Prediction of overall survival based upon a new ferroptosis-related gene signature in patients with clear cell renal cell carcinoma

• Published in: World journal of surgical oncology Vol. 20; no. 1; p. 120
• Main Authors: Sun, Zhuolun, Li, Tengcheng, Xiao, Chutian, Zou, Shaozhong, Zhang, Mingxiao, Zhang, Qiwei, Wang, Zhenqing, Zhan, Hailun, Wang, Hua
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14-04-2022; BioMed Central; BMC
• Subjects: Apoptosis; Atrophy; Bioinformatics; Biological markers; Biomarkers; Biomarkers, Tumor - genetics; Cancer; Carcinoma, Renal cell; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - pathology; Cell death; Clear cell renal cell carcinoma; Clear cell-type renal cell carcinoma; Clustering; Decision making; Diagnosis; Female; Ferroptosis; Ferroptosis - genetics; Functional analysis; Gene expression; Gene Expression Regulation, Neoplastic; Gene sequencing; Genetic aspects; Genomes; Health aspects; Humans; Kidney cancer; Kidney Neoplasms - genetics; Kidney Neoplasms - pathology; Lipid metabolism; Lipid peroxidation; Lipids; Male; Medical prognosis; Medical research; Metabolism; Metastasis; Mortality; Nomogram; Nomograms; Oncology, Experimental; Patients; Phenotypes; Prognosis; Prognostic signature; Regression analysis; Risk; Risk groups; Statistical analysis; Survival; Survival analysis; Tumorigenesis; Tumors; China; Prognostic signature; Ferroptosis; Clear cell renal cell carcinoma; Bioinformatics; Nomogram
• ISSN: 1477-7819; 1477-7819
• DOI: 10.1186/s12957-022-02555-9

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal cell carcinoma (RCC) histological subtype. Ferroptosis is a newly discovered programmed cell death and serves an essential role in tumor occurrence and development. The purpose of this study is to analyze ferroptosis-related gene (FRG) expression profiles and to construct a multi-gene signature for predicting the prognosis of ccRCC patients.
RNA-sequencing data and clinicopathological data of ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed FRGs between ccRCC and normal tissues were identified using 'limma' package in R. GO and KEGG enrichment analyses were conducted to elucidate the biological functions and pathways of differentially expressed FRGs. Consensus clustering was used to investigate the relationship between the expression of FRGs and clinical phenotypes. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to screen genes related to prognosis and construct the optimal signature. Then, a nomogram was established to predict individual survival probability by combining clinical features and prognostic signature.
A total of 19 differentially expressed FRGs were identified. Consensus clustering identified two clusters of ccRCC patients with distinguished prognostic. Functional analysis revealed that metabolism-related pathways were enriched, especially lipid metabolism. A 7-gene ferroptosis-related prognostic signature was constructed to stratify the TCGA training cohort into high- and low-risk groups where the prognosis was significantly worse in the high-risk group. The signature was identified as an independent prognostic indicator for ccRCC. These findings were validated in the testing cohort, the entire cohort, and the International Cancer Genome Consortium (ICGC) cohort. We further demonstrated that the signature-based risk score was highly associated with the ccRCC progression. Further stratified survival analysis showed that the high-risk group had a significantly lower overall survival (OS) rate than those in the low-risk group. Moreover, we constructed a nomogram that had a strong ability to forecast the OS of the ccRCC patients.
We constructed a ferroptosis-related prognostic signature, which might provide a reliable prognosis assessment tool for the clinician to guide clinical decision-making and outcomes research.