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13 January 2022 : Database Analysis  

Comprehensive Analysis of Tripterine Anti-Ovarian Cancer Effects Using Weighted Gene Co-Expression Network Analysis and Molecular Docking

Xi Long1A, Leping Liu12E, Qinyu Zhao3C, Xinyi Xu1B, Pingan Liu4D, Guoming Zhang1G, Jie Lin4F*

DOI: 10.12659/MSM.932139

Med Sci Monit 2022; 28:e932139

Abstract

BACKGROUND: Ovarian cancer has the highest mortality of gynecological cancers worldwide. The aim of this study was to identify the role of tripterine against ovarian cancer.

MATERIAL AND METHODS: GSE18520 and GSE12470 data sets were downloaded from the GEO database. WGCNA was used to analyze gene modules and hub genes related to ovarian cancer. These hub genes were intersected with tripterine targets, and GO and KEGG enrichment analyses were performed. HPA and GEPIA determined the expression of tripterine anti-ovarian hub genes in tumor tissues. Kaplan-Meier plotter was used to explore the role of hub genes in ovarian cancer prognosis. AutoDock was used to conduct molecular docking of tripterine and hub genes to observe whether the combination was stable.

RESULTS: By differential analysis of gene expression and the construction of WGCNA co-expression network, 5 hub genes, ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8, were screened by module gene screening. Seven biological processes and 20 KEGG-related pathways were obtained by gene enrichment. The expression of tripterine anti-ovarian hub genes ARHGAP11A, MUC1, and FUT8 were obtained by HPA and GEPIA. Using Kaplan-Meier plotter, the survival of ovarian cancer was negatively correlated with ARHGAP11A, MUC1, and FUT8. Molecular docking showed the combination of tripterine and FUT8 was most stable, having the greatest potential role.

CONCLUSIONS: Tripterine may be involved in megakaryocyte development and platelet production through potential genes ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8 and may have an anti-ovarian cancer effect in immune factors signaling, transporting and exchanging oxygen pathways, and autophagy pathways, through these 5 key genes.

Keywords: Molecular Docking Simulation, ovarian cancer, Survival Analysis, Tripterine

Comments

Figures

Figure 1. The targets of tripterine.Figure 2. Determination of soft threshold (β) for WGCNA analysis. (A) This diagram was used to identify significant outliers. (B) Analysis of the scale-free fit index for various soft-thresholding powers (β). (C) Analysis of the mean connectivity for various soft-thresholding powers.Figure 3. (A) Gene-clustering dendrogram. Each end of the clustering dendrogram corresponds to a gene, and different colors correspond to the different modules in which genes are clustered, and some modules which need to be merged are highly similar. (B) Topological-overlap matrix graph, each row and column represents a module and the genes of the module. This diagram shows the degree of correlation within the module. (C) Module-trait relationship. Each row corresponds to a module, and each column corresponds to a clinical trait. Each cell contains a corresponding correlation. (D) Module membership in each module.Figure 4. (A) Determination of anti-ovarian cancer targets of tripterine. Venn diagram of modules and compound-predicted targets. (B) Kyoto Encyclopedia of Genes and Genomes-enriched pathways. (C) Gene Ontology functional analysis.Figure 5. (A–J) The expression of 5 hub genes in the adjacent tissues and tumor tissues in the Human Protein Atlas database.Figure 6. (A–E) The expression of 5 hub genes in the adjacent tissues and tumor tissues in the Gene Expression Profiling Interactive Analysis database.Figure 7. (A–C) The survival analysis of ARHGAP11A, MUC1, and FUT8 genes in the Kaplan-Meier plotter database. The patients were stratified into a high-level group or a low-level group according to the median expression.Figure 8. The docking diagram of FUT8 and MUC1 with tripterine. The picture on the left is an enlarged version of the picture on the right.

Editorial

01 January 2022 : Editorial  

Editorial: Current Status of Oral Antiviral Drug Treatments for SARS-CoV-2 Infection in Non-Hospitalized Patients

Dinah V. Parums
Science Editor, Medical Science Monitor, International Scientific Information, Inc., Melville, NY, USA

DOI: 10.12659/MSM.935952

Med Sci Monit 2022; 28:e935952

SARS-CoV-2/COVID-19

25 January 2022 : Editorial  

Editorial: The 2022 World Health Organization (WHO) Priority Recommendations and Response to the Omicron Va...

Med Sci Monit In Press; DOI: 10.12659/MSM.936199  

25 January 2022 : Clinical Research  

Experience of the Polish Medical Air Rescue Service During the First Year of the COVID-19 Pandemic and Meas...

Med Sci Monit In Press; DOI: 10.12659/MSM.935474  

11 January 2022 : Clinical Research  

A Remote Questionnaire-Based Study to Compare Alcohol Use in 1030 Final-Year High School Students in Split-...

Med Sci Monit In Press; DOI: 10.12659/MSM.935567  

31 December 2021 : Clinical Research  

A Prospective Observational Study of 42 Patients with COVID-19 infection and a History of Hepatitis C Virus...

Med Sci Monit 2021; 27:e935075

In Press

28 Jan 2022 : Database Analysis  

Evaluation of Structural Neural Connectivity Between the Primary Auditory Cortex and Cognition-Related Brai...

Med Sci Monit In Press; DOI: 10.12659/MSM.936131  

27 Jan 2022 : Clinical Research  

Retrospective Study of the Association Between Platelet-to-Lymphocyte Ratio in Patients with Acute Coronary...

Med Sci Monit In Press; DOI: 10.12659/MSM.935002  

25 Jan 2022 : Editorial  

Editorial: The 2022 World Health Organization (WHO) Priority Recommendations and Response to the Omicron Va...

Med Sci Monit In Press; DOI: 10.12659/MSM.936199  

25 Jan 2022 : Clinical Research  

Experience of the Polish Medical Air Rescue Service During the First Year of the COVID-19 Pandemic and Meas...

Med Sci Monit In Press; DOI: 10.12659/MSM.935474  

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750