GSE28750

Development and Validation of a Novel Molecular Biomarker Diagnostic Test for the Early Detection of Sepsis

Whole-study status Not suitable

Dataset does not meet the current suitability threshold.

Whole-study downloads — these do not apply your sample requirements.

Selected cohort requirements

Samples meeting selected requirements: 0 cases / 0 controls

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Patient pairs verified in indexed sample metadata: 0

A case/control comparison meeting these requirements is not confirmed.

Counts describe samples, not independent patients. Pair verification uses explicit subject identifiers in indexed sample metadata. Zero verified pairs does not establish that the study is unpaired; reported matched-normal design alone is insufficient.

Summary

Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

Overall design

This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

Source Evidence

4 sources
Source Contribution Status
GEO

Original dataset metadata, sample metadata, source links, and GEO data availability flags.

Sample records
41 GSM samples
Primary source
recount3

Planned trusted source for processed count availability.

No match found
Expression Atlas

Planned source for curated expression experiment links.

No match found
PubMed

Primary GEO citations and related PubMed articles that mention this dataset.

21 linked publications 21 DOIs

Primary publication

  1. Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis. Critical care (London, England), 2011

Related publications

  1. Identification and mechanistic analysis of shared biomarkers and pathogenesis in acute pancreatitis and sepsis based on differential gene expression and protein interaction networks. Functional & integrative genomics, 2025
  2. Integrative analysis identifies lactylation-related biomarkers in sepsis via bioinformatics and machine learning. Biochemical and biophysical research communications, 2026
  3. Unveiling the therapeutic potential of Resveratrol against neutrophil-mediated hyperinflammation in sepsis: An in-silico, molecular docking, and ex-vivo approach. Biochemical and biophysical research communications, 2026
  4. Integrated multiomics and Mendelian randomization identify CHIT1 as a novel sepsis biomarker and therapeutic target. Scientific reports, 2025
  5. Identification of Molecular Subtypes and Prognostic Characteristics in Sepsis Based on T-Helper 17 Cell Differentiation-Related Genes. Shock (Augusta, Ga.), 2026
  6. Identification and Experimental Validation of Biomarkers Associated With Mitochondria and Macrophage Polarization in Sepsis. Emergency medicine international, 2025
  7. Immunomodulatory Roles and Clinical Significance of GZMM and DDX24 in Sepsis: A Multiomics Integrative Analysis With Experimental Validation. Human mutation, 2026
  8. Identification and validation of the important role of tryptophan-related gene CYP1B1 in the development and progression of sepsis. PloS one, 2025
  9. Bioinformatics analysis of pyroptosis-related differentially expressed genes in sepsis and diabetes mellitus. PloS one, 2026
  10. Combined Transcriptomic and Mendelian Randomisation Explores the Diagnostic Value of Ubiquitination-Related Genes in Sepsis. Journal of inflammation research, 2025
  11. Multi-Omics Analysis Identifies Immune Regulatory Networks in Sepsis-Associated Liver Injury: Experimental Validation and Clinical Relevance. Journal of inflammation research, 2025
  12. Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury. Frontiers in genetics, 2025
  13. IL7R, GZMA and CD8A serve as potential molecular biomarkers for sepsis based on bioinformatics analysis. Frontiers in immunology, 2024
  14. Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques. Frontiers in immunology, 2025
  15. Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, and machine learning. Frontiers in immunology, 2025
  16. Identification of FTO as a key m6A demethylase linking immune dysregulation to sepsis pathogenesis. Frontiers in immunology, 2026
  17. Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis. Frontiers in pharmacology, 2025
  18. [Exploration of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics and the depiction of their immune profiles characterization]. Zhonghua wei zhong bing ji jiu yi xue, 2024
  19. Bioinformatics analysis identifies IL-23A and JAK2 as regulatory factors in sepsis immune response via the JAK/STAT3 pathway. Journal of infection in developing countries, 2026
  20. Identification of hub genes and prediction of the ceRNA network in adult sepsis. PeerJ, 2025
Available

Metadata Quality Components

Analysis Readiness

Review before analysis
85% DE readiness

Contrast completeness

25/25 pts

Both case and control groups are present in the proposed grouping.

Minimum group size

20/20 pts

Each contrast group has at least 2 samples.

Processed data availability

20/20 pts

Processed data is marked available in the GEO index.

Sample decision clarity

3/15 pts

Unassigned or review-needed samples remain before analysis.

Blocking warnings

7/10 pts

Warnings are present, but none are critical blockers.

Metadata normalization

10/10 pts

Metadata quality is high enough to support downstream handoff.

Blockers

  • 11 samples are still unassigned.

Next Steps

  • Resolve unassigned or review-needed samples in the grouping table.

Analysis Preparation Checklist

4/6 ready

Group labels reviewed

Needs review

Siftome proposed case and control labels, but no reviewer confirmation is stored yet.

Open the sample grouping CSV and confirm group labels before analysis.

Minimum sample count met

Ready

Both groups meet the minimum of 2 samples.

Confirm whether the cohort is large enough for the intended statistical analysis.

Processed counts available

Ready

Processed data availability is marked true in the GEO index.

Verify that downloaded processed files contain usable count or expression identifiers.

Ambiguous samples handled

Needs review

0 review-needed samples and 11 unassigned samples remain.

Resolve ambiguous and unassigned samples before finalizing the contrast.

Exclusion decisions recorded

Ready

No hard-excluded samples were detected by the current rules.

Record any manual exclusions in the downstream analysis notes.

