Pathway Analysis of Renal Cell Carcinoma Genome-Wide Association Studies Identifies Novel Associations

Genetic Analysis Implicates Dysregulation of SHANK2 in Renal Cell Carcinoma Progression

SH3 and multiple ankyrin repeat domains (SHANK) is a family of scaffold proteins that were first identified to be involved in balancing synaptic transmission via regulation of intracellular signalling crosstalk and have been linked to various cancers. However, the role of the SHANK genes in renal cell carcinoma (RCC) remains to be elucidated. In this study, we aimed to evaluate whether genetic variants in SHANK family genes affect the risk of RCC and survival of patients. A genetic association study was conducted using logistic regression and Cox regression analyses, followed by the correction for a false discovery rate (FDR), in 630 patients with RCC and controls. A pooled analysis was further performed to summarise the clinical relevance of SHANK gene expression in RCC. After adjustment for known risk factors and the FDR, the SHANK2 rs10792565 T allele was found to be associated with an increased risk of RCC (adjusted odds ratio = 1.79, 95% confidence interval = 1.32-2.44, p = 1.96 × 10^-4, q = 0.030), whereas no significant association was found with RCC survival. A pooled analysis of 19 independent studies, comprising 1509 RCC and 414 adjacent normal tissues, showed that the expression of SHANK2 was significantly lower in RCC than in normal tissues (p < 0.001). Furthermore, low expression of SHANK2 was correlated with an advanced stage and poor prognosis for patients with clear cell and papillary RCC. This study suggests that SHANK2 rs10792565 is associated with an increased risk of RCC and that SHANK2 may play a role in RCC progression.

Frequency of pathogenic germline variants in cancer susceptibility genes in 1336 renal cell carcinoma cases

Renal cell carcinoma (RCC) occurs in a number of cancer predisposition syndromes, but the genetic architecture of susceptibility to RCC is not well defined. We investigated the frequency of pathogenic and likely pathogenic (P/LP) germline variants in cancer susceptibility genes (CSGs) within a large series of unselected RCC participants. Whole-genome sequencing data on 1336 RCC participants and 5834 controls recruited to the UK 100 000 Genomes Project, a nationwide multicentre study, was analyzed to identify rare P/LP short variants (single nucleotide variants and insertions/deletions ranging from 1 to 50 base pairs) and structural variants in 121 CSGs. Among 1336 RCC participants [mean: 61.3 years (±12 SD), range: 13-88 years; 64% male], 85 participants [6.4%; 95% CI (5.1, 7.8)] had one or more P/LP germline variant in a wider range of CSGs than previously recognized. A further 64 intragenic variants in CSGs previously associated with RCC were classified as a variant of uncertain significance (VUS) (24 'hot VUSs') and were considered to be of potential clinical relevance as further evaluation might results in their reclassification. Most patients with P variants in well-established CSGs known to predispose to renal cell carcinoma (RCC-CSGs) were aged <50 years. Burden test analysis for filtered variants in CSGs demonstrated a significant excess of CHEK2 variants in European RCC participants compared with the healthy European controls (P = 0.0019). Approximately, 6% of the patients with RCC unselected for family history have a germline variant requiring additional follow-up analysis. To improve diagnostic yield, we suggest expanding the panel of RCC-CSGs tested to include CHEK2 and all SDHx subunits and raising the eligibility criteria for age-based testing.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

An Investigation of Post-radiation Gene Expression Profiles: A System Biology Study

Introduction: Genomics and bioinformatics are useful methods for exploring unclear aspects of radiation effects on biological systems. Many radiation-induced alterations in irradiated samples are post-radiation time-dependent. This study aims to evaluate the post-irradiation effects of the gamma ray on human Jurkat cells. Methods: Gene expression profiles of the samples harvested 6 and 24 hours after radiation to find the critical differential expressed genes and the related pathways. Samples are provided from Gene Expression Omnibus (GEO) and analyzed by ClueGO. Results: Twnety-nine critical genes were determined as the important affected genes and 7 classes of related pathways were introduced. CCNE2, PSMD11, CDC25C, ANAPC1, PLK1, AURKA, and CCNB1 that were associated with more than 6 pathways were related to one of the determined pathway groups. Conclusion: Cell protecting pathways were associated with the genes (HSPA5, HSPA8, HSP90B1, HMMR, CEBPB, RXRA, and PSMD11) which were related to the minimum numbers of pathways. The finding of this study corresponds to repair processes which depend on post-radiation time. It seems these sets of genes are suitable candidates for further investigation.

Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach

Simple Summary

We applied the genomic fabric principles for personalized gene therapy to a case of clear cell renal cell carcinoma (ccRCC). Despite decades of research, the process of finding the molecular mechanisms responsible for the disease and, more importantly, the therapeutic solution is still a work in progress. We analyzed the transcriptomes of the chest wall metastasis, two distinct cancer nodules, and the cancer-free surrounding tissue in the surgically removed right kidney of a Fuhrman grade 3 metastatic ccRCC patient. The studies revealed that even histopathologically equally classified cancer nodules from the same kidney have different transcriptomic topologies, requiring tailored therapeutic solutions not only for each patient but even for each cancer nodule. We identified death-associated protein kinase 3 (DAPK3); transcription activation suppressor (TASOR); family with sequence similarity 27, member C, long non-coding RNA (FAM27C); and UDP-N-acetylglucosaminyltransferase subunit (ALG13) as the gene master regulators of the four profiled regions and proposed molecular mechanisms by which expression manipulation of TASOR and ALG13 may selectively destroy the cancer cells without affecting many of the normal cells.

Abstract

Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3,TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.