Identification of Functional Gene Modules and Biomarkers of Apatinib Against Lung Adenocarcinoma Based on Weighted Gene Co-expression Network Ansalysis (WGCNA)

Zhang Yanli,Wu Guosong,Luo Shiqiong,Zhang Zhidong,Zhong Yixuan

doi:10.11648/j.sjcm.20211002.17

Abstract

Objective: Apatinib is a drug for the treatment of gastric cancer. In recent years, studies have found that it also has good efficacy in the treatment of lung adenocarcinoma, but its mechanism of action is still not clear. Therefore, this study uses bioinformatics and Experimental verification explores the mechanism of apatinib in the treatment of lung adenocarcinoma. To explore the potential biological targets of apatinib against lung adenocarcinoma based on weighted gene co-expression network analysis (WGCNA). Methods: The PharmMapper server was first used to reversely predict the potential targets of apatinib. Subsequently, it was combined with WGCNA to mine the GSE10072 data set in the GEO database of the National Center for Biotechnology Information (NCBI) to obtain the co-expressed gene module. Next, it was combined with apatinib Predict the target matching mapping to obtain the potential anti-lung adenocarcinoma target of apatinib. The STRING database was combined with Cytoscape software to visualize the protein interaction network of apatinib's potential anti-lung adenocarcinoma target protein and perform network topology analysis, as well as to obtain the core target from the network. Kaplan Meier plotter database was applied to analyze the relationship between key genes and the prognosis of patients with lung adenocarcinoma. Molecular docking technology was used for the potential of apatinib. Anti-lung adenocarcinoma target protein was finally verified by molecular interaction. Finally, Western blot was employed to analyze the expression level of the key target protein. Results: A total of 300 targets of apatinib were obtained, and 50 potential targets of apatinib against lung adenocarcinoma were screened. From the protein interaction network analysis, the key genes included CASP3, EGFR, MMP9, SRC, CASP8, CASP9, STAT3 and MAPK1. High expression of EGFR, SRC, MAPK1 and STAT3, and low expression of CASP3, CASP8, CASP9 and MMP9 were closely related to poor prognosis of patients with lung adenocarcinoma. Molecular docking showed that the interaction of apatinib with the targets of MAPK1, CASP3, EGFR, SRC, MMP9 and STAT3 was comparable to the positive control. Western blot showed that with the increasing of drug concentration, STAT3 were down-regulated, and CASP8 were up-regulated. Conclusion: The mechanism of apatinib in the treatment of lung adenocarcinoma is mainly through the regulation of the signal transduction pathway and the apoptosis pathway, which provides a scientific basis for the study of its anti-lung adenocarcinoma mechanism.

Highlights

The incidence rate of lung cancer is increasing gradually
There were hydrogen bonds between apatinib and IEU-401, LEU-348 and GLU-406 of CASP3 (Figure 11A); there were hydrogen bonds between apatinib and PHE-250, SER-251 and SER-205 of epidermal growth factor receptor (EGFR) (Figure 11B); there were hydrogen bonds between apatinib and GLU-81, HIS-84 and ILE-10 of SRC (Figure 11C); there were hydrogen bonds between apatinib and VAL-298, THR-308, GLY-306, TYR-324, GLN-319 and GLU-314 of MAPK1 (Figure 11D); there were hydrogen bonds between apatinib and LYS-623, ALA-597 and LEU-595 of MMP9 (Figure 11E); there were hydrogen bonds between apatinib and TVR-454 and LEU-488 of STAT3 (Figure 11F). These results indicated that there was a stable interaction between apatinib and eight predicted targets, among which the binding with MAPK1, CASP3, EGFR, SRC, MMP9 and STAT3 was the best
According to the above studies, there is no report about the effect of apatinib on the progression of lung adenocarcinoma by regulating the targets of CASP8 and STAT3, the present study found that low expression of CASP8 and high expression of STAT3 were closely related to poor prognosis of lung adenocarcinoma patients by using public database mining

Summary

Introduction

The incidence rate of lung cancer is increasing gradually. Every year, more than 5 million new lung cancer cases are diagnosed, with nearly ten thousand deaths [1]. Is lung adenocarcinoma [2,3] Patients with this tumor have no obvious symptoms in the early stage, and their condition is difficult to detect. It has already been in middle and late stage when first diagnosed, with a relatively low 5-year survival rate [4]. Further development of new drugs with the effect of non-small cell lung cancer has important clinical significance. In the future, this may provide the basis of drug selection and molecular target information for early diagnosis and treatment of lung cancer

Methods

Results

Conclusion