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中华肺部疾病杂志(电子版)

中华肺部疾病杂志(电子版) ›› 2022, Vol. 15 ›› Issue (04) : 462 -467. doi: 10.3877/cma.j.issn.1674-6902.2022.04.002

论著

基于转录因子的肺癌预后模型构建及临床意义
何晓梅1, 王兴敏1, 熊浚智1, 陈荣荣1, 闵迁1, 张克斌1,†(), 李园园1,†()   
  1. 1. 400037　重庆，陆军军医大学第二附属医院临床医学研究中心
  • 收稿日期:2022-03-17 出版日期:2022-08-25
  • 通信作者: 张克斌, 李园园
  • 基金资助:
    国家自然科学基金资助项目(31872634)

Construction of prognostic model of lung cancer based on transcription factors and its clinical significance

Xiaomei He1, Xingmin Wang1, Junzhi Xiong1, Rongrong Chen1, Qian Min1, Kebin Zhang1,†(), Yuanyuan Li1,†()   

  1. 1. Clinical Medical Research Center, Second Affiliated Hospital, Army Medical University, Chongqing 400037, China
  • Received:2022-03-17 Published:2022-08-25
  • Corresponding author: Kebin Zhang, Yuanyuan Li
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何晓梅, 王兴敏, 熊浚智, 陈荣荣, 闵迁, 张克斌, 李园园. 基于转录因子的肺癌预后模型构建及临床意义[J]. 中华肺部疾病杂志(电子版), 2022, 15(04): 462-467.

Xiaomei He, Xingmin Wang, Junzhi Xiong, Rongrong Chen, Qian Min, Kebin Zhang, Yuanyuan Li. Construction of prognostic model of lung cancer based on transcription factors and its clinical significance[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2022, 15(04): 462-467.

目的

分析转录因子(transcription factors, TFs)在肺腺癌(lung adenocarcinoma, LUAD)中的表达及与预后的关系，寻找潜在干预靶点。

方法

利用生物信息学方法挖掘癌症基因组图谱(The Cancer Genome Atlas Program, TCGA)数据库中LUAD组织转录组和临床数据，利用R语言软件对肺癌及癌旁组织中的TFs相关基因(transcription factors-related genes, TFGs)进行表达差异分析，筛选出肺癌组织中差异表达的TFGs，进行GO功能和KEGG通路富集分析。通过COX回归分析筛选关键TFGs，建立风险评分模型并对其预测能力进行评价。

结果

与癌旁组织比较，LUAD组织中有391个TFGs表达异常。TFGs主要涉及胚胎发育、转录调控及干细胞多能行调节等生物学过程和信号通路。单因素COX回归分析显示11个TFs显著影响LUAD预后，逐步多因素COX回归分析筛选出其中5个TFGs (IRX4、ID3、CDX2、E2F7、NPAS2)作为肺癌预后的危险因子用于构建风险评分模型。生存分析显示，低风险组总生存时间显著长于高风险组。风险评分可作为危险因素预测LUAD预后(风险比为1.906，P<0.01)，而患者年龄、性别、肿瘤分期与预后无相关性(P>0.05)。

结论

LUAD中多个TFs基因存在异常表达，基于5个TFGs成功构建了风险评分模型，可有效预测LUAD预后，为临床诊疗提供参考。

关键词: 支气管肺癌, 转录因子, 预后模型, 临床意义
Objective

To investigated the relationship between aberrant expression of transcription factor genes (TFGs) and tumor prognosis in lung adenocarcinoma (LUAD), and to explore potential tumor transcriptional therapeutic targets.

Methods

Bioinformatics was used to mine the transcriptome and clinical data of lung adenocarcinoma (LUAD) in the Cancer Genome Atlas program (TCGA) database. R language software was adopted to screen the differentially expressed TFGs between LUAD and paracancerous tissues. Then, GO and KEGG enrichment analyses were performed. COX regression was applied to construct a risk score model to predict the prognosis of LUAD patients.

Results

391 TFGs were differentially expressed in LUAD tissues. These TFGs are mainly involved in biological processes and signal pathways such as embryonic development, transcriptional regulation and pluripotent regulation of stem cells. Univariate COX regression analysis showed that 11 TFGs were associated with prognosis. Five TFGs (IRX4, ID3, CDX2, E2F7, NPAS2) were further selected via stepwise multivariate COX regression analysis to construct the risk score model. Survival analysis revealed that the overall survival time of low-risk group was significantly longer than that of high-risk group. Risk score could be used as a risk factor to predict the prognosis of LUAD (hazard ratio=1.906, P<0.01), but there was no correlation between age, gender and tumor stage and prognosis (P>0.05).

Conclusion

Lots of transcription factor genes in LUAD have abnormal expression, which is closely concern to the prognosis of patients. The risk score model based on the 5-TFGs could effectively predict the LUAD patients prognosis and provide a new reference for clinical diagnosis and treatment of lung cancer.

Key words: Bronchial lung cancer, Transcription factors, Prognostic model, Clinical significance
图1 TFGs在LUAD与癌旁组织中的差异表达；注：A：所有mRNA表达谱中|log2 fold change|≥1且P<0.5的391个TFs在各个样本中的表达情况，以热图展示；B: TFGs表达分为上调、下调和没有显著差异，分别用蓝色、红色和灰色以火山图展示
图2 391个差异表达转录因子基因GO和KEGG富集分析结果；注：A: GO富集分析的气泡图，391个差异TFGs与胚胎发育、转录调节等有密切联系；B: KEGG通路分析的圈图，391个差异TFGs与癌症转录紊乱、干细胞多能性调节等通路相关
图3 COX回归分析筛选预后相关TFGs森林图；注：A：单因素COX回归分析筛选到11个与LUAD预后相关的TFGs(P<0.001)；B:逐步多因素COX回归分析筛选到5个与LUAD预后相关的TFGs
图4 风险评分模型的建立与评价；注：A:所有LUAD患者基于风险评分的曲线图，从左到右患者风险评分依次升高，黑色虚线将患者分为低、高风险两组；B：随访终点所有患者生存状态，红色、绿色分别表示死亡和存活；C:低、高风险患者中风险TFGs的表达热图，绿色、红色分别表示低表达和高表达；D:低、高风险患者KM曲线；E：风险评分模型对患者1、3、5年生存预测的ROC曲线，AUC值越高代表预测的准确性越好
图5 患者临床参数COX回归分析的森林图；注：A:单因素COX回归分析中，肿瘤stage、TN分期及风险评分均与LUAD患者OS显著相关：B:多因素COX回归分析中，仅有T分期和风险评分与LUAD患者OS显著相关，P分别为0.005和0.001，风险比分别为1.106和1.906
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