Identification of Key Genes and Signaling Pathways in Entrectinibresistant Non-small Cell Lung Cancer Using Bioinformatic Analysis and Experimental Verification.

BACKGROUND

Entrectinib, a ROS1 inhibitor, is effective in patients with ROS1-positive non-small-cell lung cancer (NSCLC). However, entrectinib resistance remains a challenge worldwide. The biomarkers of entrectinib resistance and molecular mechanisms have not been clarified based on the Gene Expression Omnibus (GEO) database.

OBJECTS

The aim of this study is to identify key genes and signaling pathways involved in the development of entrectinib-resistant NSCLC through bioinformatics analysis and experimental validation.

METHODS

Differentially expressed genes (DEGs) were screened between entrectinib resistant and parental human NSCLC cell lines of the GSE214715 dataset, lung adenocarcinoma (LUAD) and non-tumor adjacent tissues of the GSE75037 dataset, and NSCLC and non-tumor adjacent tissues of the GSE18842 dataset. Functional enrichment analyses were performed, including Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Overlapped DEGs among those three datasets were identified using the Venn diagram package. The transcriptional levels of key genes were investigated using the University of ALabama at Birmingham CANcer data analysis Portal (UALCAN). The association between transcriptional levels of key genes and survival was analyzed using Kaplan-Meier Plotter (https://www.kmplot.com/analysis/). The correlations between hub genes and immune cell infiltration were investigated using the Tumor Immune Estimation Resource (TIMER) database. Specific signaling pathway enrichment analysis was performed using Gene Set Enrichment Analysis (GSEA) of LinkedOmics. Competitive endogenous RNA (ceRNA) networks, genome-wide association studies (GWAS), and drug sensitivity analyses of key genes were further investigated. The expression of ZEB2 was subsequently confirmed in both parental HCC78 cells and entrectinib-resistant HCC78 cells using real-time quantitative polymerase chain reaction (qRT-PCR).

RESULTS

708 DEGs were identified between entrectinib-resistant CUTO28 (CUTO28-ER) and parental CUTO28 cell lines in the GSE214715 dataset. One thousand three hundred and ninety-five DEGs were identified between entrectinib resistant (CUTO37-ER) and parental CUTO37 cell lines in the GSE214715 dataset. Eight hundred and forty-nine DEGs were identified between LUAD and non-tumor adjacent tissues in the GSE75037 dataset. Seven hundred and sevety-three DEGs were identified between NSCLC and non-tumor adjacent tissues in the GSE18842 dataset. Among these three datasets, seven overlapped DEGs were identified, including ZBED2, CHI3L2, CELF2, SEMA5A, ZEB2, S100A12, and PDK4. Among these seven overlapped DEGs, the expression levels of CHI3L2, ZEB2, and S100A12 were downregulated in those three datasets. The results of analysis using the UALCAN database showed that these three genes were significantly downregulated in LUAD and LUSC patients compared with the normal population. However, only the lower transcriptional level of ZEB2 was linked to worse survival in patients with lung cancer. GSEA analysis revealed that ZEB2 was significantly negatively correlated with nucleotide excision repair (NER) in LUAD, and homologous recombination (HR) and NER in LUSC, which were linked to drug resistance. A ceRNA network of THRB-AS1/ has-miR-1293/ ZEB2 in LUAD was established.

CONCLUSION

We have identified core genes associated with non-small cell resistance to entrectinib, including CHI3L2, ZEB2, and S100A12. ZEB2 is a core gene associated with acquired resistance to entetinib in NSCLC.