Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma. Issue 1 (24th November 2022)

• Record Type: Journal Article
• Authors: Wang, Yi; Ji, Hao; Zhu, Bingye; Xing, Qianwei; Xie, Huyang
• Is Part Of: European journal of immunology. Volume 53:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-24
• Subjects: biomarkers -- clear cell renal cell carcinoma -- immune responses -- metabolic genes -- molecular subtypes; Immunology -- Periodicals; 616.079
• DOI: 10.1002/eji.202250105
• Languages: English
• ISSNs: 0014-2980

Abstract

Abstract: Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism‐associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor‐infiltrating immune cells, oncogenic pathways, pan‐cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore‐related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT‐PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism‐related ccRCC tumor heterogeneity and facilitating individualized therapy for future work. Abstract : We first classified ccRCC patients into three clusters based on metabolism‐associated genes (MAGs) with significant associations with immunity, providing novel insights into metabolism‐related ccRCC tumor heterogeneity. Besides, by combining LDA and WGCNA, seven out of 10 hub genes were finally obtained and validated by qRT‐PCR, facilitating individualized therapy for ccRCC.