Yang Yan, Lu Suqiong, Gu Guomin
Department of Pulmonary Medicine, Cancer Hospital of Xinjiang Medical University, 789 Suzhou Street, Urumqi, 830011, Xinjiang, China.
Heliyon. 2024 Aug 30;10(17):e36816. doi: 10.1016/j.heliyon.2024.e36816. eCollection 2024 Sep 15.
Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide. Despite advances in treatment, prognosis remains poor, necessitating the identification of reliable prognostic biomarkers. Costimulatory molecules (CMs) have shown to enhance antitumor immune responses. We aimed to explore their prognostic signals in NSCLC.
This study is a combination of bioinformatics analysis and laboratory validation. Gene expression profiles from The Cancer Genome Atlas (TCGA), GSE120622, and GSE131907 datasets were collected. NSCLC samples in TCGA were clustered based on CMs using consensus clustering. We used LASSO regression to identify CMs-related signatures and constructed nomogram and risk models. Differences in immune cells and checkpoint expressions between risk models were evaluated. Enrichment analysis was performed for differentially expressed CMs between NSCLC and controls. Key results were validated using qRT-PCR and flow cytometry.
NSCLC samples in TCGA were divided into two clusters based on CMs, with cluster 1 showing poor overall survival. Ten CMs-related signatures were identified using LASSO regression. NSCLC samples in TCGA were stratified into high- and low-risk groups based on the median risk score of these signatures, revealing differences in survival probability, drug sensitivity, immune cell infiltration and checkpoints expression. The area under the ROC curve values (AUC) for EDA, ICOS, PDCD1LG2, and VTCN1 exceeded 0.7 in both datasets and considered as hub genes. Expression of these hub genes was significance in GSE131907 and validated by qRT-PCR. Macrophage M1 and T cell follicular helper showed high correlation with hub genes and were lower in NSCLC than controls detected by flow cytometry.
The identified hub genes can serve as prognostic biomarkers for NSCLC, aiding in treatment decisions and highlighting potential targets for immunotherapy. This study provides new insights into the role of CMs in NSCLC prognosis and suggests future directions for clinical research and therapeutic strategies.
非小细胞肺癌（NSCLC）是全球癌症相关死亡的主要原因。尽管治疗取得了进展，但其预后仍然很差，因此需要确定可靠的预后生物标志物。共刺激分子（CMs）已被证明可增强抗肿瘤免疫反应。我们旨在探索它们在NSCLC中的预后信号。
本研究结合了生物信息学分析和实验室验证。收集了来自癌症基因组图谱（TCGA）、GSE120622和GSE131907数据集的基因表达谱。使用一致性聚类方法基于CMs对TCGA中的NSCLC样本进行聚类。我们使用LASSO回归来识别与CMs相关的特征，并构建列线图和风险模型。评估风险模型之间免疫细胞和检查点表达的差异。对NSCLC与对照之间差异表达的CMs进行富集分析。使用qRT-PCR和流式细胞术对关键结果进行验证。
基于CMs，TCGA中的NSCLC样本被分为两个聚类，聚类1显示总体生存率较差。使用LASSO回归确定了10个与CMs相关的特征。根据这些特征的中位风险评分，将TCGA中的NSCLC样本分为高风险组和低风险组，揭示了生存概率、药物敏感性、免疫细胞浸润和检查点表达的差异。在两个数据集中，EDA、ICOS、PDCD1LG2和VTCN1的受试者工作特征曲线下面积（AUC）值均超过0.7，被视为枢纽基因。这些枢纽基因的表达在GSE131907中具有显著性，并通过qRT-PCR进行了验证。巨噬细胞M1和T细胞滤泡辅助细胞与枢纽基因显示出高度相关性，并且通过流式细胞术检测发现NSCLC中的这些细胞低于对照组。
所确定的枢纽基因可作为NSCLC的预后生物标志物，有助于治疗决策并突出免疫治疗的潜在靶点。本研究为CMs在NSCLC预后中的作用提供了新的见解，并为临床研究和治疗策略提出了未来的方向。
