解剖学报 ›› 2023, Vol. 54 ›› Issue (4): 445-452.doi: 10.16098/j.issn.0529-1356.2023.04.010

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通过生物信息学分析寻找影响结肠癌患者预后的铁死亡相关基因

李晓军1 张雅敏2*   

  1. 1.天津医科大学第一中心临床学院,天津 300070; 2.天津市第一中心医院肝胆外科,天津 300192
  • 收稿日期:2022-02-24 修回日期:2022-04-15 出版日期:2023-08-06 发布日期:2023-08-06
  • 通讯作者: 张雅敏 E-mail:13802122219@163.com
  • 基金资助:
    天津市科技计划项目

Screening ferroptosis related genes influencing prognosis of colon cancer through bioinformatics analysis

LI  Xiao-jun1  ZHANG Ya-min2*   

  1. 1.Tianjin Fist Central Hospital Clinic Institute, Tianjin Medical University, Tianjin 300070, China  2.Department of Hepatobiliary Surgery, Tianjin First Central Hospital, Tianjin 300192, China
  • Received:2022-02-24 Revised:2022-04-15 Online:2023-08-06 Published:2023-08-06
  • Contact: ZHANG Ya-min E-mail:13802122219@163.com

摘要:

目的  探讨结肠癌(CC)中具有预后意义的铁死亡相关长链非编码RNA(lncRNAs),并构建预后相关的预测评分模型,最后得到与预后相关lncRNAs共表达的铁死亡相关差异基因。   方法  从FerrDb数据库下载铁死亡相关基因(FGs),在TCGA数据库中共下载41例癌旁正常组织、473例肿瘤组织的表达数据和452位患者的临床数据,通过共表达和差异分析确定铁死亡相关的差异lncRNA(DEFlncRNAs),使用单因素Cox回归分析确定预后相关的DEFlncRNAs,再运用多因素Cox回归分析构建预后模型、计算患者的风险得分,根据风险得分的中位数将患者分为高风险组和低风险组,通过Kaplan-Meier风险曲线、单因素和多因素Cox回归分析和受试者工作特征(ROC)曲线验证模型的准确性,而后绘制列线图预测患者生存情况。最终,通过共表达分析找到调控DEFlncRNAs的铁死亡差异基因,并用免疫组织化学实验验证表达差异。   结果  从TCGA数据库下载CC样本的表达及临床数据,并成功构建了含28个lncRNAs的风险预后模型,模型可较好地预测结肠癌患者的预后;成功构建了可预测患者总体生存期的列线图,该模型具有良好的临床应用价值。最后通过DEFlncRNAs和差异表达的铁死亡相关基因(DEFGs)共表达分析,得到相关系数过滤标准(|corFilter|)>0.4,P 值过滤标准(P value filter)<0.05的共表达网络,包含17个关键DEFGs。免疫组织化学实验验证ANGPTL7在CC患者癌旁组织中高表达。   结论  成功构建了含28个DEFlncRNAs的结肠癌预后模型,最后得到17个关键DEFGs。

关键词: 铁死亡, 结肠细胞癌, 预后模型, 生物信息学, Cox回归分析,

Abstract:

Objective  To explore ferroptosis-related long non-coding RNAs (lncRNAs) with prognostic significance in colon cancer (CC), and then construct a prognosis-related predictive scoring model. To search for ferroptosis-related differential expressed genes co-expressed with prognosis-related lncRNAs.    Methods  Ferroptosis-related genes (FGs) were downloaded from FerrDb database; The expression data of 41 adjacent normal tissues and 473 tumor tissues, and clinical data of 452 patients were successfully downloaded. Co-expression and differential expression analysis was performed to identify differentially expressed ferroptosis-related lncRNAs (DEFlncRNAs), and univariate Cox regression analysis was used to screen statistically significant prognosis-related DEFlncRNAs, and then multivariate Cox regression analysis was used to construct a prognostic model, calculate risk score among CC patients and divide patients by the median risk score. Kaplan-Meier curves, univariate and multivariate Cox regression analyses, and receiver operationg characteristic(ROC) curve were used to reveale great accuracy of the model. Then, a nomogram was drawed to predict the survival among CC patients. Finally, the differentially expressed ferroptosis-related genes regulating DEFlncRNAs were found by co-expression analysis, and the different expression was verified by immunohistochemical experiments.    Result  Expression and clinical data among colon cancer (CC) patients were downloaded from TCGA database. A risk prognostic model containing 28 lncRNAs to predict the prognosis among CC patients was successfully constructed. An effective clinical nomogram for predicting the overall survival of CC patients was successfully constructed. Finally, the co-expression analysis of DEFlncRNAs and differentially expressed ferroptosis-related genes (DEFGs) was preformed to obtain a co-expression network, including17 key DEFGs, with the correlation coefficient filter criteria (|corFilter|)>0.4 and  P value filter criteria (P value filter)<0.05. Immunohistochemical experiments confirmed ANGPTL7 was highly expressed in the adjacent tissues among CC patients.    Conclusion  Successfully constructed a prognostic-related model among CC patients containing 28 DEFlncRNAs, and 17 DEFGs was finally obtained. 

Key words: Ferroptosis, Coloncellular carcinoma, Prognostic model, Bioinformatics, Cox regression analysis, Human

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