A Weighted Gene Co-Expression Network Analysis–Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer

Abstract

BackgroundGastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.MethodsWe obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.ResultsOf the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.ConclusionThis study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.

Molecular Pathways and Key Genes Associated With Breast Width and Protein Content in White Striping and Wooden Breast Chicken Pectoral Muscle

“…In particular, co-expression network analysis has rapidly become a prevalent and powerful approach to elucidate the specific molecular processes underlying physiological mechanisms and pathological pathways (Hocquette et al, 2009;van Dam et al, 2018). Currently, Weighted Gene Co-expression Network Analyses (WGCNA) have been successfully employed in the study of various human diseases, most notably in different cancer research (Bai et al, 2020;Jia et al, 2020;Chen et al, 2021), to identify patterns of co-expressed genes (i.e., modules) associated with specific disease features. Combining the identification of key gene modules with hub gene analyses has proved to be useful in detecting molecular mechanisms and candidate genes that may be at the base of the physiological changes characterizing different human and animal disorders (van Dam et al, 2018).…”

A novel prognostic 6-gene signature for osteoporosis

“…Weighted gene co-expression network analysis (WGCNA) is a latest systems biology approach with particular practicability under such circumstances, which can assist in establishing the free-scale gene co-expression networks for identifying the relationships of gene sets with clinical features or among diverse gene sets ( 15 , 16 ). WGCNA has been widely adopted to identify clinical features-associated hub genes for diverse disorders, such as heart failure ( 17 ), gastric cancer ( 18 ) and ovarian cancer ( 19 ).…”

Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer

“…ARHGAP32 encodes a neuron-associated GTPase-activating protein that regulates dendritic spine morphology and strength by modulating Rho GTPase [16]. According to the literature, ARHGAP32 is widely involved in the occurrence and development of gastric cancer and liver cancer [17,18], but its relationship with colon cancer has not been reported. hsa_circ_0007331 located at chr3:195101737-195112876 is encoded by the ACAP2 gene, a homolog of Caenorhabditis elegans CNT-1, which has a proapoptotic function and an identical phosphoinositide-binding pattern to that of CNT-1.…”