Single-nucleus RNA sequencing and deep tissue proteomics reveal distinct tumour microenvironment in stage-I and II cervical cancer
Journal of Experimental & Clinical Cancer Research volume 42, Article number: 28 (2023)
Cervical cancer (CC) is the 3rd most common cancer in women and the 4th leading cause of deaths in gynaecological malignancies, yet the exact progression of CC is inconclusive, mainly due to the high complexity of the changing tumour microenvironment (TME) at different stages of tumorigenesis. Importantly, a detailed comparative single-nucleus transcriptomic analysis of tumour microenvironment (TME) of CC patients at different stages is lacking.
In this study, a total of 42,928 and 29,200 nuclei isolated from the tumour tissues of stage-I and II CC patients and subjected to single-nucleus RNA sequencing (snRNA-seq) analysis. The cell heterogeneity and functions were comparatively investigated using bioinformatic tools. In addition, label-free quantitative mass spectrometry based proteomic analysis was carried out. The proteome profiles of stage-I and II CC patients were compared, and an integrative analysis with the snRNA-seq was performed.
Compared with the stage-I CC (CCI) patients, the immune response relevant signalling pathways were largely suppressed in various immune cells of the stage-II CC (CCII) patients, yet the signalling associated with cell and tissue development was enriched, as well as metabolism for energy production suggested by the upregulation of genes associated with mitochondria. This was consistent with the quantitative proteomic analysis that showed the dominance of proteins promoting cell growth and intercellular matrix development in the TME of CCII group. The interferon-α and γ responses appeared the most activated pathways in many cell populations of the CCI patients. Several collagens, such as COL12A1, COL5A1, COL4A1 and COL4A2, were found significantly upregulated in the CCII group, suggesting their roles in diagnosing CC progression. A novel transcript AC244205.1 was detected as the most upregulated gene in CCII patients, and its possible mechanistic role in CC may be investigated further.
Our study provides important resources for decoding the progression of CC and set the foundation for developing novel approaches for diagnosing CC and tackling the immunosuppressive TME.
Cervical cancers (CC) are the 3rd most common cancer in women worldwide , and human papillomavirus (HPV)16 and 18 account for more than 70% of CC . Clinically, the International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging system is the most powerful prognostic factor in patients with cervical cancer and useful guidance for treatment [3, 4]. Stage-I CC cells have grown from the surface of the cervix into deeper tissues, while stage-II cancer is 4 cm or larger, and grows beyond the cervix and uterus, but hasn’t spread to the walls of the pelvis or the lower part of the vagina. Surgical, radio- and chemotherapy or a combination of these therapies are used for the treatment of CC . Immune checkpoint inhibitor therapy has been recommended as a second-line treatment for PD-L1 positive or MSI-h/dMMR tumours . However, the immunopathological profiles of these two stages have not been studied clearly, leading to the difficulty in the selection of stage-specific immunotherapy strategy.
Research over the last two decades has demonstrated that both innate and adaptive immune systems participate in the elimination, equilibrium, and escape stages of the immune editing process [7, 8]. Immune surveillance and subsequent immune editing theory suggest that the immune system can either positively or negatively influence tumour development. Reduced immune recognition, increased tumour cell survival, or development of an immunosuppressive tumour microenvironment (TME) contribute to the tumour escape stage . The TME establishment is a slow process, and recent studies showed that the early- and late-stage TMEs of multiple cancers have cells with different cell heterogeneity and functions [10,11,12,13,14]. Recent advances in multi-omics and single-cell RNA sequencing (scRNA-seq) techniques have contributed significantly to the characterisation of TME, which have resulted in the discovery of more cell types and their response to therapies [15,16,17,18]. As an example, in a TC-1 tumour model, six tumour-associated macrophage populations with distinct genomic signatures were present in the TME, reflecting a complex macrophage development, compared with the traditional M1 and M2 paradigm [19, 20].
In terms of the TME of CC, a recent scRNA-seq analysis has found the enrichment of PI3K/AKT pathway supported by differentially expressed genes between chemoresistant and chemosensitive patients . The mutation in NFKB1 (G430E) was shown to significantly increase mutant allele frequency after radiotherapy, indicating its role as a potential molecular target in CC radiation therapy . Another study compared the CC and the adjacent normal tissues by scRNA-seq analysis, which discovered a subset of cancer stem cells (CSCs) significantly related to tumour progression; in addition, metabolism-related signalling pathway was enhanced in the endothelial cells of the TME, associated with upregulation of TAGLN2, KLF5, STAT1, and STAT2 . Besides, the immune cells and mesenchymal cells of the normal cervix, intraepithelial neoplasia, primary tumour, and metastatic lymph node tissues were comparatively investigated using scRNA-seq, which identified a low and late activated TME in intraepithelial neoplasia, whereas metastatic lymph node showed early activated immune response . However, the efforts in profiling all cell types of the TME of different stage CC to understand their cellular biology are still limited.
In this study, we comprehensively compared the cell heterogeneity in the TME of stage-I and II CC patients. We performed high-precision single-nucleus RNA sequencing (snRNA-seq) analysis with the surgical tissues isolated from four stage-I and three stage-II CC patients, respectively. We detected, on average, 1,900 genes at a depth of ~ 250,000 reads per nucleus in about 80,000 tumour cells. The tumour cells were clustered into different types according to their transcriptome profiles, and the subtypes of selected immune cells were identified. We then employed label-free quantitative proteomic analysis to compare the overall proteome profiles of the tumour tissues, and their correlation with snRNA-seq analysis was revealed to address the basic questions in cell type and function raised above. We identified the distinct phenotypes in the TMEs of the two stages, and the marker genes specific to stage-II CC included many collagens and a novel transcript AC244205.1. Survival analysis based on The Cancer Genome Atlas data further supported the correlation between the collagen expression and CC patient survival, suggesting that they can serve as candidate targets for tumour therapy of late-stage CC patients. Our work provides novel insights into the molecular characteristics of the progression of CC.
Pathological confirmed tumour samples of cervical cancer patients without chemo-, radiotherapy, HBV, EBV and HIV negative underwent surgical operation were collected and stored in liquid nitrogen till use. The patient information is summarised in Table S1. The human ethnics number for conducting the current research is L2016.
Isolation of tumours and single-nucleus transcriptomics
Freshly obtained CC tissues were immediately processed for nucleus isolation, sequencing, and library preparation, according to the guidelines of 10 × Genomics (10X Genomics, USA). Approximately 500 mg of tumour tissue from each patient was dissociated into a single-nucleus suspension. The tumour tissue was homogenised in ice-cold homogenisation buffer (0.25 M sucrose, 5 mM CaCl2, 3 mM MgAc2, 10 mM Tris–HCl (pH = 8.0), 1 mM DTT, 0.1 mM EDTA, 1 × protease inhibitor (Thermo Scientific, cat no. 78425), and 1 U/μL RiboLock RNase Inhibitor (Thermo Scientific, cat no. O0381)) with pestle strokes. Next, the homogenates were filtered through a 70 μm cell strainer to collect the nuclear fraction in to 15 ml Falcon tube, with a volume about 1 ml. The nuclear fraction was mixed with an equal volume of 50% iodixanol solution (0.16 M sucrose, 1 0 mM NaCl2, 3 mM MgCl2, 10 mM Tris HCl (pH 7.4), 1 U/μl RiboLock R Nase Inhibitor, 1 mM DTT, 0.1 mM PMSF Protease Inhibitor (Thermo Scientific, cat no. 36978)), to a final concentration of 25%, then add 1 mL 33% iodixanol solution to the bottom of the tube, followed by adding on top of a 30% iodixanol solution. This solution was mixed by inverting for 10 times and then centrifuged for 8 min at 500 × g at 4 °C. After the myelin layer was removed from the top of the gradient, the nuclei were collected from the 30% iodixanol interface. The nuclei were resuspended in nuclear wash buffer and resuspension buffer (0.04% bovine serum albumin, 0.2 U/μL RiboLock RNase inhibitor, 500 mM mannitol and 0.1 mM PMSF protease inhibitor in PBS) and pelleted for 5 min at 500 × g and 4 °C. The nuclei were filtered through a 40 μm cell strainer to remove cell debris and large clumps. The nuclear concentration was assessed using trypan blue counterstaining by a Bio-rad TC20. Finally, the nuclear concentration was adjusted to 700–1200 nuclei/μl, and the nuclei were examined with a 10X Chromium platform. Reverse transcription, cDNA amplification and library preparation were performed based on the protocol from the manufacturer.
Raw reads were preprocessed using Cell Ranger (version 3.1.0) with the default parameters and aligned to the pre-mRNA reference (Ensemble_release 108.38, Homo sapiens). For quality control, cells with UMI counts < 8,000 or a percentage of mitochondrial genes < 10%, and gene counts between 500 and 4,000 per nucleus were retained. Then, the global-scaling normalisation method “LogNormalise” was used to normalise the gene expression measurements for each cell by total expression, multiplied this by a scale factor (10,000 by default), and log-transformed the result with the following formula. Seurat  was used to minimise the effects of batch effect and behavioural conditions, which identified 2,000 highly variable genes in each sample based on a variance stabilising transformation, to generate an integrated expression matrix.
After data integration and scaling, principal component analysis (PCA) was used dimensional reduction, and appropriate principal components were selected for clustering and subsequent analysis. The detailed method for clustering cells based on gene expression was described in detail elsewhere [19, 20]. In brief, a shared-nearest neighbour (SNN) graph was used to draw edges around cells with similar gene expression based on the euclidean distance in PCA space. The edge weights were refined between any two cells according to their Jaccard distance. The modularity optimisation techniques were applied to iteratively group cells together , for the purpose of optimising the standard modularity function.
Differentially expressed gene (DEP)
Expression value of each gene in given cluster were compared against the rest of cells using Wilcoxon rank sum test. Significant upregulated genes were identified using three criteria: (i) the expression of the genes ≥ 1.28-fold in the target cluster relative to other clusters; (ii) the genes were expressed in more than 25% of the cells of the target cluster; and (iii) P-value is < 0.05.
Cell cycle analysis
The Seurat R package was used to assign a cell cycle score to each cell based on the 100 marker genes for G1/S phase, 113 marker genes for S phase, 133 marker genes for G2/M phase, 151 marker genes for M phase and 106 marker genes for M/G1 phase, respectively . Cells with the highest score less than 0.3 was identified as non-cycling cells .
Protein extraction and trypsin digestion
Tumour tissue samples were transferred into lysis buffer (2% SDS, 7 M urea, 1 mg/mL protease inhibitor cocktail), and homogenised for 3 min on ice using an ultrasonic homogeniser (Sonics & Materials Inc VCX130). The homogenate was centrifuged at 15,000 rpm for 15 min at 4℃, and the supernatant was collected. BCA Protein Assay Kit (ThermoFisher Scientific, MA, US) was used to determine the protein concentration of the supernatant. A total of 50 μg protein extracted from each sample was suspended in 50 μL solution, reduced by adding 1 μL 1 M dithiotreitolat 55 °C for 1 h, alkylated by adding 5 μL 20 mM iodoacetamide in the dark at 37 °C for 1 h. Then the sample was precipitated using 300 μL prechilled acetone at -20 ℃ overnight. The precipitate was washed twice with cold acetone and then resuspended in 50 mM ammonium bicarbonate, followed by digestion with sequence-grade modified trypsin (Promega, Madison, WI) at a substrate/enzyme ratio of 50:1 (w/w) at 37 °C for 16 h.
High pH reverse phase separation
The peptide mixture was resuspended in the buffer A (buffer A: 20 mM ammonium formate in water, pH 10.0, adjusted with ammonium hydroxide), and then fractionated by high pH separation using Ultimate 3000 system (ThermoFisher Scientific, MA, US) connected to a reverse phase column (XBridge C18 column, 4.6 mm × 250 mm, 5 μm (Waters Corporation, MA, USA). High pH separation was performed using a linear gradient, starting from 5% B to 45% B in 40 min (B: 20 mM ammonium formate in 80% acetonitrile (ACN), pH 10.0, adjusted with ammonium hydroxide). The column was re-equilibrated at the initial condition for 15 min. The column flow rate was maintained at 1 mL/min and the column temperature was maintained at 30℃. Twelve fractions were collected and lyophilised.
The peptides were re-dissolved in 30 μL solvent A (A: 0.1% formic acid in water) and analysed by online nanospray LC–MS/MS on an Orbitrap Fusion Lumos coupled to EASY-nLC 1200 system (Thermo Fisher Scientific, MA, US). Briefly, 3μL peptide sample was loaded onto the analytical column (Acclaim PepMapC18, 75 μm × 25 cm) with a 120-min gradient, from 5 to 35% B (B: 0.1% formic acid in ACN). The column flow rate was maintained at 200 nL/min with a column temperature of 40 °C. The electrospray voltage of 2 kV versus the inlet of the mass spectrometer was used. The mass spectrometer was run under data-independent acquisition (DIA) mode, and automatically switched between MS and MS/MS mode. The parameters were: (1) MS: scan range (m/z) = 350–1200, resolution = 120,000, AGC target = 1E6 and maximum injection time = 50 ms; (2) HCD-MS/MS: resolution = 30,000, AGC target = 1E6, collision energy = 32 and stepped CE = 5%; (3) DIA was performed with variable isolation window, and each window overlapped 1 m/z, and the window number was set to 60.
Protein identification and quantification
Raw data of DIA were processed and analysed by Spectronaut X (Biognosys AG, Switzerland) with default parameters. The protein database derived from Homo sapiens genome was downloaded from NCBI (March 2021). Retention time prediction type was set to dynamic iRT. Data extraction was determined based on the extensive mass calibration. Q-value (FDR) cutoff on precursor and protein level was applied at 1%. Decoy generation was set to mutate, scrambled with a random number of AA position swamps (min = 2, max = length/2). All selected precursors passing the filters were used for quantification. The average top 3 filtered peptides were used to calculate the major group quantities. Normalisation was performed on averaging the abundance of all peptides. Medians were used for averaging. After Student’s t-test, differently expressed proteins (DEPs) were filtered if their Q-value < 0.05 and absolute AVG log2 ratio > 0.58. Principal component analysis (PCA) and correlation analysis were performed with R package gmodels. The correlation coefficient between two replicas was calculated to evaluate repeatability between samples.
The protein domain and transcription factor (TF) analysis
The prediction of the protein domain used the Pfam_scan program . The protein sequence was compared with the Pfam database to obtain the relevant annotation information of protein structure. The predicted protein sequences were compared by hmmscan with the TF database (animalTFDB ).
Protein–protein interaction (PPI) analysis
Interactions among significantly regulated proteins were predicted using STRING . All resources were selected to generate the network and ‘confidence’ was used as the meaning of network edges and the required interaction score of 0.700 was selected for all PPI, to highlight the most confident interactions. Neither the 1st nor 2nd shell of the PPI was included in this study. Protein without any interaction with other proteins was excluded from displaying in the network.
Functional annotation and enrichment analysis
DEGs and DEPs were annotated against GO, KEGG and COG/KOG database to obtain their functions. Significant GO functions and pathways were examined within differentially expressed proteins with Q-value < 0.05. The enrichment of the pathways was analysed by Gene Set Enrichment Analysis (GSEA) with P-value < 0.05 using GSEA v4.1.0 .
The survival analyses were performed by the Cox proportional hazard model provided that the proportional hazard assumption was met based on weighted residuals using TIMER2.0 . Hazard ratio was estimated relative to the lowest-risk group and assessed by a two-sided Wald test, P-value < 0.05 was significant.
Identification of the tumour cell composition of stage-I and II cervical cancer
We performed snRNA-seq experiments using all nuclei isolated from the tumour tissues of stage-I (n = 4) and II (n = 3) stage donors, respectively (Fig. 1A). After quality control, a total of 72,128 nuclei (42,928 stage-I and 29,200 stage-II) were used for downstream analysis (Fig. 1B and Fig. S1). Unsupervised clustering analysis revealed 22 cell types, which were present in both the stage-I (CCI) and II (CCII) groups, indicating that the cell-type identity was not strongly confounded by the ageing and stage effects (Fig. 1B; Figs. S2A and 2B). However, these cell types were not detected in all individual patients (Fig. S2C). The proportions of different cell types at the two stages were compared (Fig. 1C), and the population comparison among different samples were displayed in Figure S2D. Overall, the percentage populations of the cluster 3, 4, 9, 14, 15, 16, 17, 18, 19 were largely increased in the CCII group, whereas reduced proportions were observed for the cluster 2, 5, 7 and 20 (Fig. 1C and Table S1). Many of cluster 0 and 3 cells did not show the differential expression of the marker genes for definitive cell-cycle (non-cycling) (Fig. 1D). Cluster 1, 2 and 5 possessed high populations of cells at S or G1 cell cycle, while S and M cell cycles were more represented in cluster 7 and 8, respectively (Figs. S3A and 3B). CCI-4, CCII-2 and CCII-3 showed high proportion of non-cycling cells, while the other three CCI samples possessed more cells at G1 and S cell cycles (Figs. S3C and 3D).
The differentially expressed genes (DEGs) were analysed to determine cell type-specific marker genes (Fig. 1E and Table S2). The clusters were annotated with predicted cell-type identities based on known marker genes derived from the expert annotation in literature . Correlation analysis of the 22 clusters showed that cluster 17 was least correlated with the other clusters, followed by cluster 13, implying distinct phenotypes present in these two clusters (Fig. S4). The cell types directly associated with immune response included T cells (cluster 4; marker genes: NELL2, ITK, and IL7R), macrophage (MΦ, cluster 6; SIGLEC1, FPR3, and MSR1) , γδT cells (cluster 8; TOP2A, ASPM, and CENPF), naïve B cells (cluster 9; DCC, CD38, POU2AF1) , regulatory T cells (Treg, cluster 10; CTLA4, IL2RA, and F5) , NK cells (cluster 11; KLRC1, GNLY, and NCR1), mature B cells (cluster 14; BLK, FCRL1, MS4A1) , CD141+CLEC9A+ DC (cluster 16; SLC24A4, FLT3, and ZNF366) , and plasmacytoid dendritic cells (pDCs, cluster 21; CLEC4C, IL3RA, and IRF8) [36, 38]. Several clusters had the features of stem cells, including cancer stem cells (CSCs) (cluster0; CLDN10-AS1, TOX3, and PROM1), mesenchymal stem cells (MSCs) (cluster 3; ADAMTS2, LAMA2, and ABI3BP), vascular stem cells (VSCs) (cluster 7; RRM2, EXO1, and SKA3), and endothelial cell/submandibular gland stem cells (cluster 12; FLT1, PCDH17, and VWF) . Two clusters were annotated as progenitor cells, i.e., neural progenitor cells (NPCs, cluster 19; NRXN1, ADAMTSL1, and PPP2R2B) [40, 41] and granulocyte-monocyte progenitor cells (GMPCs, cluster 20; CPA3, MS4A2, and TPSAB1) . Cluster 1 was characterised as basal cells, with significantly high expression of KRT15, KRT17, and KRT5. Epithelial cells and lymphatic endothelial cells were mainly detected in cluster 2 (IL1RN, GPRC5A, SPRR1B) and 18 (FLT4, PROX1, and CD34) , respectively. In addition, adipose-derived stromal cells (cluster 5; NTRK2, PTPRZ1, and CHL1), myofibroblast (cluster 13; SPARC, COL1A1, and COL6A2) , pericytes (cluster 15; RGS5, ABCC9, and ADGRF5), and astrocytes (cluster 17; FOSB, ATF3, and ITGB4)  were also present.
