Bioinformatics analysis and experimental validation of TTK as a biomarker for prognosis in non-small cell lung cancer

Background: Despite the prominent development of medical technology in recent years, the prognosis of non-small-cell lung cancer (NSCLC) is still not optimistic. It is crucial to identify more reliable diagnostic biomarkers for the early diagnosis and personalized therapy of NSCLC and clarify the molecular mechanisms underlying NSCLC progression.

Methods: Bioinformatics analysis was performed on 3 datasets obtained from the Gene Expression Omnibus to identify the NSCLC-associated differentially expressed genes (DEGs). Immunohistochemistry (IHC) based tissue microarray of human NSCLC was used to experimental validating the potential targets obtained from bioinformatics analysis.

Results: By using PPI network analysis, Kaplan Meier plotter, and GEPIA, we selected 40 core DEGs for further study. Then, a re-analysis of 40 selected genes via KEGG pathway enrichment showed that 9 key genes involved in the cell cycle and p53 signaling pathway participated in the development of NSCLC. Then we checked the protein level of 9 key genes by semi-quantitative of IHC and checked the distribution at a single-cell level. Finally, we validated dual-specificity protein kinase TTK as a biomarker for prognosis in a tissue microarray. High TTK expression associated with a higher histological stage, advanced TNM stage, high frequency of positive lymph nodes, and worse 5-year overall survival.

Conclusions: We found 9 key genes were enriched in the cell cycle and p53 signaling pathway. TTK could be considered as a potential therapeutic target and for the prognosis biomarker of NSCLC. These findings will provide new insights for the development of individualized therapeutic targets for NSCLC.

Keywords: biomarkers; differentially expressed genes; overall survival; single-cell RNA sequencing.