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pmid:33791209
Subgroup-Independent Mapping of Renal Cell Carcinoma-Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries.
André Marquardt · Antonio Giovanni Solimando · Alexander Kerscher · Max Bittrich · Charis Kalogirou · Hubert Kübler · Andreas Rosenwald · Ralf Bargou · Philip Kollmannsberger · Bastian Schilling · Svenja Meierjohann · Markus Krebs
Frontiers in oncology · 2021 doi:10.3389/fonc.2021.621278
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Complete file
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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.", "claims": [{"id": "claim-method", "statement": "If not stated otherwise, statistical tests were performed using Kruskal-Wallis-Test—using scipy.stats module including indicated significances, for which we used the statannot module for python (version 0.2.3; https://github.com/webermarcolivier/statannot).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33791209", "locator": "sections[7] \"Plots and Statistical Analysis\" paragraph 3", "quote": "If not stated otherwise, statistical tests were performed using Kruskal-Wallis-Test—using scipy.stats module including indicated significances, for which we used the statannot module for python (version 0.2.3; https://github.com/webermarcolivier/statannot)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. 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