A seven‐DNA methylation signature as a novel prognostic biomarker in breast cancer

Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer

Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.

DNA methylation patterns associated with breast cancer prognosis that are specific to tumor subtype and menopausal status

Tumor subtype and menopausal status are strong predictors of breast cancer (BC) prognosis. We aimed to find and validate subtype- or menopausal-status-specific changes in tumor DNA methylation (DNAm) associated with all-cause mortality or BC progression. Associations between site-specific tumor DNAm and BC prognosis were estimated among The Cancer Genome Atlas participants (n = 692) with Illumina Infinium HumanMethylation450 BeadChip array data. All-cause mortality and BC progression were modeled using Cox proportional hazards models stratified by tumor subtypes, adjusting for age, race, stage, menopausal status, tumor purity, and cell type proportion. Effect measure modification by subtype and menopausal status were evaluated by incorporating a product term with DNAm. Site-specific inference was used to identify subtype- or menopausal-status-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset (GSE72308; n = 180). We identified a total of fifteen unique CpG probes that were significantly associated ( P ≤ 1 × 10 - 7 with survival outcomes in subtype- or menopausal-status-specific manner. Seven probes were associated with overall survival (OS) or progression-free interval (PFI) for women with luminal A subtype, and four probes were associated with PFI for women with luminal B subtype. Five probes were associated with PFI for post-menopausal women. A majority of significant probes showed a lower risk of OS or BC progression with higher DNAm. We identified subtype- or menopausal-status-specific DMRs and functional pathways of which top associated pathways differed across subtypes or menopausal status. None of significant probes from site-specific analyses met genome-wide significant level in validation analyses while directions and magnitudes of coefficients showed consistent pattern. We have identified subtype- or menopausal-status-specific DNAm biomarkers, DMRs and functional pathways associated with all-cause mortality or BC progression, albeit with limited validation. Future studies with larger independent cohort of non-post-menopausal women with non-luminal A subtypes are warranted for identifying subtype- and menopausal-status-specific DNAm biomarkers for BC prognosis.

Methylation-based reclassification and risk stratification of skull-base chordomas

BACKGROUND

Skull-base chordomas are rare malignant bone cancers originating from the remnant of the notochord. Survival is variable, and clinical or molecular factors cannot reliably predict their outcomes. This study therefore identified epigenetic subtypes that defined new chordoma epigenetic profiles and their corresponding characteristics.

METHODS

Methylation profiles of 46 chordoma-resected neoplasms between 2008 and 2014, along with clinical information, were collected. K-means consensus clustering and principal component analysis were used to identify and validate the clusters. Single-sample gene set enrichment analysis, methylCIBERSORT algorithm, and copy number analysis were used to identify the characteristics of the clusters.

RESULTS

Unsupervised clustering analysis confirmed two clusters with a progression-free survival difference. Gene set enrichment analysis indicated that the early and late estrogen response pathways and the hypoxia pathway were activated whereas the inflammatory and interferon gamma responses were suppressed. Forty-six potential therapeutic targets corresponding to differentially methylated sites were identified from chordoma patients. Subgroups with a worse outcome were characterized by low immune cell infiltration, higher tumor purity, and higher stemness indices. Moreover, copy number amplifications mostly occurred in cluster 1 tumors and the high-risk group. Additionally, the presence of a CCNE1 deletion was exclusively found in the group of chordoma patients with better outcome, whereas RB1 and CDKN2A/2B deletions were mainly found in the group of chordoma patients with worse outcome.

CONCLUSIONS

Chordoma prognostic epigenetic subtypes were identified, and their corresponding characteristics were found to be variable.

Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models

BACKGROUND

The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.

METHODS

Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.

RESULTS

The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations.

CONCLUSIONS

The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.

DNA methylation and breast cancer: A way forward (Review)

