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甲状腺癌中与谷氨酰胺代谢相关的预测标志物的RNA测序分析

RNA‑seq analysis of predictive markers associated with glutamine metabolism in thyroid cancer.

作者信息

You Yi, Zhou Yuheng, Chen Zilu, Deng Longcheng, Shen Yaping, Wang Qin, Long Wei, Xiong Yan, Tan Foxing, Du Haolin, Yang Yan, Zhong Jiang, Ge Yunqian, Li Youchen, Huang Yan

机构信息

Department of Ultrasound, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210022, P.R. China.

出版信息

Mol Med Rep. 2025 Jun;31(6). doi: 10.3892/mmr.2025.13510. Epub 2025 Apr 4.

DOI:10.3892/mmr.2025.13510
PMID:40183409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11980536/
Abstract

The incidence of thyroid cancer (TC) increases year by year. It is necessary to construct a prognostic model for risk stratification and management of TC patients. Glutamine metabolism is essential for tumor progression and the tumor microenvironment. The present study aimed to develop a predictive model for TC using a glutamine metabolism gene set. Differentially expressed genes in cells with high glutamine metabolism levels from single cell RNA‑sequencing data were compared with genes differentially expressed between normal and TC tissues from The Cancer Genome Atlas Program data. Through Boruta feature selection methods and multivariate Cox regression, six crucial genes were identified for a risk‑scoring system to develop a prognostic model. The role of each gene was verified in TC cells . A risk‑scoring system was developed according to the glutamine gene set to forecast the overall survival of TC patients. This risk score could stratify TC patients and minimize unnecessary surgeries and invasive treatments. In addition, signal induced proliferation associated 1 like 2 (SIPA1L2), an important gene in the prognostic model, knockdown in TPC‑1 and BCPAP cell lines enhanced TC cell proliferation, migration and invasion. A risk model was developed based on a glutamine metabolism gene set. The model has reference values for TC stratification.

摘要

甲状腺癌（TC）的发病率逐年上升。有必要构建一个用于TC患者风险分层和管理的预后模型。谷氨酰胺代谢对于肿瘤进展和肿瘤微环境至关重要。本研究旨在使用谷氨酰胺代谢基因集开发一种TC预测模型。将来自单细胞RNA测序数据的谷氨酰胺代谢水平高的细胞中的差异表达基因与来自癌症基因组图谱计划数据的正常组织和TC组织之间差异表达的基因进行比较。通过Boruta特征选择方法和多变量Cox回归，确定了六个关键基因用于风险评分系统以开发预后模型。每个基因的作用在TC细胞中得到验证。根据谷氨酰胺基因集开发了一个风险评分系统来预测TC患者的总生存期。该风险评分可以对TC患者进行分层，并尽量减少不必要的手术和侵入性治疗。此外，预后模型中的一个重要基因——信号诱导增殖相关1样2（SIPA1L2），在TPC-1和BCPAP细胞系中的敲低增强了TC细胞的增殖、迁移和侵袭。基于谷氨酰胺代谢基因集开发了一个风险模型。该模型对TC分层具有参考价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/9d4f1e80e838/mmr-31-06-13510-g09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/28fa8e893591/mmr-31-06-13510-g00.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/e75df9b6e4d0/mmr-31-06-13510-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/7dad6ae19467/mmr-31-06-13510-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/1830ed406e5d/mmr-31-06-13510-g03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/80e713851014/mmr-31-06-13510-g04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/b98906345db5/mmr-31-06-13510-g05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/d38d9cfc2f7d/mmr-31-06-13510-g06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/5bb4953e2291/mmr-31-06-13510-g07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/e8819debf1d1/mmr-31-06-13510-g08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/9d4f1e80e838/mmr-31-06-13510-g09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/28fa8e893591/mmr-31-06-13510-g00.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/e75df9b6e4d0/mmr-31-06-13510-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/7dad6ae19467/mmr-31-06-13510-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/1830ed406e5d/mmr-31-06-13510-g03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/80e713851014/mmr-31-06-13510-g04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/b98906345db5/mmr-31-06-13510-g05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/d38d9cfc2f7d/mmr-31-06-13510-g06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/5bb4953e2291/mmr-31-06-13510-g07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/e8819debf1d1/mmr-31-06-13510-g08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/518e/11980536/9d4f1e80e838/mmr-31-06-13510-g09.jpg

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本文引用的文献

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Targeting glutamine metabolism exhibits anti-tumor effects in thyroid cancer.靶向谷氨酰胺代谢在甲状腺癌中显示出抗肿瘤作用。
J Endocrinol Invest. 2024 Aug;47(8):1953-1969. doi: 10.1007/s40618-023-02294-y. Epub 2024 Feb 22.
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Application of single-cell sequencing to the research of tumor microenvironment.单细胞测序在肿瘤微环境研究中的应用。
Front Immunol. 2023 Oct 27;14:1285540. doi: 10.3389/fimmu.2023.1285540. eCollection 2023.
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Management of Advanced Thyroid Cancer: Overview, Advances, and Opportunities.晚期甲状腺癌的管理：概述、进展与机遇
Am Soc Clin Oncol Educ Book. 2023 May;43:e389708. doi: 10.1200/EDBK_389708.
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Thyroid cancer.甲状腺癌。
Lancet. 2023 May 6;401(10387):1531-1544. doi: 10.1016/S0140-6736(23)00020-X. Epub 2023 Apr 3.
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CD40 signal rewires fatty acid and glutamine metabolism for stimulating macrophage anti-tumorigenic functions.CD40 信号重塑脂肪酸和谷氨酰胺代谢以刺激巨噬细胞抗肿瘤功能。
Nat Immunol. 2023 Mar;24(3):452-462. doi: 10.1038/s41590-023-01430-3. Epub 2023 Feb 23.
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