Establishment of a Lung Cancer Discriminative Model Based on an Optimized Support Vector Machine Algorithm and Study of Key Targets of Wogonin in Lung Cancer

作者：Lin Wang, Jianhua Zhang, Guoyong Shan, Junting Liang, Wenwen Jin, Yingyue Li, Fangchu Su, Yan-hua Ba, Tian XiFeng, Xiaoyan Sun, Da‐Yong Zhang, Weihua Zhang, Chuan liang Chen · 发表于：Frontiers in Pharmacology · 年份：2021 · DOI：10.3389/fphar.2021.728937 · 被引用次数：20 · 研究领域：Flavonoids in Medical Research、Machine Learning in Bioinformatics、Traditional Chinese Medicine Analysis

An optimized support vector machine model was used to construct a lung cancer diagnosis model based on serological indicators, and a molecular regulation model of Wogonin, a component of Scutellaria baicalensis , was established. Serological indexes of patients were collected, the grid search method was used to identify the optimal penalty coefficient C and parameter g of the support vector machine model, and the benign and malignant auxiliary diagnosis model of isolated pulmonary nodules based on serological indicators was established. The regulatory network and key targets of Wogonin in lung cancer were analyzed by network pharmacology, and key targets were detected by western blot. The relationship between serological susceptibility genes and key targets of Wogonin was established, and the signaling pathway of Wogonin regulating lung cancer was constructed. After support vector machine parameter optimization ( C = 90.597, g = 32), the accuracy of the model was 90.8333%, with nine false positives and two false negative cases. Ontology functional analysis of 67 common genes between Wogonin targets and lung cancer–related genes showed that the targets were associated with biological processes involved in peptidye-serine modification and regulation of protein kinase B signaling; cell components in the membrane raft and chromosomal region; and molecular function in protein serine/threonine kinase activity and heme binding. Kyoto Encyclopedia of Genes and Genomes analysis showed...