Abstract
Cervical cancer is the fourth most common cancer among women worldwide. Calcium ion channel-related genes (CICRGs) play an important role in the proliferation, differentiation, migration and angiogenesis of cervical cancer (CC). However, the diagnostic potential of calcium channel-related genes in cervical cancer remains underexplored. This study combined the TCGA-CC, GSE9750, and GSE63514 datasets with gene set map04020 to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to filter key module genes, followed by enrichment analysis and protein-protein interaction (PPI) network construction. Candidate prognostic genes were identified through three machine learning methods, with expression verified and survival differences assessed via Kaplan–Meier (KM) curves. Gene Set Enrichment Analysis (GSEA) correlated these genes with tumor-related processes. Additionally, immunological analysis and a ceRNA network were constructed. A total of 30 candidate genes were identified by intersecting 5089 DEGs, 3417 key module genes, and 240 CICRGs, and a PPI network was constructed. Expression verification and survival analysis identified two prognostic genes, VEGFA and CALML3. GSEA revealed VEGFA was positively correlated with tumor-related processes, while CALML3 was negatively correlated. Immunological analysis confirmed CALML3 as an effective indicator of immune cell infiltration. Finally, the drug-disease network revealed three disease processes associated with CALML3 and VEGFA, and the drug–gene network highlighted bisphenol A, tetra chlorodibenzo dioxin (TCDD), and Estradiol as co-acting agents of the prognostic genes. Immunohistochemistry showed higher expression levels of CALML3 and VEGFA in CC tissues, suggesting their potential as prognostic biomarkers. This study not only identifies key prognostic genes but also provides valuable insights into the molecular mechanisms of cervical cancer, offering potential therapeutic targets and biomarkers for prognosis and immune infiltration, thus contributing to the advancement of personalized treatment strategies for CC. The novelty of this study lies in the systematic integration of the KEGG calcium signaling gene set (map04020) with multi-cohort transcriptome data using a prior-constrained, multi-algorithm consensus workflow, which enhances robustness and reduces false positives. Moreover, we demonstrate the immune-infiltration relevance of CALML3 and develop a high-performance two-gene nomogram together with regulatory and drug-association networks, offering new clues for calcium-channel-related prognostic assessment and therapeutic exploration in cervical cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-026-04683-0.