Dataset Information

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis.

ABSTRACT:

Background

Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.

Methods

GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the "least absolute shrinkage and selection operator" (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.

Results

Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946.

Conclusions

These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.

SUBMITTER: Li C

PROVIDER: S-EPMC7938783 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.MethodsWe downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.ResultsTen gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).ConclusionThe brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.