Development of a metabolism-associated prognostic risk model based on immune landscape stratification in prostate cancer

Background: Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy.Method: In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) analysis and systematically correlated with genomic characteristics and clinicopathological features of IDC patients. Furthermore, an immune signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A random forest algorithm was applied to identify the most important somatic gene mutations associated with the constructed immune signature. A nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed by multivariate Cox regression.Results: The IDC were clustered into low immune infiltration, intermediate immune infiltration, and high immune infiltration by the immune landscape. The high infiltration group had a favorable survival probability compared with that of the low infiltration group. The low-risk score subtype identified by the immune signature was characterized by T cell-mediated immune activation. Additionally, activation of the interferon-α response, interferon-γ response, and TNF-α signaling via the NFκB pathway was observed in the low-risk score subtype, which indicated T cell activation and may be responsible for significantly favorable outcomes in IDC patients. A random forest algorithm identified the most important somatic gene mutations associated with the constructed immune signature. Furthermore, a nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed, revealing that the immune signature was an independent prognostic biomarker. Finally, the relationship of VEGFA, PD1, PDL-1, and CTLA-4 expression with the immune infiltration landscape and the immune signature was analyzed to interpret the responses of IDC patients to immunotherapy.Conclusion: Taken together, we performed a comprehensive evaluation of the immune landscape of IDC and constructed an immune signature related to the immune landscape. This analysis of TME immune infiltration landscape has shed light on how IDC respond to immunotherapy and may guide the development of novel drug combination strategies.

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

Glioma is the most common primary malignancy of the adult central nervous system (CNS), with a poor prognosis and no effective prognostic signature. Since late 2019, the world has been affected by the rapid spread of SARS-CoV-2 infection. Research on SARS-CoV-2 is flourishing; however, its potential mechanistic association with glioma has rarely been reported. The aim of this study was to investigate the potential correlation of SARS-CoV-2-related genes with the occurrence, progression, prognosis, and immunotherapy of gliomas. SARS-CoV-2-related genes were obtained from the human protein atlas (HPA), while transcriptional data and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Glioma samples were collected from surgeries with the knowledge of patients. Differentially expressed genes were then identified and screened, and seven SARS-CoV-2 related genes were generated by LASSO regression analysis and uni/multi-variate COX analysis. A prognostic SARS-CoV-2-related gene signature (SCRGS) was then constructed based on these seven genes and validated in the TCGA validation cohort and CGGA cohort. Next, a nomogram was established by combining critical clinicopathological data. The correlation between SCRGS and glioma related biological processes was clarified by Gene set enrichment analysis (GSEA). In addition, immune infiltration and immune score, as well as immune checkpoint expression and immune escape, were further analyzed to assess the role of SCRGS in glioma-associated immune landscape and the responsiveness of immunotherapy. Finally, the reliability of SCRGS was verified by quantitative real-time polymerase chain reaction (qRT-PCR) on glioma samples. The prognostic SCRGS contained seven genes, REEP6, CEP112, LARP4B, CWC27, GOLGA2, ATP6AP1, and ERO1B. Patients were divided into high- and low-risk groups according to the median SARS-CoV-2 Index. Overall survival was significantly worse in the high-risk group than in the low-risk group. COX analysis and receiver operating characteristic (ROC) curves demonstrated excellent predictive power for SCRGS for glioma prognosis. In addition, GSEA, immune infiltration, and immune scores indicated that SCRGS could potentially predict the tumor microenvironment, immune infiltration, and immune response in glioma patients. The SCRGS established here can effectively predict the prognosis of glioma patients and provide a potential direction for immunotherapy.

BackgroundThe extracellular matrix (ECM) plays a crucial role in the development and tumor microenvironment of lung adenocarcinoma (LUAD). This study aimed to establish a risk score of ECM-related genes in LUAD and explore the association between the risk score and patient survival as well as immune cell infiltration, somatic mutations, and therapy response. MethodsGene expression data from The Cancer Genome Atlas (TGCA) and eight Gene Expression Omnibus (GEO) databases were used to analyze and identify differentially expressed genes (DEGs). Prognostic ECM-related genes were identified and utilized to formulate a prognostic signature. A nomogram was constructed using TCGA dataset and validated in two GEO datasets. Differences between high- and low-risk patients were analyzed for function enrichment, immune cell infiltration, somatic mutations, and therapy response. Finally, Quantitative real-time PCR (qRT-PCR) was used to detect the mRNA expression of DEGs in LUAD. ResultsA risk score based on four ECM-related genes, ANOS1, CD36, COL11A1, and HMMR, was identified as an independent prognostic factor for overall survival (OS) compared to other clinical variables. Subsequently, a nomogram incorporating the risk score and TNM staging was developed using the TCGA dataset. Internal and external validation of the nomogram, conducted through calibration plots, C-index, time-dependent receiver operating characteristics (ROC), integrated discrimination improvement (IDI), and decision curve analyses (DCA), demonstrated the excellent discriminatory ability and clinical practicability of this nomogram. The risk score correlated with the distribution of function enrichment, immune cell infiltration, and immune checkpoint expression. More somatic mutations occurred in the high-risk group. The risk score also demonstrated a favorable ability to predict immunotherapy response and drug sensitivity. ConclusionA novel signature based on four ECM-related genes is developed to help predict LUAD prognosis. This signature correlates with tumor immune microenvironment and can predict the response to different therapies in LUAD patients.

