Evidence map›Paper›PMID 40304330›Full record

ArticleCurrent medicinal chemistry2026

Integrating Transcriptomic Data and Mendelian Randomization Analyses Reveals Potentially Novel Sepsis-related Targets.

Wenting Tao, Liang Chen

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Article in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Wenting TaoDepartment of Critical Care Medicine, Nanjing Lishui People's Hospital, Zhongda Hospital Lishui Branch, Southeast University, Nanjing, 211200, China.
Liang ChenDepartment of Infectious Diseases, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical College of Nanjing University, Nanjing, 210046, China.

Funding

Nanjing Medical Science and Technology Development Fund YKK22239Talent Introduction Special Funds 08
6 · The paper itself

Abstract

backgroundSepsis remains a leading cause of global morbidity and mortality.

objectiveTo identify candidate biomarkers that may be mechanistically related to the pathogenesis of sepsis.

methodsThe Gene Expression Omnibus database was leveraged to identify differentially expressed genes (DEGs) between the healthy control and septicemia groups. Genes causally related to sepsis were probed through the integration of GWAS and expression quantitative trait loci (eQTL) data in a two-sample Mendelian randomization (MR) analysis. A set of key sepsis-related genes was then selected based on the overlap between these putative causal genes and the DEGs. These genes were then subjected to enrichment analyses, testing set validation, and analyses of their expression dynamics in clinical samples.

resultsAn examination of the overlap between 228 sepsis-related DEGs identified in the training dataset and 275 candidate causal genes linked to sepsis derived from the MR analysis led to the selection of four overlapping (SLC22A15, IL5RA, HDC, and SLC46A2) that may play a key role in sepsis. Enrichment analyses indicated that these genes were involved in the regulation of histidine metabolism and immune/inflammatory responses. In immune cell infiltration analyses, these genes were positively correlated with inflammatory response activation and the suppression of adaptive immunity. Consistent findings were obtained through qPCR verification in clinical samples.

conclusionThese results offer potential insight into the mechanisms that govern septicemia and thus suggest a promising series of candidates that may be amenable to targeting to prevent or treat sepsis more effectively.

Indexed as

Mendelian Randomization AnalysisSepsisTranscriptomeBiomarkersDatabases, GeneticGene Expression ProfilingGenome-Wide Association StudyHumansQuantitative Trait LociBiomarkersdifferentially expressed geneshistidine metabolismimmune responseskey genemendelian randomizationSepsis

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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.