Shao Mengqi, Cao Jieming, Wang Ruoyao, Liu Yazhuo, Hu Yan, Lei Weixuan, Wang Li, Liu Wenliang
Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, China.
Hunan Key Laboratory of Early Diagnosis and Precision Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, China.
Transl Lung Cancer Res. 2025 Aug 31;14(8):3090-3106. doi: 10.21037/tlcr-2025-747. Epub 2025 Aug 26.
Ground-glass opacities (GGOs) are a common computed tomography (CT) imaging feature in various pulmonary diseases, including early-stage lung adenocarcinoma (LUAD). The investigation of tumor-specific biomarkers for the diagnosis of early LUAD holds significant clinical importance. The aim of this study was to integrate highly specific tumor-educated platelet (TEP)-derived biomarkers to optimize preoperative evaluation.
After identifying differentially expressed genes (DEGs), six machine learning methods were applied to the sequencing results of cohort 1, generating a DEG-TEP data set. The DEGs in cohort 2 were considered to be the DEG-TISSUES data set. The intersection of these two gene sets was designated as the intersection-17 data set. Validation of the genes in intersection-17 was conducted in cohort 3 (tissues) and cohort 4 (platelets) via real-time reverse-transcription quantitative polymerase chain reaction (RT-qPCR). si- cell lines were established in PC9 and H1299 cell lines to validate the role of in LUAD . Finally, a single-gene gene set enrichment analysis (GSEA) was carried out for .
A total of 7,753 DEGs were identified in cohort 1. After machine learning was applied, the DEG-TEP data set, consisting of 108 DEGs, was established, while the DEG-TISSUES data set contained 1,909 genes. The RT-qPCR results in cohort 3 indicated that the messenger RNA expression of , , and in tissues was consistent with the sequencing results. In cohort 4, only exhibited differential expression, with an area under receiver operative characteristic curve of 0.78 (95% confidence interval: 0.61-0.95; P<0.01). We found that has a suppressive effect on the proliferation, invasion, and metastatic abilities of LUAD. The single-gene GSEA was applied to preliminarily characterize the biological functions of in tumors, revealing that is predominantly enriched in ribosome-related pathways.
We found that is significantly downregulated in tumor tissues and TEPs and that the low expression of promotes the proliferation, invasion, and metastatic abilities of tumor cells. We further discovered that had a low expression in the tissues and TEPs of patients with LUAD presenting as GGOs, and we further validated its diagnostic efficacy and tumor-suppressive effects. The downregulation in AHNAK may be associated with changes in the ribosomal pathway, affecting the malignancy of tumors.
磨玻璃影(GGOs)是包括早期肺腺癌(LUAD)在内的多种肺部疾病常见的计算机断层扫描(CT)成像特征。研究用于早期LUAD诊断的肿瘤特异性生物标志物具有重要的临床意义。本研究旨在整合高度特异性的肿瘤驯化血小板(TEP)衍生生物标志物以优化术前评估。
在鉴定出差异表达基因(DEGs)后,将六种机器学习方法应用于队列1的测序结果,生成DEG-TEP数据集。队列2中的DEGs被视为DEG-TISSUES数据集。这两个基因集的交集被指定为交集-17数据集。通过实时逆转录定量聚合酶链反应(RT-qPCR)在队列3(组织)和队列4(血小板)中对交集-17中的基因进行验证。在PC9和H1299细胞系中建立si-细胞系以验证其在LUAD中的作用。最后,对进行单基因基因集富集分析(GSEA)。
在队列1中总共鉴定出7753个DEGs。应用机器学习后,建立了由108个DEGs组成的DEG-TEP数据集,而DEG-TISSUES数据集包含1909个基因。队列3中的RT-qPCR结果表明,组织中、和的信使核糖核酸表达与测序结果一致。在队列4中,只有表现出差异表达,其受试者操作特征曲线下面积为0.78(95%置信区间:0.61-0.95;P<0.01)。我们发现对LUAD的增殖、侵袭和转移能力具有抑制作用。应用单基因GSEA初步表征其在肿瘤中的生物学功能,揭示主要富集于核糖体相关途径。
我们发现其在肿瘤组织和TEPs中显著下调,且的低表达促进肿瘤细胞的增殖、侵袭和转移能力。我们进一步发现,在表现为GGOs的LUAD患者的组织和TEPs中表达较低,并进一步验证了其诊断效能和肿瘤抑制作用。AHNAK的下调可能与核糖体途径的变化有关,影响肿瘤的恶性程度。