Evidence map›Paper›PMID 37556419›Full record

ArticlePloS one2023

Integrated bioinformatics analyses identifying potential biomarkers for type 2 diabetes mellitus and breast cancer: In SIK1-ness and health.

Ilhaam Ayaz Durrani, Attya Bhatti, Peter John

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed, 9 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Ilhaam Ayaz DurraniDepartment of Healthcare Biotechnology, Atta ur Rehman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H12, Islamabad, Islamabad Capital Territory, Pakistan.ORCID 0000-0002-9951-1227
Attya BhattiDepartment of Healthcare Biotechnology, Atta ur Rehman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H12, Islamabad, Islamabad Capital Territory, Pakistan.ORCID 0000-0002-2781-5221
Peter JohnDepartment of Healthcare Biotechnology, Atta ur Rehman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H12, Islamabad, Islamabad Capital Territory, Pakistan.
National University of Sciences and Technology · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The bidirectional causal relationship between type 2 diabetes mellitus (T2DM) and breast cancer (BC) has been established by numerous epidemiological studies. However, the underlying molecular mechanisms are not yet fully understood. Identification of hub genes implicated in T2DM-BC molecular crosstalk may help elucidate on the causative mechanisms. For this, expression series GSE29231 (T2DM-adipose tissue), GSE70905 (BC- breast adenocarcinoma biopsies) and GSE150586 (diabetes and BC breast biopsies) were extracted from Gene Expression Omnibus (GEO) database, and analyzed to obtain differentially expressed genes (DEGs). The overlapping DEGs were determined using FunRich. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Transcription Factor (TF) analyses were performed on EnrichR software and a protein-protein interaction (PPI) network was constructed using STRING software. The network was analyzed on Cytoscape to determine hub genes and Kaplan-Meier plots were obtained. A total of 94 overlapping DEGs were identified between T2DM and BC samples. These DEGs were mainly enriched for GO terms RNA polymerase II core promoter proximal region sequence and its DNA binding, and cAMP response element binding protein, and KEGG pathways including bladder cancer, thyroid cancer and PI3K-AKT signaling. Eight hub genes were identified: interleukin 6 (IL6), tumor protein 53 (TP53), interleukin 8 (CXCL8), MYC, matrix metalloproteinase 9 (MMP9), beta-catenin 1 (CTNNB1), nitric oxide synthase 3 (NOS3) and interleukin 1 beta (IL1β). MMP9 and MYC associated unfavorably with overall survival (OS) in breast cancer patients, IL6, TP53, IL1β and CTNNB1 associated favorably, whereas NOS3 did not show any correlation with OS. Salt inducible kinase 1 (SIK1) was identified as a significant key DEG for comorbid samples when compared with BC, also dysregulated in T2DM and BC samples (adjusted p <0.05). Furthermore, four of the significant hub genes identified, including IL6, CXCL8, IL1B and MYC were also differentially expressed for comorbid samples, however at p < 0.05. Our study identifies key genes including SIK1, for comorbid state and 8 hub genes that may be implicated in T2DM-BC crosstalk. However, limitations associated with the insilico nature of this study necessitates for subsequent validation in wet lab. Hence, further investigation is crucial to study the molecular mechanisms of action underlying these genes to fully explore their potential as diagnostic and prognostic biomarkers and therapeutic targets for T2DM-BC association.

Indexed as

Breast NeoplasmsDiabetes Mellitus, Type 2BiomarkersBiomarkers, TumorComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansInterleukin-6Matrix Metalloproteinase 9Neoplasm ProteinsPhosphatidylinositol 3-KinasesProtein Serine-Threonine KinasesBiomarkersBiomarkers, TumorInterleukin-6Matrix Metalloproteinase 9Neoplasm ProteinsPhosphatidylinositol 3-KinasesProtein Serine-Threonine KinasesSIK1 protein, human

Identifiers

PMID37556419
PMCPMC10411810
OpenAlexW4385689721

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.