A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes
Background: The study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. Methods: We downloaded the gene ch...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Biochemistry and Biophysics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405580823000213 |
| _version_ | 1797813707558879232 |
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| author | Jingyu Zhao Yu Liu Qianwen Cui Rongli He Jia-Rong Zhao Li Lu Hong-Qiang Wang Haiming Dai Hongzhi Wang Wulin Yang |
| author_facet | Jingyu Zhao Yu Liu Qianwen Cui Rongli He Jia-Rong Zhao Li Lu Hong-Qiang Wang Haiming Dai Hongzhi Wang Wulin Yang |
| author_sort | Jingyu Zhao |
| collection | DOAJ |
| description | Background: The study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. Methods: We downloaded the gene chip dataset GSE79973 (n = 20) of GAD from the Gene Expression Omnibus (GEO) database to compare differentially expressed genes (DEGs) between normal and tumor tissues. We then extracted MRGs from these DEGs and systematically investigated the prognostic value of these differential MRGs for predicting patients' overall survival by univariable and multivariable Cox regression analysis. Six metabolic genes (ACOX3, APOE, DIO2, HSD17B4, NUAK1, and WHSC1L1) were identified as prognosis-associated hub genes, which were used to build a prognostic model in the training dataset GSE15459 (n = 200), and then validated in the dataset GSE62254 (n = 300). Results: Patients were divided into high-risk and low-risk subgroups based on the model's risk score, and it was found that patients in the high-risk subgroup had shorter overall survival than those in the low-risk subgroup, both in the training and testing datasets. In addition, for the training and testing cohorts, the area under the ROC curve of the prognostic model for one-year survival prediction was 0.723 and 0.667, respectively, indicating that the model has good predictive performance. Furthermore, we established a nomogram based on tumor stage and risk score to effectively predict the overall survival (OS) of GAD patients. The expression of 6 MRGs at the protein level was confirmed by immunohistochemistry (IHC). Kaplan-Meier survival analysis further confirmed that their expression influenced OS in GAD patients. Conclusion: Collectively, the 6 MRGs signature might be a reliable tool for assessing OS in GAD patients, with potential application value in clinical decision-making and individualized therapy. |
| first_indexed | 2024-03-13T07:56:44Z |
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| id | doaj.art-0e3a602b06ad4d5a8e76c073c1070da4 |
| institution | Directory Open Access Journal |
| issn | 2405-5808 |
| language | English |
| last_indexed | 2024-03-13T07:56:44Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
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| series | Biochemistry and Biophysics Reports |
| spelling | doaj.art-0e3a602b06ad4d5a8e76c073c1070da42023-06-02T04:23:20ZengElsevierBiochemistry and Biophysics Reports2405-58082023-07-0134101440A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genesJingyu Zhao0Yu Liu1Qianwen Cui2Rongli He3Jia-Rong Zhao4Li Lu5Hong-Qiang Wang6Haiming Dai7Hongzhi Wang8Wulin Yang9Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China; Anhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, ChinaAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China; Science Island Branch, Graduate School of USTC, Hefei, 230026, ChinaAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China; Science Island Branch, Graduate School of USTC, Hefei, 230026, ChinaDepartment of Anatomy, Shanxi Medical University, Taiyuan, 030024, ChinaHefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, ChinaDepartment of Anatomy, Shanxi Medical University, Taiyuan, 030024, ChinaScience Island Branch, Graduate School of USTC, Hefei, 230026, China; Biological Molecular Information System Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China; Science Island Branch, Graduate School of USTC, Hefei, 230026, ChinaAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China; Science Island Branch, Graduate School of USTC, Hefei, 230026, ChinaAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China; Science Island Branch, Graduate School of USTC, Hefei, 230026, China; Corresponding author. Institute of Health and Medical Technology, Hefei Institutes of Physical Science, CAS, No. 350, Shushanhu Road, Hefei, 230031, Anhui, China.Background: The study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. Methods: We downloaded the gene chip dataset GSE79973 (n = 20) of GAD from the Gene Expression Omnibus (GEO) database to compare differentially expressed genes (DEGs) between normal and tumor tissues. We then extracted MRGs from these DEGs and systematically investigated the prognostic value of these differential MRGs for predicting patients' overall survival by univariable and multivariable Cox regression analysis. Six metabolic genes (ACOX3, APOE, DIO2, HSD17B4, NUAK1, and WHSC1L1) were identified as prognosis-associated hub genes, which were used to build a prognostic model in the training dataset GSE15459 (n = 200), and then validated in the dataset GSE62254 (n = 300). Results: Patients were divided into high-risk and low-risk subgroups based on the model's risk score, and it was found that patients in the high-risk subgroup had shorter overall survival than those in the low-risk subgroup, both in the training and testing datasets. In addition, for the training and testing cohorts, the area under the ROC curve of the prognostic model for one-year survival prediction was 0.723 and 0.667, respectively, indicating that the model has good predictive performance. Furthermore, we established a nomogram based on tumor stage and risk score to effectively predict the overall survival (OS) of GAD patients. The expression of 6 MRGs at the protein level was confirmed by immunohistochemistry (IHC). Kaplan-Meier survival analysis further confirmed that their expression influenced OS in GAD patients. Conclusion: Collectively, the 6 MRGs signature might be a reliable tool for assessing OS in GAD patients, with potential application value in clinical decision-making and individualized therapy.http://www.sciencedirect.com/science/article/pii/S2405580823000213Gastric adenocarcinomaMetabolism-related genesPrognostic modelImmunohistochemistrySurvival analysis |
| spellingShingle | Jingyu Zhao Yu Liu Qianwen Cui Rongli He Jia-Rong Zhao Li Lu Hong-Qiang Wang Haiming Dai Hongzhi Wang Wulin Yang A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes Biochemistry and Biophysics Reports Gastric adenocarcinoma Metabolism-related genes Prognostic model Immunohistochemistry Survival analysis |
| title | A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes |
| title_full | A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes |
| title_fullStr | A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes |
| title_full_unstemmed | A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes |
| title_short | A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes |
| title_sort | prediction model for prognosis of gastric adenocarcinoma based on six metabolism related genes |
| topic | Gastric adenocarcinoma Metabolism-related genes Prognostic model Immunohistochemistry Survival analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405580823000213 |
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