Immunosuppressive and tumour-growth-promoting phenotypes dominated the macrophages in the TME of CCII patients
Relatively high populations of MΦs were identified in CCI (5.30%) and CCII (5.65%) patients, respectively. The gene expression analysis revealed 122 upregulated and 102 downregulated DEGs in the CCII groups relative to the CCI, with the log-transformed expression of the top 50 DEGs hierarchically compared in Fig. 2A (see Table S3 for the full list of DEGs and annotations). The expression of MΦ marker genes was comparatively downregulated in the CCII group, for instance, C1QB, C1QC, STAT1 and IFI44L. In addition, many of chemokines, cytokines and interleukins exhibited significantly higher expression in the CCI group, including the signatures of M1-like MΦs, such as IL12RB1, IL2RA and IL20RB (Fig. 2B). The genes (CCL19 and MMP11) that correlated with immune suppressive MΦs were elevated in the CCII group. The canonical pathways were analysed based on the respective transcriptome profiles using GSEA. It was evident that the epithelial-mesenchymal transition (EMT) was the most enriched pathway in the CCII group, supported by the upregulation of collagen family members, such as COL1A1, COL1A2, COL6A2 and COL3A1. Furthermore, these collagens might function collaboratively with extracellular matrix synthesis regulating genes (e.g., SFRP4, LUM, SPARC, and DCN), to enhance tissue growth (Fig. 2C and 2D; Table S3). Besides, the enrichment of ‘P53 pathway’, ‘TNFα signalling via NFκB’, and ‘apoptosis’ was detected in the CCII group, and so were several other pathways closely related to the development of cell cytoskeleton. The elevation of genes encoding interferon-induced proteins in the CCI group strongly supported the activation of IFN-α and IFN-β response pathways, including IFI44L, IFIT3, IFI44, IFI35, and IFIH1 (Fig. 2E).
The MΦ subtypes were analysed to unveil the change in cell heterogeneity between the two stages. There were five subtypes (i.e., cluster-0 to 4) identified, with the expression of the top five marker genes compared in Fig. 3A. Cluster-0 had the phenotype of resident-like macrophage, characterised by MS4A6A, CD163 and CD163L, thus it was referred to as C0-Res. Several marker genes of cluster-1 were associated with tumorigenesis, such as PARD3, EGFR, and SMAD3, was thus labelled as C1-TAM (Table S4). The third subtype showed the significant upregulation of the signatures of M2-like MΦ, including SLC16A10, SLC11A1 and CTSL, and thus we named it C2-M2. The fourth subtype showed the marker genes of both dendritic cells (ADAM19, HDAC9, and MCOLN2)[52,53,54] and macrophages (SLC8A1, RUNX3, and LCP1)[55,56,57], which was referred to as C3-DC. The fifth subtype had several marker genes (SEMA3A, ESRRG, and IL18)[58,59,60] representing M1-like MΦ, which was labelled as C4-M1. The enrichment of biological processes (BPs) in each subtype was analysed (Fig. S5). More immune response associated BPs were observed in C2-M2, C3-DC and C4-M1, with several inflammation relevant processes only present in the C4-M1, such as ‘interleukin-21-mediated signalling pathway’, ‘inflammatory response’ and ‘regulation of cytokine production’. C1-TAM was highly enriched with cell development and tissue growth processes, and occupied a higher proportion in the CCII group, while other subtypes were more abundant in the CCI group (Fig. S6A).
The DEGs relevant to immune response appeared mostly upregulated in all the subtypes of the CCI group, especially in C0-Res, C1-TAM and C2-M2 (Fig. S6B). The average expression of selected genes associated with macrophage functions was compared across the subtypes, showing that STAT1, HLA-DRB1, TMSB4X, C1QC and FGL2 were upregulated in all subtypes of the CCI group with a higher percentage of expression (Fig. 3B). Although S100A8, DUSP1, MT2A, and JUNB had higher average expression in the subtypes of the CCII group, their percentage of expression was comparatively low. The expression of the genes related to cell proliferation and extracellular matrix development, such as CCN2, AEBP1, MGP, and several members of the collagen family, was highly elevated in all subtypes of the CCII group. Notably, the upregulation of several genes encoding mitochondrial proteins associated with oxidative phosphorylation, for instance, MT-CYO2, MT-CYO3, MT-ND4 and MT-ND1, were observed in the C0-Res, C1-TAM and C2-M2 of the CCII group (Table S4), which might be associated with active cellular metabolism during tumour growth. We then projected MΦs onto the two-dimensional state-space defined by Monocle3 for pseudotime analysis, to infer lineage trajectory for MΦ development (Fig. 3C). The trajectory began with the C0-Res and C2-M2, and then developed to separate directions, with one direction leading to C3-DC and C4-M1, while the other direction eventually linked to C1-TAM with a branch composed of C1-TAM, C3-DC and C4-M1. It appeared that the C3-DC and C4-M1 had similar developmental lineage along the pseudotime.
A total of nine states were thus derived from the trajectory, with large proportions of C0-Res and C2-M2 cells detected at State-1, State-3, and State-5, while C1-TAM cells gradually became the largest population at late pseudotime (Fig. S6C). There were high proportion of C3-DC in State-2, 4 and 9. It was evident C0-Res, C1-TAM and C2-M2 cells aligned more with the cells derived from the CCI group relative to the CCII group, except for State-9. The DEGs of each state were compared between the CCI and CCII groups (Table S4), and the macrophage marker genes showed higher percentage and/or average expression in most states of the CCI group, except for State-3 showing the upregulation of CXCR4 and DOCK8 in the CCII group (Figure S6D). The expression of mitochondria-associated marker genes and several collagens were elevated along the pseudotime, particularly for the State-5, 6, 7, 8 and 9 (Fig. S6E). The composition of each state within each subtype was displayed; C4-M1 and C2-M2 MΦs were respectively dominated by one cell state, whose population was mostly derived from the CCI group. State-4 and 9 cells occupied nearly 90% of C3-DC (Figure S6F).
There were four branches present along the pseudotime. Analysing the genes that were significantly dependent on Branch-1, we found upregulated expression of immune response relevant genes in State-1 and 2 cells, such as CXCL9, CXCL10, IL15, IL18, DOCK8, DOCK10, and STAT1 (Fig. S7A). The expression of DOCK8 and STAT1 was significantly upregulated in State-1 cells of the CCI group, and so were IFI44L, IFI44 and IFNGR2, which resulted in the activation of IFN-γ response. The genes largely downregulated in the other states post-branching were mainly expressed by M2-like MΦs, playing roles in various metabolisms, whereas resident-like MΦs appeared more active in cell proliferation. In terms of Brach-2, many DEGs in State-1, 3 and 4 were derived from the CCI group and associated with innate immune system post-branching (Fig. S7B). State-6 cells had comparatively lower level of antigen processing and presentation post branching, as suggested by the downregulation of MHC class I proteins, such as HLA-A, B and C, as well as HLA-DQA1, HLA-DRB1 and HLA-DRA (Fig. S7C). Separated by Branch-4, the genes associated with cell adhesion and development were upregulated in State-8, which was more representative in the CCII group; State-8 cells of the CCI group exhibited functions in ion import and response to cytokine (Table S4). The genes relevant to chemokine production and neutrophil activation showed higher expression post-branching, such as DENND1B, NLRP3, CD53, FGR, PTPRC, MNDA, and CD58 (Fig. S7D). This largely reflected the activation of cytokine-mediated signalling in the State-9 cells of the CCI group; in contrast, ECM-receptor interaction and PI3K-Akt signalling were more pronounced in CCII State-9 cells.
The function of T cells was suppressed in the TME of CCII patients
Three populations of T cells were identified, including CD8+ T, γδT and Treg cells. The proportion of CD8+ T cells was much higher in the CCII group (12.85%) than that in the CCI group (8.58%) (Table S1). Considering the entire T cell population, the populations of CD8+ T and γδT cells were largely modulated at different stages, with the former increased from 57% (CCI) to 80% (CCII) while the latter reduced by nearly 70% (from 28% in CCI to 9% in CCII) (Fig. 4A). The patients of the CCI group had a higher number of Treg cells than the CCII group (Fig. 4B). The distribution of three T cell populations between the two groups was displayed in 2D-tSNE space (Fig. 4C). It was evident that CD8+ T cells separated into five major subtypes (a, b, c, d, and e), with subtype-a possessing a significantly higher number of cells derived from the CCII group. The CCI group had more cells derived from subtype-b, c, d, and e. The expression of selected maker genes characterising Treg cells was compared between the CCI and CCII groups (Fig. 4C). CD8A and PRF1 were highly expressed by the subcluster-d cells of CD8+ T, while the cells expressing elevated levels of CD4 and CTLA4 were mainly detected in Treg cells of the CCI group. The expression of IFNGR1 and IFNGR2 was more expressed in γδT cells, whereas IFIT3 was upregulated in the Treg cells of the CCI group.
