Identification of novel biomarkers for hepatocellular carcinoma using transcriptome analysis

Abstract

Hepatocellular carcinoma (HCC) is the third leading cause of death from cancer in the world. To comprehensively investigate the utility of microRNAs (miRNAs) and protein-encoding transcripts (messenger RNAs [mRNAs]) in HCC as potential biomarkers for early detection and diagnosis, we exhaustively mined genomic data from three available omics datasets (GEO, Oncomine, and TCGA), analyzed the overlaps among gene expression studies from 920 hepatocellular carcinoma samples and 508 healthy (or adjacent normal) liver tissue samples available from six laboratories, and identified 178 differentially expressed genes (DEGs) associated with HCC. Paired with miRNA and lncRNA data, we identified 23 core genes that were targeted by nine differentially expressed miRNAs and 21 HCC-specific lncRNAs. We further demonstrated that alterations in these 23 genes were quite frequent, with five genes altered in over 5% of the population. Patients with high levels of YWHAZ, ENAH, and HMGN4 tended to have high-grade tumors and shorter overall survival, suggesting that these genes could be promising candidate biomarkers for disease and poor prognosis in patients with HCC. Our comprehensive mRNA, miRNA, and lncRNA omics analyses from multiple independent datasets identified robust molecules that may be used as biomarkers for early HCC detection and diagnosis.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

“…The unprecedented proliferation of recent large-scale and multiomics databases of cancers provides numerous new insights into genomic and epigenomic dysregulation in cancer discovery (Rappoport and Shamir, 2018). Publicly available databases like TCGA, METABRIC, or GEO, which are common in the cancer research community, help better understand tumor heterogeneity, detect biomarker genes, and define hidden molecular mechanisms in multi-omics research (Xia Q. et al, 2019). Moreover, lines of previous evidence indicate notable relationships between CNA and mRNA, such as there is a high association of CNA with the development and progression of cancers by regulating gene expression level (Huang et al, 2017;Samulin Erdem et al, 2017;Zhou et al, 2017;Gut et al, 2018), as well as the similar regulatory associations between MET and mRNA (Herman and Baylin, 2003;Shen and Laird, 2013).…”

<p>Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD</p>

“…24,25 Therefore, multi-omics studies can help determine tumor heterogeneity and screen therapeutic targets and tumor biomarkers, which have greater advantages. 26 The study screens and identifies 9-gene signature associated with COAD prognosis by analyzing multi-omics data, including transcriptome data, copying number variation data and mutation data. The 9-gene signature established by screening has strong robustness and stable prediction performance in both internal verification set and external verification set.…”

LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling

“…In recent years, high-throughput sequencing provides a better understanding of HCC-related lncRNAs. A large number of dysregulated lncRNAs have been identified, and they are associated with a variety of biological processes in HCC, such as cell proliferation, apoptosis, migration, invasion, and angiogenesis 19 – 21 . Emerging researches have revealed that SNHG family plays important roles in tumorigenesis and the immune escape of cancer via sponging miRNA 22 – 24 .…”