| Lingyan Xiao,Yongbiao Huang,Wan Qin,Chaofan Liu,Hong Qiu,Bo Liu,Xianglin Yuan. A metabolism-associated gene signature with prognostic value in colorectal cancer. Oncol Transl Med, 2022, 8: 43-54. |
| 基于代谢相关基因的结直肠癌预后模型 |
| A metabolism-associated gene signature with prognostic value in colorectal cancer |
| Received:September 19, 2021 Revised:March 07, 2022 |
| DOI：10.1007/s10330-021-0521-1 |
| 中文关键词: 结直肠癌，预后，代谢，转录组测序，TCGA |
| 英文关键词: colorectal cancer (CRC); prognostic; metabolism; RNA-seq; The Cancer Genome Atlas (TCGA) |
| 基金项目: |
| Author Name | Affiliation | E-mail | | Lingyan Xiao | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | 15580329472@163.com | | Yongbiao Huang | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | | | Wan Qin | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | | | Chaofan Liu | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | | | Hong Qiu | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | | | Bo Liu* | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | boliu888@hotmail.com | | Xianglin Yuan | Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | |
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| 中文摘要: |
| 目的：本研究旨在探究结直肠癌（CRC）中代谢相关基因的作用并构建结预后模型。方法：对TCGA中 CRC 的 RNA 测序数据进行差异表达分析，并进行富集分析以找出差异的代谢相关基因的功能。蛋白质-蛋白质相互作用(PPI)网络、Kaplan-Meier曲线和Cox 回归分析用于筛选关键的代谢相关基因。通过 LASSO cox 回归分析构建预后模型并以列线图的形式进行可视化。在TCGA 和GEO数据库进行生存分析以显示模型的预测能力。结果：从TCGA中筛选出332个CRC的差异表达代谢相关基因，差异表达的代谢相关基因主要参与磷酸核苷、磷酸核糖、脂质和脂肪酸的代谢。我们构建了一个由328个关键基因组成的蛋白质-蛋白质相互作用（PPI）网络，基于 5 个预后基因（ALAD、CHDH、ISYNA1、NAT1 和 P4HA1）建立了预后模型，该模型被证明能够在TCGA和GEO中准确预测生存。结论：我们的研究构建了一个可以预测 CRC 患者生存的代谢基因相关预后模型，我们的工作是对以往寻找结直肠癌预后因素的工作的补充，可以为进一步的机制探索奠定基础。 |
| 英文摘要: |
| Objective In this study, our goal was to explore the role of metabolism-associated genes in colorectal
cancer (CRC) and construct a prognostic model for patients with CRC.
Methods Differential expression analysis was conducted using RNA-sequencing data from The Cancer
Genome Atlas (TCGA) dataset. Enrichment analyses were performed to determine the function of
dysregulated metabolism-associated genes. The protein-protein interaction (PPI) network, Kaplan-Meier
curves, and stepwise Cox regression analyses identified key metabolism-associated genes. A prognostic
model was constructed using LASSO Cox regression analysis and visualized as a nomogram. Survival
analyses were conducted in the TCGA and Expression Omnibus (GEO) cohorts to demonstrate the
predictive ability of the model.
Results A total of 332 differentially expressed metabolism-associated genes in CRC were screened
from the TCGA cohort. Differentially expressed metabolism-associated genes mainly participate in
the metabolism of nucleoside phosphate, ribose phosphate, lipids, and fatty acids. A PPI network was
constructed out of 328 key genes. A prognostic model was established based on five prognostic genes
(ALAD, CHDH, ISYNA1, NAT1, and P4HA1) and was demonstrated to predict survival in the TCGA and
GEO cohorts accurately.
Conclusion The metabolism-associated prognostic model can predict the survival of patients with
CRC. Our work supplements previous work focusing on determining prognostic factors of CRC and lays a
foundation for further mechanistic exploration. |
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