The hierarchical clustering of the top 100 DEGs across the three T cell populations of the two groups was displayed in Fig. 4D. The expression of DEGs associated with the activation of T cell immune response was upregulated in CD8+ T of the CCI group significantly, such as CD7, RUNX3, CD3D, CD3G, GZMB, CD8B, and CCL5. This was also partially observed for the Treg cells. Several genes closely related to interferon response were upregulated in the CCI γδT cells, for example, IFIH1, ISG15, GBP1, GBP4, GBP5, OAS1, OAS2 and OAS3. Notably, the expression of two genes (CD38 and CD48) related to antigen-presenting were elevated in the CD8+ T and Treg cells, but downregulated in the γδT cells of the CCI group. The genes regulating complement and coagulation cascades, and inflammation were clustered and showed higher expression in CD8+ T and Treg cells of the CCII group, for instance, S100A8, S100A9, CYBA, CD55, C3, and PLAU. These observations meant that, compared to the CCI group, the T cells of the CCII group were more immunosuppressive, although the population of CD8+ T was significantly higher.
B cells and DCs in the TME of CCII showed suppressed MHC class I antigen process
The CCI group had a higher population of pDCs, while the population of B cells was more abundant in the CCII group (Fig. 5A). For both CD141+CLECL9A+ DCs and pDCs, the DEGs associated with antigen-presenting processes, such as HLA-DRB5, HLA-DQB1, and HLA-DPA1, were upregulated in the CCI group, whereas the CCII group had a higher average expression of HLA-B, HLA-A, HLA-C, and HLA-DMB (Fig. 5B). In addition, several members of the proteasome subunit gene family appeared more pronounced in terms of both average expression and percentage of expression in pDCs of the CCI group, for instance, PSMB2, PSMD14, PMSC6, and PSM8. The biological process analysis suggested that tissue development signalling was highly activated in the DCs of the CCII group, such as ‘angiogenesis’, ‘blood vessel morphogenesis’, ‘circulatory system development’, and ‘cell–cell adhesion’ (Fig. 5C). In contrast, many immune response processes were highly activated in the DCs of the CCI group, including ‘oxidation reduction process’, ‘immune effector process’, ‘response to virus’, ‘defence response to virus’, and ‘antigen processing and presentation’. It appeared the multicellular organism process was enriched in the CD141+CLECL9A+ DCs of the CCII group, but was suppressed significantly in the pDCs of the CCI group. The expression of selected DEGs associated with MHC class I antigen-processing was compared, and their upregulation in the B cells of the CCI group can be seen (Fig. 5D). The elevation of COL3A1, COLA1 and COLA2 was present in the native B cells of the CCII group (Fig. 5E). Two clusters of genes associated with the signalling of type I and II interferons were upregulated in B cells of the CCI group, such as STAT1, OAS1, OAS2, ISG15, HLA-DPA1, IL12RB2, and JAK2. The signalling of IL-18 and chemokine activity appeared more enhanced in the native B cells of the CCII group, supported by the higher expression of CD81, MMP2, LTB, ITK, and CX3CL1. The thymic stromal lymphopoietin (TSLP) and B cell receptor signalling were overrepresented by the upregulation of TEC, FYN, IL7R, and STAT1 in the B cells of the CCII group.
NK cells were more activated in the CCI group
The populations of NK cells occupied 2.52% and 0.85% of CCI and CCII cells, respectively (Table S1). The top 50 DEGs relevant to immune system and/or response KEGG pathways were compared, which clearly showed that most NK cells of the CCI group expressed higher levels of the marker genes positively associated with activated NK cell function, such as KLRD1, KLRC1, GZMA, GZMB and NKG7 (Fig. 6A). The genes regulating IL17 and TNF signalling were highly expressed by the NK cells of the CCII group, including TRAF4, FOSB, CXCL1, and FOS; in addition, HSPB1, APOE and IL7R, which were related to the apoptosis due to altered Notch3, were more abundant and closely hierarchically clustered. The distribution of cells expressing selected marker genes of activated NK cells was displayed in a 2D-tSNE space, showing that GZMA, KLRC1, GZMB and NKG7 were expressed only in a few NK cells of the CCII group (Fig. 6B). The DEGs associated with chemokine and cytokine signalling mostly had elevated average expression and percentage of expression in the CCI group, such as CD7, IL2RB, CCL5, and CCL4 (Fig. 6C). The GSEA detected ‘natural killer cell mediated cytotoxicity’ as the only KEGG pathway enriched in the CCI group, supported by the upregulation of GZMB, PRF1, KLRC1/2 and CD244 (Fig. 6D). On the other hand, the enrichment of ‘ECM receptor interaction’ and ‘focal adhesion’ was detected in the CCII group (Table S5). In terms of Hallmark pathways, ‘INF-γ response’ and ‘allograft rejection’ were the only two pathways highly activated in the CCI group, whereas EMT and ‘coagulation’ were significantly enriched in the CCII group (Fig. S8A).
We then analysed the subtypes of the NK cells, which showed that the CCI group possessed higher populations of mature (characterised by the marker genes, PRF1, GZMA, GZMB and CFL1), CD56bright (KLRC1, NCAM1 and GNLY) and terminal (WDR74, HIST1H1D, and HIST1H1E) NK cells in general (Fig. 6E). The population of CD56dim (IL23R, IL7R, and TCF7) NK cells was more present in the CCII group, so were the transitional (FOS, FOSB, and JUNB) NK cells. The development of NK cells in the two groups was inferred based on the trajectory analysis, where many cells present at early pseudotime belonged to in the CCI group, as well on the two branches along the time (Fig. 6F and Table S5). A total of seven states was thus identified, with higher proportion of mature NK cells detected in the CCI group (Fig. 6G). The proportion of terminal NK cells was the second largest in state-1, and then decreased in state-2 and 7 along the time, whereas the proportion of transitional NK cells increased. State-6 was mostly composed of CD56bright and mature NK cells. The expression of the marker genes associated with the activated NK cells, including GZMA, GZMB, NKG7, KLRC1, CCL5 and CCL4, was significantly upregulated in all states of the CCI group except for the state-5 (containing only mature NK), compared with the CCII group (Fig. 6H). The CCII group had more state-2 NK cells, while significantly less NK cells of the other states. For branch point 2, the CD56bright cells of state-1 and 2 expressed higher level of DEGs associated with ‘natural killer cell mediated cytotoxicity’, such as CD247, KLRC1, and NCAM1, with respect to the other states post-branch (Fig. S8B). While the mature NK cells exhibited activated KEGG pathways related to tissue and cell development, such as ‘focal adhesion’, ‘regulation of actin cytoskeleton’, and ‘PI3K-Akt signalling pathway’, due to the upregulation of ITGAT, ITGA1 and BCL2 for state-1 and 2. Thus, in terms of immune response, CCI NK cells were more activated relative to those of the CCII group.
Cancer cells of CCI and CCII patients showed distinct heterogeneity
Several other cell types associated with tumour progression, including stem cells and epithelial cells, were identified with the highest populations (Fig. 1; Table S1 and 2). The proportion of CSCs increased largely in the CCII group to 24.75%, compared to only 15.63% in the CCI group. Similarly, MSC population was higher (13.88%) in the CCII group. In contrast, the ADSCs showed lower proportion (3.13%) in the CCII group. The relative expression density of selected marker genes was displayed in a 2D-tSNE space (Fig. 7A). The top 3 marker genes of CSCs, including CLDN10-AS1, AC024230.1 and FYB2, also showed certain expression in epithelial cells. A cluster of DEGs, such as CFTR, PROM1, CD55, and RHEX, were significantly upregulated in the CSCs and more pronounced in the CCII group, implying a high level of cell growth (Fig. 7B). The genes associated with inflammatory response, such as STAT1, ITGB4, SAT1, and ANTXR1, as well as several collagens, had higher expression in MSCs with respect to other stem cells. The GSEA identified that the top 6 pathways enriched in the CSCs of the CCI group were relevant to immune response, such as ‘cellular immune response to IFN-γ’, ‘IFN-γ mediated signalling’, and ‘B cell mediated immunity’ (Fig. 7C). In contrast, the pathways associated with cell growth were highly activated in the CCII CSCs. The signalling of TGFβ and extracellular matrix organisation appeared more enriched in ADSCs of the CCII group, whereas the activation of ion membrane transport was detected in those of the CCI group (Fig. 7D).
The signalling associated with multiple cancers was among the top KEGG pathways enriched in both basal and epithelial cells, with ‘human papillomavirus infection’ more present in the former (Fig. 7E and 7F). Since many genes expressing keratins and collagens were detected as the top DEGs of the four non-stem cell populations, the average expression of these genes with respect to their expression percentage were compared (Fig. 7G). Notably, the expression of collagens was upregulated in these cells of the CCII group in general, which was also observed for keratins except in the lymphatic endothelial cells. The keratinocytes showed high expression of most keratins and collagens. Furthermore, several members of collagen family were upregulated in multiple cell types of the CCII group, such as COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, COL6A1, and COL18A1 (Fig. S9A). The aberrant expression of many of them were positively correlated with the risk of cervical cancer, with the KM curves of four conveying significance, including COL4A1, COL4A2, COL5A1, and COL12A1 (Fig. S9B), which implied their potential application in clinical diagnosis.