Hepatocellular carcinoma (HCC) is known for its high invasiveness, high fatality rate. Both hypoxia and senescence play crucial roles in the initiation and progression of cancer, yet their prognostic implications in HCC are yet to be fully understood. The hypoxia-senescence co-related genes (HSCRGs) were screened from public databases. Transcriptome data and clinical information were obtained from patients with HCC using the Cancer Genome Atlas, GSE76427, and International Cancer Genome Consortium (ICGC). The random forest tree algorithm was used to identify the characteristic genes of the disease, and the genes were verified by related experiments. SVM algorithm was used to classify HCC patients based on HSCRGs. The prediction model based on HSCRGs was established by LASSO, univariate and multivariate COX regression analysis. We used the ICGC for outside validation. The risk score model was analyzed from subgroup analysis, immune infiltration, and functional strength. The expression patterns of key prognostic genes in tumor microenvironment were decoded by single cell analysis. A total of 184 HSCRGs were identified. The expression pattern and functional characteristics of MLH1 gene in HCC were verified. Two HCC subtypes were identified based on HSCRGs. Then, a prediction model based on HSCRGs was established, and risk score was identified as an independent prognostic indicator of HCC. A new nomogram is constructed and shows good prediction ability. We further determined that the level of infiltration of immune cells and the expression of immune checkpoints are significantly affected by the risk score. The immune microenvironment was different between the two risk groups. The high-risk group was dominated by immunosuppressed cells, and the prognosis was poor. Single-cell analysis revealed the expression of seven key prognostic genes in the tumor microenvironment. Finally, qPCR results further verified the expression levels of seven prognostic genes. HSCRGs are of great significance in the prognosis prediction, risk stratification and targeted therapy of patients with HCC.

Purpose To investigate the expression of LPCAT1 in liver hepatocellular carcinoma (LIHC) and its relationship with prognosis and immune infiltration and predict its upstream nonencoding RNAs (ncRNAs). Method In this study, expression analysis and survival analysis for LPCAT1 in pan cancers were first performed by using The Cancer Genome Atlas (TCGA) data, which suggested that LPCAT1 might be a potential LIHC oncogene. Then, ncRNAs contributing to the overexpression of LPCAT1 were explored in starBase by a combination of expression analysis, correlation analysis, and survival analysis. Immune cell infiltration of LPCAT1 in LIHC was finally investigated via Tumor Immune Estimation Resource (TIMER). Result SNHG3 was observed to be the most promising upstream lncRNA for the hsa-miR-139-5p/LPCAT1 axis in LIHC. In addition, the LPCAT1 level was significantly positively associated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in LIHC. Conclusion To summarize, the upregulation of LPCAT1 mediated by ncRNAs is associated with poor prognosis, immune infiltration, and immune checkpoint expression in LIHC.

Background Hepatocellular carcinoma (HCC) is aggressive cancer with a poor prognosis. It has been suggested that the aberrant expression of LOXL2 is associated with the development of HCC, but the exact mechanism remains unclear. This research is aimed at examining the expression level and prognostic value of LOXL2 in hepatocellular carcinoma and its relationship with immune infiltration and at predicting its upstream noncoding RNAs (ncRNAs). Method The transcriptome data of HCC was first downloaded from The Cancer Genome Atlas (TCGA) database to investigate the expression and prognosis of LOXL2. Then, the starBase database was used to find the upstream ncRNAs of LOXL2, and correlation analysis and expression analysis were performed. Finally, the Tumor Immune Estimation Resource (TIMER) was used to explore the association between LOXL2 and immune cell infiltration. Result CARMN was considered to be the potential upstream lncRNA for the hsa-miR-192-5p/LOXL2 axis in HCC. Furthermore, the level LOXL2 was markedly positively associated with tumor immune cell infiltration and immune checkpoint expression in HCC. Conclusion Higher expression of LOXL2 mediated by microRNA (miRNA) and long noncoding RNAs (lncRNA) is associated with poor overall survival (OS), immune infiltration, and immune checkpoint expression in HCC.

