Evidence map›Paper›PMID 42775302›Full record

ArticleJournal of inflammation research2026

Identification of Potential Key Biomarkers for Comorbid Depression and Obesity Through Integrated Bioinformatics Analysis and Experimental Verification.

Wanrong Wu, Hui Chen, Liying Yan, Linbo Jiang, Tong Luo, Lijing Zhang, Cheng Xiang, Su An, Yang Yang, Tian-Rui Xu

Abstract read
In one paragraph

Article in Journal of inflammation research, 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
–field-weighted citation impact
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

10 authors.

Wanrong Wu *The Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Hui Chen *The Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Liying YanThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Linbo JiangThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Tong LuoThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Lijing ZhangThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Cheng XiangThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Su AnThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Yang YangThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.
Tian-Rui XuThe Academy for Cell and Life Health, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Depression and obesity impair quality of life and burden economies. Growing evidence shows a strong link between these two diseases. The objective of this study is to identify the shared core genes associated with both depression and obesity and to evaluate their diagnostic potential. Methods: Gene expression profile data were retrieved from the Gene Expression Omnibus (GEO) database to analyze the shared differentially expressed genes (DEGs) in major depression and obesity. Weighted Gene Co-expression Network Analysis (WGCNA) identified co-expression modules. STRING was used to construct and analyze the protein-protein interaction (PPI) network. Six cytoHubba algorithms identified key genes. Two different machine learning methods were used to identify core genes and develop diagnostic nomograms. The association between core genes and immune cells was assessed by immune infiltration analysis. Finally, RT-qPCR tested how inflammation affects Mmp8 and Ltf expression in mast cells and how they influence inflammatory factor expression. Results: Integration of DEGs and WGCNA yielded 98 key genes. The results of PPI network analysis were imported into the Cytoscape software, and 14 hub genes were screened using the cytoHubba plugin. Five core genes-MMP8, LTF, LCN2, CEACAM8, and ITGB3-were selected through machine learning. The previous four genes were strongly linked to different functions of immune cells in cases of coexisting depression and obesity. In the validation cohort, LTF, LCN2, CEACAM8 were downregulated after bariatric surgery, while ITGB3 unchanged and MMP8 undetected. In P815 cells, Lipopolysaccharide (LPS) induced MMP8 and LTF, and knockdown of either reduced proinflammatory cytokines. Conclusion: This study suggests that MMP8, LTF, LCN2, and CEACAM8 may be associated with therapeutic the comorbid mechanisms of depression and obesity and could potentially be used as candidates for treatment. The results of this research could offer new directions for future investigations into the causes and treatments of depression and obesity.

Indexed as

bioinformatics analysisdepressionmachine learningmast cellobesity

Identifiers

PMID42775302
PMCPMC13596460

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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.