# Cell-in-cell associated lncRNA signature predicts prognosis and immunotherapy response in gastric cancer

## TL;DR

This study identifies a long non-coding RNA signature that predicts survival and immunotherapy response in gastric cancer patients.

## Contribution

A novel lncRNA-based risk model is developed to predict prognosis and treatment response in gastric cancer.

## Key findings

- The CICRlncRNA model stratified patients into low- and high-risk groups with significantly different survival outcomes.
- High-risk patients showed an immunosuppressive tumor environment and poor response to immunotherapy.
- Low-risk patients responded better to specific targeted therapies like gefitinib and entinostat.

## Abstract

Gastric cancer (GC) remains a leading cause of cancer mortality, necessitating robust prognostic biomarkers and personalized therapeutic strategies.

We developed a risk model integrating three cell-in-cell-associated lncRNAs (CICRlncRNAs: AP003392.1, AP000695.2, AL161785.1) using transcriptomic data from 367 TCGA-GC patients. The cohort was randomly split into training (n = 184) and test sets (n = 183) for model construction and external validation. Statistical rigor included LASSO-Cox regression, Kaplan-Meier analysis, and ROC curves assessing 1/3/5-year AUC.

The model stratified patients into low- and high-risk groups with distinct overall survival (OS, HR = 2.62, P <0.001) and progression-free survival (PFS, HR = 1.94, P <0.001). High-risk patients exhibited an immunosuppressive tumor microenvironment (TME), characterized by elevated Tregs (P <0.05) and M2 macrophages (P <0.05), correlating with poor response to immune checkpoint inhibitors (TIDE score, P <0.001). Drug sensitivity analysis revealed low-risk patients responded better to gefitinib/entinostat, while high-risk patients benefited from dasatinib/foretinib. Experimental validation confirmed AP000695.2 promoted proliferation and invasion in GC cells (P <0.01).

This study establishes CICRlncRNAs as prognostic biomarkers and provides insights for precision therapy, though clinical applicability requires prospective validation.