Identification of Potential Prognostic and Predictive Biomarkers for Immune-Checkpoint Inhibitor Response in Small Cell Lung Cancer

BACKGROUND: Immune-checkpoint inhibitors have propelled the field of therapeutics for small cell lung cancer (SCLC) treatment, but are only beneficial to some patients. The objective of this study was to identify valid biomarkers for good potential response to immunotherapy. MATERIAL/METHODS: We performed an integrated analysis of the available datasets from the Gene Expression Omnibus (GEO) projects, Cancer Cell Line Encyclopedia (CCLE), TISIDB database, and Lung Cancer Explorer (LCE) database. Six prognosis-related genes (MCM2, EZH2, CENPK, CHEK1, CDKN2A, and EXOSC2) were identified utilizing the meta workflow of data analysis methods. We performed subclass mapping to compare their expression profiles to other datasets of patients who responded to immunotherapy. A drug sensitivity predictive model was used to predict the chemotherapeutic response to cisplatin and etoposide. RESULTS: Our results showed that the expression of the 6 key genes was significantly associated with the overall survival of patients with SCLC. Lower expression of these 6 genes was correlated to the response to anti-PD-1 treatment. Additionally, low expression of MCM2, EZH2, CENPK, and CHEK1 was correlated with increased sensitivity to cisplatin, but not etoposide. CONCLUSIONS: Overall, our data showed that MCM2, EZH2, CENPK, CHEK1, CDKN2A, and EXOSC2 are potential prognostic and predictive biomarkers for response to immune-checkpoint inhibitor treatment in patients with SCLC. Further studies with large sample sizes are required to validate our findings and to explore the detailed mechanisms underlying the role of these genes in SCLC.