Dataset Information

Multi-omic analyses of hepatocellular carcinoma to determine immunological characteristics and key nodes in gene-expression network.

ABSTRACT: Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide, but effective immunotherapy is still limited for those affected. Therefore, there is an urgent need to explore the specific mechanisms governing tumor immunity to improve the survival rate for those diagnosed with HCC. In the present study, we performed a new immune stratification of HCC samples into two subclasses (A and B) from The Cancer Genome Atlas and the International Cancer Genome Consortium databases, and comprehensive multi-omic analyses of major histocompatibility complex genes, gene copy-number variations, somatic mutations, DNA methylation, and non-coding RNAs. Subclass A was found to have a higher survival rate compared with subclass B, and there were significant immunological differences between the two clusters. Based on these differences, we identified DRD1 and MYCN as key hub genes in the immune-phenotype gene expression regulatory network. These results provide novel ideas and evidence for HCC regulatory mechanisms that may improve immunotherapy for this cancer.

SUBMITTER: Wang Z

PROVIDER: S-EPMC8276092 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Multi-omic analyses of hepatocellular carcinoma to determine immunological characteristics and key nodes in gene-expression network.

Wang Zhihui Z Zhang Shuijun S

Bioscience reports 20210701 7

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide, but effective immunotherapy is still limited for those affected. Therefore, there is an urgent need to explore the specific mechanisms governing tumor immunity to improve the survival rate for those diagnosed with HCC. In the present study, we performed a new immune stratification of HCC samples into two subclasses (A and B) from The Cancer Genome Atlas and the International Cancer Genome Consortium databases, and comprehensiv ...[more]

PMID: 34212175

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Project description:To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

Project description:The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

Dataset Information

Multi-omic analyses of hepatocellular carcinoma to determine immunological characteristics and key nodes in gene-expression network.

Publications

Multi-omic analyses of hepatocellular carcinoma to determine immunological characteristics and key nodes in gene-expression network.

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