Transcriptome-proteome integration of archival human renal cell carcinoma biopsies enables identification of molecular mechanisms

Abstract

Renal cell cancer is among the most common forms of cancer in humans, with around 35,000 deaths attributed to kidney carcinoma in the European Union in 2012 alone. Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and the most lethal of all genitourinary cancers. Here, we apply omics technologies to archival core biopsies to investigate the biology underlying ccRCC. Knowledge of these underlying processes should be useful for the discovery and/or confirmation of novel therapeutic approaches and ccRCC biomarker development. From partial or full nephrectomies of 11 patients, paired core biopsies of ccRCC-affected tissue and adjacent (“peritumorous”) nontumor tissue were both sampled and subjected to proteomics analyses. We combined proteomics results with our published mRNA sequencing data from the same patients and with published miRNA sequencing data from an overlapping patient cohort from our institution. Statistical analysis and pathway analysis were performed with JMP Genomics and Ingenuity Pathway Analysis (IPA), respectively. Proteomics analysis confirmed the involvement of metabolism and oxidative stress-related pathways in ccRCC, whereas the most affected pathways in the mRNA sequencing data were related to the immune system. Unlike proteomics or mRNA sequencing alone, a combinatorial cross-omics pathway analysis approach captured a broad spectrum of biological processes underlying ccRCC, such as mitochondrial damage, repression of apoptosis, and immune system pathways. Sirtuins, immunoproteasome genes, and CD74 are proposed as potential targets for the treatment of ccRCC.

Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

“…The highest positive correlations indicate that immune responses play a central role, mostly due to immune cell infiltration and immunotherapy treatment. The protein organization in the membrane changes the capacity of the motility of the cell, and both events have been described in this type of cancer previously [42]. The observed biological categories that anti-correlate with the proteomics (and transcriptomics) data show important effects centered in the metabolism and the respiratory chain in the cell, indicating decoupling between the respiratory chain and metabolism in the cell, as expected in many types of cancer [43,44].…”

High Cytoplasmic and Membranous Versus Nuclear Talin-1 Expression Associated With Malignancy Potential and Poor Survival in Clear Cell Renal Cell Carcinoma

“…Search on PRoteomics IDEnti cations database (PRIDE) (https://www.ebi.ac.uk/pride/) 44 form 2019 to 2020 led to identifying three RCC studies that each of them included the proteomics information of one of the RCC subtypes and adjacent normal tissues. The results of proteomics analysis were downloaded from the supplementary information of their published articles 37,45,46 . Venn diagram analysis was performed to nd common signi cant differential proteins in three subtypes of RCC by OriginPro 2019 (https://www.originlab.com/2019, OriginLab Corporation, Northampton, MA, USA).…”

“…The RCC incidence rates have been increasing during the decade 3 . According to the American Cancer Society, an estimated 73,750 new cases (45,520 in males and 28,230 in females) and 14,830 deaths (9,860 in males and 4,970 in females) from RCC will occur in 2020 4 . If cancer has not metastasized, the 5-year survival rate is 65-90%, but this is lowered considerably when cancer has spread so that the 5-year survival rates for patients with stage IV disease are less than 20% 5 .…”

Proteomic approaches for characterizing renal cell carcinoma

“…Interestingly, in this particular dataset, CFL1, PFN1, and ALDOA were found to be decreased in early stages relative to NATs. In another study, FFPE ccRCC tissues and patient matched NATs that were previously characterized at the transcript level, were characterized at the protein level, wherein the authors found disparate cellular pathways over-represented at each biological domain [50]. At the protein level, metabolic pathways were robustly shown to be altered, whereas transcriptomics indicated immune response pathways were more significantly impacted.…”