结肠癌(COAD)是世界上第三大常见恶性肿瘤和第四大癌症死亡原因。内质网应激对细胞生长有很大的影响,迁移,扩散,入侵,血管生成,和巨大肿瘤的化学耐药性。尽管已知内质网应激在各种类型的癌症中起重要作用,基于ER应激相关基因(ERSRGs)的结肠癌预后模型尚未建立.在这项研究中,我们建立了一个ERSRGs预后风险模型来评估COAD患者的生存率.
从GEO数据库(GSE40967)获得训练集的COAD基因表达谱和临床信息数据,从TCGA数据库下载测试集COAD基因表达谱和临床信息数据。内质网应激相关基因(ERSRG)从基因集富集分析(GSEA)网站获得。通过R\“limma\”包装鉴定正常样品和COAD样品之间的差异表达ERSRG。基于单变量,套索,和多变量Cox回归分析,我们建立了一个ERSRGs预后风险模型来预测COAD患者的生存率.最后,我们通过体外实验验证了WFS1在COAD中的功能。
我们建立了基于单变量的9基因预后风险模型,套索,和多变量Cox回归分析。Kaplan-Meier生存分析和受试者工作特征(ROC)曲线显示,预后风险模型具有良好的预测性能。随后,我们筛选了60个化合物,这些化合物在高风险和低风险组之间的估计半数最大抑制浓度(IC50)存在显著差异.此外,我们发现ERSRGs预后风险模型与免疫细胞浸润和免疫检查点分子的表达有关.最后,我们确定敲低WFS1的表达抑制结肠癌细胞的增殖。
我们建立的预后风险模型可以帮助临床医生准确预测COAD患者的生存率。我们的发现为ERSRGs在COAD中的作用提供了有价值的见解,并可能为COAD治疗提供新的靶点。
Colon cancer (COAD) is the third-largest common malignant tumor and the fourth major cause of cancer death in the world. Endoplasmic reticulum (ER) stress has a great influence on cell growth, migration, proliferation, invasion, angiogenesis, and chemoresistance of massive tumors. Although ER stress is known to play an important role in various types of cancer, the prognostic model based on ER stress-related genes (ERSRGs) in colon cancer has not been constructed yet. In this study, we established an ERSRGs prognostic risk model to assess the survival of COAD patients.
The COAD gene expression profile and clinical information data of the training set were obtained from the GEO database (GSE40967) and the test set COAD gene expression profile and clinical informative data were downloaded from the TCGA database. The endoplasmic reticulum stress-related genes (ERSRGs) were obtained from Gene Set Enrichment Analysis (GSEA) website. Differentially expressed ERSRGs between normal samples and COAD samples were identified by R \"limma\" package. Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients. Finally, we verified the function of WFS1 in COAD through in vitro experiments.
We built a 9-gene prognostic risk model based on the univariate, lasso, and multivariate Cox regression analysis. Kaplan-Meier survival analysis and Receiver operating characteristic (ROC) curve revealed that the prognostic risk model has good predictive performance. Subsequently, we screened 60 compounds with significant differences in the estimated half-maximal inhibitory concentration (IC50) between high-risk and low-risk groups. In addition, we found that the ERSRGs prognostic risk model was related to immune cell infiltration and the expression of immune checkpoint molecules. Finally, we determined that knockdown of the expression of WFS1 inhibits the proliferation of colon cancer cells.
The prognostic risk model we built may help clinicians accurately predict the survival of patients with COAD. Our findings provide valuable insights into the role of ERSRGs in COAD and may provide new targets for COAD therapy.