The current management of breast cancer (BC) lacks specific non‑invasive biomarkers able to provide an early diagnosis of the disease. Epigenetic‑sensitive signatures are influenced by environmental exposures and are mediated by direct molecular mechanisms, mainly guided by DNA methylation, which regulate the interplay between genetic and non‑genetic risk factors during cancerogenesis. The inactivation of tumor suppressor genes due to promoter hypermethylation is an early event in carcinogenesis. Of note, targeted tumor suppressor genes are frequently hypermethylated in patient‑derived BC tissues and peripheral blood biospecimens. In addition, epigenetic alterations in triple‑negative BC, as the most aggressive subtype, have been identified. Thus, detecting both targeted and genome‑wide DNA methylation changes through liquid‑based assays appears to be a useful clinical strategy for early detection, more accurate risk stratification and a personalized prediction of therapeutic response in patients with BC. Of note, the DNA methylation profile may be mapped by isolating the circulating tumor DNA from the plasma as a more accessible biospecimen. Furthermore, the sensitivity to treatment with chemotherapy, hormones and immunotherapy may be altered by gene‑specific DNA methylation, suggesting novel potential drug targets. Recently, the use of epigenetic drugs administered alone and/or with anticancer therapies has led to remarkable results, particularly in patients with BC resistant to anticancer treatment. The aim of the present review was to provide an update on DNA methylation changes that are potentially involved in BC development and their putative clinical utility in the fields of diagnosis, prognosis and therapy.

Role of Immune Cell-Specific Hypermethylation Signatures in Classification and Risk Stratification of Breast Cancer

Background: Epigenetic regulation, including DNA methylation, plays a major role in shaping the identity and function of immune cells. Innate and adaptive immune cells recruited into tumor tissues contribute to the formation of the tumor immune microenvironment (TIME), which is closely involved in tumor progression in breast cancer (BC). However, the specific methylation signatures of immune cells have not been thoroughly investigated yet. Additionally, it remains unknown whether immune cells-specific methylation signatures can identify subgroups and stratify the prognosis of BC patients. Methods: DNA methylation profiles of six immune cell types from eight datasets downloaded from the Gene Expression Omnibus were collected to identify immune cell-specific hypermethylation signatures (IC-SHMSs). Univariate and multivariate cox regression analyses were performed using BC data obtained from The Cancer Genome Atlas to identify the prognostic value of these IC-SHMSs. An unsupervised clustering analysis of the IC-SHMSs with prognostic value was performed to categorize BC patients into subgroups. Multiple Cox proportional hazard models were constructed to explore the role of IC-SHMSs and their relationship to clinical characteristics in the risk stratification of BC patients. Integrated discrimination improvement (IDI) was performed to determine whether the improvement of IC-SHMSs on clinical characteristics in risk stratification was statistically significant. Results: A total of 655 IC-SHMSs of six immune cell types were identified. Thirty of them had prognostic value, and 10 showed independent prognostic value. Four subgroups of BC patients, which showed significant heterogeneity in terms of survival prognosis and immune landscape, were identified. The model incorporating nine IC-SHMSs showed similar survival prediction accuracy as the clinical model incorporating age and TNM stage [3-year area under the curve (AUC): 0.793 vs. 0.785; 5-year AUC: 0.735 vs. 0.761]. Adding the IC-SHMSs to the clinical model significantly improved its prediction accuracy in risk stratification (3-year AUC: 0.897; 5-year AUC: 0.856). The results of IDI validated the statistical significance of the improvement (p < 0.05). Conclusions: Our study suggests that IC-SHMSs may serve as signatures of classification and risk stratification in BC. Our findings provide new insights into epigenetic signatures, which may help improve subgroup identification, risk stratification, and treatment management.

A High Epigenetic Risk Score Shapes the Non-Inflamed Tumor Microenvironment in Breast Cancer

Background: Epigenetic dysregulation via aberrant DNA methylation has gradually become recognized as an efficacious signature for predicting tumor prognosis and response to therapeutic targets. However, reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC).

Methods: Whole-genome methylation datasets integrated from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were profiled (n = 1,268). A three-stage selection procedure (discovery, training, and external validation) was utilized to screen out the prominent biomarkers and establish a robust risk score from more than 300,000 CpG sites after quality control, rigorous filtering, and reducing dimension. Moreover, gene set enrichment analyses guided us to systematically correlate this epigenetic risk score with immunological characteristics, including immunomodulators, anti-cancer immunity cycle, immune checkpoints, tumor-infiltrating immune cells and a series of signatures upon modulating components within BC tumor microenvironment (TME). Multi-omics data analyses were performed to decipher specific genomic alterations in low- and high-risk patients. Additionally, we also analyzed the role of risk score in predicting response to several treatment options.

