Evidence map›Paper›PMID 42221222›Full record

ArticleJournal of inflammation research2026

Characterization and Clinical Diagnostic Potential of IHRDEGs in Renal Interstitial Fibrosis: An Integrative Data Analysis and Model Construction Study.

Jie Zhang, Xinyu Dang, Enlai Dai

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.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jie ZhangSchool of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, People's Republic of China.
Xinyu DangSchool of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, People's Republic of China.
Enlai DaiSchool of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Renal interstitial fibrosis (RIF) is a critical pathological process in the progression of chronic kidney disease (CKD). This study aimed to identify and validate inflammation- and hypoxia-related differentially expressed genes (IHRDEGs) associated with RIF and to construct a robust diagnostic model with potential clinical applications. Patients and Methods: Three public GEO datasets (GSE22459, GSE76882, GSE53605) comprising 76 RIF and 142 control samples were integrated following batch correction and normalization. Differentially expressed IHRDEGs were screened and analyzed using GO and KEGG pathway enrichment. A diagnostic model was constructed using logistic regression and optimized through SVM and LASSO algorithms. Immune infiltration was evaluated using ssGSEA, and consensus clustering was used to define molecular subtypes. Experimental validation was conducted in a rat model of RIF using RT-qPCR, Western blotting, and immunohistochemistry. Results: A total of five hub IHRDEGs (EDN1, HLA-G, MYC, HIF1A, and TLR2) were identified and incorporated into a diagnostic model that demonstrated strong predictive ability (AUC 0.7-0.9; sensitivity and specificity > 70-90%). These genes were significantly correlated with immune cell infiltration patterns. Subtype analysis revealed two distinct molecular clusters of RIF with different immunopathological features. Co-expression and regulatory interaction analyses further elucidated the involvement of hub genes in fibrotic mechanisms. Experimental validation confirmed the upregulation of hub genes at both mRNA and protein levels in the RIF model. Conclusion: This study uncovers the diagnostic and mechanistic significance of inflammation- and hypoxia-related genes in RIF. The five identified hub genes may serve as promising biomarkers and therapeutic targets. These findings provide novel insights into the immune-hypoxia interplay in renal fibrosis and offer a potential framework for early diagnosis and targeted treatment of CKD-related fibrosis.

Indexed as

bioinformatics analysisbiomarker discoveryfibrotic progressionhypoxia signalingimmune cell infiltrationmachine learning model

Identifiers

PMID42221222
PMCPMC13217453

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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.