Feng Shicheng, Zhang Xiuxiu, Gu Xuyu, Zhou Min, Chen Yan, Wang Cailian
Department of Oncology, Zhongda Hospital, Southeast University, Nanjing 210009, China.
School of Medicine, Southeast University, Nanjing 210009, China.
Comb Chem High Throughput Screen. 2023;26(5):938-949. doi: 10.2174/1386207325666220427121619.
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.
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.
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.
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.
作为肺癌的一个亚组，小细胞肺癌（SCLC）的特征是肿瘤倍增时间短、早期远处癌症转移率高且预后差。我们的研究旨在确定与SCLC预后相关的新分子标志物。
从基因表达综合数据库（GEO）获取SCLC肿瘤及配对正常组织的微阵列数据。在数据集中，通过比较正常肺组织和SCLC样本之间的基因表达来鉴定差异表达基因（DEGs），使用R语言进行筛选。利用STRING数据库绘制蛋白质 - 蛋白质相互作用（PPI）网络，并用Cytoscape软件进行可视化。使用Metascape数据库进行基因本体（Go）富集分析和预测，并对结果进行可视化。通过单因素COX回归分析确定自噬相关的预后基因。随后，使用赤池信息准则（AIC）进行逐步模型选择并构建多因素COX回归模型以构建DEGs特征。生存受试者工作特征（ROC）分析用于评估生存预测的性能。最后，我们评估了两组患者对常用化疗药物和小分子靶向药物的药物敏感性差异。
共鉴定出441个DE基因，包括412个下调基因和29个上调基因。基因本体富集分析表明，DEGs在含胶原蛋白的细胞外基质和细胞外基质组织中显著富集。单因素分析中16个基因分别与总生存期（OS）相关。6个基因（HIST1H4L、RP11 - 16O9.2、SNORA71A、SELV、FAM66A和BRWD1 - AS1）的高表达与SCLC患者的不良预后相关。为了预测患者的预后，我们基于这6个基因开发了个体风险评分模型。我们发现低风险评分的SCLC患者的生存期明显优于高风险评分的患者。此外，临床病理因素与基因特征之间的关联分析表明，临床分期或T分期较高的患者风险评分更高。此外，高风险评分组的患者对依托泊苷和多西他赛的治疗效果更好。这表明我们的模型可以指导临床治疗决策。
确定了一种用于SCLC预后预测的新型六基因特征。我们的研究结果可能为SCLC的精准治疗和预后预测提供新的见解。
