Genetic alteration of histone lysine methyltransferases and their significance in renal cell carcinoma

Computational analysis of protein-protein interactions of cancer drivers in renal cell carcinoma

Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep-learning methods based on AlphaFold to predict protein-protein interactions (PPIs) involving RCC drivers. We predicted high-confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS-related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2-MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies.

Tumor-suppressive functions of protein lysine methyltransferases

Protein lysine methyltransferases (PKMTs) play crucial roles in histone and nonhistone modifications, and their dysregulation has been linked to the development and progression of cancer. While the majority of studies have focused on the oncogenic functions of PKMTs, extensive evidence has indicated that these enzymes also play roles in tumor suppression by regulating the stability of p53 and β-catenin, promoting α-tubulin-mediated genomic stability, and regulating the transcription of oncogenes and tumor suppressors. Despite their contradictory roles in tumorigenesis, many PKMTs have been identified as potential therapeutic targets for cancer treatment. However, PKMT inhibitors may have unintended negative effects depending on the specific cancer type and target enzyme. Therefore, this review aims to comprehensively summarize the tumor-suppressive effects of PKMTs and to provide new insights into the development of anticancer drugs targeting PKMTs.

Loss of SETD2-mediated downregulation of intracellular and exosomal miRNA-10b determines MAPK pathway activation and multidrug resistance in renal cancer

SET domain-containing 2 (SETD2) is the most frequently mutated gene among all the histone methyltransferases in clear cell renal cell carcinoma (ccRCC). Microarrays, RNA sequencing analysis and exosomes analysis of cellular supernatant were performed after transfection A498 cells with si-SETD2 or siRNA of negative control. Chromatin immunoprecipitation and Luciferase reporter assay were conducted to evaluate the interaction between SETD2 and miR-10b. Functional and drug experiments in vitro and in vivo were performed to verify the role of SETD2, miR-10b and MAP4K4. The results showed that loss of SETD2 mediated downregulation of intracellular and exosomal microRNA-10b. MAP4K4 were relevant to oncogenesis of ccRCC caused by loss of SETD2 and miR-10b. SETD2 could directly target miR-10b and regulate the expression of multidrug resistance (MDR)-1 (P-gp170) through JNK pathway, which was one of the downstream pathways of MAP4K4. The coordinated expression of SETD2/H3K36me3/miR-10b/MAPKs/JNK/MDR pathway was revealed to the progression of ccRCC.

Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of papillary renal cell carcinoma

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.

Expression analysis of hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, and hsa_circ_0006990 in renal cell carcinoma

Renal cell carcinoma (RCC) is a prevalent heterogeneous kidney cancer. So far, different genes have been reported for RCC development. However, its particular molecular mechanism remains unclear. Circular RNAs (circRNAs), a class of non-coding RNAs, are involved in numerous biological processes in different malignancies such as RCC. This study aims to assess the expression and underlying mechanism of four circRNAs (hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, hsa_circ_0006990) with possible new roles in RCC. In the experimental step, we investigated the expression of these four circRNAs in our RCC samples using quantitative real-time polymerase chain reaction. In the bioinformatics step, the differential expressed mRNAs (DEmRNAs), and miRNAs (DEmiRNAs) were obtained from the GEO datasets using the GEO2R tool. A protein-protein interaction network was constructed using the STRING database, and hub genes were identified by Cytoscape. Molecular pathways associated with hub genes were detected using KEGG pathway enrichment analysis. Then, we utilized the ToppGene database to detect the relationships between DEmiRNAs and hub genes. Furthermore, interactions between circRNAs and DEmiRNAs were predicted by the StarBase and circinteractome databases. Finally, a circRNA-DEmiRNA-hub gene triple network was constructed. Our results revealed that the expression of hsa_circ_0020397, hsa_circ_0005986, and hsa_circ_0006990 was downregulated in RCC tissues. Moreover, these circRNAs had a significantly lower expression in patients with a history of kidney disease. Furthermore, hsa_circ_0003028 and hsa_circ_0006990 showed higher expression in the tumor of participants with Lymphovascular/perineural invasion and oncocytoma type, respectively. Based on bioinformatic results, 15 circRNA-DEmiRNA-hub gene ceRNA regulatory axes were predicted, which included three hub genes, five miRNAs, and four selected circRNAs. In conclusion, the current work is the first to emphasize the expression of the hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, and hsa_circ_0006990 in RCC patients presents a novel perspective on the molecular processes underlying the pathogenic mechanisms of RCC.

Lysine Methyltransferase NSD1 and Cancers: Any Role in Melanoma?

Simple Summary

Epigenetic events, which comprise post-translational modifications of histone tails or DNA methylation, control gene expression by altering chromatin structure without change in the DNA sequence. Histone tails modifications are driven by specific cellular enzymes such as histone methyltransferases or histone acetylases, which play a key role in regulating diverse biological processes. Their alteration may have consequences on growth and tumorigenesis.

