A prognostic signature based on genes associated with m6A/m5C/m1A/m7G modifications and its immunological characteristics in clear cell renal cell carcinoma - Research findings and risk score model

Sep 21, 2026

Uploaded by Liam

Clear cell renal cell carcinoma (ccRCC) shows poor outcomes despite advances in therapy, motivating the search for biomarkers tied to RNA methylation. Transcriptomic and clinical data from The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress were integrated to identify 57 methylation-related genes, then a Methylation-Related Risk Score (MARS) was built using DESeq2, Cox regression, and LASSO. The study defines six key prognostic genes, validates the signature in two external cohorts, and links risk stratification to immune infiltration and immunotherapy response, including two methylation gene clusters.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Background

Clinical need in ccRCC

RNA methylation modifications

Methods

Data sources and identification of methylation-related genes

Model construction with DESeq2, Cox and LASSO

Bioinformatic evaluations (clustering, GO, immune infiltration, mutation)

Results

Six pivotal methylation-related prognostic genes

MARS validation across external cohorts

Two methylation gene clusters and immunologic implications

Significance

Potential for precision treatment and immunotherapy prediction

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN A prognostic signature based on genes associated with m6A/ m5C/m1A/m7G modificationsand its immunological characteristics in clear cell renal cell carcinoma Wei He1,2,9, Zixiang Cong3,9, Chengtao Niu1, Fajuan Cheng4, Tinghai Yi5, Zhongshun Yao6, Yiming Zhang7, Xue Jiang8, Xintong Sun2, Zhihong Niu1,2* & Qiang Fu1,2* Clear cell renal cell carcinoma (ccRCC) is characterized by a high incidence and mortality rate. Despite advancements in therapeutic interventions, the prognosis for renal cancer patients remains suboptimal. Of late, methylation modifications have emerged as promising molecular targets for tumor assessment and treatment, yet their potential has not been fully investigated in the context of ccRCC. Transcriptomic and clinical data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress databases, leading to the identification of 57 methylationrelated genes (MRGs). Utilizing DESeq2 analysis, Cox regression analysis, and the LASSO regression algorithm, a Methylation-Related Risk Score (MARS) was constructed. Cluster analysis, Gene Ontology (GO) analysis, clinical feature analysis, immune infiltration analysis, and mutation analysis were further employed to evaluate the model. Our investigation identified six pivotal prognostic MRGs and established a risk score predicated on m6A/m5C/m1A/m7G regulatory factors. This score was validated across two external cohorts and can be utilized to assess individual immune infiltration statuses and predict responses to immunotherapy. Moreover, cluster analysis delineated two distinct m6A/m5C/m1A/m7G gene clusters. We have developed and validated a robust prognostic signature based on genes associated with m6A, m5C, m1A, and m7G modifications. This gene signature demonstrates significant prognostic value in assessing survival outcomes, clinical characteristics, immune infiltration, and responses to immunotherapy in ccRCC patients. This finding provides valuable insights for refining precision treatment strategies. Annually, there are still in excess of 400,000 individuals diagnosed with kidney cancer on a global scale1. Patients with distant metastases have a 5-year relative survival rate of 15.3% . Clear cell renal cell carcinoma (ccRCC) is the predominant subtype of kidney cancer, making up approximately 70% of all instances2. The therapeutic landscape for metastatic ccRCC is continually evolving, yet it remains encumbered by heterogeneity in treatment responses, the emergence of resistance to therapeutic agents, and the attendant toxicities associated with treatment regimens3. In light of the significant incidence and mortality associated with ccRCC, the exploration of 1Department of Urology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China. 2Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China. 3Department of Urology, Weihai Municipal Hospital Affiliated to Shandong University, Weihai, Shandong, China. 4Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China. 5Department of Urology, Traditional Chinese Hospital of Yiyuan County, Zibo, Shandong, China. 6Department of Emergency, Liaocheng People’s Hospital, Liaocheng, Shandong, China. 7Department of Urology, People’s Hospital of Changle County, Weifang, Shandong, China. 8Medical School, Shandong Xiehe University, Jinan, Shandong, China. 9These authors contributed equally: Wei He and Zixiang Cong.* email: [nzh1789@163.com](nzh1789@163.com); [qiangfu68@163.com](qiangfu68@163.com) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) novel biomarkers is essential to enhance early detection, assess risk, and predict the effectiveness of therapeutic interventions. Post-transcriptional modifications of RNA represent a pivotal com

FAQs

What datasets were used to develop the prognostic model?

Transcriptomic and clinical data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress.

How was the Methylation-Related Risk Score (MARS) constructed?

The work used DESeq2 to analyze methylation-related genes, Cox regression, and the LASSO regression algorithm to build MARS.

What biological and clinical aspects does the gene signature relate to in ccRCC?

The signature is associated with survival outcomes, clinical characteristics, immune infiltration patterns, and predicted responses to immunotherapy.

Sep 21, 2026

Uploaded by Liam