Dataset Information

Comprehensive data analysis of genomics, epigenomics, and transcriptomics to identify specific biomolecular markers for prostate adenocarcinoma.

ABSTRACT:

Background

Multiomics data analysis based on high-throughput sequencing technology has become a hotspot in tumor investigation. The present study aimed to explore prognostic biomarkers via investigating DNA copy number variation (CNV) and methylation variation (MET) data in prostate cancer.

Methods

We obtained the messenger RNA (mRNA) expression, CNV, and methylated data of prostate adenocarcinoma (PRAD) samples via The Cancer Genome Atlas (TCGA)-PRAD cohort. We calculated and assessed the associations between CNV and RNA sequencing (RNA-seq), and between MET and RNA-seq via Pearson correlation coefficients. We then used the "iCluster" package to perform multigroup cluster analysis with CNVcor gene CNV data, METcor gene methylation data, and CNVcor and METcor gene mRNA data. The univariate Cox analysis was used to screen significant hub genes, and multivariate Cox analysis was used to construct risk a model. The nomogram was constructed based on "rms" package, and the immune infiltrating patterns were compared between high- and low-risk groups.

Results

A total of 477 PRAD samples with complete CNV, methylation, mRNA, and matched clinical information were included in our study. A list of 10,073 CNVcor genes and 9841 METcor genes were confirmed with a significance level of P<0.01. We found that CNVcor is more likely to appear on chromosome (chr)8, chr17, and chr10, while METcor is more likely to appear on chr1, chr19, and chr17. Based on the core genes, we finally classified the samples into 4 subtypes, incorporating iC1 (iCluster) (92 samples), iC2 (79 samples), iC3 (165 samples), and iC4 (141 samples). Furthermore, we constructed the prognostic model for PRAD based on the 5 genes (IER3, AOX1, PRKCDBP, UBD, and FBLN5). Nomograms incorporating risk score and other clinical variables were further constructed, and these nomograms exhibited superior predictive ability. We further compared the differential immune infiltrating patterns in 2 risk groups and found significantly low levels of infiltrating cluster of differentiation (CD)8⁺ T cells in high-risk samples.

Conclusions

Our study integrated the multi-omics data to elucidate the molecular features of PRAD and pivotal genes for predicting prognosis.

SUBMITTER: Ye C

PROVIDER: S-EPMC8350225 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundProstate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages' prognosis value in PCa.MethodsSingle-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured.ResultsA total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05).ConclusionsWe uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

Project description:With unknown etiology, scleroderma (SSc) is a multifaceted disease characterized by immune activation, vascular complications, and excessive fibrosis in internal organs. Genetic studies, including candidate gene association studies, genome-wide association studies, and whole-exome sequencing have supported the notion that while genetic susceptibility to SSc appears to be modest, SSc patients are genetically predisposed to this disease. The strongest genetic association for SSc lies within the MHC region, with loci in HLA-DRB1, HLA-DQB1, HLA-DPB1, and HLA-DOA1 being the most replicated. The non-HLA genes associated with SSc are involved in various functions, with the most robust associations including genes for B and T cell activation and innate immunity. Other pathways include genes involved in extracellular matrix deposition, cytokines, and autophagy. Among these genes, IRF5, STAT4, and CD247 were replicated most frequently while SNPs rs35677470 in DNASE1L3, rs5029939 in TNFAIP3, and rs7574685 in STAT4 have the strongest associations with SSc. In addition to genetic predisposition, it became clear that environmental factors and epigenetic influences also contribute to the development of SSc. Epigenetics, which refers to studies that focus on heritable phenotypes resulting from changes in chromatin structure without affecting the DNA sequence, is one of the most rapidly expanding fields in biomedical research. Indeed extensive epigenetic changes have been described in SSc. Alteration in enzymes and mediators involved in DNA methylation and histone modification, as well as dysregulated non-coding RNA levels all contribute to fibrosis, immune dysregulation, and impaired angiogenesis in this disease. Genes that are affected by epigenetic dysregulation include ones involved in autoimmunity, T cell function and regulation, TGFβ pathway, Wnt pathway, extracellular matrix, and transcription factors governing fibrosis and angiogenesis. In this review, we provide a comprehensive overview of the current findings of SSc genetic susceptibility, followed by an extensive description and a systematic review of epigenetic research that has been carried out to date in SSc. We also summarize the therapeutic potential of drugs that affect epigenetic mechanisms, and outline the future prospective of genomics and epigenomics research in SSc.