Integrated multi-level transcriptomic and bioinformatics analysis identifies lipid metabolism-ubiquitination signatures and reveals ATG5, ASB4, LDLR, and CD36 as Candidate biomarkers in insulin resistance.

Jicui Chen, Gang Wu, Wei Shi, Xianjun Ma

Abstract read

In one paragraph

Article in Cell cycle (Georgetown, Tex.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Multi-omics data integration in bioinformatics: methods, applications, and the path toward biological reasoning.Functional & integrative genomics · 2026
Review

4 · The record

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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

4 authors.

Jicui ChenDepartment of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, Shandong, China.ORCID 0009-0007-9594-5785

Gang WuEmergency Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.ORCID 0009-0001-4258-2227

Wei ShiDepartment of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, Shandong, China.ORCID 0009-0005-1075-9090

Xianjun MaDepartment of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, Shandong, China.ORCID 0000-0003-0737-5369

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insulin resistance (IR) is the core pathological basis of type 2 diabetes mellitus (T2DM). This study aimed to identify core lipid metabolism-ubiquitination-related feature genes in IR. Integrative transcriptomic and functional analysis screened differential genes via multiple machine learning models. Gene set enrichment analysis (GSEA), regulatory network prediction, and immune infiltration analysis were performed. C2C12 IR models were constructed, and

Indexed as

Autophagy-Related Protein 5CD36 AntigensComputational BiologyInsulin ResistanceLipid MetabolismReceptors, LDLSuppressor of Cytokine Signaling ProteinsTranscriptomeUbiquitinationAnimalsAutophagyBiomarkersCell LineDiabetes Mellitus, Type 2Gene Expression ProfilingGene Regulatory NetworksAutophagy-Related Protein 5BiomarkersCD36 AntigensGlucoseMicroRNAsReceptors, LDLSuppressor of Cytokine Signaling ProteinsATG5immune infiltrationInsulin resistancetype 2 diabetes mellitus

Identifiers

PMID42503036

DOI10.1080/15384101.2026.2707583

PMCPMC13418714

What OpenQuestion holds

Textmetadata

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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.