Dataset Information

Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays.

ABSTRACT:

Background

The incidence and mortality rates of hepatocellular carcinoma are among the highest of all cancers all over the world. However the survival rates are relatively low due to lack of effective treatments. Efforts to elucidate the mechanisms of HCC and to find novel prognostic markers and therapeutic targets are ongoing. Here we tried to identify prognostic genes of HCC through co-expression network analysis.

Methods

We conducted weighted gene co-expression network analysis with a microarray dataset GSE14520 of HCC from Gene Expression Omnibus database and identified a hub module associated with HCC prognosis. Function enrichment analysis of the hub module was performed. Clinical information was analyzed to select candidate hub genes. The expression profiles and survival analysis of the selected genes were performed using additional datasets (GSE45267 and TCGA-LIHC) and the hub gene was identified. GSEA and in vitro experiments were conducted to further verify the function of the hub gene.

Results

Genes in the hub module were mostly involved in the metabolism pathway. Four genes (SLC27A5, SLC10A1, PCK2 and FMO4) from the module were identified as candidate hub genes according to correlation analysis with prognostic indicators. All these genes were significantly down-regulated in tumor tissues compared with non-tumor tissues in additional datasets. After survival analysis and network construction, SLC27A5 was selected as a prognostic marker. GSEA analysis and in vitro assays suggested that SLC27A5 downregulation promoted tumor cell migration via enhancing epithelial-mesenchymal transition.

Conclusion

SLC27A5 is a potential biomarker of HCC and SLC27A5 downregulation promoted HCC progression by enhancing EMT.

SUBMITTER: Zhang F

PROVIDER: S-EPMC7968262 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays.

Zhang Fan F Xue Mengjuan M Jiang Xin X Yu Huiyuan H Qiu Yixuan Y Yu Jiaming J Yang Fan F Bao Zhijun Z

Cancer cell international 20210317 1

<h4>Background</h4>The incidence and mortality rates of hepatocellular carcinoma are among the highest of all cancers all over the world. However the survival rates are relatively low due to lack of effective treatments. Efforts to elucidate the mechanisms of HCC and to find novel prognostic markers and therapeutic targets are ongoing. Here we tried to identify prognostic genes of HCC through co-expression network analysis.<h4>Methods</h4>We conducted weighted gene co-expression network analysis ...[more]

PMID: 33731144

Similar Datasets

Project description:BackgroundIntervertebral disc degeneration (IVDD) is an age-related orthopedic degenerative disease characterized by recurrent episodes of lower back pain, the pathogenesis of which is not fully understood. This study aimed to identify key biomarkers of IVDD and its causes.MethodsWe acquired three gene expression profiles from the Gene Expression Omnibus (GEO) database, GSE56081, GSE124272, and GSE153761, and used limma fast differential analysis to identify differentially expressed genes (DEGs) between normal and IVDD samples after removing batch effects. We applied weighted gene co-expression network (WGCNA) to identify the key modular genes in GSE124272 and intersected these with DEGs. Next, A protein-protein interaction network (PPI) was constructed, and Cytoscape was used to identify the Top 10 hub genes. Functional enrichment analyses were performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Three key genes were validated using Western Blot (WB) and qRT-PCR. Additionally, we predicted miRNAs involved in hub gene co-regulation and analyzed miRNA microarray data from GSE116726 to identify four differentially expressed miRNAs.ResultsWe identified 10 hub genes using bioinformatics analysis, gene function enrichment analysis revealed that they were primarily enriched in pathways, such as the TNF signaling pathway. We chose JUNB, SOCS3, and CEBPB as hub genes and used WB and qRT-PCR to confirm their expression. All three genes were overexpressed in the IVDD model group compared to the control group. Furthermore, we identified four miRNAs involved in the co-regulation of the hub genes using miRNet prediction: mir-191-5p, mir-20a-5p, mir-155-5p, and mir-124-3p. Using limma difference analysis, we discovered that mir-191-5p, mir-20a-5p, and mir-155-5p were all down-regulated and expressed in IVDD samples, but mir-124-3p showed no significant change.ConclusionJUNB, SOCS3, and CEBPB were identified as key genes in IVDD, regulated by specific miRNAs, providing potential biomarkers for early diagnosis and therapeutic targets.

Project description:BackgroundHepatocellular carcinoma (HCC) is a leading cause of tumor-associated death worldwide, owing to its high 5-year postoperative recurrence rate and inter-individual heterogeneity. Thus, a prognostic model is urgently needed for patients with HCC. Several researches have reported that copy number amplification of the 8q24 chromosomal region is associated with low survival in many cancers. In the present work, we set out to construct a multi-gene model for prognostic prediction in HCC.MethodsRNA sequencing and copy number variant data of tumor tissue samples of HCC from The Cancer Genome Atlas (n=328) were used to identify differentially expressed messenger RNAs of genes located on the chromosomal 8q24 region by the Wilcox test. Univariate Cox and Lasso-Cox regression analyses were carried out for the screening and construction of a prognostic multi-gene signature in The Cancer Genome Atlas cohort (n=119). The multi-gene signature was validated in a cohort from the International Cancer Genome Consortium (n=240). A nomogram for prognostic prediction was built, and the underpinning molecular mechanisms were studied by Gene Set Enrichment Analysis.ResultsWe successfully established a 7-gene prognostic signature model to predict the prognosis of patients with HCC. Using the model, we divided individuals into high-risk and low-risk sets, which showed a significant difference in overall survival in the training dataset (HR =0.17, 95% CI: 0.1-0.28; P<0.001) and in the testing dataset (HR = 0.42, 95% CI: 0.23-0.74; P=0.002). Multivariate Cox regression analysis showed the signature to be an independent prognostic factor of HCC survival. A nomogram including the prognostic signature was constructed and showed a better predictive performance in short-term (1 and 3 years) than in long-term (5 years) survival. Furthermore, Gene Set Enrichment Analysis identified several pathways of significance, which may aid in explaining the underlying molecular mechanism.ConclusionsOur 7-gene signature is a reliable prognostic marker for HCC, which may provide meaningful information for therapeutic customization and treatment-related decision making.

Dataset Information

Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays.

Background

Methods

Results

Conclusion

Publications

Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets