■在肿瘤免疫微环境中,先天和适应性免疫细胞对肿瘤进展的贡献已得到一致证明.然而,肺腺癌(LUAD)的可靠预后生物标志物尚未确定.因此,我们开发并验证了免疫学长非编码RNA(lncRNA)签名(ILLS),以促进高风险和低风险患者的分类,并提供潜在的“定制”治疗选择。
■从癌症基因组图谱(TCGA)和基因表达综合(GEO)的公共数据库获得并处理了LUAD数据集。通过共识聚类计算免疫浸润的丰度及其相关途径,加权基因共表达网络分析(WGCNA),和整合的ImmLnc以鉴定免疫相关的lncRNAs并提取免疫相关的预后lncRNAs。基于整合程序,最佳的算法组成是最小绝对收缩和选择算子(LASSO)和逐步Cox回归在两个方向上开发的ILLSTCGA-LUAD数据集和验证4个独立数据集的预测能力,通过生存分析GSE31210、GSE37745、GSE30219和GSE50081,接收机工作特性(ROC)分析,和多元Cox回归。将一致性指数(C-index)分析与上述5个数据集中49个已发表的签名进行横向比较,以进一步证实其稳定性和优越性。最后,进行药物敏感性分析以探索潜在的治疗药物.
■与低风险组相比,来自高风险组的患者的总生存期(OS)始终较差。ILLS被证明是独立的预后因素,具有良好的敏感性和特异性。在4个GEO数据集中,与其他文献报道的相比,ILLS保持稳定的预测能力,更适合作为共识风险分层工具。然而,癌症免疫图谱和IMsporch210数据集证明了在识别具有有效免疫疗法的目标人群方面的实际实用性。虽然高危人群表现出某些化疗药物的潜在靶点,比如卡莫司汀,依托泊苷,三氧化二砷,和阿列替尼。
■ILLS表现出优越且稳定的预后预测能力,因此有可能作为辅助LUAD患者进行风险分类和临床决策的工具。
UNASSIGNED: In the tumor
immune microenvironment, the contribution of innate and adaptive
immune cells to tumor progression has been consistently demonstrated. However, reliable prognostic biomarkers for lung adenocarcinoma (LUAD) have not yet been identified. We thus developed and validated an immunologic long noncoding RNA (lncRNA) signature (ILLS) to facilitate the classification of patients with high and low risk and provide potential \"made-to-measure\" treatment choices.
UNASSIGNED: The LUAD data sets were obtained and processed from public databases of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The abundance of
immune infiltration and its related pathways were calculated through
consensus clustering, weighted gene coexpression network analysis (WGCNA), and an integrated ImmLnc to identify
immune-related lncRNAs and extract
immune-related prognostic lncRNAs. Based on the integrative procedure, the best algorithm composition was least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression in both directions to develop the ILLS in the TCGA-LUAD data set and validate the predictive power of 4 independent data sets, GSE31210, GSE37745, GSE30219, and GSE50081 through survival analysis, receiver operating characteristic (ROC) analysis, and multivariate Cox regression. The concordance index (C-index) analysis was transversely compared with 49 published signatures in the above 5 data sets to further confirm its stability and superiority. Finally, drug sensitivity analysis was conducted to explore potential therapeutic agents.
UNASSIGNED: Patients from the high-risk groups consistently had worse overall survival (OS) compared to the low-risk groups. ILLS proved to be an independent prognostic factor with favorable sensitivity and specificity. Among the 4 GEO data sets, compared to those reported in the other literature, ILLS maintained stable prediction ability and was more suitable as a
consensus risk-stratification tool. However, The Cancer Immunome Atlas and IMvigor210 data sets demonstrated practical utility in recognizing target populations with effective immunotherapy, while the high-risk group exhibited potential targets for certain chemotherapy drugs, such as carmustine, etoposide, arsenic trioxide, and alectinib.
UNASSIGNED: ILLS demonstrated superior and stable prognostic prediction ability and thus has potential as a tool for assisting in risk classification and clinical decision-making in patients with LUAD.