Ding Dongxiao, Wang Liangbin, Zhang Yunqiang, Shi Ke, Shen Yaxing
Department of Thoracic Surgery, The People's Hospital of Beilun District, Ningbo, 315800, Zhejiang, China.
Department of Anorectal Surgery, The People's Hospital of Beilun district, Ningbo, 315800, Zhejiang, China.
Transl Oncol. 2023 Dec;38:101784. doi: 10.1016/j.tranon.2023.101784. Epub 2023 Sep 16.
Lung cancer is the leading cause of cancer-related deaths worldwide with poor prognosis. Programmed cell death (PCD) plays a crucial function in tumor progression and immunotherapy response in lung adenocarcinoma (LUAD).
Integrative machine learning procedure including 10 methods was performed to develop a prognostic cell death signature (CDS) using TCGA, GSE30129, GSE31210, GSE37745, GSE42127, GSE50081, GSE68467, GSE68571, and GSE72094 dataset. The correlation between CDS and tumor immune microenvironment was evaluated using various methods and single cell analysis. qRT-PCR and CCK-8 assay were conducted to explore the biological functions of hub gene.
The prognostic CDS developed by Lasso + survivalSVM method was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting the clinical outcome of LUAD and served as an independent risk factor in TCGA and 8 GEO datasets. The C-index of CDS was higher than that of clinical stage and many developed signatures for LUAD. LUAD patients with low CDS score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score, indicating a better immunotherapy benefit. Single cell analysis revealed a strong and frequent communication between epithelial cells and cancer-related fibroblasts by specific ligand-receptor pairs, including COL1A2-SDC4 and COL1A2-SDC1. Vitro experiment showed that SLC7A5 was upregulated in LUAD and knockdown of SLC7A5 obviously suppressed tumor cell proliferation.
Our study developed a novel CDS for LUAD. The CDS served as an indicator for predicting the prognosis and immunotherapy benefits of LAUD patients.
肺癌是全球癌症相关死亡的主要原因，预后较差。程序性细胞死亡（PCD）在肺腺癌（LUAD）的肿瘤进展和免疫治疗反应中起关键作用。
使用TCGA、GSE30129、GSE31210、GSE37745、GSE42127、GSE50081、GSE68467、GSE68571和GSE72094数据集，通过包括10种方法的综合机器学习程序来开发一种预后性细胞死亡特征（CDS）。使用各种方法和单细胞分析评估CDS与肿瘤免疫微环境之间的相关性。进行qRT-PCR和CCK-8试验以探索枢纽基因的生物学功能。
由Lasso + survivalSVM方法开发的预后性CDS被视为最佳预后模型。CDS在预测LUAD的临床结果方面具有稳定且强大的性能，并在TCGA和8个GEO数据集中作为独立危险因素。CDS的C指数高于临床分期以及许多已开发的LUAD特征。CDS评分低的LUAD患者具有更高的PD1&CTLA4免疫表型评分、更高的TMB评分、更低的TIDE评分和更低的肿瘤逃逸评分，表明免疫治疗获益更好。单细胞分析揭示了上皮细胞与癌症相关成纤维细胞之间通过特定配体-受体对进行强烈且频繁的通讯，包括COL1A2-SDC4和COL1A2-SDC1。体外实验表明，SLC7A5在LUAD中上调，敲低SLC7A5明显抑制肿瘤细胞增殖。
我们的研究为LUAD开发了一种新型CDS。该CDS可作为预测LUAD患者预后和免疫治疗获益的指标。
