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

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 GS54129 dataset from GEO by supervised learning method artiﬁcial neural network (ANN) algorithm. Results: 12 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 four 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. 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 26 bearing the top 25% standard deviations among all the samples in training set were performed to 27 systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. 28 Then, hub genes were further screened by using the “least absolute shrinkage and selection 29 operator” (LASSO) logistic regression. Finally, hub genes were validated in GS54129 dataset 30 from GEO by supervised learning method artificial neural network (ANN) algorithm. 31 Results: 12 modules with strong preservation were identified by using WGCNA methods in 32 training set. Of which, five modules significantly related to gastric cancer were selected as 33 clinically significant modules, and 713 candidate genes were identified from these four modules. Then, , , C1orf95 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 38 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.