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基于优化支持向量机算法的肺癌判别模型的建立及汉黄芩素在肺癌中的关键靶点研究

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.

作者信息

Wang Lin, Zhang Jianhua, Shan Guoyong, Liang Junting, Jin Wenwen, Li Yingyue, Su Fangchu, Ba Yanhua, Tian Xifeng, Sun Xiaoyan, Zhang Dayong, Zhang Weihua, Chen Chuan Liang

机构信息

Department of Radiotherapy, People's Hospital of Zhengzhou, Zhengzhou, China.

Medical Engineering Technology and Data Mining Institute, Zhengzhou University, Zhengzhou, China.

出版信息

Front Pharmacol. 2021 Sep 22;12:728937. doi: 10.3389/fphar.2021.728937. eCollection 2021.

DOI:10.3389/fphar.2021.728937
PMID:34630106
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8493220/
Abstract

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 , 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 ( = 90.597, = 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 that the regulation pathways involved the PI3K-Akt signaling pathway, ERBB signaling pathway, and EGFR tyrosine kinase inhibitor resistance. analyses using lung cancer cells showed that Wogonin led to significantly increased levels of cleaved caspase-3 and Bad and significantly decreased Bcl-2 expression in a concentration-dependent manner. ErbB4 expression also significantly decreased in lung cancer cells after treatment with Wogonin. A regulatory network of Wogonin regulating lung cancer cell apoptosis was constructed, including the participation of serological susceptibility genes. There is a certain regulatory effect between the serological indexes that can be used in the diagnosis of lung cancer and the key targets of Chinese herbal medicine treatment of lung cancer, which provides a new idea for the diagnosis, treatment and prognosis of clinical lung cancer.

摘要

采用优化的支持向量机模型,基于血清学指标构建肺癌诊断模型,并建立汉黄芩素(一种[具体物质]成分)的分子调控模型。收集患者血清学指标,采用网格搜索法确定支持向量机模型的最佳惩罚系数C和参数g,建立基于血清学指标的孤立性肺结节良恶性辅助诊断模型。通过网络药理学分析汉黄芩素在肺癌中的调控网络和关键靶点,并采用蛋白质免疫印迹法检测关键靶点。建立血清学易感基因与汉黄芩素关键靶点的关系,构建汉黄芩素调控肺癌的信号通路。支持向量机参数优化后(准确率 = 90.597,召回率 = 32),模型准确率为90.8333%,假阳性9例,假阴性2例。对汉黄芩素靶点与肺癌相关基因之间的67个共同基因进行本体功能分析,结果显示这些靶点与肽-丝氨酸修饰和蛋白激酶B信号调控所涉及的生物学过程相关;与膜筏和染色体区域中的细胞成分相关;与蛋白质丝氨酸/苏氨酸激酶活性和血红素结合的分子功能相关。京都基因与基因组百科全书分析表明,调控途径涉及PI3K-Akt信号通路、ERBB信号通路和表皮生长因子受体酪氨酸激酶抑制剂抗性。对肺癌细胞的分析表明,汉黄芩素导致裂解的半胱天冬酶-3和Bad水平显著升高,Bcl-2表达显著降低,且呈浓度依赖性。用汉黄芩素处理后,肺癌细胞中ErbB4表达也显著降低。构建了汉黄芩素调控肺癌细胞凋亡的调控网络,其中包括血清学易感基因的参与。可用于肺癌诊断的血清学指标与肺癌中药治疗的关键靶点之间存在一定的调控作用,为临床肺癌的诊断、治疗及预后提供了新思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/e46b872aa48a/fphar-12-728937-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/a53f2ee0903a/fphar-12-728937-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/eea2cdce86e1/fphar-12-728937-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/ada673b0cda8/fphar-12-728937-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/e46b872aa48a/fphar-12-728937-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/a53f2ee0903a/fphar-12-728937-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/eea2cdce86e1/fphar-12-728937-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/ada673b0cda8/fphar-12-728937-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0d9/8493220/e46b872aa48a/fphar-12-728937-g004.jpg

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