Integrative analysis-based identification and validation of a prognostic immune cell infiltration-based model for patients with advanced gastric cancer

Advanced gastric cancer (GC) remains difficult to conduct individualized prognostic evaluations owing to the highly heterogeneous nature and the low level of immune cell infiltration (ICI) within GC tumors. This study thus sought to develop a model capable of classifying GC patients according to the degree of tumor ICI and gauging prognosis.The degree of ICI in GC patients from the GSE15459, GSE57303, and GSE62254 datasets were estimated, and these values were used to group patients via an unsupervised clustering approach, after which ICI cluster-related genes were identified the association with prognosis through Cox and LASSO regression analyses. The primary risk genes were then verified by immunohistochemical staining of GC tumor tissue samples.570 patients were clustered into three clusters and 289 ICI cluster-related genes were identified. A prognostic model based on the expression of six crucial ICI risk genes (CXCL11, RBPMS2, LOC400043, JCHAIN, CT83, and ORM1) wa constructed. Patients identified as being high risk based upon the model have poorer clinical features and survival outcomes compared to the other patients. Adjuvant intervention was found to be more beneficial for patients expressing high levels of RBPMS2, JCHAIN, or ORM1. Furthermore, patients expressing low levels of JCHAIN or CT83 in GC tumor tissues were verified to exhibit a significantly better prognosis in a CMU cohort.Advanced GC patients were successfully grouped into clusters based on the degree of intratumoral ICI, and a prognostic evaluation model based on 6 ICI risk genes was developed and validated.