Evidence map›Paper›PMID 38956704›Full record

ArticleGenomics & informatics2024

Identification of common genetic factors and immune-related pathways associating more than two autoimmune disorders: implications on risk, diagnosis, and treatment.

Aruna Rajalingam, Anjali Ganjiwale

Abstract read
In one paragraph

Article in Genomics & informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Vitiligo Information Resource database v3.NPJ systems biology and applications · 2026
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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

2 authors.

Aruna RajalingamDepartment of Life Science, Bangalore University, Bangalore, Karnataka, 560056, India.ORCID http://orcid.org/0000-0003-0210-7744
Anjali GanjiwaleDepartment of Life Science, Bangalore University, Bangalore, Karnataka, 560056, India. anjalig@bub.ernet.in.ORCID http://orcid.org/0000-0003-1327-5487

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune disorders (ADs) are chronic conditions resulting from failure or breakdown of immunological tolerance, resulting in the host immune system attacking its cells or tissues. Recent studies report shared effects, mechanisms, and evolutionary origins among ADs; however, the possible factors connecting them are unknown. This study attempts to identify gene signatures commonly shared between different autoimmune disorders and elucidate their molecular pathways linking the pathogenesis of these ADs using an integrated gene expression approach. We employed differential gene expression analysis across 19 datasets of whole blood/peripheral blood cell samples with five different autoimmune disorders (rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Crohn's disease, and type 1 diabetes) to get nine key genes-EGR1, RUNX3, SMAD7, NAMPT, S100A9, S100A8, CYBB, GATA2, and MCEMP1 that were primarily involved in cell and leukocyte activation, leukocyte mediated immunity, IL-17, AGE-RAGE signaling in diabetic complications, prion disease, and NOD-like receptor signaling confirming its role in immune-related pathways. Combined with biological interpretations such as gene ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network, our current study sheds light on the in-depth research on early detection, diagnosis, and prognosis of different ADs.

Indexed as

Autoimmune disorders (ADs)BiomarkersDifferential gene expressionGenetic factorsImmune-related pathwaysImmunological tolerance

Identifiers

PMID38956704
PMCPMC11221123

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