Identification and validation of Kif15 as a potential diagnostic and prognostic biomarker in colon cancer.

objectiveThis study aimed to systematically identify and validate KIF15 as a potential diagnostic and prognostic biomarker in colon cancer (CC) using integrated bioinformatics analyses. We further explored its role in the tumor immune microenvironment and its potential value in immunotherapy, and validated its expression and clinical significance through immunohistochemical analysis.

methodsGene expression profiles of CC were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). A protein–protein interaction (PPI) network was constructed, and key genes were identified using the MCODE plugin in Cytoscape. External validation of candidate gene expression was performed using The Cancer Genome Atlas (TCGA) cohort. Kaplan–Meier survival analysis was conducted to evaluate prognostic significance. Receiver operating characteristic (ROC) and time-dependent ROC analyses were applied to assess diagnostic and prognostic performance. A nomogram model was established based on multivariate Cox regression, and its predictive accuracy and stability were evaluated using calibration curves and decision curve analysis (DCA). To investigate the role of KIF15 in the tumor microenvironment, single-cell RNA sequencing data from the TISCH database were analyzed to determine its expression distribution across immune cell subsets. Functional enrichment and immune correlation analyses were performed to elucidate the potential molecular mechanisms of KIF15 in CC progression and to identify downstream hub genes. Finally, immunohistochemistry (IHC) was conducted to validate the tissue expression pattern and clinical relevance of KIF15.

resultsA total of 611 DEGs were identified by integrating three GEO datasets (GSE24550, GSE21815, and GSE44076). Based on PPI network construction and MCODE analysis, 13 key genes, including KIF15, were identified. Combined univariate Cox regression and pan-cancer analysis ultimately determined KIF15 as the core gene of interest. TCGA analysis demonstrated that KIF15 was significantly upregulated in CC tissues (P < 0.0001), with a diagnostic ROC AUC of 0.874. Kaplan–Meier analysis showed that high KIF15 expression was significantly associated with poor prognosis (P = 0.004). Multivariate Cox regression confirmed that KIF15 was an independent prognostic factor. The nomogram model constructed based on KIF15 yielded a C-index of 0.783. Time-dependent ROCanalysis showed AUC values of 0.788, 0.777, and 0.712 for 1-, 3-, and 5-year survival, respectively. Calibrationcurves and DCA indicated good predictive consistency and clinical net benefit. Single-cell analysis revealedthat KIF15 was highly expressed in proliferating T cells (Tprolif). Functional enrichment analysis indicatedthat KIF15 was primarily involved in the cell cycle, DNA replication, mitosis, and the p53 signaling pathway.Immune correlation analysis showed significant associations between KIF15 expression and multipleimmune-infiltrating cells as well as immune checkpoint genes. Four downstream hub genes (TTK, CDK1,CHEK1, and KIF2C) were further identified and were all significantly upregulated in CC. IHC results confirmedthat KIF15 was highly expressed in CC tissues (P < 0.001) and was significantly associated with poorprognosis. Pearson correlation analysis demonstrated that KIF15 expression was positively correlated withimmune markers, including PD-1 and PD-L1.

conclusionKIF15 is significantly overexpressed in CC tissues and is strongly associated with unfavorable prognosis, suggesting its potential clinical value in the diagnosis and prognostic assessment of CC. Notably, KIF15 expression is closely correlated with immune cell infiltration and multiple immune checkpoint molecules, indicating a potential role in regulating the tumor immune microenvironment. Therefore, KIF15 may serve as a promising biomarker and potential therapeutic target for immunotherapy, as well as a candidate marker for diagnosis, prognosis evaluation, and individualized treatment in CC.