INCLIVA Biomedical Research Institute
Published March 2, 2026 | Version v1
Dataset Open

Integrated DNA methylation datasets (Illumina EPIC) from septic, septic shock, PICS and non‑PICS patients – GEO datasets (GSE319925, GSE247524, GSE285813)

Authors/Creators

  • 1. ROR icon INCLIVA Health Research Institute
  • 2. ROR icon Universitat de València
  • 3. Hospital Clínic Universitari de València
  • 4. ROR icon Hospital Clínico Universitario de Valencia

Description

The project encompasses three GEO datasets, containing human blood simples analysed with Illumina Infinium MethylationEPIC arrays. The datasets consist of raw .idat files and processed data files containing beta values, as well as metadata files describing each sample.

All samples come from septic patients and controls.

1) GSE247524 (public)
Dataset associated with the publication in Frontiers in Immunology (2024) by Beltrán‑García et al.
Includes methylation data from leukocytes of septic patients with severe clinical phenotypes and immunosuppression.
Publication:
Beltrán‑García J. et al., 2024. Front Immunol. 14:1333705. DOI: 10.3389/fimmu.2023.1333705.
GEO Accession: GSE247524.

2) GSE285813 (public)
Dataset associated with the 2025 study in Frontiers in Cellular and Infection Microbiology by López‑Cruz et al.
Compares methylation profiles of septic vs. non‑septic patients with similar infectious conditions, exploring their utility as biomarkers.
Publication:
López‑Cruz I. et al., 2025. Front Cell Infect Microbiol. 14:1532417. DOI: 10.3389/fcimb.2024.1532417.
GEO Accession: GSE285813.

3) GSE319925 (private)

Contains raw .idat files generated with Illumina EPIC arrays.
The dataset is private in GEO until the associated manuscript is published.

Abstract (English)

This project investigates epigenetic alterations associated with sepsis, septic shock, and the clinical progression toward PICS (Persistent Inflammation, Immunosuppression and Catabolism Syndrome) or non‑PICS.
The aim is to identify epigenetic biomarkers based on DNA methylation capable of distinguishing clinical phenotypes and predicting disease progression, morbidities and frailty.

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Additional details

Related works

Describes
Dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE247524 (URL)
Dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE285813 (URL)
Dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319925 (URL)
Is described by
Journal article: 10.3389/fimmu.2023.1333705 (DOI)
Journal article: 10.3389/fcimb.2024.1532417 (DOI)

Funding

Instituto de Salud Carlos III
Acción Estratégica de Salud PI22/00481