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

Abstract

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.

Keywords: kidney cancer; mTOR; machine learning; mitochondrial DNA; mtDNA; pan-RCC.

Figure 1

(A) t-SNE-plot for RNA-sequencing data from ccRCC (red), pRCC (green) and chRCC (blue) specimen within the TCGA database. (B) Visually identified clusters—I to III: distinct pRCC subgroups; IV: ccRCC samples; V: mixed subgroup containing ccRCC, pRCC and chRCC tumors. (C) Manually defined clusters based on visual separation. (D) Pie charts illustrating absolute numbers and proportions of RCC samples inside/outside the mixed subgroup for each RCC subgroup.

Figure 2

StringDB network of the top 200 genes identified as relevant classifiers for RCC sample clusters from Figure 1C. Genes affiliated with oxidative phosphorylation and respiratory electron transport chain are marked in red and blue, genes related to blood vessel morphogenesis and blood vessel development are marked in green and yellow.

Figure 3

Unprocessed FPKM values of exemplary candidate genes–(A,B) MT-CO2 and MT-CO3, (C,D) FLT1 and KDR. ns, not significant, ****p < 0.0001.

Figure 4

Color-coded presentation of the Pearson R correlation matrix of mitochondrial genes and angiogenesis-associated genes for ccRCC samples from the (A) TCGA, (B) the ICGC RECA-EU, and (C) the CPTAC-3-Kidney cohort as well as (D) Fumarate hydratase-deficient RCC samples contained within the GSE157256 cohort.

Figure 5

(A,B) KM plots illustrating overall survival of patients with ccRCC (A), chRCC (B) and pRCC (C) from TCGA database depending on mixed subgroup affiliation. (D) Protein expression levels of bona fide candidate genes from mTOR-associated, angiogenesis-related and immune-related signaling for ccRCC, pRCC and chRCC samples inside (blue) and outside (red) the mixed subgroup (TCPA database). ns, not significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.