Screening of genes characteristic of pancreatic cancer by LASSO regression combined with support vector machine and recursive feature elimination, and immune correlation analysis
Longhui Zeng, Zheng Chen
Abstract
BACKGROUND: Pancreatic cancer is a malignant tumor of the digestive tract that shows increased mortality, recurrence, and morbidity year on year. METHODS: Differentially expressed genes between pancreatic cancer and healthy tissues were first analyzed from four datasets within the Gene Expression Omnibus (GEO). Gene ontology, disease ontology, and gene set enrichment analysis of differentially expressed genes were performed, and genes identified as characteristic of pancreatic cancer were screened using LASSO regression combined with support vector machine and recursive feature elimination (SVM-RFE). Differential analysis and receiver operating characteristic curve analysis were performed on the identified eigengenes, and validation was carried out using another dataset from the GEO database. Differences and correlations between characteristic pancreatic cancer genes and immune cells were analyzed. RESULTS: A total of 90 differentially expressed genes were identified by screening, and six genes characteristic of pancreatic cancer were obtained by taking the intersection of two characteristic genes identified by machine learning. Immunoassays yielded multiple immune cells associated with pancreatic cancer signature genes. CONCLUSION: The six characteristic genes screened by a combination of LASSO regression and SVM-RFE are potential new biomarkers for the early diagnosis and prognosis of pancreatic cancer, and could be a novel therapeutic target.