Background Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract which seriously endangers the health of human beings worldwide. Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of GC. This study aimed to investigate the impact of epigenetically regulated genes on the prognosis, immune microenvironment, and potential treatment of GC. Results Under the premise of verifying significant co-regulation of the aberrant frequencies of microRNA (miRNA) correlated (MIRcor) genes and DNA methylation-correlated (METcor) genes. Four GC molecular subtypes were identified and validated by comprehensive clustering of MIRcor and METcor GEPs in 1521 samples from five independent multicenter GC cohorts: cluster 1 was characterized by up-regulated cell proliferation and transformation pathways, with good prognosis outcomes, driven by mutations, and was sensitive to 5-fluorouracil and paclitaxel; cluster 2 performed moderate prognosis and benefited more from apatinib and cisplatin; cluster 3 was featured by an up-regulated ligand–receptor formation-related pathways, poor prognosis, an immunosuppression phenotype with low tumor purity, resistant to chemotherapy (e.g., 5-fluorouracil, paclitaxel, and cisplatin), and targeted therapy drug (apatinib) and sensitive to dasatinib; cluster 4 was characterized as an immune-activating phenotype, with advanced tumor stages, benefit more from immunotherapy and displayed worst prognosis. Conclusions According to the epigenetically regulated GEPs, we developed four robust GC molecular subtypes, which facilitated the understanding of the epigenetic mechanisms underlying GC heterogeneity, offering an optimized decision-making and surveillance platform for GC patients.

BackgroundDilated cardiomyopathy (DCM) is characterized by left ventricular dilatation and systolic dysfunction. The pathogenesis and etiologies of DCM remain elusive. This study aims to identify the key genes to construct a genetic diagnosis model of DCM.MethodsA total of 257 DCM samples from five independent cohorts were enrolled. The Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to identify the key modules associated with DCM. The latent mechanisms and protein-protein interaction network underlying the key modules were further revealed. Subsequently, we developed and validated a LASSO diagnostic model in five independent cohorts.ResultsTwo key modules were identified using WGCNA. Novel mechanisms related to the extracellular, mitochondrial matrix or IL-17 signaling pathway were pinpointed, which might significantly influence DCM. Besides, 23 key genes were screened out by combining WGCNA and differential expression analysis. Based on the key genes, a genetic diagnosis model was constructed and validated using five cohorts with excellent AUCs (0.975, 0.954, 0.722, 0.850, 0.988). Finally, significant differences in immune infiltration were observed between the two groups divided by the diagnostic model.ConclusionOur study revealed several novel pathways and key genes to provide potential targets and biomarkers for DCM treatment. A key genes’ diagnosis model was built to offer a new tool for diagnosing DCM.

Data mining from RNA-seq or microarray data has become an essential part of cancer biomarker exploration. Certain existing web servers are valuable and broadly utilized, but the meta-analysis of multiple datasets is absent. Most web servers only contain tumor samples from the TCGA database with only one cohort for each cancer type, which also means that the analysis results mainly derived from a single cohort are thin and unstable. Indeed, consistent performance across multiple independent cohorts is the foundation for an excellent biomarker. Moreover, many analytical functions researchers require remain adequately unmet by these tools. Thus, we introduce BEST (Biomarker Exploration for Solid Tumors), a web application for comprehensive biomarker exploration on large-scale data in solid tumors. BEST includes more than 50,000 samples of 27 cancer types. To ensure the comparability of genes between different sequencing technologies and the legibility of clinical traits, we re-annotated transcriptome data based on the GRCh38 patch 13 sequences and unified the nomenclature of clinical traits. BEST delivers fast and customizable functions, including clinical association, survival analysis, enrichment analysis, cell infiltration, immunomodulator, immunotherapy, candidate agents, and genomic alteration. Together, our web server provides multiple cleaned-up independent datasets and diverse analysis functionalities, helping unleash the value of current data resources. It is freely available at https://rookieutopia.com/.

BackgroundEsophageal adenocarcinoma (EAC) remains a leading cause of cancer-related deaths worldwide, and demonstrates a predominant rising incidence in Western countries. Recently, immunotherapy has dramatically changed the landscape of treatment for many advanced cancers, the benefit in EAC thus far been limited to a small fraction of patients. MethodsUsing somatic mutations data of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we delineated somatic mutation landscape of EAC patients from US and England. Bioinformatics algorithms were utilized to perform function annotation, immune cell infiltration analysis, and immunotherapy response assessment.ResultsWe found that RYR2 was a common frequently mutated gene (FMG) in both cohorts, and patients with RYR2 mutation suggested higher tumor mutation burden (TMB), better prognosis, and superior expression of immune checkpoints. Moreover, RYR2 mutation upregulated the signaling pathways implicated in immune response and enhanced antitumor immunity in EAC. Multiple bioinformatics algorithms for assessing immunotherapy response demonstrated that patients with RYR2 mutation might benefit more from immunotherapy. In order to provide additional reference for antitumor therapy of different RYR2 status, we identified nine latent antitumor drugs associated with RYR2 status in EAC. ConclusionsThis study reveals a novel gene whose mutation could be served as a potential biomarker for prognosis, TMB, and immunotherapy of EAC patients.

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