Quantitative proteomic analysis revealed significant immunosuppression in CCII patients
A total of 14,855 proteins belonging to 6,181 protein groups were identified from the CCI and CCII groups (Table S6). The PCA analysis showed a coverage of 55.0% and 29.6% of the difference between the CCI and CCI groups in PC1 and PC2, respectively (Fig. S10A), and the relatively close correlation between certain replicates of the two groups was present (Fig. S10B). The transcription factor (TF) ‘HMG’ was highly identified in the proteins corresponding to 73 genes, followed by ‘zf-C2H2’ (57) and ‘Homeobox’ (53) (Fig. S10C and Table S7). Notably, several TFs encoding proteins regulating cell development were identified, such as ‘MYB’, ‘CSD’ and ‘ARID’, as well as those relevant to immune response regulation, including ‘STAT’, ‘IRF’ and ‘NF-Y’. There were 541 differentially expressed proteins (DEPs) determined as quantifiable between two groups (Fig. 8A and Table S6), with the top 60 DEPs displayed comparatively with respect to the P-values (Fig. 8B). The DEPs significantly upregulated in the CCII group showed intensive interactions with high-confidence, with AKT1 (FC = 1.91) being the node with the highest degree, which interacts with several other high-degree nodes associated with the development of extracellular matrix, such as FN1, MMP9, PXN, CAT and CUL1 (Fig. 8C and Table S6). The interactions between MMP9 and the nodes related to immune response were present, for instance, CTSG, MPO, ELANE and AZU1. In addition, the downstream nodes interacting with CTSG included several members of SERPIN family and IGFBP3. The PPIs between DEPs upregulated in the CCI group showed a large cluster of histones (Fig. S10D). Several nodes regulating mitochondria-associated metabolism interacted with each other, such as CYC1, NDUFS1, NDUFS8 and UQCRC1. Importantly, three immune response relevant clusters were identified, which were composed of proteins related to antigen processing and presentation, stress response, and interferon response, respectively.
The GSEA revealed the enrichment of IFN-α (P-value = 2.2E-3) and IFN-β (P-value = 1.5E-2) response in the CCI group, whereas TNFα signalling via NFκB was enriched in the CCII group (Fig. 8D). Then, we compared the expression of genes encoding the DEPs detected on the IFN-α response pathway across the immune cells detected by snRNA-seq analysis, which showed that they were more expressed in MΦs, Treg, NK, B cells and DCs, with MΦs exhibiting the highest level of expression (Fig. 8E). The correlation between the protein profile and the cell types was assessed based on the FC values of DEPs and DEGs (Fig. S11). CSCs showed low correlation with all the other cell types and the proteome. The DEPs had the highest correlation (P-value < 0.001) with ADSCs, followed by NK cells and MΦs (P-value < 0.01). The FC values of the top 20 significantly regulated proteins in the CCII group relevant to immune processes (based on GO terms) were correlated to the expressions of their genes in different immune cell populations (Fig. 8F and Table S7). The expression of S100A8 and S100A9 was upregulated in most cell types of the CCII group, while CD55 and PXN was remarkably upregulated in γδT cells. Thus, overall, a more immunosuppressive TME was formed in the CCII with respect to the CCI patients.
This study comparatively investigated the cell heterogeneity and functions within the TME of stage-I and II CC patients using snRNA-seq analysis. We found that the CCI patients had MΦs showing proinflammatory function, as indicated by significantly activated IFN-α and IFN-γ response signalling, whereas the MΦs of the CCII patients exhibited the activation of many pathways related to cell growth and tissue development. The CD8+ T cells appeared more activated in the CCI group with a lower population, and the populations of Treg and γδT were largely reduced in the CCII group. Immune response, particularly the MHC class-I pathway, was more pronounced in the DCs and B cells of the CCI group, whereas metabolic and developmental processes were enriched in the CCII group. The proportion of NK cells was reduced more than 60% in the CCII relative to the CCI group, which had the upregulation of many DEGs marking the activation of NK cell function, especially for the mature and CD56bright NK cells. The immune response relevant pathways were enriched in several stem cell types of the CCI group in general, while the cell and tissue growth, as well as metabolic pathways, were highly present in those of the CCII group. In addition, the quantitative proteomics revealed the activation of IFN-α and IFN-γ response signalling in the CCI group, which was accorded with the observation of snRNA-seq analysis.
Significant upregulations of more than 30 collagens were present in different cell types with snRNA-seq analysis. Collagens are major components of the TME and play roles in cancer fibrosis, as well as interacting with receptors, exosomes, and microRNAs to influence tumour cell behaviours . It has been previously found that COL1A1 was significantly elevated at both mRNA and protein level in CC tissues relative to normal tissues, and correlated negatively to radiosensitivity . COL6A1 was suggested as an oncogene in the initiation and progression of CC and a predictor for poor prognosis . The high expression of COL14A1 in residual CC after a 50-Gy dose of irradiation was detected by quantitative PCR . COL7A1 and COL8A1 were identified bioinformatically as two of five genes associated with the collagen assembly that might be used as a single combinatorial prognostic marker for stage-II CC . The role of COL10A1 in CC progression was suggested . The co-expression of COL1A1, COL4A1, COL5A1, COL5A2 and COL7A1 with a potential therapeutic target for CC P4HA2, was identified . However, the correlation between the other collagens and CC, and their roles in progression remains largely unclear. The novel transcript AC244205.1 was found as one of the most upregulated DEGs in many cell types of the CCII group, such as DCs, MΦs, T cells, MSCs, NPCs, and GMPCs, implying it may play a role in the progression of CC. A recent study has revealed that the upregulation of AC244205.1 was observed in cholangiocarcinoma patients with better overall survival . However, there is no report about the molecular function of AC244205.1 in CC, which will be investigated in our future studies.
The expression of DEGs related to mitochondrial respiratory machinery, such as MT-CYB, MT-CO2, MT-CO3, MT-ND1, MT-ND2, and MT-ND4, were elevated in multiple cell types of the CCII group. This implied the activation of cytochromes on the respiratory chain, resulting in high level of mitochondrial biogenesis, which might be due to the significant energy consumption for tumour cell growth at stage-II with respect to stage-I, such as EMT, ‘myogenesis’, ‘hypoxia’, as well as multiple metabolic processes, suggested by the snRNA-seq analysis. A previous study showed that the inhibition of mitochondrial complex III, subsequently affecting mitochondrial respiration by atovaquone, can inhibit the proliferation and induce apoptosis in certain CC cell lines in vitro and in vivo on a mouse model . The impairment of mitochondrial function via interfering certain signalling pathways, such as mTOR and CaMKII/Parkin/mitophagy, was targeted by several studies to tackle metabolic stress in CC cell lines, to inhibit the cancer cell growth [70,71,72]. Our study provided more target genes for interfering upregulated mitochondria activity in CC.
Compared with the CCI group, the snRNA-seq analysis found immunosuppression in nearly all immune cells of the CCII group, which led to an overall immunosuppressive TME collectively. IGLL5 was the DEG upregulated in the MΦs of the CCII group following the collagens and mitochondrial genes. It has been recently shown to be closely correlated with tumour‐infiltrating immune cells, including MΦs, in clear cell renal cell carcinoma based the TCGA data . The fusion of IGLL5 was suggested to promote metastasis of the lymph nodes and play a role in breast cancer development , though its role in MΦs in the TME of CC remains unclear. MMP11 was identified nearly unique to the MΦs of the CCII group. The immunotherapeutic role of MMP11 in different cancers has been suggested previously [75,76,77]. Its overexpression was characterised in CC cell lines  and in cervical precursor lesions . In breast cancer, MMP11 expression was considered as a biomarker for prognosis , and correlated with a high CD68/(CD3 + CD20) ratio in CD68+ macrophages , which caused the polarisation of macrophages in the tumour centre, resulting in a higher metastatic phenotype . This implies a similar process involved MMP11 might occur in the TME of the CCII group.
Besides, CCL19, IL7R, SPARC, MGP, LUM, and CXCR4 were highly expressed in the MΦs of the CCII group, and in each MΦ subtype. The overexpression of CCL19 was found in CC cell lines and patient tissues, and its knockdown via siRNA inhibited the proliferation of CC cells in vitro via apoptosis pathway . SPARC was associated with epithelial-mesenchymal transition and overexpressed in CC patients with poor prognosis , and was highly elevated in the CCII group. Increased MGP was significantly observed in high-grade cervical premalignant lesions with elevated hTERT mRNA expression . In uterine CC tissues, LUM was expressed in most cancer cells and stromal fibroblasts, indicating its roles in the growth or invasion of CC . The CXCL12/CXCR4 chemokine pathway was targeted for improving the therapeutic ratio in patient-derived CC models with radio-chemotherapeutic treatment .
Although there were higher number of CD8+ T cells in the CCII group, their function was largely suppressed compared with the CCI group. Many interferon-induced genes and receptors were expressed by γδT cells of the CCI group, while the population of γδT cells was very small in the CCII group. γδT cells link the innate with adaptive immunity, to protect the epithelium from trauma and infection, which have been suggested as potential therapeutics against HPV in patients . The number of γδT cells was negatively associated with the progression of CC . γδT cells alone were found to inhibit tumour growth, and if combined with galectin-1 antibody, they could provide a more effective immunotherapy for CC . However, highly expressed HPV16 oncoproteins at the cancer stage induced a reorganisation of the local epithelial-associated γδT cell subpopulations, to promote angiogenesis and cancer development . Thus, the significant reduction in the number of γδT cells may be associated with the high degree of tumorigenesis in stage-II CC.
