分子动力学模拟与单细胞及空间转录组学验证结直肠癌免疫和预后生物标志物并构建临床预后模型

Colorectal cancer (CRC) represents a huge global health challenge characterized by significant morbidity and mortality. The urgent need to identify biomarkers through integrative validation research to enhance diagnostic accuracy and prognostic stratification has prompted the exploration of immune and prognostic genes. This study aimed to systematically identify differentially expressed genes (DEGs) associated with both immunity and prognosis in CRC, validate their clinical significance, and construct a reliable prognostic model.

This research sought to identify DEGs associated with immunity and prognosis in CRC. We examined clinical and RNA sequencing data from 698 CRC patients obtained from The Cancer Genome Atlas (TCGA). Utilizing the Xiantao Academic Platform, we conducted differential expression analysis and identified hub genes associated with immunity and prognosis through Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses, alongside five machine learning algorithms to construct a prognostic model. The hub genes were validated using the Gene Expression Omnibus (GEO) database, molecular docking, molecular dynamics simulation, single-cell and spatial transcription analyses.

LASSO and Cox regression analyses, along with five machine learning algorithms, were employed to identify significant genes linked to immunity and prognosis, yielding three hub genes: ULBP2 , INHBB , and STC2 . Validation of these genes in the GEO dataset GSE21815 demonstrated significant diagnostic performance, with area under the curve (AUC) values of 0.908, 0.742, and 0.934, respectively. A prognostic model integrating clinical factors and hub genes was developed, demonstrating high predictive accuracy for 1-, 3-, and 5-year survival rates. Further analysis revealed significant enrichment in the TGF-β signaling pathway and natural killer cell-mediated cytotoxicity, as evidenced by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The single-sample Gene Set Enrichment Analysis (ssGSEA)-based immune infiltration analysis revealed immune infiltration differences between groups with high and low immune phenotype scores. Molecular docking and dynamics simulations revealed valproic acid, cyclosporine, and genistein as potential therapeutic compounds with strong binding affinities to the hub genes. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics provided insights into hub gene expression patterns and interactions within the tumor microenvironment.

This comprehensive study highlights the potential of ULBP2 , INHBB , and STC2 as promising biomarkers for CRC, emphasizing their roles in regulating tumor progression and immune responses. Future studies should focus on targeted therapeutic strategies that utilize these biomarkers to enhance treatment efficacy and patient prognosis.