Integrated analysis of a ceRNA network reveals potential prognostic lncRNAs in gastric cancer.
Qi Mingran,Yu Bingxin,Yu Huiyuan,Li Fan
Long noncoding RNAs (lncRNAs) have important biological functions as competing endogenous RNAs (ceRNAs) in tumors, yet the functions and regulatory mechanisms of lncRNA-related ceRNAs in gastric cancer have not been fully elucidated. In this study, we constructed a lncRNA-miRNA-mRNA ceRNA network and identified potential lncRNA biomarkers in gastric cancer. Basing on the RNA profiles downloaded from The Cancer Genome Atlas (TCGA) platform, the gastric cancer-specific differentially expressed lncRNAs, miRNAs, and mRNAs were screened for constructing a ceRNA network using bioinformatic tools. The enrichment analysis of the biological processes in Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathways was performed on the ceRNA-related DEmRNAs. According to the modularization of protein-protein interaction (PPI) network, we extracted a ceRNA subnetwork and analyzed the correlation between the expression of the lncRNAs involved and specific clinical features of patients. Next, the expression of highly up-regulated in liver cancer (HULC) and RP11-314B1.2 showed significant changes in several pathological processes involved in gastric cancer, and nine lncRNAs were found to be correlated with the overall survival of patients with gastric cancer. Through the univariate and multivariate Cox regression analyses, two lncRNAs (LINC00106 and RP11-999E24.3) were identified and utilized to establish a risk score model for assessing the prognosis of patients. The analysis results were also partially verified using quantitative real-time PCR. The findings from this study indicate that HULC, RP11-314B1.2, LINC00106, and RP11-999E24.3 could be considered as potential therapeutic targets or prognostic biomarkers in gastric cancer, and provide a new perspective for cancer pathogenesis research.
A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer.
Chen Qingchuan,Tan Yuen,Zhang Chao,Zhang Zhe,Pan Siwei,An Wen,Xu Huimian
Frontiers in oncology
Background:Gastric 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. Methods:We 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. Results:Of 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 ()] + [(-0.8272) * Expression ()] + [1.09 * Expression ()] + [0.5174 * Expression ()] + [1.66 * Expression ()]. 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. Conclusion:This study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.