Huang Yongkang, Li Na, Jiang Jie, Pei Yongjian, Gao Shiyuan, Qian Yajuan, Xing Yufei, Zhou Tong, Lian Yixin, Shi Minhua
Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, 215004, Jiangsu, China.
Department of Respiratory and Critical Care Medicine, the Fourth Affiliated Hospital of Soochow University, 9 Chongwen Road, Suzhou, 215004, Jiangsu, China.
Heliyon. 2024 Aug 31;10(17):e37214. doi: 10.1016/j.heliyon.2024.e37214. eCollection 2024 Sep 15.
The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.
目前用于区分肺恶性结节与良性结节的现有分类器在有效性或临床实用性方面存在局限性。在我们的研究中，我们旨在开发并验证一种用于肺癌诊断的基因分类器。为了识别参与此过程的基因，我们使用加权基因共表达网络分析来分析来自基因表达综合数据库（GEO）的基因表达数据集。我们确定了与恶性结节相关的三个最相关模块，并对其进行了功能富集分析。结果表明，这些模块显著参与了代谢、免疫相关、细胞周期和病毒相关过程。所有三个模块在代谢重编程途径中均表现出富集。基于这些基因，我们将三个模块中的基因与代谢重编程相关基因进行交集分析，然后进一步与差异表达基因进行交集分析，得到78个基因。经过机器学习算法和人工筛选，我们最终得到了一个由SEC24D、RPSA、PSME3、PSMD8、PSMB7、NCOA1、MED12、LPCAT1和AKR1C3组成的九基因分类器。我们开发并验证的基于分类器的模型显示出良好的区分能力，在开发队列中的曲线下面积（AUC）为0.763，在内部验证队列中为0.744，在外部验证队列中为0.718，并且优于先前的临床模型。此外，加入结节大小提高了分类器的预测能力。我们使用TCGA肺癌样本进一步验证了分类器中基因的表达，发现其中八个基因在肺腺癌中表现出显著差异表达，而所有九个基因在肺鳞癌中均表现出显著差异表达。我们的研究结果强调了代谢重编程途径在恶性肺结节患者中的重要性，并且我们的基因分类器可以帮助临床医生在临床环境中区分良性与恶性肺结节。
