Evidence map›Paper›PMID 39045407›Full record

ArticleBioMed research international2024

Identification of Hub Genes for Psoriasis and Cancer by Bioinformatic Analysis.

Yao Yu, Shaoze Ma, Jinzhe Zhou

Abstract read
In one paragraph

Article in BioMed research international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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.

Yao YuDepartment of Dermatology Shanghai Putuo District Liqun Hospital, Shanghai 200333, China.ORCID https://orcid.org/0000-0002-2514-9683
Shaoze MaDepartment of Urology Surgery Baoshan Branch of Shanghai Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201999, China.ORCID https://orcid.org/0009-0003-2435-6490
Jinzhe ZhouDepartment of General Surgery Tongji Hospital Tongji University School of Medicine, Shanghai 200065, China.ORCID https://orcid.org/0000-0002-6434-9952

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Psoriasis increases the risk of developing various cancers, including colon cancer. The pathogenesis of the co-occurrence of psoriasis and cancer is not yet clear. This study is aimed at analyzing the pathogenesis of psoriasis combined with cancer by bioinformatic analysis. Skin tissue data from psoriasis (GSE117239) and intestinal tissue data from colon cancer (GSE44076) were downloaded from the GEO database. One thousand two hundred ninety-six common differentially expressed genes and 688 common shared genes for psoriasis and colon cancer were determined, respectively, using the limma R package and weighted gene coexpression network analysis (WGCNA) methods. The results of the GO and KEGG enrichment analyses were mainly related to the biological processes of the cell cycle. Thirteen hub genes were selected, including AURKA, DLGAP5, NCAPG, CCNB1, NDC80, BUB1B, TTK, CCNB2, AURKB, TOP2A, ASPM, BUB1, and KIF20A. These hub genes have high diagnostic value, and most of them are positively correlated with activated CD4 T cells. Three hub transcription factors (TFs) were also predicted: E2F1, E2F3, and BRCA1. These hub genes and hub TFs are highly expressed in various cancers. Furthermore, 251 drugs were predicted, and some of them overlap with existing therapeutic drugs for psoriasis or colon cancer. This study revealed some genetic mechanisms of psoriasis and cancer by bioinformatic analysis. These hub genes, hub TFs, and predicted drugs may provide new perspectives for further research on the mechanism and treatment.

Indexed as

Computational BiologyGene Regulatory NetworksPsoriasisBRCA1 ProteinColonic NeoplasmsDatabases, GeneticE2F1 Transcription FactorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansBRCA1 ProteinBRCA1 protein, humanE2F1 protein, humanE2F1 Transcription Factorbioinformatic analysiscancerhub genespsoriasis

Identifiers

PMID39045407
PMCPMC11265948

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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.