Abstract
BACKGROUND: Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a dismal prognosis and a lack of effective biomarkers at an early stage. Calcium plays a crucial role in immunomodulation and immunotherapy, and can effectively predict the prognosis of tumors. This study aimed to probe into the potential value of calcium-related genes (CRGs) in the prognosis of LUAD. METHODS: RNA sequencing data, somatic mutation data, and demographic and clinical data of LUAD were collected from the TCGA database. GSE31210 data were collected from the GEO database, and data on CRGs were from GeneCards. Univariate, LASSO, multivariate Cox regression analyses, Kaplan-Meier survival analysis, ROC curve analysis, gene set enrichment analysis (GSEA), and other methods were employed to establish the prediction model and validate and annotate the functions. The tumor immune microenvironment in different risk groups was evaluated using ESTIMATE, single-sample GSEA, and CIBERSORT algorithms. The immunophenoscore and tumor immune dysfunction and exclusion scores were examined to predict immunotherapy response sensitivity. Anti-tumor drugs were screened through correlation analysis and differential comparison. RESULTS: Eight calcium-related biomarkers (GRIA1, BTK, CLCA1, PDGFB, S100P, TRPA1, F2RL1, FBN2) significantly associated with the prognosis of LUAD were identified. A reliable risk-scoring model was constructed and its capability was validated. Patients with LUAD with worse clinical features (advanced stage, higher tumor burden, and lymph node metastases) had higher riskscores and a worse prognosis. Patients in the low-risk (LR) group exhibited a strong immune response, especially significantly increased mast cells, B cells, and Tfh cells. The high-risk (HR) group exhibited enrichment of immunosuppressive cells (e.g., Tregs), suggesting that the LR group may benefit more from the immune checkpoint suppressive therapy. Moreover, we predicted the clinical potential of drug candidates such as Erlotinib, Afatinib, and Barasertib. CONCLUSION: We discovered that multiple CRGs were significantly associated with the survival of LUAD and were differentially expressed in LUAD. We created a risk model for prognosis prediction based on CRGs in LUAD, which can not only effectively predict prognosis but also reflect changes in the tumor immune microenvironment among different risk groups. These findings may be beneficial for clinical decision-making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-025-09571-3.