Dataset Information

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

ABSTRACT:

Background

Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.

Methods

Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.

Results

A list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, 'pathways in cancer,' was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982-0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.

Discussion

The study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, 'pathways in cancer.' The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

SUBMITTER: Yang H

PROVIDER: S-EPMC5354077 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

Yang Hong H Zhang Xin X Cai Xiao-Yong XY Wen Dong-Yue DY Ye Zhi-Hua ZH Liang Liang L Zhang Lu L Wang Han-Lin HL Chen Gang G Feng Zhen-Bo ZB

PeerJ 20170314

<h4>Background</h4>Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.<h4>Methods</h4>Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling p ...[more]

PMID: 28316892

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Project description:Hepatocellular carcinoma (HCC), the most malignant form of primary liver cancer, is the fourth most prevalent cause of cancer mortality globally. It was recently discovered that the dietary fermentable fiber, inulin, can reprogram the murine liver to favor HCC development in a gut microbiota-dependent manner. Determining the molecular pathways that are either over expressed or repressed during inulin-induced HCC would provide a platform of potential therapeutic targets. In the present study, we have combined analysis of the novel inulin-induced HCC murine model and human HCC samples to identify differentially expressed genes (DEGs) in hepatocarcinogenesis. Hepatic transcriptome profiling revealed that there were 674 DEGs in HCC mice compared to mice safeguarded from HCC. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis uncovered enrichment in ECM-receptor interaction, steroid hormone biosynthesis, PPAR signaling pathway, focal adhesion and protein digestion and absorption during inulin-induced HCC. Tandem mass tag based quantitative, multiplexed proteomic analysis delineated 57 differentially expressed proteins, where the over-expressed proteins were associated with cell adhesion molecules, valine, leucine and isoleucine degradation and ECM-receptor interaction. After obtaining the human orthologs of the mouse genes, we did a comparison analysis to level 3 RNA-seq data found in the Cancer Genome Atlas (TCGA) database, corresponding to human HCC (n = 361) and healthy liver (n = 50) samples. Out of the 549 up-regulated and 68 down-regulated human orthologs identified, 142 genes (137 significantly over-expressed and 5 significantly under-expressed) were associated with human HCC. Using univariate survival analysis, we found 27 over-expressed genes involved in cell-cell adhesion and cell division that were associated with poor HCC patient survival. Overall, the genetic and proteomics signatures highlight potential underlying mechanisms in inulin-induced HCC and support that this murine HCC model is human relevant.

Dataset Information

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

Background

Methods

Results

Discussion

Publications

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

Similar Datasets

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