■免疫疗法已成为肺癌患者的一线治疗选择,但有些病人对免疫疗法反应不佳,尤其是肺腺癌(LUAD)患者。需要新的工具来筛查LUAD患者对免疫治疗的潜在反应者,从而更好地预测预后,指导临床决策。尽管已经做出了许多努力来预测LUAD患者的反应性,结果有限。在免疫疗法时代,本研究试图通过在具有不同免疫治疗反应的患者中利用差异表达基因(DEGs)构建一种新的LUAD预后模型.
■598例LUAD患者的转录组数据从癌症基因组图谱(TCGA)数据库下载,其中包括539个肿瘤样本和59个正常对照样本,平均随访时间为29.69个月(63.1%的患者在随访结束时仍存活).分析了其他数据源,包括来自基因表达综合(GEO)数据库的三个数据集,免疫疗法应答者和非应答者之间的DEG被鉴定和筛选。应用单变量Cox回归分析,TCGA队列作为训练集,GSE72094队列作为验证集,和最小绝对收缩和选择算子(LASSO)Cox回归被应用在预后相关基因中,这些基因满足过滤标准以建立预后公式,然后用时间相关的接收器工作特性(ROC)分析进行测试。用基因本体论(GO)和京都基因和基因组百科全书(KEGG)富集分析分析了预后相关基因的富集途径,和肿瘤免疫微环境(TIME),肿瘤突变负担,在R(R统计计算基金会)中使用适当的软件包完成药物敏感性测试。最后,建立了包含预后公式的列线图。
■总共确定了1,636个DEG,提取了1,163个与预后相关的DEGs,选择34个DEGs并将其纳入免疫治疗反应性相关风险评分(IRRS)公式.IRRS公式在预测LUAD患者的总体预后方面具有良好的性能,并且在所有LUAD亚组中都具有出色的预后预测性能。此外,IRRS公式可以预测LUAD患者的抗癌药物敏感性和免疫治疗反应性。机械上,两个IRRS组之间的免疫微环境差异很大;高IRRS组和低IRRS组之间最显著变化的途径是核糖核蛋白复合物生物发生,与TP53和TTN突变负荷密切相关。此外,我们建立了一个包含IRRS的列线图,年龄,性别,临床分期,T-stage,N级,和M阶段作为可以预测1年预后的预测因子,3年,LUAD患者的5年生存率,曲线下面积(AUC)分别为0.718、0.702和0.68。
■本研究建立的模型可以预测LUAD患者的预后,帮助识别对抗癌药物和免疫疗法反应良好的患者,并作为指导临床决策的宝贵工具。
UNASSIGNED: Immune therapy has become first-line treatment option for patients with lung cancer, but some patients respond poorly to immune therapy, especially among patients with lung adenocarcinoma (LUAD). Novel tools are needed to screen potential responders to immune therapy in LUAD patients, to better predict the prognosis and guide clinical decision-making. Although many efforts have been made to predict the responsiveness of LUAD patients, the results were limited. During the era of immunotherapy, this study attempts to construct a novel prognostic model for LUAD by utilizing differentially expressed genes (DEGs) among patients with differential immune therapy responses.
UNASSIGNED: Transcriptome data of 598 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, which included 539 tumor samples and 59 normal control samples, with a mean follow-up time of 29.69 months (63.1% of patients remained alive by the end of follow-up). Other data sources including three datasets from the Gene Expression Omnibus (GEO) database were analyzed, and the DEGs between immunotherapy responders and nonresponders were identified and screened. Univariate Cox regression analysis was applied with the TCGA cohort as the training set and GSE72094 cohort as the validation set, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied in the prognostic-related genes which fulfilled the filter criteria to establish a prognostic formula, which was then tested with time-dependent receiver operating characteristic (ROC) analysis. Enriched pathways of the prognostic-related genes were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and tumor immune microenvironment (TIME), tumor mutational burden, and drug sensitivity tests were completed with appropriate packages in R (The R Foundation of Statistical Computing). Finally, a nomogram incorporating the prognostic formula was established.
UNASSIGNED: A total of 1,636 DEGs were identified, 1,163 prognostic-related DEGs were extracted, and 34 DEGs were selected and incorporated into the immunotherapy responsiveness-related risk score (IRRS) formula. The IRRS formula had good performance in predicting the overall prognoses in patients with LUAD and had excellent performance in prognosis prediction in all LUAD subgroups. Moreover, the IRRS formula could predict anticancer drug sensitivity and immunotherapy responsiveness in patients with LUAD. Mechanistically, immune microenvironments varied profoundly between the two IRRS groups; the most significantly varied pathway between the high-IRRS and low-IRRS groups was ribonucleoprotein complex biogenesis, which correlated closely with the TP53 and TTN mutation burdens. In addition, we established a nomogram incorporating the IRRS, age, sex, clinical stage, T-stage, N-stage, and M-stage as predictors that could predict the prognoses of 1-year, 3-year, and 5-year survival in patients with LUAD, with an area under curve (AUC) of 0.718, 0.702, and 0.68, respectively.
UNASSIGNED: The model we established in the present study could predict the prognosis of LUAD patients, help to identify patients with good responses to anticancer drugs and immunotherapy, and serve as a valuable tool to guide clinical decision-making.