Source traceability preserved

Ready

The dataset and sample accessions can be traced back to GEO.

Keep GEO accessions with exported metadata.

Bioinformatician Review

Bioinformatician review required

Ask a bioinformatician to review grouping, count availability, exclusions, and design before DE.

Analysis readiness has blockers that must be resolved before DE.

One or more samples are unassigned and need manual grouping decisions.

Assay context may require expert handling before DESeq2-style analysis.

Why This Is Ranked Here

Tumor vs normal
Not suitable Dataset does not meet the current suitability threshold.

Applied Review Decisions

No active review decisions affect this ranking.

Derived Classifications

  • Assay differs from search intent Microarray Search intent expects bulk RNA-seq. Assay came from Normalization rules.
  • Disease context Sepsis, systemic inflammatory response syndrome Disease context came from Normalization rules.
  • Likely case group Sepsis or systemic inflammatory response 10 samples
  • Likely control group Control 20 samples

Intent Interpretation

  • Downgrade or exclusion reason Downgraded for tissue search because it is a biofluid cohort. Derived from current-intent ranking reasons.

Ranking Facts

Supporting evidence

  • Comparison: Disease/model vs control. Specimen: Biofluid. Comparison suitability: 0%.
  • Interpreted disease metadata: Infectious disease or host response, Sepsis, Systemic inflammatory response syndrome.
  • Processed data is available for downstream review.
  • External evidence: PubMed links 21 publications; 21 DOIs available: PMID 21682927, PMID 39586719, PMID 39654893, PMID 40129981, PMID 40201575.
  • Publication context: Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis.. Critical care (London, England), 2011. PMID 21682927. DOI 10.1186/cc10274.
  • Publication context: [Exploration of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics and the depiction of their immune profiles characterization].. Zhonghua wei zhong bing ji jiu yi xue, 2024. PMID 39586719. DOI 10.3760/cma.j.cn121430-20240524-00457.
  • Publication context: IL7R, GZMA and CD8A serve as potential molecular biomarkers for sepsis based on bioinformatics analysis.. Frontiers in immunology, 2024. PMID 39654893. DOI 10.3389/fimmu.2024.1445858.
  • Publication context: 18 additional linked PubMed publications are available.

Cautions

  • Downgraded for tissue search because it is a biofluid cohort.
  • Downgraded assay: microarray cohorts are separated from the RNA-seq intent.
  • Disease/control design without a tumor/normal comparison is outside tumor/normal triage.
  • Blood, plasma, or serum cohort downgraded because this ranking targets tissue tumor-vs-normal comparisons.
  • Microarray expression context downgraded below primary bulk RNA-seq for the default tissue ranking.
  • Overall score capped because comparison suitability is weak for the selected search intent.
  • Overall score capped because microarray cohorts should be compared separately from RNA-seq cohorts.
  • Comparison suitability score is below the Phase 1 recommended threshold.

Dataset Warnings

Normalized Fields

138 fields

Sample Group Preview

41 rows shown
Show Hide
Study totals: Case 10 | Control 20 | Unassigned 11 | Excluded 0 | Ambiguous 0 41 rows shown
Group Role Accession Title Source / characteristics Reason
Sepsis or systemic inflammatory response Case GSM712478 Experiment_Sepsis_1 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712479 Experiment_Sepsis_2 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712480 Experiment_Sepsis_3 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712481 Experiment_Sepsis_4 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712482 Experiment_Sepsis_5 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712483 Experiment_Sepsis_6 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712484 Experiment_Sepsis_7 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712485 Experiment_Sepsis_8 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712486 Experiment_Sepsis_9 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Sepsis or systemic inflammatory response Case GSM712487 Experiment_Sepsis_10 Whole blood Tissue: Whole blood; Health status: SEPSIS Explicit sample label case marker detected.
Control Control GSM712488 Control_Healthy_1 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712489 Control_Healthy_2 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712490 Control_Healthy_3 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712491 Control_Healthy_4 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712492 Control_Healthy_5 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712493 Control_Healthy_6 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712494 Control_Healthy_7 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712495 Control_Healthy_8 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712496 Control_Healthy_9 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712497 Control_Healthy_10 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712498 Control_Healthy_11 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712499 Control_Healthy_12 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712500 Control_Healthy_13 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712501 Control_Healthy_14 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712502 Control_Healthy_15 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712503 Control_Healthy_16 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712504 Control_Healthy_17 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712505 Control_Healthy_18 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712506 Control_Healthy_19 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Control Control GSM712507 Control_Healthy_20 Whole blood Tissue: Whole blood; Health status: HEALTHY Sample-local control marker detected.
Unassigned Unassigned GSM712508 Experiment_Post_Surgical_1 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712509 Experiment_Post_Surgical_2 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712510 Experiment_Post_Surgical_3 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712511 Experiment_Post_Surgical_4 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712512 Experiment_Post_Surgical_5 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712513 Experiment_Post_Surgical_6 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712514 Experiment_Post_Surgical_7 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712515 Experiment_Post_Surgical_8 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712516 Experiment_Post_Surgical_9 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712517 Experiment_Post_Surgical_10 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.
Unassigned Unassigned GSM712518 Experiment_Post_Surgical_11 Whole blood Tissue: Whole blood; Health status: POST_SURGICAL No explicit case or control marker detected.

Use as Discovery Cohort

External Validation Finder

Carry Siftome's disease, organism, specimen, assay, data type, and accession into a validation project. Uncertain fields remain Unknown.