Neoadjuvant, endocrine, and targeted therapies have significantly improved the prognosis of breast cancer (BC). However, due to the high heterogeneity of cancer, some patients cannot benefit from existing treatments. Increasing evidence suggests that amino acids and their metabolites can alter the tumor malignant behavior through reshaping tumor microenvironment and regulation of immune cell function. Breast cancer cell lines have been identified as methionine-dependent, and methionine restriction has been proposed as a potential cancer treatment strategy. We integrated transcriptomic and single-cell RNA sequencing (ScRNA-seq) analyses based on The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Then we applied weighted gene co-expression network analysis (WGCNA) and Cox regression to evaluate methionine metabolism-related genes (MRGs) in BC, constructing and validating a prognostic model for BC patients. Immune landscapes and immunotherapy were further explored. Finally, in vitro experiments were conducted to assess the expression and function of key genes APOC1. In this study, we established and validated a prognostic signature based on eight methionine-related genes to predict overall survival (OS) in BC patients. Patients were further stratified into high-risk and low-risk groups according to prognostic risk score. Further analysis revealed significant differences between two groups in terms of pathway alterations, immune microenvironment characteristics, and immune checkpoint expression. Our study shed light on the relationship between methionine metabolism and immune infiltration in BC. APOC1, a key gene in the prognostic signature, was found to be upregulated in BC and closely associated with immune cell infiltration. Notably, APOC1 was primarily expressed in macrophages. Subsequent in vitro experiments demonstrated that silencing APOC1 reduced the generation of tumor-associated macrophages (TAMs) with an M2 phenotype while significantly decreasing the proliferation, invasion, and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines. We established a prognostic risk score consisting of genes associated with methionine metabolism, which helps predict prognosis and response to treatment in BC. The function of APOC1 in regulating macrophage polarization was explored.

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

Long noncoding RNAs (lncRNAs) participate in cancer immunity. We characterized the clinical significance of an immune-related lncRNA model and evaluated its association with immune infiltrations and chemosensitivity in bladder cancer. Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed, and receiver operator characteristic curves of one-, three- and five-year survival were established. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups. Totally, 90 immune-related lncRNA pairs were identified, 15 of which were screened for constructing the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered patients into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. The risk model was related to survival status, age, stage and TNM. Compared with conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and served as an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate. Collectively, the model conducted by paring immune-related lncRNAs regardless of expressions exhibits a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.

BackgroundGlioblastoma (GBM) originates from neuroepithelial tissue and is one of the most common intracranial malignant tumors in adults, with high recurrence rate and poor prognosis. In recent years, SOX9 has been reported to play an important role in many diseases and cancers, and is a promising target, but it has been rarely reported in GBM.MethodsRNA sequencing data of GBM were obtained from the Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database for analysis of SOX9 expression and differentially expressed genes (DEGs). Moreover, functional enrichment analysis of GBM-related DEGs was performed by GO/KEGG, GSEA, and protein-protein interaction (PPI) network. Additionally, the clinical significance of SOX9 in GBM was assessed by Kaplan-Meier Cox regression and prognostic model. What’s more, we analyzed SOX9-related immune cell infiltration and expression of immune checkpoints in GBM. The incorporated studies were analyzed using the R package.ResultsSOX9 was highly expressed in a range of malignant tumor tissues, including GBM. Surprisingly, high SOX9 expression was remarkably associated with better prognosis in the lymphoid invasion subgroups in a sample of 478 cases (P < 0.05). Totally, 126 differentially significant genes (DSGs) were identified between high- and low- expression group, of which 29 genes were upregulated and 97 genes were downregulated. Furthermore, high expression of SOX9 was an independent prognostic factor for IDH (isocitrate dehydrogenase)-mutant in Cox regression analysis. Screening was performed by LASSO coefficients to select non-zero variables that satisfied the coefficients of lambda. min, and four genes were screened out. OR4K2 and IDH status were prognostic factors associated with THCA in multifactorial COX regression analysis. SOX9, OR4K2 and IDH status were included in the nomogram prognostic model. Correlation analysis indicated SOX9 expression was correlated with immune cell infiltration and expression of immune checkpoints in GBM.ConclusionSOX9 was identified as a diagnostic and prognostic biomarker in glioblastoma, particularly in IDH-mutant cases. Its expression was closely correlated with immune infiltration and checkpoint expression, indicating its involvement in the immunosuppressive tumor microenvironment. SOX9-based gene signatures further supported a robust nomogram model, underscoring its potential as a therapeutic and prognostic target in GBM.

