Abstract

Objective: As a subgroup of lung cancer, small cell lung cancer (SCLC) is characterized by a short tumor doubling time, high rates of early occurred distant cancer spread and poor outcomes. Our study aimed to identify novel molecular markers associated with SCLC prognosis.

Methods: Microarray data from the Gene Expression Omnibus (GEO) database of SCLC tumors and paired normal tissues were obtained. In the dataset, Differentially expressed genes (DEGs) which were identified by comparing gene expression between normal lung and SCLC samples, were screened using the R language. The STRING database was used to map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Go enrichment analysis and prediction were performed using the Metascape database and the results were visualized. Autophagy-related prognostic genes were identified by univariate COX regression analysis. Subsequently, stepwise model selection using the Akaike information criterion (AIC) and multivariate COX regression model was performed to construct DEGs signature. Survival receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. At last, we evaluated the differences in drug sensitivity of the two groups of patients to common chemotherapeutic drugs and small-molecule targeted drugs.

Results: A total of 441 identified DE genes, including 412 downregulated and 29 upregulated genes were identified. GO enrichment analyses showed that DEGs were significantly enriched in the collagen-containing extracellular matrix and extracellular matrix organization. 16 genes were individually associated with OS in univariate analyses. The high expression of 6 genes (HIST1H4L, RP11-16O9.2, SNORA71A, SELV, FAM66A and BRWD1-AS1)) was associated with the poor prognosis of SCLC patients. To predict patients’ outcomes, we developed an individual’s risk score model based on the 6 genes. We found that SCLC patients with a low-risk score had significantly better survival than those with a high-risk score. What’s more, association analysis between clinicopathological factors and gene signature showed the risk score was higher in patients with higher clinical stage or T stage. What’s more, the patients in the high-risk score group had better treatment effects for etoposide and docetaxel. This suggests that our model can guide clinical treatment decisions.

Conclusion: A novel six-gene signature was determined for prognostic prediction in SCLC. Our findings may provide new insights into the precise treatment and prognosis prediction of SCLC.

Keywords: SCLC, Gene signature, Biomarker, Risk score, Prognostic model