Results: A 10-CpG-based prognostic signature which could significantly and independently categorize BC patients into distinct prognoses was established and sufficiently validated. And we hypothesize that this signature designs a non-inflamed TME in BC based on the evidence that the derived risk score is negatively correlated with tumor-associated infiltrating immune cells, anti-cancer immunity cycle, immune checkpoints, immune cytolytic activity, T cell inflamed score, immunophenoscore, and the vast majority of immunomodulators. The identified high-risk patients were characterized by upregulation of immune inhibited oncogenic pathways, higher TP53 mutation and copy number burden, but lower response to cancer immunotherapy and chemotherapy.

Conclusion: Our work highlights the complementary roles of 10-CpG-based signature in estimating overall survival in BC patients, shedding new light on investigating failed events concerning immunotherapy at present.

The epigenetics of breast cancer - Opportunities for diagnostics, risk stratification and therapy

The stage and molecular pathology-dependent prognosis of breast cancer, the limited treatment options for triple-negative carcinomas, as well as the development of resistance to therapies illustrate the need for improved early diagnosis and the development of new therapeutic approaches. Increasing data suggests that some answers to these challenges could be found in the area of epigenetics. In this study, we focus on the current research of the epigenetics of breast cancer, especially on the potential of epigenetics for clinical application in diagnostics, risk stratification and therapy. The differential DNA methylation status of specific gene regions has been used in the past to differentiate breast cancer cells from normal tissue. New technologies as detection of circulating nucleic acids including microRNAs to early detect breast cancer are emerging. Pattern of DNA methylation and expression of histone-modifying enzymes have been successfully used for risk stratification. However, all these epigenetic biomarkers should be validated in larger clinical studies. Recent preclinical and clinical studies show a therapeutic benefit of epigenetically active drugs for breast cancer entities that are still difficult to treat (triple negative, UICC stage IV). Remarkably, epigenetic therapies combined with chemotherapies or hormone-based therapies represent the most promising strategy. At the current stage, the integration of epigenetic substances into established breast cancer therapy protocols seems to hold the greatest potential for a clinical application of epigenetic research.

Identification of a DNA Methylation-Based Prognostic Signature for Patients with Triple-Negative Breast Cancer

BACKGROUND Aberrant DNA methylation is an important biological regulatory mechanism in malignant tumors. However, it remains underutilized for establishing prognostic models for triple-negative breast cancer (TNBC). MATERIAL AND METHODS Methylation data and expression data downloaded from The Cancer Genome Atlas (TCGA) were used to identify differentially methylated sites (DMSs). The prognosis-related DMSs were selected by univariate Cox regression analysis. Functional enrichment was analyzed using DAVID. A protein-protein interaction (PPI) network was constructed using STRING. Finally, a methylation-based prognostic signature was constructed using LASSO method and further validated in 2 validation cohorts. RESULTS Firstly, we identified 743 DMSs corresponding to 332 genes, including 357 hypermethylated sites and 386 hypomethylated sites. Furthermore, we selected 103 prognosis-related DMSs by univariate Cox regression. Using a LASSO algorithm, we established a 5-DMSs prognostic signature in TCGA-TNBC cohort, which could classify TNBC patients with significant survival difference (log-rank p=4.97E-03). Patients in the high-risk group had shorter overall survival than patients in the low-risk group. The excellent performance was validated in GSE78754 (HR=2.42, 95%CI: 1.27-4.59, log-rank P=0.0055). Moreover, for disease-free survival, the prognostic performance was verified in GSE141441 (HR=2.09, 95%CI: 1.28-3.44, log-rank P=0.0027). Multivariate Cox regression analysis indicated that the 5-DMSs signature could serve as an independent risk factor. CONCLUSIONS We constructed a 5-DMSs signature with excellent performance for the prediction of disease-free survival and overall survival, providing a guide for clinicians in directing personalized therapeutic regimen selection of TNBC patients.

Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

Integrative Analysis of DNA Methylation and Gene Expression to Determine Specific Diagnostic Biomarkers and Prognostic Biomarkers of Breast Cancer

Background: DNA methylation is a common event in the early development of various tumors, including breast cancer (BRCA), which has been studies as potential tumor biomarkers. Although previous studies have reported a cluster of aberrant promoter methylation changes in BRCA, none of these research groups have proved the specificity of these DNA methylation changes. Here we aimed to identify specific DNA methylation signatures in BRCA which can be used as diagnostic and prognostic markers.