The population of NK cells was remarkably reduced in the CCII group, so was the immune response, compared with those of the CCI group, which showed remarkably high activation of ‘INF-γ response’ and ‘allograft rejection’ pathways. The most enriched pathways in the CCII group were associated with cell organisation and tissue development, possibly correlated to more active tumour growth. This was particularly significant for the state-2 NK cells of the NK development, which showed the remarkable activation of EMT pathway (P-value ≈ 0). This was largely attributed to the upregulation of FBN1, FN1, SERPINE1, VCAN, and many collagens in the CCII group. Previous study discovered that collagens promote the accumulation of NK cells in Foci of infection near the lymph node capsule . We thus speculate that the high abundance of collagens in NK cells might be cellular mechanism responding to extremely low NK cell number in the TME of stage-II CC, seeking to recruit more active NK cells.
The proteomic analysis revealed distinct protein profiles in the CCI and CCII groups. The activation of IFN-α and γ response in the CCI group was accorded with the comparative observation in multiple cells, especially immune cell types, by snRNA-seq analysis. The DEPs upregulated in the CCII group were largely associated with extracellular matrix, cell and tissue development, and metabolism, suggesting the higher degree of tumorigenesis. AKT1 was the node DEP with the highest degree of interactions. The signalling of PI3K/AKT/mTOR was found to regulate the virus/host cell crosstalk in HPV-positive CC , and the elevated level of pAKT is associated with radiation resistance in CC . The inhibition of AKT, subsequently disrupting the signalling with mTOR, induced higher degree of cell death and decreased glucose uptake in CC . FN1 was the most upregulated DEP interacting with multiple other DEPs of the CCII group, it was shown to promote the tumorigenesis of CC via activating FAK signalling pathway . FN1 interacts with MPO, which has controversial role in different diseases , and a type of its gene polymorphism leads to reduced anti-tumour activity that may play a role in development of CC . In terms of DEPs upregulation in the CCI group, there was a big cluster of histones intensively interacting with each other, including H2A/H2B and H4 members, which play important roles in transcription, DNA replication and repair . It was identified that histone genes can be used as independent prognostic factors for survival prediction among CC patients, including HIST1H4A, HIST1H4E and HIST1H4K that were detected DEPs in this study. This implies that other histones might also be considered as markers, in combination with other DEPs upregulated in the CCI group, such as HSPs and interferon response relevant proteins, for the early detection of CC.
In this study, we have comparatively investigated the TME of stage-I and II CC patients using snRNA-seq and label-free quantitative proteomic analysis. It was evident that the heterogeneity of nearly all immune cells of stage-II CC patients, including MΦs, T cells, B cells, NK cells and dendritic cells, were largely modulated, with their phenotypes to be significantly immunosuppressive. In contrast, these cells showed elevated immune response in stage-I CC patients, with INF-α and γ response being the most activated signalling pathways. Several histones showed the potential for diagnosing CC at an early stage. Notably, for the stage-II CC patients, the significant upregulation of collagens identified respectively by snRNA-seq analysis in multiple cell types and proteomic analysis, suggested that they might be used as the prognostic markers. For the first time, the correlation between aberrant expression of novel transcript AC244205.1 and stage-II CC was revealed. These discoveries provide important clues for clinical diagnosis and immunotherapy of CC, which warrant further research.
Availability of data and materials
The snRNA-seq data is available on the Institute Single Cell Portal [https://singlecell.broadinstitute.org/single_cell] under accession number SCP1950. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD029103.
The Cancer Genome Atlas
Single-cell RNA sequencing
Single-nucleus RNA sequencing
Differentially expressed gene
Differentially expressed protein
Tumour necrosis factor-α
The International Federation of Gynecology and Obstetrics
Major histocompatibility complex
Cervical cancer stage-I
Cervical cancer stage-II
Gene set enrichment analysis
Tuyaerts S, Van Nuffel AMT, Naert E, Van Dam PA, Vuylsteke P, De Caluwe A, Aspeslagh S, Dirix P, Lippens L, De Jaeghere E, et al. PRIMMO study protocol: a phase II study combining PD-1 blockade, radiation and immunomodulation to tackle cervical and uterine cancer. BMC Cancer. 2019;19(1):506.
Kjaer SK, Dehlendorff C, Belmonte F, Baandrup L. Real-World Effectiveness of Human Papillomavirus Vaccination Against Cervical Cancer. J Natl Cancer Inst. 2021;113(10):1329–35.
Xie L, Chu R, Wang K, Zhang X, Li J, Zhao Z, Yao S, Wang Z, Dong T, Yang X, et al. Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach. Front Oncol. 2020;10:1353.
Olthof EP, van der Aa MA, Adam JA, Stalpers LJA, Wenzel HHB, van der Velden J, Mom CH. The role of lymph nodes in cervical cancer: incidence and identification of lymph node metastases-a literature review. Int J Clin Oncol. 2021;26(9):1600–10.
Wipperman J, Neil T, Williams T. Cervical Cancer: Evaluation and Management. Am Fam Physician. 2018;97(7):449–54.
Abu-Rustum NR, Yashar CM, Bean S, Bradley K, Campos SM, Chon HS, Chu C, Cohn D, Crispens MA, Damast S, et al. NCCN Guidelines Insights: Cervical Cancer, Version 1.2020. J Natl Compr Canc Netw. 2020;18(6):660–666.
Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ. Natural innate and adaptive immunity to cancer. Annu Rev Immunol. 2011;29:235–71.
Moynihan KD, Irvine DJ. Roles for Innate Immunity in Combination Immunotherapies. Cancer Res. 2017;77(19):5215–21.
Mittal D, Gubin MM, Schreiber RD, Smyth MJ. New insights into cancer immunoediting and its three component phases–elimination, equilibrium and escape. Curr Opin Immunol. 2014;27:16–25.
Marzagalli M, Ebelt ND, Manuel ER. Unraveling the crosstalk between melanoma and immune cells in the tumor microenvironment. Semin Cancer Biol. 2019;59:236–50.
Karamitopoulou E. Tumour microenvironment of pancreatic cancer: immune landscape is dictated by molecular and histopathological features. Br J Cancer. 2019;121(1):5–14.
Roma-Rodrigues C, Mendes R, Baptista PV, Fernandes AR. Targeting Tumor Microenvironment for. Cancer Therapy. 2019;20(4):840.
Patel S, Fu S, Mastio J, Dominguez GA, Purohit A, Kossenkov A, Lin C, Alicea-Torres K, Sehgal M, Nefedova Y, et al. Unique pattern of neutrophil migration and function during tumor progression. Nat Immunol. 2018;19(11):1236–47.
Long W, Chen J, Gao C, Lin Z, Xie X, Dai H. Brief review on the roles of neutrophils in cancer development. J Leukoc Biol. 2021;109(2):407–13.
Li C, Guo L, Li S, Hua K. Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and transcriptional activities of ECs in CC. Mol Ther Nucleic Acids. 2021;24:682–94.
Zhang J, Rashmi R, Inkman M, Jayachandran K, Ruiz F, Waters MR, Grigsby PW, Markovina S, Schwarz JK. Integrating imaging and RNA-seq improves outcome prediction in cervical cancer. J Clin Invest. 2021;131(5):e139232.
Gu M, He T, Yuan Y, Duan S, Li X, Shen C. Single-Cell RNA Sequencing Reveals Multiple Pathways and the Tumor Microenvironment Could Lead to Chemotherapy Resistance in Cervical Cancer. Front Oncol. 2021;11: 753386.
Yin R, Zhai X, Han H, Tong X, Li Y, Deng K. Characterizing the landscape of cervical squamous cell carcinoma immune microenvironment by integrating the single-cell transcriptomics and RNA-Seq. Immun Inflamm Dis. 2022;10(6): e608.
Ni G, Liu X, Li H, Fogarty CE, Chen S, Zhang P, Liu Y, Wu X, Wei MQ, Chen G, et al. Topical Application of Temperature-Sensitive Gel Containing Caerin 1.1 and 1.9 Peptides on TC-1 Tumour-Bearing Mice Induced High-Level Immune Response in the Tumour Microenvironment. Front Oncol. 2021;11.
Ni G, Yang X, Li J, Wu X, Liu Y, Li H, Chen S, Fogarty CE, Frazer IH, Chen G, et al. Intratumoral injection of caerin 1.1 and 1.9 peptides increases the efficacy of vaccinated TC-1 tumor-bearing mice with PD-1 blockade by modulating macrophage heterogeneity and the activation of CD8(+) T cells in the tumor microenvironment. Clin Transl Immunology. 2021;10(8).
Yang D, Zhang W, Liang J, Ma K, Chen P, Lu D, Hao W. Single cell whole genome sequencing reveals that NFKB1 mutation affects radiotherapy sensitivity in cervical cancer. Oncotarget. 2018;9(7):7332–40.
Li C, Hua K. Dissecting the Single-Cell Transcriptome Network of Immune Environment Underlying Cervical Premalignant Lesion, Cervical Cancer and Metastatic Lymph Nodes. Front Immunol. 2022;13: 897366.
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-1902.e1821.
