Evidence map›Paper›PMID 38678059›Full record

ArticleScientific reports2024

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis.

Wenhao Chen, Wentao Guo, Yang Li, Muhu Chen

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

14 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Sepsis and the immunometabolic inflammatory response.npj metabolic health and disease · 2026
    Review
  3. Review
  4. Genetic associations in sepsis and ARDS.Frontiers in pharmacology · 2026
    Review
  5. Multi-omics reveals the protective mechanisms ofFrontiers in pharmacology · 2026
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Dissecting inflammation in the immunemetabolomic era.Cellular and molecular life sciences : CMLS · 2025
    Review
  11. Review
  12. Review
  13. Review
  14. Multi Omics Applications in Biological Systems.Current issues in molecular biology · 2024
    Review
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

4 authors at 1 institution in 1 country.

Wenhao ChenDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Wentao GuoDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Yang LiDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Muhu ChenDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China. cmh6186@swmu.edu.cn.
Affiliated Hospital of Southwest Medical University · CN

Funding

Luzhou Municipal People's Government-Southwest Medical University Science and Technology Strategic Cooperation Applied Basic Research Project 2021LZXNYD-J13
6 · The paper itself

Abstract

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC-MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

Indexed as

BiomarkersMetabolomicsSepsisAdultAgedChromatography, LiquidFemaleGene Expression ProfilingHumansMaleMiddle AgedSystemic Inflammatory Response SyndromeTandem Mass SpectrometryTranscriptomeBiomarkersBiomarkersMetabolomicsRNA sequencingSepsisSingle-cell RNA sequencing

Identifiers

PMID38678059
PMCPMC11055861
OpenAlexW4395694740

What OpenQuestion holds

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LicenceCC BY
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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.