Evidence map›Paper›PMID 42196513›Full record

ArticleInternational journal of molecular sciences2026

Deciphering the Diagnostic and Natural Therapeutic Implications of Necrosis by Sodium Overload and NK Signatures in Endometriosis Patients.

Juan Du, Zili Lv

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Juan DuSchool of Medicine and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
Zili LvSchool of Medicine and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID 0000-0001-9553-781X

Funding

Medical Research Project of the Health Commission of Chengdu 2023026Medical Research Project of the Health Commission of Sichuan Province 24QNMP002Natural Science Foundation of Sichuan Province 2024NSFSC1825
6 · The paper itself

Abstract

Endometriosis (EMT) is characterized by a chronic inflammatory disorder in the female reproductive system, posing significant challenges to global women's health. Necrosis by Sodium Overload (NESCO) is a novel immunogenic programmed cell death (PCD) pattern that may potentially inhibit natural killer (NK) cell activation by increasing cytotoxicity and the inflammatory response in the EMT microenvironment. By integrating three bulk datasets to compare endometrium tissues between endometriosis patients and normal controls and the NESCO gene list from a public database, we identified NK- and NESCO (NN)-associated hub genes via integrative bioinformatic analyses utilizing Limma, WGCNA, CIBERSORT and machine learning frameworks. The diagnostic performance of NN-associated hub genes was evaluated across the three aforementioned datasets and two independent validation sets. Furthermore, their molecular and immune features were estimated at the bulk and single-cell transcriptomic levels. In addition, endometriosis patients were classified into two novel molecular subgroups based on consensus clustering of NN. Finally, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and molecular docking were used to identify compounds in Chinese traditional medicine (CTM) that can target NN-associated hub genes for endometriosis treatment.

Indexed as

EndometriosisKiller Cells, NaturalNecrosisSodiumComputational BiologyEndometriumFemaleGene Expression ProfilingGene Regulatory NetworksHumansMedicine, Chinese TraditionalMolecular Docking SimulationTranscriptomeSodiumdiagnosisendometriosisnatural killer cellnecrosis by sodium overloadtherapeutic agents

Identifiers

PMID42196513
PMCPMC13208043

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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.