Microarray-based identification of genes associated with cancer progression and prognosis in hepatocellular carcinoma

Abstract

BackgroundHepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths. The average survival and 5-year survival rates of HCC patients still remains poor. Thus, there is an urgent need to better understand the mechanisms of cancer progression in HCC and to identify useful biomarkers to predict prognosis.MethodsPublic data portals including Oncomine, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) profiles were used to retrieve the HCC-related microarrays and to identify potential genes contributed to cancer progression. Bioinformatics analyses including pathway enrichment, protein/gene interaction and text mining were used to explain the potential roles of the identified genes in HCC. Quantitative real-time polymerase chain reaction analysis and Western blotting were used to measure the expression of the targets. The data were analysed by SPSS 20.0 software.ResultsWe identified 80 genes that were significantly dysregulated in HCC according to four independent microarrays covering 386 cases of HCC and 327 normal liver tissues. Twenty genes were consistently and stably dysregulated in the four microarrays by at least 2-fold and detection of gene expression by RT-qPCR and western blotting showed consistent expression profiles in 11 HCC tissues compared with corresponding paracancerous tissues. Eleven of these 20 genes were associated with disease-free survival (DFS) or overall survival (OS) in a cohort of 157 HCC patients, and eight genes were associated with tumour pathologic PT, tumour stage or vital status. Potential roles of those 20 genes in regulation of HCC progression were predicted, primarily in association with metastasis. INTS8 was specifically correlated with most clinical characteristics including DFS, OS, stage, metastasis, invasiveness, diagnosis, and age.ConclusionThe significantly dysregulated genes identified in this study were associated with cancer progression and prognosis in HCC, and might be potential therapeutic targets for HCC treatment or potential biomarkers for diagnosis and prognosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13046-016-0403-2) contains supplementary material, which is available to authorized users.

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

“…A total of 522 patient samples with LUAD were retrieved from a cohort of TCGA database, while only 516 samples with mRNA expression value were available to analyze the association of gene expression with clinical characteristics. The gene expression level was categorized as high or low based on the median value referring to a previous study (Yin et al, ).…”

“…An increasing number of studies used these public databases as powerful evidence to screen and identify novel biomarkers for diagnosis and prognosis. For instance, by retrieving data from Oncomine and TCGA, Yin et al () successfully identified a group of genes related to cancer progression and prognosis in hepatocellular carcinoma. Liu et al () identified six genes that may be potential therapeutic targets and biomarkers for diagnosis and prognosis in ovarian cancer, based on data retrieved from Oncomine, GEO, and TCGA.…”

Identification of differentially expressed genes in hepatocellular carcinoma by integrated bioinformatic analysis

“…Additionally, Wang et al provided a pipeline for the identification of prognostic signatures for HCC OS prediction[5]. Microarray technology has since been applied to the study of genetic changes in HCC, has defined several different genetic variants of the disease, and has identified genetic features that predict poor outcomes and metastasis [6–8]. Although the cellular and molecular genetic alterations of HCC have been more well-understood through these technologies, the molecular mechanisms have not yet been fully elucidated.…”

Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profiles

“…In current study, CLEC4G , GDF5 , CLEC1B , CLEC4M and FCN2 are among LCN-5-RNA signature that can classify cancer and normal tissue sample with high precision. Earlier, CLEC4M and GDF5 have been recognized as differentially expressed gene in various other cancers [68-70], while CLEC4G, CLEC1B and FCN2 were identified as genes that associated with cancer progression and prognosis in hepatocellular carcinoma [64, 71–73]. Overall this analysis emphasizes the role of these signature genes in oncogenesis.…”