Machine learning and multi-omics analyses reveal CLDN1 as a candidate marker associated with colorectal cancer invasion and nutlin-3a (-) response.

OBJECTIVE: Colorectal cancer (CRC) is molecularly, cellularly and spatially heterogeneous, and current staging systems and biomarkers incompletely explain prognosis and treatment response. This study integrated transcriptomics, machine learning, single-cell RNA sequencing, spatial transcriptomics and in vitro experiments to prioritize candidate CRC-associated markers and characterize their potential biological and clinical relevance. METHODS: GSE44861 and GSE44076 from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) were used for differential expression, consensus differentially expressed gene (DEG) identification and functional enrichment analyses. Four machine-learning algorithms were applied for feature selection. Candidate genes were prioritized by differential expression, model importance and receiver operating characteristic (ROC) performance. GSE231559 single-cell data and spatial transcriptomics characterized the cellular source, pseudotime dynamics and spatial distribution of the core gene. TCGA-COAD assessed prognosis, pathways and computationally predicted drug sensitivity. Western blotting, Transwell and MTT assays were used for experimental validation, and public immunohistochemistry images from the Human Protein Atlas were used as supplementary protein-expression evidence. RESULTS: We identified 544 consensus DEGs enriched in leukocyte migration, chemotaxis, extracellular matrix organization, cytokine activity and metabolic pathways. Six genes were jointly selected: CLDN1, CDH3, KRT24, MMP7, NEBL and SCGN. CLDN1 was markedly upregulated and showed the highest tumour-versus-normal discrimination among the six candidates in the TCGA-COAD cohort. Single-cell and spatial analyses showed that CLDN1 was mainly expressed in CRC epithelial cells, displayed increasing expression across the inferred pseudotime ordering, and was enriched in malignant and mixed regions. In the TCGA-COAD cohort, high CLDN1 expression was associated with poorer overall survival and pathways involving the cell cycle, proteasome, autophagy, Hippo, ErbB and CRC. CLDN1 manipulation altered CRC-cell invasion and in vitro response to Nutlin-3a (-). CONCLUSION: These findings support CLDN1 as a candidate CRC-associated marker linked to malignant epithelial-cell states, tumour-associated spatial heterogeneity, invasive phenotype and Nutlin-3a (-) response. Further independent and prospective validation is required to determine its clinical diagnostic, prognostic and treatment-response relevance.