Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results

Li, Jia; Huang, Zhong-Xi; Wei, Lixin

doi:10.5114/wo.2016.58497

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…For instance, Ho, Kai & Ng (2015) employed TCGA whole-transcriptome sequencing data and discovered the significantly enriched KEGG pathways of cell cycle and p53 signaling, which matches our results in the present one with DEGs from TCGA and supports the credibility and reliability of the current research. Other studies employed Gene Expression Omnibus (GEO) data to perform KEGG pathways enrichment analysis but their results did not coincide with ours ( Jin et al, 2015a ; Li, Huang & Wei, 2016 ). Such findings might be related to the methods and sample sizes adopted.…”

Section: Discussioncontrasting

confidence: 60%

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

Yang

Zhang

Cai

et al. 2017

PeerJ

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BackgroundLiver 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.MethodsBig 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.ResultsA 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.DiscussionThe 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.

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Section: Discussioncontrasting

confidence: 60%

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

Yang

Zhang

Cai

et al. 2017

PeerJ

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“…Hepatocellular carcinoma (HCC) accounts for 90% of primary liver cancers and it represents the third most common cause of death from cancer worldwide, with an increasing incidence expected in the next decades [1]. The major risk factors are chronic viral hepatitis B and C (HBV and HCV), alcohol abuse, primary biliary cirrhosis, xenobiotics, diabetes, non-alcoholic fatty liver disease and genetic disorders such as hemochromatosis and α1-antitrypsin deficiency [2, 3]. To date, surgery remains the most effective treatment with curative potential.…”

Section: Introductionmentioning

confidence: 99%

MicroRNA-122a as a non-invasive biomarker for HCV genotype 4-related hepatocellular carcinoma in Egyptian patients

El‐Ahwany¹,

Mourad²,

Zoheiry³

et al. 2019

aoms

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IntroductionHepatitis C virus (HCV) infection persists in most infected individuals and can lead to the development of chronic hepatitis, cirrhosis and hepatocellular carcinoma (HCC). MicroRNAs (miRNAs) have a crucial role in various liver diseases, especially HCC. The expression profiles of circulating microRNAs have been studied aiming at the identification of novel non-invasive biomarkers. This study aims to develop a non-invasive diagnostic tool based on measuring the serum levels of different miRNAs in order to detect HCV-induced HCC at the early stages of the disease.Material and methodsFive main miRNAs (miRNA-122a, miRNA-125a, miRNA-139, miRNA-145, and miRNA-199a) were selected according to the literature that demonstrated their unique expression pattern during HCC development. Serum samples were collected from 42 cases of chronic hepatitis C (CHC) without cirrhosis, 45 cases of CHC with cirrhosis (LC), 38 cases of HCC with HCV, and 40 healthy individuals serving as a control. The five miRNAs were measured using real-time reverse transcription PCR. The conventional HCC markers α-fetoprotein (AFP) and des-γ-carboxyprothrombin (DCP) were measured with commercial kits.ResultsSerum levels of miRNA-122a, miRNA-125a, miRNA-139, miRNA-145, and miRNA-199a were significantly lower (p < 0.01) in HCC than in CHC and LC groups. As a single marker, miRNA-122a had the highest sensitivity for HCC, followed by miRNA-199a, miRNA-145, miRNA-139, and miRNA-125a.ConclusionsThese findings indicate that measurement of serum levels of miRNA-122a, miRNA-125a, miRNA-139, miRNA-145, and miRNA-199a can differentiate HCC from CHC and LC. Our results suggest that serum miR-122 might serve as a novel and potential noninvasive biomarker for HCV-induced HCC.

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“…DEGs from TCGA and supports the credibility and reliability of the current research. Other studies employed Gene Expression Omnibus (GEO) data to perform KEGG pathways enrichment analysis but their results did not coincide with ours (Jin et al 2015a;Li et al 2016). Such findings might be related to the methods and sample sizes adopted.…”

Section: Discussionmentioning

confidence: 61%

Peer Review #1 of "From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma (v0.1)"

2017

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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.

show abstract

Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results

Cited by 10 publications

References 22 publications

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

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

MicroRNA-122a as a non-invasive biomarker for HCV genotype 4-related hepatocellular carcinoma in Egyptian patients

Peer Review #1 of "From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma (v0.1)"

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