Constructing a folate metabolism gene signature for predicting prognosis in pulmonary neuroendocrine carcinomas.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-10-14 eCollection Date: 2024-01-01 DOI:10.7150/jca.102186
Quanying Tang, Luoyi Li, Ruiyao Wang, Xin Jin, Xuewang Jia, Yifan Zhu, Xiaoyue Sun, Jianguo Zhong, Huangsheng Xie, Yurong Da, Lingling Zu, Song Xu
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Abstract

Folate metabolism is a crucial biological process in cell proliferation and exhibits its pro-tumorigenic functions in multiple tumor types. However, its role in pulmonary neuroendocrine carcinomas remains uncertain. Folate metabolism related genes were obtained from previous studies, and the gene expression data and clinical data were collected from GEO database. The expression patterns of folate metabolism related genes were measured across normal and tumor tissues. We subsequently assessed their prognostic role using Kaplan-Meier and univariate Cox regression analysis. The core genes were isolated from 16 prognostic genes through four algorithms. Based on the expression of core genes, patients were divided into two clusters employing consensus clustering algorithm. Furthermore, we evaluated immune infltration level, biological mechanisms, and drug sensitivity. ALDH1L2 was finally identified through qRT-PCR and its pro-tumorigenic function was confirmed via in vitro experiments. The expression patterns of 26 folate metabolism related genes were evaluated between normal lung tissues and PNEC tumor tissues, and 20 of them exhibited differential expression. All of folate metabolism related genes were related to the prognosis of PNECS and 16 genes were identified as prognostic genes. Using SVM-RFE, RF, Xgboost and LASSO algorithm, three core genes were isolated from 16 prognostic genes. Based on the expression patterns of core genes, PNECs patients were divided into two clusters through consensus clustering algorithm. Cluster 1 was characterized by the worse survival, higher immune infiltration level, and sensitivity to chemotherapy. Compared with the HBEC cells, ALDH1L2 was notably overexpressed in NCI-H446 cells (SCLC cell line). ALDH1L2 knockdown significantly repressed the proliferation and migration capacity of tumor cells and increased the cell proportion in S phase. Our results indicated that folate metabolism gene signature is a reliable biomarker for PNECs. Classification based on this signature could be utilized to guide the treatment of PNECs patients and improve its prognosis.

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构建用于预测肺神经内分泌癌预后的叶酸代谢基因特征。
叶酸代谢是细胞增殖过程中的一个关键生物过程,在多种肿瘤类型中都表现出促癌功能。然而,它在肺神经内分泌癌中的作用仍不确定。叶酸代谢相关基因来自以往的研究,基因表达数据和临床数据来自 GEO 数据库。我们测量了叶酸代谢相关基因在正常组织和肿瘤组织中的表达模式。随后,我们使用 Kaplan-Meier 和单变量 Cox 回归分析评估了这些基因的预后作用。通过四种算法从16个预后基因中分离出核心基因。根据核心基因的表达情况,采用共识聚类算法将患者分为两组。此外,我们还评估了免疫炎症水平、生物学机制和药物敏感性。通过qRT-PCR最终确定了ALDH1L2,并通过体外实验证实了其促癌功能。评估了正常肺组织和 PNEC 肿瘤组织之间 26 个叶酸代谢相关基因的表达模式,其中 20 个基因表现出差异表达。所有叶酸代谢相关基因都与 PNECS 的预后有关,其中 16 个基因被确定为预后基因。利用 SVM-RFE、RF、Xgboost 和 LASSO 算法,从 16 个预后基因中分离出 3 个核心基因。根据核心基因的表达模式,PNECs 患者通过共识聚类算法被分为两组。聚类1的特点是生存率较低、免疫浸润水平较高和对化疗敏感。与HBEC细胞相比,ALDH1L2在NCI-H446细胞(SCLC细胞系)中明显过表达。敲除 ALDH1L2 能显著抑制肿瘤细胞的增殖和迁移能力,并增加 S 期细胞的比例。我们的研究结果表明,叶酸代谢基因特征是 PNECs 的可靠生物标志物。基于该特征的分类可用于指导PNECs患者的治疗并改善其预后。
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CiteScore
7.20
自引率
4.30%
发文量
567
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