Subgroup-Independent Mapping of Renal Cell Carcinoma—Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries

Frontiers in Oncology

Philip Kollmannsberger¹  Alexander Kerscher²  Ralf Bargou²  Bastian Schilling³  Max Bittrich⁴  Markus Krebs⁵  Hubert Kübler⁵  Charis Kalogirou⁵  Antonio Giovanni Solimando⁷  Andreas Rosenwald⁸  André Marquardt⁹  Svenja Meierjohann⁹ 

[1] Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany;Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, Würzburg, Germany;Department of Dermatology, University Hospital Würzburg, Würzburg, Germany;Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany;Department of Urology and Pediatric Urology, University Hospital Würzburg, Würzburg, Germany;Guido Baccelli Unit of Internal Medicine, Department of Biomedical Sciences and Human Oncology, School of Medicine, Aldo Moro University of Bari, Bari, Italy;IRCCS Istituto Tumori “Giovanni Paolo II” of Bari, Bari, Italy;Institute of Pathology, University of Würzburg, Würzburg, Germany;Interdisciplinary Center for Clinical Research, University Hospital Würzburg, Würzburg, Germany;

关键词: kidney cancer;    pan-RCC;    machine learning;    mitochondrial DNA;    mtDNA;    mTOR;   

DOI  :  10.3389/fonc.2021.621278

来源: DOAJ

【 摘 要 】

Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

【 授权许可】

Unknown