PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 16, 2022Scientific Reports18 citationsOpen Access

A robust gene expression signature for NASH in liver expression data

YHYehudit Hasin-BrumshteinSSSuraj SakaramPKPurvesh Khatri

Key Result

A parsimonious 19-gene expression signature accurately discriminated NASH from NAFLD and healthy controls, achieving a mean AUROC of 0.98 in discovery datasets and 0.79 in independent validation.

Study Design

Type

Meta-Analysis (n=812)

Multicenter

Yes

Structured PICO

Does a gene expression signature accurately distinguish NASH from NAFLD and healthy controls in liver biopsy samples?

P
Population
812 liver biopsy samples from patients with NASH, NAFLD, and healthy controls across 12 datasets from 7 countries, used to discover and validate a diagnostic gene signature.
E
Exposure
130-gene and 19-gene mRNA expression signatures derived from multicohort transcriptome analysis
C
Comparator
Clinical and histological diagnosis of NAFLD or Healthy Controls
O
Outcome
Diagnostic accuracy (AUROC) for distinguishing NASH from NAFLD and healthy controlssurrogate

A 19-gene expression signature derived from a multicohort analysis can robustly distinguish NASH from NAFLD and healthy controls, offering a potential basis for novel diagnostic tests.

Main Result

Effect estimate: AUROC 0.79

Limitations

  • Limited by the variability captured in the available datasets, requiring further retrospective validation in independent cohorts.
  • The 19-gene signature requires further algorithmic refinement for clinical utility.
  • The signature relies on liver biopsy data rather than a non-invasive blood test.
  • The diagnostic score is a difference of geometric means and does not produce a probability score, limiting the use of standard calibration metrics.
  • Wide variation in control populations (e.g., obese vs healthy)
  • Differences in diagnostic approach across datasets
  • Inter-individual differences in histological evaluations between pathologists

Abstract

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hasin-Brumshtein et al. (2022) conducted a meta-analysis in Non-Alcoholic SteatoHepatitis (NASH) (n=812). 19-gene expression signature vs. NAFLD and Healthy Controls was evaluated on Discrimination of NASH from NAFLD and Healthy Controls (AUROC) (AUROC 0.79). A parsimonious 19-gene expression signature accurately discriminated NASH from NAFLD and healthy controls, achieving a mean AUROC of 0.98 in discovery datasets and 0.79 in independent validation.

synapsesocial.com/papers/6a2201993081c2f8f8e22befhttps://doi.org/10.1038/s41598-022-06512-0
Ask AI
Helpful
Bookmark
Share
View Full Paper