Waltman L, van Eck NJ. A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B. 2013;86(11):471.
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015;161(5):1202–14.
Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, Richman AR, Silverbush D, Shaw ML, Hebert CM, et al. An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell. 2019;178(4):835-849.e821.
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, Qureshi M, Richardson LJ, Salazar GA, Smart A, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47(D1):D427-d432.
Hu H, Miao YR, Jia LH, Yu QY, Zhang Q, Guo AY. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 2019;47(D1):D33-d38.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447-452.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.
Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509-w514.
Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T, Xu L, Liao G, Yan M, et al. Cell Marker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 2019;47(D1):D721-d728.
Müller S, Kohanbash G, Liu SJ, Alvarado B, Carrera D, Bhaduri A, Watchmaker PB, Yagnik G, Di Lullo E, Malatesta M, et al. Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment. Genome Biol. 2017;18(1):234.
Teyssier JR, Rousset F, Garcia E, Cornillet P, Laubriet A. Upregulation of the netrin receptor (DCC) gene during activation of b lymphocytes and modulation by interleukins. Biochem Biophys Res Commun. 2001;283(5):1031–6.
Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, Kang B, Hu R, Huang JY, Zhang Q, et al. Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell. 2017;169(7):1342-1356.e1316.
Young MD, Mitchell TJ, Vieira Braga FA, Tran MGB, Stewart BJ, Ferdinand JR, Collord G, Botting RA, Popescu DM, Loudon KW, et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science. 2018;361(6402):594–9.
Villani AC, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, Griesbeck M, Butler A, Zheng S, Lazo S, et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science. 2017;356(6335):eaah4573.
Van Brussel I, Van Vré EA, De Meyer GR, Vrints CJ, Bosmans JM, Bult H. Expression of dendritic cell markers CD11c/BDCA-1 and CD123/BDCA-2 in coronary artery disease upon activation in whole blood. J Immunol Methods. 2010;362(1–2):168–75.
Acarregui MJ, England KM, Richman JT, Littig JL. Characterization of CD34+ cells isolated from human fetal lung. Am J Physiol Lung Cell Mol Physiol. 2003;284(2):L395-401.
Zhong S, Zhang S, Fan X, Wu Q, Yan L, Dong J, Zhang H, Li L, Sun L, Pan N, et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature. 2018;555(7697):524–8.
Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, Wildberg A, Gao D, Fung HL, Chen S, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352(6293):1586–90.
Karamitros D, Stoilova B, Aboukhalil Z, Hamey F, Reinisch A, Samitsch M, Quek L, Otto G, Repapi E, Doondeea J, et al. Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells. Nat Immunol. 2018;19(1):85–97.
Lokmic Z. Isolation, Identification, and Culture of Human Lymphatic Endothelial Cells. Methods Mol Biol. 2016;1430:77–90.
Gao S, Yan L, Wang R, Li J, Yong J, Zhou X, Wei Y, Wu X, Wang X, Fan X, et al. Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing. Nat Cell Biol. 2018;20(6):721–34.
Tirosh I, Venteicher AS, Hebert C, Escalante LE, Patel AP, Yizhak K, Fisher JM, Rodman C, Mount C, Filbin MG, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature. 2016;539(7628):309–13.
Li S, Huang J, Yang F, Zeng H, Tong Y, Li K. High expression of PARD3 predicts poor prognosis in hepatocellular carcinoma. Sci Rep. 2021;11(1):11078.
Hardbower DM, Coburn LA, Asim M, Singh K, Sierra JC, Barry DP, Gobert AP, Piazuelo MB, Washington MK, Wilson KT. EGFR-mediated macrophage activation promotes colitis-associated tumorigenesis. Oncogene. 2017;36(27):3807–19.
Tang PC, Chung JY, Xue VW, Xiao J, Meng XM, Huang XR, Zhou S, Chan AS, Tsang AC, Cheng AS, et al. Smad3 Promotes Cancer-Associated Fibroblasts Generation via Macrophage-Myofibroblast Transition. Adv Sci (Weinh). 2022;9(1): e2101235.
Daniel B, Nagy G, Czimmerer Z, Horvath A, Hammers DW, Cuaranta-Monroy I, Poliska S, Tzerpos P, Kolostyak Z, Hays TT, et al. The Nuclear Receptor PPARγ Controls Progressive Macrophage Polarization as a Ligand-Insensitive Epigenomic Ratchet of Transcriptional Memory. Immunity. 2018;49(4):615-626.e616.
Correa MA, Canhamero T, Borrego A, Katz ISS, Jensen JR, Guerra JL, Cabrera WHK, Starobinas N, Fernandes JG, Ribeiro OG, et al. Slc11a1 (Nramp-1) gene modulates immune-inflammation genes in macrophages during pristane-induced arthritis in mice. Inflamm Res. 2017;66(11):969–80.
Oelschlaegel D, Weiss Sadan T, Salpeter S, Krug S, Blum G, Schmitz W, Schulze A, Michl P. Cathepsin Inhibition Modulates Metabolism and Polarization of Tumor-Associated Macrophages. Cancers (Basel). 2020;12(9):2579.
Ehrnsperger A, Rehli M, Thu-Hang P, Kreutz M. Epigenetic regulation of the dendritic cell-marker gene ADAM19. Biochem Biophys Res Commun. 2005;332(2):456–64.
Ning Y, Ding J, Sun X, Xie Y, Su M, Ma C, Pan J, Chen J, Jiang H, Qi C. HDAC9 deficiency promotes tumor progression by decreasing the CD8(+) dendritic cell infiltration of the tumor microenvironment. J Immunother Cancer. 2020;8(1):e000529.
Xue D, Tabib T, Morse C, Lafyatis R. Transcriptome landscape of myeloid cells in human skin reveals diversity, rare populations and putative DC progenitors. J Dermatol Sci. 2020;97(1):41–9.
Bogdan C. Macrophages as host, effector and immunoregulatory cells in leishmaniasis: Impact of tissue micro-environment and metabolism. Cytokine X. 2020;2(4): 100041.
Sánchez-Martín L, Estecha A, Samaniego R, Sánchez-Ramón S, Vega M, Sánchez-Mateos P. The chemokine CXCL12 regulates monocyte-macrophage differentiation and RUNX3 expression. Blood. 2011;117(1):88–97.
Zakrzewska A, Cui C, Stockhammer OW, Benard EL, Spaink HP, Meijer AH. Macrophage-specific gene functions in Spi1-directed innate immunity. Blood. 2010;116(3):e1-11.
Wallerius M, Wallmann T, Bartish M, Östling J, Mezheyeuski A, Tobin NP, Nygren E, Pangigadde P, Pellegrini P, Squadrito ML, et al. Guidance Molecule SEMA3A Restricts Tumor Growth by Differentially Regulating the Proliferation of Tumor-Associated Macrophages. Cancer Res. 2016;76(11):3166–78.
Peng Z, Guan Q, Luo J, Deng W, Liu J, Yan R, Wang W. Sophoridine exerts tumor-suppressive activities via promoting ESRRG-mediated β-catenin degradation in gastric cancer. BMC Cancer. 2020;20(1):582.
Xu R, Zhu D, Guo J, Wang C. IL-18 Promotes Erythrophagocytosis and Erythrocyte Degradation by M1 Macrophages in a Calcific Microenvironment. Can J Cardiol. 2021;37(9):1460–71.
Xu S, Xu H, Wang W, Li S, Li H, Li T, Zhang W, Yu X, Liu L. The role of collagen in cancer: from bench to bedside. J Transl Med. 2019;17(1):309.
Liu S, Liao G, Li G. Regulatory effects of COL1A1 on apoptosis induced by radiation in cervical cancer cells. Cancer Cell Int. 2017;17:73.
Hou T, Tong C, Kazobinka G, Zhang W, Huang X, Huang Y, Zhang Y. Expression of COL6A1 predicts prognosis in cervical cancer patients. Am J Transl Res. 2016;8(6):2838–44.
Fu ZC, Wang FM, Cai JM. Gene expression changes in residual advanced cervical cancer after radiotherapy: indicators of poor prognosis and radioresistance? Med Sci Monit. 2015;21:1276–87.
Banerjee S, Karunagaran D. An integrated approach for mining precise RNA-based cervical cancer staging biomarkers. Gene. 2019;712: 143961.
Sun Y, Ling J, Liu L. Collagen type X alpha 1 promotes proliferation, invasion and epithelial-mesenchymal transition of cervical cancer through activation of TGF-β/Smad signaling. Physiol Int. 2022;109:204–214.
Li Q, Wang Q, Zhang Q, Zhang J, Zhang J. Collagen prolyl 4-hydroxylase 2 predicts worse prognosis and promotes glycolysis in cervical cancer. Am J Transl Res. 2019;11(11):6938–51.
Chen Z, Yu M, Yan J, Guo L, Zhang B, Liu S, Lei J, Zhang W, Zhou B, Gao J, et al. PNOC Expressed by B Cells in Cholangiocarcinoma Was Survival Related and LAIR2 Could Be a T Cell Exhaustion Biomarker in Tumor Microenvironment: Characterization of Immune Microenvironment Combining Single-Cell and Bulk Sequencing Technology. Front Immunol. 2021;12: 647209.
Tian S, Chen H, Tan W. Targeting mitochondrial respiration as a therapeutic strategy for cervical cancer. Biochem Biophys Res Commun. 2018;499(4):1019–24.
