The Comprehensive Analysis of Competitive Endogenous RNA Networks and Tumor-Infiltrating Immune Cells in Gastric Cancer With Lymph Node Metastasis

Background: Gastric cancer is a kind of tumor with strong heterogeneity. Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors. Methods: In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. Results: In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. The performance of SVM model was good in the intestinal and diffuse cohort, while the model in the whole cohort was relatively poor. Some important co-expression patterns between immune cells and ceRNAs network indicated significant correlation CD70 with dendritic cells and so on. Conclusion: Our research inferred competing endogenous RNA network of lymph node metastasis and built an excellent predicting model.