Evidence map›Paper›PMID 42032709›Full record

ArticleHereditas2026

Identification and validation of biomarkers related to lysine β-hydroxybutyrylation in chronic obstructive pulmonary disease based on transcriptomics data.

Jing Lei, Jing Zhang, Linfei Yang, Yubao Chen

Abstract read
In one paragraph

Article in Hereditas, 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

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jing Lei *Department of Respiratory and Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Qinhuai District, Nanjing, Jiangsu, 210000, China.ORCID http://orcid.org/0009-0001-4423-6387
Jing Zhang *Department of Respiratory and Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Qinhuai District, Nanjing, Jiangsu, 210000, China.ORCID http://orcid.org/0000-0002-9320-367X
Linfei Yang *Department of Respiratory and Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Qinhuai District, Nanjing, Jiangsu, 210000, China.ORCID http://orcid.org/0009-0002-1254-960X
Yubao ChenDepartment of Respiratory and Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Qinhuai District, Nanjing, Jiangsu, 210000, China. interloper9@163.com.ORCID http://orcid.org/0000-0002-5068-0601

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic obstructive pulmonary disease (COPD) involves progressive lung inflammation and tissue destruction. Lysine β-hydroxybutyrylation (Kbhb) is linked to COPD. This study aimed to identify Kbhb-related biomarkers for COPD to aid therapeutic development.

methodsCOPD data and Kbhb-related genes (Kbhb-RGs) were obtained from public databases and literature. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with Kbhb-RGs. Biomarkers were selected using machine learning. Diagnostic efficacy was evaluated via a nomogram. Functional enrichment, immune infiltration, drug prediction, and molecular docking analyses were performed. Biomarker expression was validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR).

resultsA total of 12 candidate genes were detected at the intersection of 765 DEGs and 1,493 Kbhb-RGs. Subsequently, POLD2 and OTUD7B were identified as biomarkers, and the expression of these 2 genes was found to be downregulated in COPD samples. The nomogram developed utilizing these biomarkers demonstrated a satisfactory capacity for differentiating among various sample types. Biomarkers were significantly enriched in transcriptional regulation and translation processes. Regulatory T cells displayed a significant positive linkage with OTUD7B (correlation (r) > 0.3, P < 0.05). Eosinophils were considerably negatively relevant to POLD2 and OTUD7B (r < -0.3, P < 0.05). Molecular docking studies demonstrated a strong binding affinity between biomarkers and estradiol. Compared with the control group, the expression levels of POLD2 and OTUD7B were significantly lower in the COPD group.

conclusionsThis study identified POLD2 and OTUD7B as biomarkers for COPD, offering valuable insights that could support the development of targeted therapies.

Indexed as

BiomarkersLysinePulmonary Disease, Chronic ObstructiveTranscriptomeGene Expression ProfilingHumansMolecular Docking SimulationBiomarkersLysineImmune infiltrationMachine learningMolecular dockingNomogram model

Identifiers

PMID42032709
PMCPMC13245097

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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.