Identification of Survival-Associated Gene Signature in Lung Cancer Coexisting With COPD.
Identification of Survival-Associated Gene Signature in Lung Cancer Coexisting With COPD.
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DOI：
10.3389/fonc.2021.600243
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发表时间：
2021
影响因子：
4.7
通讯作者：
Fu JJ
中科院分区：
文献类型：
--
作者：
Miao TW;Du LY;Xiao W;Mao B;Wang Y;Fu JJ
背景：慢性阻塞性肺疾病（COPD）和肺癌常并存，预后较差。已经研究了数千种与肺癌生存相关的生物标志物。然而，目前缺乏能够预测肺癌与COPD共存的生存率的方法。本研究旨在鉴定新的基因特征以预测肺癌合并COPD患者的生存率。方法：从癌症基因组图谱（TCGA）中检索肺癌和对照的RNA序列数据以及匹配的临床信息。筛选与肺癌并存COPD相关的特异性表达基因（DEG）。基因本体论（GO）和基因和基因组的京都百科全书（KEGG）进行。应用单变量和多变量考克斯回归分析来鉴定存活相关的DEG并构建存活相关的基因签名。进行Kaplan-Meier存活分析和列线图的校准图以测试基因签名的预测准确性。进行qPCR以验证预后标记中的基因。结果如下：纳入70例肺癌合并COPD患者、127例单纯肺癌患者和108例对照组织的序列数据进行分析。共2424 DEG时，确定了肺癌并存COPD与对照组相比。其生物学过程主要与DNA结合转录激活因子活性、肽酶抑制剂活性、内肽酶抑制剂活性等有关。KEGG途径主要富集于神经活性配体-受体相互作用、细胞周期和金黄色葡萄球菌感染。鉴定了由CEACAM 5、RASAL 1、CSTL 1、CNGB 1和SLC 4A 3组成的存活相关基因签名，并表示为风险评分。高危评分组生存率明显低于低危评分组（P < 0.001）。预测1、3、5年总生存率的受试者工作特征曲线下面积分别为0.943、0.773、0.888。风险评分是独立于临床因素的生存预测因子。通过应用风险评分，实际生存率与诺模图预测的生存概率高度一致。在一个独立的队列中，通过qPCR证实了肺癌合并COPD患者中五种基因的上调。结论：我们的研究构建并验证了一种新的预测肺癌合并COPD患者生存的预后基因标签，这可能有助于临床治疗决策。
Background: Chronic obstructive pulmonary disease (COPD) and lung cancer often coexist, which is associated with a worse prognosis. Thousands of biomarkers related to the survival of lung cancer have been investigated. However, those which can predict the survival of lung cancer coexisting with COPD are currently lacking. The present study aimed to identify novel gene signatures to predict the survival of patients with lung cancer coexisting COPD. Method: RNA-sequence data of lung cancer and control accompanying with matched clinical information were retrieved from the Cancer Genome Atlas (TCGA). Differently expressed genes (DEGs) associated with lung cancer coexisting COPD were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Univariate and multivariate Cox regression analyses were applied to identify survival-associated DEGs and to construct survival-associated gene signature. Kaplan-Meier survival analysis and calibration plots of the nomogram were performed to test the predictive accuracy of the gene signature. qPCR was performed to validate the genes in the prognostic signature. Results: Sequence data from 70 patients with lung cancer coexisting COPD, 127 with lung cancer alone and 108 control tissues were included for analysis. A total of 2424 DEGs were identified when comparing lung cancer coexisting COPD with controls. The biological process was primarily associated with DNA-binding transcription activator activity, peptidase inhibitor activity, endopeptidase inhibitor activity, et al. KEGG pathways were mainly enriched in neuroactive ligand-receptor interaction, cell cycle, and Staphylococcus aureus infection. A survival-associated gene signature consisting of CEACAM5, RASAL1, CSTL1, CNGB1, and SLC4A3 was identified and represented as risk score. The high-risk score group had significantly worse survival than the low-risk score group (P < 0.001). Areas under receiver operating characteristic curves were 0.943, 0.773, 0.888 for predicting overall survival at 1-, 3-, and 5-year, respectively. The risk score was an independent predictor of survival, independent of clinical factors. High conformity of the actual survival and the nomogram–predicted probability of survival by applying the risk score. Upregulation of the five genes in patients with lung cancer coexisting COPD were confirmed by qPCR in an independent cohort. Conclusion: Our study constructed and validated a novel prognostic gene signature for predicting survival of patient with lung cancer coexisting COPD, which may contribute to the clinical treatment decisions.
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DOI：
10.2147/copd.s185837
发表时间：
2019-01-01
影响因子：
2.8
作者：
Shah, Shweta;Blanchette, Christopher M.;Howden, Reuben
通讯作者：
Howden, Reuben
影响因子：
9.6
作者：
de Torres, Juan P.;Bastarrika, Gorka;Zulueta, Javier J.
通讯作者：
Zulueta, Javier J.
影响因子：
2.2
作者：
He, Yuan;Gong, Rui;Wang, Hai-Yun
通讯作者：
Wang, Hai-Yun
影响因子：
168.9
作者：
Wang, Chen;Xu, Jianying;He, Jiang
通讯作者：
He, Jiang
影响因子：
4
作者：
Bai, Yuquan;Shen, Wulin;Zhao, Jinping
通讯作者：
Zhao, Jinping