Construction of a risk scoring model based on machine learning and validation of the role of the key gene SERPINE1 in the progression of colon cancer.

BACKGROUND AND OBJECTIVE: Colon cancer (CC) is a highly prevalent malignant tumor with a high mortality rate worldwide. Despite recent advancements in diagnosis and treatment, the overall prognosis for patients remains poor, especially for those with metastasis. Exploring key genes associated with the prognosis of patients with colon cancer, establishing effective molecular models, and validating their functions are necessary to optimize patient management and develop novel therapeutic strategies. This study aimed to reveal the role of the key gene SERPINE1 in the progression of colon cancer and its potential clinical application value through bioinformatics analysis and experimental validation. METHODS: Multimodal data from colon cancer patients were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) with a fold change ≥ 2 and an adjusted P value less than 0.05 were screened. A protein‒protein interaction network was constructed using the STRING database, and 40 core genes were identified using Cytoscape software. Genes closely related to the prognosis of colon cancer patients were further selected using LASSO regression and Cox proportional hazards regression models to construct a risk scoring model for assessing patients' survival risk. The model performance was evaluated in combination with immune cell infiltration analysis and ROC curve assessment, and the associations between the target genes and the tumor immune microenvironment were explored. The expression and function of SERPINE1 were subsequently investigated. The expression level of SERPINE1 in colon cancer tissues was analyzed by Western blotting. The effects of SERPINE1 knockdown on the migration and invasion abilities of colon cancer cells were assessed by scratch wound healing and Transwell assays. The impact of SERPINE1 knockdown on tumor formation and metastasis in colon cancer cells was verified through mouse tumor and metastasis models. RESULTS: The bioinformatics analysis identified seven target genes related to the prognosis of colon cancer, namely, CAMK2B, KIF1A, OPCML, SCN5A, SERPINE1, TH, and UCHL1, based on which a risk scoring model was constructed. Further analysis revealed that the risk score was not only associated with patients' survival prognosis but also significantly correlated with the infiltration of immune cells such as T cells and macrophages in the tumor immune microenvironment. Among these seven genes, SERPINE1 was highly expressed in colon cancer and was significantly positively correlated with a poor prognosis. In vitro experiments revealed that SERPINE1 knockdown inhibited the proliferation, migration, and invasion of colon cancer cells. Western blot experiments indicated that SERPINE1 knockdown upregulated the expression of epithelial cadherin (E-cadherin, E-CAD) and downregulated the expression of neural cadherin (N-cadherin, N-CAD) and vimentin (VIM), suggesting that SERPINE1 knockdown may inhibit the epithelial‒mesenchymal transition (EMT). In a mouse subcutaneous tumor experiment, SERPINE1 knockdown significantly inhibited tumor growth. The liver metastasis model revealed that inhibiting SERPINE1 expression significantly reduced the number of liver metastases, further verifying the effect of high SERPINE1 expression on promoting the progression of colon cancer. CONCLUSIONS: A risk scoring model constructed based on the results of a bioinformatics analysis can effectively predict the survival prognosis of patients with colon cancer and reveals that SERPINE1 promotes the proliferation of colon cancer cells and accelerates the distant metastasis of tumor cells by inducing the EMT. These results provide an important reference for the prognostic assessment of colon cancer patients and pave the way for the development of therapeutic strategies targeting SERPINE1.