Department of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Cancer Med. 2022 Jun;11(11):2259-2270. doi: 10.1002/cam4.4577. Epub 2022 Mar 4.
Examining the role of immune-related genes (IRGs) in "driver gene negative" lung adenocarcinoma (LUAD) may provide new ideas for the treatment and study for this LUAD subgroup. We aimed to find the hub immune-related gene pairs can stratify the risk of "driver-gene-negative" LUAD.
IRGs were identified according to ImmPort database based on RNA sequencing results of tumors and normal tissues from 46 patients with "driver gene negative" LUAD at The First Affiliated Hospital of Sun Yat-sen University and cyclically singly paired as immune-related gene pairs (IRGPs). Multivariate Cox analysis was used to construct an immune risk model and a prognostic nomogram combining was also been developed. Immune microenvironment landscape described by CIBERSORT and drug sensitivity calculated by pRRophetic algorithm were used to explore possible treatment improvements.
A novel immune risk model with 5-IRGPs (CD1A|CXCL135, CD1A|S100A7L2, IFNA7|CMTM2, IFNA7|CSF3, CAMP|TFR2) can accurately distinguish patients in the high- and low-risk groups. Risk score act as an independent prognostic factor and is related to clinical stage. There are significant differences in tumor immune microenvironment and PD-1/PD-L1/CTLA-4 expression between groups. The low-risk patient may benefit more from the commonly used chemotherapy regimens such as gemcitabine and paclitaxel.
This study constructed 5-IRGPs as a reliable prognostic tool and may represent genes pairs that are potential rationale for choice of treatment for "driver gene negative" LUAD.
研究免疫相关基因(IRGs)在“驱动基因阴性”肺腺癌(LUAD)中的作用,可能为该 LUAD 亚组的治疗和研究提供新的思路。我们旨在寻找潜在的免疫相关基因对,以对“驱动基因阴性”LUAD 进行风险分层。
根据中山大学第一附属医院 46 例“驱动基因阴性”LUAD 患者的肿瘤和正常组织的 RNA 测序结果,从 ImmPort 数据库中确定 IRGs,并循环单对作为免疫相关基因对(IRGPs)。采用多变量 Cox 分析构建免疫风险模型,并结合建立预后列线图。采用 CIBERSORT 描述免疫微环境景观,采用 pRRophetic 算法计算药物敏感性,以探索可能的治疗改善。
一个由 5-IRGPs(CD1A|CXCL135、CD1A|S100A7L2、IFNA7|CMTM2、IFNA7|CSF3、CAMP|TFR2)组成的新型免疫风险模型可以准确区分高风险和低风险组的患者。风险评分作为一个独立的预后因素,与临床分期有关。两组间肿瘤免疫微环境和 PD-1/PD-L1/CTLA-4 表达存在显著差异。低危患者可能从吉西他滨和紫杉醇等常用化疗方案中获益更多。
本研究构建的 5-IRGPs 是一种可靠的预后工具,可能代表潜在的治疗选择的基因对“驱动基因阴性”LUAD。