Li Houqiang, Sha Xinyu, Wang Wenmiao, Huang Zhanghao, Zhang Peng, Liu Lei, Wang Silin, Zhou Youlang, He Shuai, Shi Jiahai
Department of Thoracic Surgery, Nantong Key Laboratory of Translational Medicine in Cardiothoracic Diseases, Research Institution of Translational Medicine in Cardiothoracic Diseases in Affiliated Hospital of Nantong University, Nantong, China.
Graduate School, Dalian Medical University, Dalian, China.
Transl Lung Cancer Res. 2023 Jul 31;12(7):1477-1495. doi: 10.21037/tlcr-23-14. Epub 2023 Jul 18.
Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high.
RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD.
Of the nine candidate differential prognostic LRGs, , , , and were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. and were highly expressed, while and were poorly expressed in LUAD cell lines. In addition, and were highly expressed, while and were poorly expressed in tumor tissues.
Four key prognostic biomarkers-, , , and -were used to construct a significant prognostic model for LUAD patients.
肺腺癌(LUAD)是肺癌最常见的亚型,占该肿瘤所有病例的40%。尽管在了解LUAD的分子基础、诊断和治疗方面取得了巨大进展,但其复发率仍然很高。
从基因组数据共享门户下载来自癌症基因组图谱(TCGA)LUAD队列的RNA测序数据。来自基因表达综合数据库(GEO)的GSE13213数据集用于外部验证。通过将单变量Cox回归分析获得的生存相关基因与差异表达基因(DEG)进行重叠,鉴定出差异预后溶酶体相关基因(LRG)。使用Kaplan-Meier曲线和最小绝对收缩和选择算子(LASSO)分析建立预后模型。此外,采用单变量和多变量Cox分析来确定独立的预后因素。进一步预测患者对免疫检查点抑制剂(ICI)的反应。使用pRRophetic软件包和秩和检验来计算56种化疗药物的半数最大抑制浓度(IC)及其在低风险和高风险组中的差异效应。此外,使用定量实时聚合酶链反应、蛋白质印迹和人类蛋白质图谱(HPA)数据库来验证LUAD中四种预后生物标志物的表达。
在九个候选差异预后LRG中,选择了[具体基因名称未给出]作为预后生物标志物。风险模型的预测被验证是可靠的。Cox独立预后分析显示,风险评分和分期是LUAD的独立预后因素。此外,独立预后因素的列线图和校准曲线表现良好。ICI的差异分析显示,CD276、ICOS、PDCD1LG2、CD27、TNFRSF18、TNFSF9、ENTPD1和NT5E在低风险和高风险组中的表达不同。包括埃博霉素B、JNK抑制剂VIII和AKT抑制剂VIII在内 的12种化疗药物的IC值在两个风险组之间存在显著差异。[具体基因名称未给出]在LUAD细胞系中高表达,而[具体基因名称未给出]低表达。此外,[具体基因名称未给出]在肿瘤组织中高表达,而[具体基因名称未给出]低表达。
四个关键预后生物标志物[具体基因名称未给出]用于构建LUAD患者的显著预后模型。