Constarium
← Search

Data · dataset · 2024

Screening and bioinformatics analysis of characteristics genes of sepsis and aging based on GEO database

Listed in ScienceDB

Objective To screen the shared genes of sepsis and senescence by combining gene expression database (GEO) and machine learning algorithms, and to conduct bioinformatics analysis to understand the comorbid mechanism of sepsis and aging.

Description

Methods

Sepsis-related genes were obtained from CTD, DisGeNet, and GeneCards, and aging-related genes were obtained from Aging Atlas database. GSE13904, GSE28705, and GSE8121 sepsis microarray data and aging dataset GSE173608 were obtained from the GEO database; Webstalt database was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed by CNSknowall, and immune cell infiltration analysis of the expression matrix of shared genes was performed using the CIBERSORTX platform.

Read the rest (2 more)

Finally, the BIDOS database was used to analyze the correlation between the expression level of characteristic genes and the survival time and APACHEII score of sepsis patients.Results There were 151 genes shared between sepsis and senescence, and the characteristic genes of TXN, CCL4, IL7 and SIRT1 were the common diagnostic markers of sepsis and senescence. The main gene sets that were up-regulated by the shared genes included cAMP signaling pathway, chemokine signaling pathway, PD-1 expression and PD-1 cancer checkpoint pathway, phospholipase D signaling pathway, PI3K-Akt signaling pathway, prolactin signaling pathway and other signaling pathways.

Immune filtration analysis showed that the high expression of CCL4 was positively correlated with the prognosis of sepsis, and CCL4 was significantly positively correlated with activated natural killer cells. TXN was significantly positively correlated with resting dendritic cells.Conclusion CCL4, TXN, IL7 and SIRT1 can be used as diagnostic biomarkers for sepsis and aging, and are closely related to the pathophysiological process of sepsis and aging.

Links

Where it is published

Catalogue records · 1

Topics

Provenance · 1 source records, 13 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.j00217.0307410 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · scidb cnconnector:scidb_cn@1.0.0
concepts[disease].local:disease:cancerenrichment · scidb cnkeyword-concept-rules@1.0.0title+description (75%)
concepts[field].anzsrc:group:3102enrichment · scidb cntaxonomy-embedding@1.0.0title+keywords+description (71%)
concepts[field].local:field:computer-science-aimapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:earth-environmentalmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:engineeringmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:humanitiesmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:life-sciencesmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:social-sciencemapping · scidb cnconnector:scidb_cn@1.0.0
descriptionsource · scidb cnconnector:scidb_cn@1.0.0/metadata/dc/description
licensesource · scidb cnconnector:scidb_cn@1.0.0/metadata/dc/rights
publication_datesource · scidb cnconnector:scidb_cn@1.0.0
titlesource · scidb cnconnector:scidb_cn@1.0.0/metadata/dc/title