Evidence map›Paper›PMID 40074706›Full record

ArticleAnnals of medicine2025

Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients.

Wenchao Zhou, Hui Li, Juan Zhang, Changsheng Liu, Dan Liu, Xupeng Chen, Jing Ouyang, Tian Zeng, Shuang Peng, Fan Ouyang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

12 authors.

Wenchao ZhouDepartment of Assisted Reproductive Centre, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Hui LiDepartment of Assisted Reproductive Centre, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Juan ZhangDepartment of Assisted Reproductive Centre, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Changsheng LiuDepartment of Scientific Research, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Dan LiuDepartment of Assisted Reproductive Centre, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Xupeng ChenDepartment of Gynecology, The Second Affiliated Hospital, Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, China.
Jing OuyangDepartment of Infectious Disease, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Tian ZengDepartment of Scientific Research, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Shuang PengDepartment of Infectious Disease, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Fan OuyangDepartment of Cardiology, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Yunzhu LongDepartment of Infectious Disease, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.
Yukun LiDepartment of Assisted Reproductive Centre, Xiangya Hospital Zhuzhou Central South University, Central South University, Zhuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundButyrate may inhibit SARS-CoV-2 replication and affect the development of COVID-19. However, there have been no systematic comprehensive analyses of the role of butyrate metabolism-related genes (BMRGs) in COVID-19.

methodsWe performed differential expression analysis of BMRGs in the brain, liver and pancreas of COVID-19 patients and controls in GSE157852 and GSE151803. The differentially expressed genes (DEGs) and module genes between COVID-19 patients and healthy controls in GSE171110 were screened through 'limma' and 'WGANA' R package, respectively, followed by an intersection with BMRGs via 'ggvenn' R package. Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. Moreover, the differences in immune-infiltrating cells between the COVID-19 and control groups were compared using CIBERSORT. The differences in immune cells and expression levels of biomarkers in immune cells among different tissues were analysed using GSE171668.

resultsThe BMRGs were the most different in the brain between the COVID-19 and control groups, including 21 upregulated and 16 downregulated genes. Five important common BMRGs were screened as biomarkers for COVID-19 using XGBoost, namely CCNB1, CCNA2, BRCA1, HBB and HSPA5, with increased diagnostic performance. Enrichment analysis revealed that these five genes were related to the cell cycle, cell proliferation and cell senescence. The infiltrating abundance of 12 immune cells was different between the COVID-19 and control groups. Finally, the expression levels of HSPA5, BRCA1 and HBB were higher in annotated cells than in CCNB1 and CCNA2, and there were four different types of immune cells in the liver, heart, lungs and kidneys.

conclusionsThese five genes may be potential biomarkers of butyrate metabolism in COVID-19 patients. These findings provide a direction for further studies on the molecular mechanisms underlying COVID-19.

Indexed as

ButyratesCOVID-19BiomarkersBrainGene Expression ProfilingHumansLiverROC CurveSARS-CoV-2BiomarkersButyratesbiomarkersbutyrate metabolismCOVID-19organoidsingle-cell sequencing

Identifiers

PMID40074706
PMCPMC11905318

What Socratic holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

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 Socratic graph.