Methods: Differentially methylated sites were identified using the Cancer Genome Atlas (TCGA) BRCA data set. We screened for BRCA-differential methylation by comparing methylation profiles of BRCA patients, healthy breast biopsies and blood samples. These differential methylated sites were compared to nine main cancer samples to identify BRCA specific methylated sites. A BayesNet model was built to distinguish BRCA patients from healthy donors. The model was validated using three Gene Expression Omnibus (GEO) independent data sets. In addition, we also carried out the Cox regression analysis to identify DNA methylation markers which are significantly related to the overall survival (OS) rate of BRCA patients and verified them in the validation cohort.

Results: We identified seven differentially methylated sites (DMSs) that were highly correlated with cell cycle as potential specific diagnostic biomarkers for BRCA patients. The combination of 7 DMSs achieved ~94% sensitivity in predicting BRCA, ~95% specificity comparing healthy vs. cancer samples, and ~88% specificity in excluding other cancers. The 7 DMSs were highly correlated with cell cycle. We also identified 6 methylation sites that are highly correlated with the OS of BRCA patients and can be used to accurately predict the survival of BRCA patients (training cohort: likelihood ratio = 70.25, p = 3.633 × 10⁻¹³, area under the curve (AUC) = 0.784; validation cohort: AUC = 0.734). Stratification analysis by age, clinical stage, Tumor types, and chemotherapy retained statistical significance.

Conclusion: In summary, our study demonstrated the role of methylation profiles in the diagnosis and prognosis of BRCA. This signature is superior to currently published methylation markers for diagnosis and prognosis for BRCA patients. It can be used as promising biomarkers for early diagnosis and prognosis of BRCA.

Prediction of Overall Survival Among Female Patients With Breast Cancer Using a Prognostic Signature Based on 8 DNA Repair-Related Genes

IMPORTANCE

Breast cancer (BC), a common malignant tumor, ranks first among cancers in terms of morbidity and mortality among female patients. Currently, identifying effective prognostic models has a significant association with the prediction of the overall survival of patients with BC and guidance of clinicians in early diagnosis and treatment.

OBJECTIVES

To identify a potential DNA repair-related prognostic signature through a comprehensive evaluation and to further improve the accuracy of prediction of the overall survival of patients with BC.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, conducted from October 9, 2019, to February 3, 2020, the gene expression profiles and clinical data of patients with BC were collected from The Cancer Genome Atlas database. This study consisted of a training set from The Cancer Genome Atlas database and 2 validation cohorts from the Gene Expression Omnibus, which included 1096 patients with BC. A prognostic signature based on 8 DNA repair-related genes (DRGs) was developed to predict overall survival among female patients with BC.

MAIN OUTCOMES AND MEASURES

Primary screening prognostic biomarkers were analyzed using univariate Cox proportional hazards regression analysis and the least absolute shrinkage and selection operator Cox proportional hazards regression. A risk model was completely established through multivariate Cox proportional hazards regression analysis. Finally, a prognostic nomogram, combining the DRG signature and clinical characteristics of patients, was constructed. To examine the potential mechanisms of the DRGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.

RESULTS

In this prognostic study based on samples from 1096 women with BC (mean [SD] age, 59.6 [13.1] years), 8 DRGs (MDC1, RPA3, MED17, DDB2, SFPQ, XRCC4, CYP19A1, and PARP3) were identified as prognostic biomarkers. The time-dependent receiver operating characteristic curve analysis suggested that the 8-gene signature had a good predictive accuracy. In the training cohort, the areas under the curve were 0.708 for 3-year survival and 0.704 for 5-year survival. In the validation cohort, the areas under the curve were 0.717 for 3-year survival and 0.772 for 5-year survival in the GSE9893 data set and 0.691 for 3-year survival and 0.718 for 5-year survival in the GSE42568 data set. This DRG signature mainly involved some regulation pathways of vascular endothelial cell proliferation.

CONCLUSIONS AND RELEVANCE

In this study, a prognostic signature using 8 DRGs was developed that successfully predicted overall survival among female patients with BC. This risk model provides new clinical evidence for the diagnostic accuracy and targeted treatment of BC.