Abstract

Epigenetic regulations, that comprise histone modifications and DNA methylation, are essential to processes as diverse as development and cancer. Among the histone post-translational modifications, lysine methylation represents one of the most important dynamic marks. Here, we focused on methyltransferases of the nuclear binding SET domain 1 (NSD) family, that catalyze the mono- and di-methylation of histone H3 lysine 36. We review the loss of function mutations of NSD1 in humans that are the main cause of SOTOS syndrome, a disease associated with an increased risk of developing cancer. We then report the role of NSD1 in triggering tumor suppressive or promoter functions according to the tissue context and we discuss the role of NSD1 in melanoma. Finally, we examine the ongoing efforts to target NSD1 signaling in cancers.

Structural insight into ASH1L PHD finger recognizing methylated histone H3K4 and promoting cell growth in prostate cancer

ASH1L is a member of the Trithorax-group protein and acts as a histone methyltransferase for gene transcription activation. It is known that ASH1L modulates H3K4me3 and H3K36me2/3 at its gene targets, but its specific mechanism of histone recognition is insufficiently understood. In this study, we found that the ASH1L plant homeodomain (PHD) finger interacts with mono-, di-, and trimethylated states of H3K4 peptides with comparable affinities, indicating that ASH1L PHD non-selectively binds to all three methylation states of H3K4. We solved nuclear magnetic resonance structures picturing the ASH1L PHD finger binding to the dimethylated H3K4 peptide and found that a narrow binding groove and residue composition in the methylated-lysine binding pocket restricts the necessary interaction with the dimethyl-ammonium moiety of K4. In addition, we found that the ASH1L protein is overexpressed in castrate-resistant prostate cancer (PCa) PC3 and DU145 cells in comparison to PCa LNCaP cells. The knockdown of ASH1L modulated gene expression and cellular pathways involved in apoptosis and cell cycle regulation and consequently induced cell cycle arrest, cell apoptosis, and reduced colony-forming abilities in PC3 and DU145 cells. The overexpression of the C-terminal core of ASH1L but not the PHD deletion mutant increased the overall H3K36me2 level but had no effect on the H3K4me2/3 level. Overall, our study identifies the ASH1L PHD finger as the first native reader that non-selectively recognizes the three methylation states of H3K4. Additionally, ASH1L is required for the deregulation of cell cycle and survival in PCas.

idenMD-NRF: a ranking framework for miRNA-disease association identification

Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.

Transcription Factors BARX1 and DLX4 Contribute to Progression of Clear Cell Renal Cell Carcinoma via Promoting Proliferation and Epithelial-Mesenchymal Transition

Dysregulation of transcription factors contributes to the carcinogenesis and progression of cancers. However, their roles in clear cell renal cell carcinoma remain largely unknown. This study aimed to evaluate the clinical significance of TFs and investigate their potential molecular mechanisms in ccRCC. Data were accessed from the cancer genome atlas kidney clear cell carcinoma cohort. Bioinformatics algorithm was used in copy number alterations mutations, and differentially expressed TFs’ analysis. Univariate and multivariate Cox regression analyses were performed to identify clinically significant TFs and construct a six-TF prognostic panel. TFs’ expression was validated in human tissues. Gene set enrichment analysis (GSEA) was utilized to find enriched cancer hallmark pathways. Functional experiments were conducted to verify the cancer-promoting effect of BARX homeobox 1 (BARX1) and distal-less homeobox 4 (DLX4) in ccRCC, and Western blot was performed to explore their downstream pathways. As for results, many CNAs and mutations were identified in transcription factor genes. TFs were differentially expressed in ccRCC. An applicable predictive panel of six-TF genes was constructed to predict the overall survival for ccRCC patients, and its diagnostic efficiency was evaluated by the area under the curve (AUC). BARX1 and DLX4 were associated with poor prognosis, and they could promote the proliferation and migration of ccRCC. In conclusion, the six-TF panel can be used as a prognostic biomarker for ccRCC patients. BARX1 and DLX4 play oncogenic roles in ccRCC via promoting proliferation and epithelial–mesenchymal transition. They have the potential to be novel therapeutic targets for ccRCC.

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes

Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.

Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma

PD-1 blockade has transformed the management of advanced clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of the PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors from patients with advanced ccRCC enrolled in prospective clinical trials of treatment with PD-1 blockade by whole-exome and RNA sequencing, integrated with immunofluorescence analysis, to uncover the immunogenomic determinants of the therapeutic response. Although conventional genomic markers (such as tumor mutation burden and neoantigen load) and the degree of CD8⁺ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations associated with response or resistance to PD-1 blockade. These advanced ccRCC tumors were highly CD8⁺ T cell infiltrated, with only 27% having a non-infiltrated phenotype. Our analysis revealed that infiltrated tumors are depleted of favorable PBRM1 mutations and enriched for unfavorable chromosomal losses of 9p21.3, as compared with non-infiltrated tumors, demonstrating how the potential interplay of immunophenotypes with somatic alterations impacts therapeutic efficacy.