Li H, Jiao S, Li X, Banu H, Hamal S, Wang X. Therapeutic effects of antibiotic drug mefloquine against cervical cancer through impairing mitochondrial function and inhibiting mTOR pathway. Can J Physiol Pharmacol. 2017;95(1):43–50.
Riera Leal A, Ortiz-Lazareno PC, Jave-Suárez LF, Ramírez De Arellano A, Aguilar-Lemarroy A, Ortiz-García YM, Barrón-Gallardo CA, Solís-Martínez R, Luquin De Anda S, Muñoz-Valle JF et al: 17β‑estradiol‑induced mitochondrial dysfunction and Warburg effect in cervical cancer cells allow cell survival under metabolic stress. Int J Oncol 2020, 56(1):33–46.
Zhao Q, Wang W, Cui J. Melatonin enhances TNF-α-mediated cervical cancer HeLa cells death via suppressing CaMKII/Parkin/mitophagy axis. Cancer Cell Int. 2019;19:58.
Xia ZN, Wang XY, Cai LC, Jian WG, Zhang C. IGLL5 is correlated with tumor-infiltrating immune cells in clear cell renal cell carcinoma. FEBS Open Bio. 2021;11(3):898–910.
Liang F, Qu H, Lin Q, Yang Y, Ruan X, Zhang B, Liu Y, Yu C, Zhang H, Fang X, et al. Molecular biomarkers screened by next-generation RNA sequencing for non-sentinel lymph node status prediction in breast cancer patients with metastatic sentinel lymph nodes. World J Surg Oncol. 2015;13:258.
Peruzzi D, Mori F, Conforti A, Lazzaro D, De Rinaldis E, Ciliberto G, La Monica N, Aurisicchio L. MMP11: a novel target antigen for cancer immunotherapy. Clin Cancer Res. 2009;15(12):4104–13.
Zhang X, Huang S, Guo J, Zhou L, You L, Zhang T, Zhao Y. Insights into the distinct roles of MMP-11 in tumor biology and future therapeutics (Review). Int J Oncol. 2016;48(5):1783–93.
Roscilli G, Cappelletti M, De Vitis C, Ciliberto G, Di Napoli A, Ruco L, Mancini R, Aurisicchio L. Circulating MMP11 and specific antibody immune response in breast and prostate cancer patients. J Transl Med. 2014;12:54.
Vazquez-Ortiz G, Pina-Sanchez P, Vazquez K, Duenas A, Taja L, Mendoza P, Garcia JA, Salcedo M. Overexpression of cathepsin F, matrix metalloproteinases 11 and 12 in cervical cancer. BMC Cancer. 2005;5:68.
Valdivia A, Peralta R, Matute-González M, García Cebada JM, Casasola I, Jiménez-Medrano C, Aguado-Pérez R, Villegas V, González-Bonilla C, Manuel-Apolinar L, et al. Co-expression of metalloproteinases 11 and 12 in cervical scrapes cells from cervical precursor lesions. Int J Clin Exp Pathol. 2011;4(7):674–82.
Eiró N, Fernandez-Garcia B, Vázquez J, Del Casar JM, González LO, Vizoso FJ. A phenotype from tumor stroma based on the expression of metalloproteases and their inhibitors, associated with prognosis in breast cancer. Oncoimmunology. 2015;4(7): e992222.
Eiró N, Pidal I, Fernandez-Garcia B, Junquera S, Lamelas ML, del Casar JM, González LO, López-Muñiz A, Vizoso FJ. Impact of CD68/(CD3+CD20) ratio at the invasive front of primary tumors on distant metastasis development in breast cancer. PLoS ONE. 2012;7(12): e52796.
Eiro N, González L, Martínez-Ordoñez A, Fernandez-Garcia B, González LO, Cid S, Dominguez F, Perez-Fernandez R, Vizoso FJ. Cancer-associated fibroblasts affect breast cancer cell gene expression, invasion and angiogenesis. Cell Oncol (Dordr). 2018;41(4):369–78.
Zhang X, Wang Y, Cao Y, Zhang X, Zhao H. Increased CCL19 expression is associated with progression in cervical cancer. Oncotarget. 2017;8(43):73817–25.
Shi D, Jiang K, Fu Y, Fang R, Liu XI, Chen J. Overexpression of SPARC correlates with poor prognosis in patients with cervical carcinoma and regulates cancer cell epithelial-mesenchymal transition. Oncol Lett. 2016;11(5):3251–8.
de Wilde J, Wilting SM, Meijer CJ, van de Wiel MA, Ylstra B, Snijders PJ, Steenbergen RD. Gene expression profiling to identify markers associated with deregulated hTERT in HPV-transformed keratinocytes and cervical cancer. Int J Cancer. 2008;122(4):877–88.
Naito Z, Ishiwata T, Kurban G, Teduka K, Kawamoto Y, Kawahara K, Sugisaki Y. Expression and accumulation of lumican protein in uterine cervical cancer cells at the periphery of cancer nests. Int J Oncol. 2002;20(5):943–8.
Lecavalier-Barsoum M, Chaudary N, Han K, Pintilie M, Hill RP, Milosevic M. Correction: Targeting CXCL12/CXCR4 and myeloid cells to improve the therapeutic ratio in patient-derived cervical cancer models treated with radio-chemotherapy. Br J Cancer. 2019;121(7):626.
Dogan S, Terzioglu E, Ucar S. Innate immune response against HPV: Possible crosstalking with endocervical γδ T cells. J Reprod Immunol. 2021;148: 103435.
Wu Y, Ye S, Goswami S, Pei X, Xiang L, Zhang X, Yang H. Clinical significance of peripheral blood and tumor tissue lymphocyte subsets in cervical cancer patients. BMC Cancer. 2020;20(1):173.
Li H, Wang Y, Zhou F. Effect of ex vivo-expanded γδ-T cells combined with galectin-1 antibody on the growth of human cervical cancer xenografts in SCID mice. Clin Invest Med. 2010;33(5):E280-289.
Van Hede D, Polese B, Humblet C, Wilharm A, Renoux V, Dortu E, de Leval L, Delvenne P, Desmet CJ, Bureau F, et al. Human papillomavirus oncoproteins induce a reorganization of epithelial-associated γδ T cells promoting tumor formation. Proc Natl Acad Sci U S A. 2017;114(43):E9056-e9065.
Coombes JL, Han SJ, van Rooijen N, Raulet DH, Robey EA. Infection-induced regulation of natural killer cells by macrophages and collagen at the lymph node subcapsular sinus. Cell Rep. 2012;2(1):124–35.
Bossler F, Hoppe-Seyler K, Hoppe-Seyler F: PI3K/AKT/mTOR Signaling Regulates the Virus/Host Cell Crosstalk in HPV-Positive Cervical Cancer Cells. Int J Mol Sci 2019, 20(9).
Kim TJ, Lee JW, Song SY, Choi JJ, Choi CH, Kim BG, Lee JH, Bae DS. Increased expression of pAKT is associated with radiation resistance in cervical cancer. Br J Cancer. 2006;94(11):1678–82.
Rashmi R, DeSelm C, Helms C, Bowcock A, Rogers BE, Rader JL, Rader J, Grigsby PW, Schwarz JK. AKT inhibitors promote cell death in cervical cancer through disruption of mTOR signaling and glucose uptake. PLoS ONE. 2014;9(4): e92948.
Zhou Y, Shu C, Huang Y: Fibronectin promotes cervical cancer tumorigenesis through activating FAK signaling pathway. J Cell Biochem 2019.
Castelão C, da Silva AP, Matos A, Inácio Â, Bicho M, Medeiros R, Bicho MC. Association of myeloperoxidase polymorphism (G463A) with cervix cancer. Mol Cell Biochem. 2015;404(1–2):1–4.
Mäkelä S, Hurme M, Ala-Houhala I, Mustonen J, Koivisto AM, Partanen J, Vapalahti O, Vaheri A, Pasternack A. Polymorphism of the cytokine genes in hospitalized patients with Puumala hantavirus infection. Nephrol Dial Transplant. 2001;16(7):1368–73.
Sadeghi L, Siggens L, Svensson JP, Ekwall K. Centromeric histone H2B monoubiquitination promotes noncoding transcription and chromatin integrity. Nat Struct Mol Biol. 2014;21(3):236–43.
Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47(D1):D442-d450.
We thank Professor Abigail Elizur for her valuable advice and support. We are grateful for the sequencing platform and/or bioinformation analysis and proteomic experiment of Gene Denovo Biotechnology Co., Ltd (Guangzhou, China) and Applied Protein Technology, Co Ltd (Shanghai, China). We are indebted to all patients, nurses, and doctors who participated in or contributed to this study.
This study was supported in part by the First Affiliated Hospital of Guangdong Pharmaceutical University, Deng Feng project of Foshan First People’s Hospital (2019A008), Foshan municipal Government (2015AG1003), Guangdong Science and Technology Department (2016A020213001), National Science Foundation of Guangdong province (2020A1515010855), National Science Foundation of China (31971355). The funders were not involved in the design, data collection and analysis, preparation, or publication of the manuscript.
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The ethical approval for collecting pathological samples of patients was L2016, First People’s Hospital of Foshan Ethics Committee.
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The authors declare no competing interests.
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Liu, X., Ni, G., Zhang, P. et al. Single-nucleus RNA sequencing and deep tissue proteomics reveal distinct tumour microenvironment in stage-I and II cervical cancer. J Exp Clin Cancer Res 42, 28 (2023). https://doi.org/10.1186/s13046-023-02598-0