Evidence map›Paper›PMID 36852096›Full record

ArticleBiochemistry and biophysics reports2023

A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes.

Jingyu Zhao, Yu Liu, Qianwen Cui, Rongli He, Jia-Rong Zhao, Li Lu, Hong-Qiang Wang, Haiming Dai, Hongzhi Wang, Wulin Yang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biochemistry and biophysics reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Jingyu ZhaoInstitutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China.
Yu LiuAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Qianwen CuiAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Rongli HeDepartment of Anatomy, Shanxi Medical University, Taiyuan, 030024, China.
Jia-Rong ZhaoHefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China.
Li LuDepartment of Anatomy, Shanxi Medical University, Taiyuan, 030024, China.
Hong-Qiang WangScience Island Branch, Graduate School of USTC, Hefei, 230026, China.
Haiming DaiAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Hongzhi WangAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Wulin YangAnhui Province Key Laboratory of Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
University of Science and Technology of China · CNAnhui University · CNShanxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Gastric adenocarcinomaImmunohistochemistryMetabolism-related genesPrognostic modelSurvival analysis

Identifiers

PMID36852096
PMCPMC9957706
OpenAlexW4321381319

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.