Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

Abstract

Background Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets. Method The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA. Results Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction. Conclusion The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

“…The random forest algorithm is a common machine-learning algorithm often used in cancer and biological research as a routine bioinformatics protocol; it was used to reduce the dimensions to screen more important genes. This method has been widely used in many different prognostic models of ccRCC such as chromatin-remodeling genes [ 15 ], DNA methylation patterns [ 16 ], and microRNA [ 17 ]. Cancer is usually accompanied by abnormal changes in metabolic patterns.…”

Identification of post-transcriptional modifications in nucleic acid sequences using purpose-designed molecular beacons

“…Recently, epigenetic modifications of either the genomic DNA or circulating extracellular RNAs, such as methylations, in particular m6A, as well as hydroxy-methylations, of the nitrogenous base or the corresponding sugar backbone, were described in certain cancers, and showed to provide enhance the functional context for disease progression than the classical up/down changes in their concentrations alone (3,9) . Previously, we have shown that the location of the mismatch nucleotide between the MB and the target is important for the formation, fluorescence, and electrophoretic characteristics of the resulting hybrid.…”

“…In the last decade, circulating cell-free nucleic acids 5-i7 present either in extracellular vesicles (EVs) [8][9][10] , or associated RNA-and DNA-binding proteins have received increased attention not just for their critical role in basic biological processes, but also as potential theragnostic agents for a growing number of cancers. 11-13 One of the challenges facing precision oncology is the discovery and the validation of reliable biomarkers, followed by the development of sensitive, multiplex sets of detection tests.…”

Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study

“…In clinical work, positron emission tomography–computed tomography is an approach used to detect the metastatic sites of ccRCC patients, but it is not a routine screen for early‐stage populations and has a misdiagnosis rate [ 18 ]. Nevertheless, the results are determined by abdominal imaging and clinicopathology, which may lead to an approximate margin of error of 20% [ 19 , 20 ]. To address these limitations, the development of a specific model to evaluate the metastatic tendency among ccRCC patients, especially in the early stage, is urgently needed.…”