Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: a bioinformatics analysis

Abstract

Background: Genetic factors account for approximately 35% of colorectal cancer risk. The specificity and sensitivity of previous diagnostic biomarkers for colorectal cancer could not meet the need of clinical application. The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning to build informative and predictive models of the underlying biological processes. The aim of this study is to identify diagnostic genes of colorectal cancer by using machine learning methods. Methods:The GSE41328 and GSE106582 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The gene expression differences between colon cancer and normal tissues were analyzed.The key colorectal cancer genes were screened and validated by Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) regression. Immune cell infiltration and the correlation with the key genes in patients with colon cancer were further analyzed by CIBERSORT.Results: Eleven key genes were identified as biomarkers for colon cancer, namely ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4. The mean area under the receiver operating characteristic (ROC) curve (AUC) of all 11 genes for colon cancer diagnosis were 0.94 with a range of 0.91-0.97. In the validation set, the expression of the 11 key genes was significantly different between colon cancer and normal subjects (P<0.05) and the mean AUCs were 0.82 with a range of 0.70-0.88.Immune cell infiltration analyses demonstrated that the relative quantity of plasma cells, T cells, B cells, NK cells, MO, M1, Dendritic cells resting, Mast cells resting, Mast cells activated, and Neutrophils in the tumor group were significantly different to the normal group.

Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning

“…We employed the R software’s “glmnet” and “randomforest” packages to conduct LASSO and RF analyses, respectively. The intersection of the two results can serve as the candidate hub genes for diagnosis ( 22 , 23 ).…”

TRIB3, as a robust prognostic biomarker for HNSC, is associated with poor immune infiltration and cancer cell immune evasion

“…Notably, TRIB3 is expressed in CD4T/CD8T cells and monocyte-macrophages across almost all datasets, consistent with the findings from single-cell data on renal cancer ( 12 ). Moreover, TRIB3 has been reported to affect the immune infiltration of NK, T, and B cells in colorectal cancer ( 39 ). This suggests, to some extent, that TRIB3 participates in the progression of HNSC, not just through malignant cells.…”

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

“…LASSO analysis was performed using the R package "glmnet" (version 4.1.7) [ 40 ]. Support vector machines (SVM) are machine algorithms that establish a threshold between two categories and make predictions based on one or several feature vectors [ [41] , [42] , [43] ]. At the same time, we used the SVM-REF algorithm by R package "e1071"(version 1.26.1) and repeated the 10-fold cross-validation with 5 repetitions, choosing the "random" repeated cross-validation method, SIZE = 1:10, to extract the genes with higher variable importance.…”