Evidence map›Paper›PMID 40794949›Full record

ArticleBriefings in bioinformatics2025

Differential causal networks highlight sex-based differences in human tissues.

Annamaria Defilippo, Kimberly Glass, Federico M Giorgi, Tamer Kahveci, Pierangelo Veltri, Pietro Hiram Guzzi

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Annamaria DefilippoDepartment of Surgical and Medical Sciences, University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy.ORCID 0009-0004-0421-7650
Kimberly GlassChanning Division of Network Medicine, Brigham and Women's Hospital & Harvard Medical School,181 Longwood Avenue, Boston, MA 02115, United States.
Federico M GiorgiDepartment of Pharmacy and Biotechnology, University of Bologna, Via Irnerio, 48, 40126 Bologna, Italy.ORCID 0000-0002-7325-9908
Tamer KahveciDepartment of Computer and Information Science and Engineering, University of Florida, 432 Newell Dr, Gainesville, FL 32611, United States.
Pierangelo VeltriDepartment of Informatics, Modelling, Electronics and Systems, University of Calabria, Via P. Bucci Edificio 42C, 87036, Rende (CS), Italy.ORCID 0000-0003-2494-0294
Pietro Hiram GuzziDepartment of Surgical and Medical Sciences, University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy.ORCID 0000-0001-5542-2997

Funding

Networks Tools to Understand Sex- and Gender-Specific Drivers of DiseaseR01HG011393 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMEO, DAWN L, QUACKENBUSH, JOHN · 2021 to 2024
$2.1M
NHGRI NIH HHS R01 HG011393NHGRI NIH HHS R01HG011393
6 · The paper itself

Abstract

Sex differences appear in healthy and pathological conditions and may influence sex-specific therapeutic responses. Understanding such differences is a key activity for developing precision medicine strategies. This study investigates sex differences in gene expression across 40 human tissues by applying a Differential Causal Network (DCN) analysis using data from the Genotype-Tissue Expression project. We identified sex-based DCNs that highlight distinct molecular mechanisms influencing both health and disease in men and women. For example, in pancreas tissue, genes associated with immune system show significant differences in their regulatory patterns between sexes, demonstrating a possible different response to diseases such as diabetes mellitus and cancer. Our findings provide valuable information on the biological underpinnings of sex differences, offering potential pathways for the development of precision medicine strategies.

Indexed as

Gene Regulatory NetworksSex CharacteristicsFemaleGene Expression ProfilingHumansMaleOrgan SpecificitySex Factorsdifferential causal networksgene differentiationhuman tissuessex differences

Identifiers

PMID40794949
PMCPMC12342583

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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.