Development and validation of a novel 15-CpG-based signature for predicting prognosis in triple-negative breast cancer

DNA methylation is an important biological regulatory mechanism that changes gene expression without altering the DNA sequence. Increasing studies have revealed that DNA methylation data play a vital role in the field of oncology. However, the methylation site signature in triple‐negative breast cancer (TNBC) remains unknown. In our research, we analysed 158 TNBC samples and 98 noncancerous samples from The Cancer Genome Atlas (TCGA) in three phases. In the discovery phase, 86 CpGs were identified by univariate Cox proportional hazards regression (CPHR) analyses to be significantly correlated with overall survival (P < 0.01). In the training phase, these candidate CpGs were further narrowed down to a 15‐CpG‐based signature by conducting least absolute shrinkage and selector operator (LASSO) Cox regression in the training set. In the validation phase, the 15‐CpG‐based signature was verified using two different internal sets and one external validation set. Furthermore, a nomogram comprising the CpG‐based signature and TNM stage was generated to predict the 1‐, 3‐ and 5‐year overall survival in the primary set, and it showed excellent performance in the three validation sets (concordance indexes: 0.924, 0.974 and 0.637). This study showed that our nomogram has a precise predictive effect on the prognosis of TNBC and can potentially be implemented for clinical treatment and diagnosis.

A DNA Methylation-Based Panel for the Prognosis and Dagnosis of Patients With Breast Cancer and Its Mechanisms

Objective

To identify DNA methylation related biomarkers in patients with breast cancer (BC).

Materials and Methods

A total of seven BC methylation studies including 1,438 BC patients or breast tissues were included in this study. An elastic net regularized Cox proportional hazards regression (CPH) model was used to build a multi-5′-C-phosphate-G-3′ methylation panel. The diagnosis and prognosis power of the panel was evaluated and validated using a Kaplan–Meier curve, univariate and multivariable CPH, subgroup analysis. A nomogram containing the panel was developed. The relationships between the panel-based methylation risk and the immune landscape and genomic metrics were investigated.

Results

Sixty-eight CpG sites were significantly correlated with the overall survival (OS) of BC patients, and based on the result of penalized CPH, a 28-CpG site based multi CpG methylation panel was found. The prognosis and diagnosis role of the panel was validated in the discovery set, validation set, and six independent cohorts, which indicated that higher methylation risk was associated with poor OS, and the panel outperformed currently available biomarkers and remained an independent factor after adjusting for other clinical features. The methylation risk was negatively correlated with innated and adaptive immune cells, and positively correlated with total mutation load, SCNA, and MATH.

Conclusions

We validated a multi CpG methylation panel that could independently predict the OS of BC patients. The Th2-mediated tumor promotion effect—suppression of innate and adaptive immunity—participated in the progression of high-risk BC. Patients with high methylation risk were associated with tumor heterogeneity and poor survival.

HPV-related methylation-based reclassification and risk stratification of cervical cancer

Human papillomavirus (HPV) is a clear etiology of cervical cancer (CC). However, the associations between HPV infection and DNA methylation have not been thoroughly investigated. Additionally, it remains unknown whether HPV‐related methylation signatures can identify subtypes of CC and stratify the prognosis of CC patients. DNA methylation profiles were obtained from The Cancer Genome Atlas to identify HPV‐related methylation sites. Unsupervised clustering analysis of HPV‐related methylation sites was performed to determine the different CC subtypes. CC patients were categorized into cluster 1 (Methylation‐H), cluster 2 (Methylation‐M), and cluster 3 (Methylation‐L). Compared to Methylation‐M and Methylation‐L, Methylation‐H exhibited a significantly improved overall survival (OS). Gene set enrichment analysis (GSEA) was conducted to investigate the functions that correlated with different CC subtypes. GSEA indicated that the hallmarks of tumors, including KRAS signaling, TNFα signaling via NF‐κB, inflammatory response, epithelial–mesenchymal transition, and interferon‐gamma response, were enriched in Methylation‐M and Methylation‐L. Based on mutation and copy number variation analyses, we found that aberrant mutations, amplifications, and deletions among the MYC, Notch, PI3K‐AKT, and RTK‐RAS pathways were most frequently detected in Methylation‐H. Additionally, mutations, amplifications, and deletions within the Hippo, PI3K‐AKT, and TGF‐β pathways were presented in Methylation‐M. Genes within the cell cycle, Notch, and Hippo pathways possessed aberrant mutations, amplifications, and deletions in Methylation‐L. Moreover, the analysis of tumor microenvironments revealed that Methylation‐H was characterized by a relatively low degree of immune cell infiltration. Finally, a prognostic signature based on six HPV‐related methylation sites was developed and validated. Our study revealed that CC patients could be classified into three heterogeneous clusters based on HPV‐related methylation signatures. Additionally, we derived a prognostic signature using six HPV‐related methylation sites that stratified the OS of patients with CC into high‐ and low‐risk groups.