Evidence map›Paper›PMID 37582873›Full record

ArticleDoklady. Biochemistry and biophysics2023

A Prognostic Model Based on Metabolism-Related Genes for Patients with Ovarian Cancer.

Dong Jian, Zhai Lianghao, Gao Yunge, Chen Ligang, Chen Biliang, Lv Xiaohui

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Article in Doklady. Biochemistry and biophysics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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6 authors.

Dong JianDepartment of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, 710032, Shaanxi Xi'an, China.
Zhai LianghaoDepartment of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, 710032, Shaanxi Xi'an, China.
Gao YungeDepartment of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, 710032, Shaanxi Xi'an, China.
Chen LigangDepartment of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, 710032, Shaanxi Xi'an, China.
Chen BiliangDepartment of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, 710032, Shaanxi Xi'an, China.
Lv XiaohuiDepartment of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, 710032, Shaanxi Xi'an, China. lvxiaohuiss@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolism-associated genes (MAGs) are important regulators of tumor progression and can affect a variety of physiological processes. In this study, we focused on the relationship between MAGs and Ovarian cancer (OC) prognosis.

methodMetabolism-related genes were extracted from the Cancer Genome Atlas (TCGA) database. Through univariate COX and lasso regression models, a dynamic risk model based on MAGs was established. Compared with other clinical factors, demonstrated the ability of the model to predict the prognosis of patients with OC. The clinical samples were used to verify the expression of these MAGs.

resultsA metabolism-associated gene signature was constructed by LASSO Cox regression analysis in OC, which was composed of 3-MAGs (PTGIS, AOC3, and IDO1). The signature was used to classify the OC patients into high-risk and low-risk groups. The overall survival of the low-risk group was significantly better than that of the high-risk group. The analysis of the therapeutic effect of bevacizumab showed that bevacizumab was not conducive to improving the prognosis of the low-risk group.

conclusionsWe constructed a prognostic model of MAGs in OC, which can be used to predict the prognosis of OC patients and may have a good guiding significance in the individualized treatment of patients.

Indexed as

Ovarian NeoplasmsFemaleHumansPrognosisBevacizumabMetabolism-associated geneOvarian cancerPrognosisrisk factor

Identifiers

PMID37582873

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