构建和评估非小细胞肺癌患者肿瘤转移相关基因的预后风险模型。
Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer.
机构信息
Changchun University of Traditional Chinese Medicine, No. 1035 Boshuo Road, Jingyue National High-Tech Industrial Development Zone, Changchun, 130117, China.
Affiliated Hospital of Changchun University of Chinese Medicine, No. 1478, Gongnongda Road, Changchun, 130021, China.
出版信息
BMC Med Genomics. 2022 Sep 2;15(1):187. doi: 10.1186/s12920-022-01341-6.
BACKGROUND
Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80-85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).
METHODS
NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan-Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.
RESULTS
In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.
CONCLUSION
Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.
背景
肺癌是一种高发癌症，也是全球癌症死亡的最常见原因。80-85%的肺癌病例可归类为非小细胞肺癌（NSCLC）。
方法
从 TCGA 数据库和 GEO 数据库下载 NSCLC 转录组数据和临床信息。首先，我们分析并鉴定了 TCGA 数据库中非转移性 NSCLC 组和转移性 NSCLC 组之间的差异表达基因（DEGs），GO、KEGG 通路注释来探索 DEGs 的功能。然后，应用单因素 Cox 回归和 LASSO Cox 回归算法识别预后相关的转移基因signature，构建预测 NSCLC 患者生存的风险评分模型和列线图。GSEA 分析高低风险组中差异表达基因相关的信号通路。Kaplan-Meier 法分析 NSCLC 患者的生存情况。绘制 ROC 曲线评估模型的准确性。最后，进一步应用 GEO 数据库验证转移相关的预后 signature。
结果
共鉴定出 2058 个 DEGs。GO 功能和 KEGG 通路分析结果表明，DEGs 主要集中在表皮发育、皮肤发育和癌症中的神经活性配体-受体相互作用通路。构建了一个包含 6 个基因（C1QL2、FLNC、LUZP2、PRSS3、SPIC 和 GRAMD1B）的转移相关风险 signature，用于预测 NSCLC 患者的总生存期。在 GSE13213 中验证了基因 signature 的可靠性。根据风险评分的中位数将 NSCLC 患者分为低风险组和高风险组。低风险患者的风险评分较低，生存时间较长。单因素和多因素 Cox 回归验证了该 signature 是 NSCLC 的独立危险因素。
结论
本研究鉴定了 NSCLC 中的 6 个转移生物标志物。这些标志物可能有助于个体风险评估和生存预后。
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