The immunological landscape and silico analysis of key paraptosis regulator LPAR1 in gastric cancer patients.

This study aims to identify key regulators of paraptosis in gastric cancer (GC) and explore their potential in guiding therapeutic strategies, especially in stomach adenocarcinoma (STAD). Genes associated with paraptosis were identified from the references and subjected to Cox regression analysis in the TCGA-STAD cohort. Using machine learning models, LPAR1 consistently ranked highest in feature importance. Multiple sequencing data showed that LPAR1 was significantly overexpressed in cancer-associated fibroblasts (CAFs). LPAR1 expression was significantly higher in normal tissues, and ROC analysis demonstrated its discriminative ability. Copy number alterations and microsatellite instability were significantly associated with LPAR1 expression. High LPAR1 expression correlated with advanced tumor grades and specific cancer immune subtypes, and multivariate analysis confirmed LPAR1 as an independent predictor of poor prognosis. LPAR1 expression was associated with different immune response metrics, including immune effector activation and upregulated chemokine secretion. High LPAR1 expression also correlated with increased sensitivity to compounds, such as BET bromodomain inhibitors I-BET151 and RITA, suggesting LPAR1 as a biomarker for predicting drug activity. FOXP2 showed a strong positive correlation with LPAR1 transcriptional regulation, while increased methylation of LPAR1 promoter regions was negatively correlated with gene expression. Knockdown of LPAR1 affected cell growth in most tumor cell lines, and in vitro experiments demonstrated that LPAR1 influenced extracellular matrix (ECM) contraction and cell viability in the paraptosis of CAFs. These findings suggest that LPAR1 is a critical regulator of paraptosis in GC and a potential biomarker for drug sensitivity and immunotherapy response. This underscores the role of CAFs in mediating tumorigenic effects and suggests that targeting LPAR1 could be a promising strategy for precision medicine in GC.