Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer

Yue Wang; Xulong Huang; Bin Xian; Huajuan Jiang; Tao Zhou; Siyu Chen; Feiyan Wen; Jin Pei

doi:10.3389/fgene.2022.1005896

Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer

Front Genet. 2022 Oct 28:13:1005896. doi: 10.3389/fgene.2022.1005896. eCollection 2022.

Authors

Yue Wang¹, Xulong Huang¹, Bin Xian¹, Huajuan Jiang¹, Tao Zhou¹, Siyu Chen¹, Feiyan Wen¹, Jin Pei¹

Affiliation

¹ State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Abstract

Background: Lung cancer has the highest mortality rate among cancers worldwide, and non-small cell lung cancer (NSCLC) is the major lethal factor. Saponins in Paris polyphylla smith exhibit antitumor activity against non-small cell lung cancer, but their targets are not fully understood. Methods: In this study, we used differential gene analysis, lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) to screen potential key genes for NSCLC by using relevant datasets from the GEO database. The accuracy of the signature genes was verified by using ROC curves and gene expression values. Screening of potential active ingredients for the treatment of NSCLC by molecular docking of the reported active ingredients of saponins in Paris polyphylla Smith with the screened signature genes. The activity of the screened components and their effects on key genes expression were further validated by CCK-8, flow cytometry (apoptosis and cycling) and qPCR. Results: 204 differential genes and two key genes (RHEBL1, RNPC3) stood out in the bioinformatics analysis. Overall survival (OS), First-progression survival (FP) and post-progression survival (PPS) analysis revealed that low expression of RHEBL1 and high expression of RNPC3 indicated good prognosis. In addition, Polyphyllin VI(PPVI) and Protodioscin (Prot) effectively inhibited the proliferation of non-small cell lung cancer cell line with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67μM, respectively. The number of apoptotic cells increased significantly with increasing concentrations of PPVI and Prot. Prot induces G1/G0 phase cell cycle arrest and PPVI induces G2/M phase cell cycle arrest. After PPVI and Prot acted on this cell line for 48 h, the expression of RHEBL1 and RNPC3 was found to be consistent with the results of bioinformatics analysis. Conclusion: This study identified two potential key genes (RHEBL1 and RNPC3) in NSCLC. Additionally, PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Our findings provide a reference for clinical treatment of NSCLC.

Keywords: Paris polyphylla smith; bioinformatics; key genes; machine learning; non-small cell lung cancer.