Evidence map›Paper›PMID 37798609›Full record

ArticleReproductive sciences (Thousand Oaks, Calif.)2024

The Identification of Gamma-Glutamyl Hydrolase in Uterine Corpus Endometrial Carcinoma: a Predictive Model and Machine Learning.

Cheng Zhu, Yishan Du, Qian Huai, Nana Fang, Wentao Xu, Jing Yang, Xingyu Li, Yanyan Zhang, Xu Zhang, Hanren Dai and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in Reproductive sciences (Thousand Oaks, Calif.), 2024. 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
0.5field-weighted citation impact, top 30% 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, 3 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

13 authors at 1 institution in 1 country.

Cheng Zhu *Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Yishan Du *Department of Geriatrics, Affiliated Provincial Hospital of Anhui Medical University, Hefei, 230001, China.
Qian Huai *Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Nana Fang *Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, 230032, China.
Wentao XuDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Jing YangDepartment of Breast Surgery, Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, 230036, China.
Xingyu LiDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Yanyan ZhangDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Xu ZhangDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Hanren DaiDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Xiaolei LiDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. li-xiaolei0913@163.com.
Hua WangDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. wanghua@ahmu.edu.cn.
Ying DaiDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. dabora20051@hotmail.com.ORCID 0000-0002-7251-2617
Anhui Medical University · CN

Funding

Anhui Natural Science Foundation Youth Program 2008085QH424
6 · The paper itself

Abstract

backgroundPoor neoplastic differentiation contributes to the rapid progression of uterine corpus endometrial carcinoma (UCEC). Thus, it is essential to identify candidate genes, clarifying the carcinogenesis and progression of UCEC.

methodsWe screened genes that affect differentiation and prognosis in UCEC. Least absolute selection and shrinkage operator (LASSO) regression, univariate Cox, and multivariate Cox proportional risk regression analyses were performed to screen out γ-glutamyl hydrolase (GGH) as the candidate gene. The clinical value of GGH on prognosis was evaluated. The relationship between GGH and immune infiltration was assessed by CIBERSORT, EPIC, ssGSEA, unsupervised clustering and immunohistochemistry (IHC). Additionally, we investigated the effect of GGH knockdown in vitro.

resultsAmong the GGH, CDKN2A, and SIX1 genes, the impact of GGH was predominant on immune infiltration in UCEC. A nomogram containing GGH and other clinical features showed good predictive performance via curve analysis (DCA). In the functional analysis, GGH affected differentiation, tumour proliferation, and immune regulation. The immunosuppressive components were enriched in the GGH-high group, with poor immunotherapy efficacy. The study suggests that GGH may influence the progression of UCEC by regulating the glycolytic process.

conclusionsGGH is closely associated with various immune cell infiltrations. Our study demonstrates the prognostic role of GGH in carcinogenesis in UCEC.

Indexed as

Carcinoma, EndometrioidEndometrial NeoplasmsCarcinogenesisCell DifferentiationFemalegamma-Glutamyl HydrolaseHomeodomain ProteinsHumansMachine LearningPrognosisgamma-Glutamyl HydrolaseHomeodomain ProteinsSIX1 protein, humanGGHImmune cell infiltrationUterine corpus endometrial carcinoma

Identifiers

PMID37798609
OpenAlexW4387364037

What OpenQuestion holds

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Registered trials

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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.