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6‐methylguanine‐DNA methyl‐transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

Pan-cancer analysis revealing DAAM1 as a novel predictive biomarker for PD-1/PD-L1 blockade in clear cell renal cell carcinoma.

Skin Cutaneous Melanoma (SKCM) is a malignant tumor and the prediction of its prognosis remains challenging. Sex determining region Y-box 10 (SOX10) is over-expressed in SKCM and reported to accelerate tumor invasion and immunosuppression. Although studies have suggested the correlation of immune infiltration between SOX10 and SKCM, further in-depth explore of the immunomodulatory role of SOX10 is still needed. Therefore, we assessed the prognostic role of SOX10 and its correlation with immune infiltration and checkpoint expression. RNA sequencing data were obtained for analysis of SOX10 expression and differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA). Moreover, functional enrichment analysis of SOX10-related DEGs was performed by GO/KEGG, GSEA. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of SOX10 in SKCM. Kaplan-Meier method was conducted to assess the effect of SOX10 on survival. Additionally, the clinical significance of SOX10 in SKCM was figured out by LASSO and prognostic nomogram model. We analyzed SOX10-related immune cell infiltration and expression of immune checkpoints. Finally, validations were performed through immunohistochemical analysis. SOX10 was low expressed in a range of malignant tumor tissues except SKCM. Totally, 1029 differentially significant genes (DSGs) were identified between SOX10 low- and high- expression group, of which 50 genes were upregulated and 979 genes were downregulated. Additionally, SOX10 high expression was remarkably associated with pathologic stage, age and breslow depth in a sample of 472 cases (P < 0.05). Screening was performed by LASSO coefficients to select non-zero variables that satisfied the coefficients of lambda, and 8 genes were screened out. The forest plot results showed that only OCA2 and TRAT1 had statistical significance (P < 0.05) by multi-factor COX regression analysis. SOX10, OCA2, TRAT1, pathologic stage, age and breslow depth were included in the nomogram prognostic model. Furthermore, upregulation of SOX10 expression inhibited immune infiltration in SKCM. Overall, high expression of SOX10 was correlated with poor prognosis in SKCM, which may be related to suppression of immune infiltration. The DSGs and pathways identified in our research have initially provided an insight into the molecular mechanisms underlying the progression of SKCM.

Understanding the role of N6-adenosine methylation (m6A) in the tumor microenvironment (TME) is important since it can contribute to tumor development. However, the research investigating the association between m6A and TME and cervical cancer is still in its early stages. The aim of this study was to discover the possible relationship between m6A RNA methylation regulators, TME, PD-L1 expression levels, and immune infiltration in cervical cancer. We gathered RNA-seq transcriptome data and clinical information from cervical cancer patients using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. To begin, researchers assessed the differences in m6A regulatory factor expression levels between cervical cancer and normal tissues. Clustering analysis was adapted to assess PD-L1 expression, immunological score, immune cell infiltration, TME, and probable pathways in cervical cancer samples. The majority of m6A regulators were found to be considerably overexpressed in cervical cancer tissues. Using consensus clustering of 21 m6A regulators, we identified two subtypes (clusters 1/2) of cervical cancer, and we found that WHO stage and grade were associated with the subtypes. PD-L1 expression increased dramatically in cervical cancer tissues and was significantly linked to ALKBH5, FTO, METTL3, RBM15B, YTHDF1, YTHDF3, and ZC3H13 expression levels. Plasma cells and regulatory T cells (Tregs) were considerably elevated in cluster 2. Cluster 1 is involved in numerous signature pathways, including basal transcription factors, cell cycle, RNA degradation, and the spliceosome. The prognostic signature-based riskscore (METTL16, YTHDF1, and ZC3H13) was found to be an independent prognostic indicator of cervical cancer. The tumor immune microenvironment (TIME) was linked to m6A methylation regulators, and changes in their copy number will affect the quantity of tumor-infiltrating immune cells dynamically. Overall, our research discovered a powerful predictive signature based on m6A RNA methylation regulators. This signature correctly predicted the prognosis of cervical cancer patients. The m6A methylation regulator could be a critical mediator of PD-L1 expression and immune cell infiltration, and it could have a significant impact on the TIME of cervical cancer.