Evidence map›Paper›PMID 36514742›Full record

ArticleInternational journal of general medicine2022

Elucidating the Gene Signatures and Immune Cell Types in HIV-Infected Immunological Non-Responders by Bioinformatics Analyses.

Qing Xiao, Junyan Han, Fengting Yu, Liting Yan, Qun Li, Xiaojie Lao, Hongxin Zhao, Fujie Zhang

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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

8 authors at 1 institution in 1 country.

Qing XiaoDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.ORCID 0000-0002-2014-9959
Junyan HanBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Beijing, People's Republic of China.
Fengting YuDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.
Liting YanDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.
Qun LiDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.
Xiaojie LaoDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.
Hongxin ZhaoDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.
Fujie ZhangDepartment of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People's Republic of China.
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Numerous studies have reported on the pathogenesis of poor immune reconstitution (PIR) after antiretroviral treatment in human immunodeficiency virus (HIV) patients. However, fewer studies focused on both immune-related genes (IRGs) and immune cells, and the correlation between IRGs and immune cells was evaluated via bioinformatics analyses. Methods: Gene expression profiling of GSE143742 from the Gene Expression Omnibus (GEO) database was analyzed to get differentially expressed immune-related genes (DEIRGs). The enrichment analysis and protein-protein interaction (PPI) networks of DEIRGs were established. The relative fractions of 22 immune cell types were detected using the "CIBERSORT". The correlation analysis between DEIRGs and immune cells was constructed to discover the potential IRGs associated with immune cells. A logistic regression diagnostic model was built, and a receiver operating characteristic (ROC) curve was performed to evaluate the model's diagnostic efficacy. The CMap database was used to find molecules with therapeutic potential. RT-qPCR was used to verify the expression of the hub DEIRGs. Results: We identified eight types of significantly changed immune cells and five hub IRGs in INRs. The DEIRGs were mainly enriched in lymphocyte activation, receptor-ligand activity, and T cell receptor signaling pathway. The correlation analysis showed that the expression of TNF, CXCR4 and TFRC correlate with CD8 cells, resting mast cells, activated NK cells, and naïve CD4 cells in INRs. Meanwhile, TFRC and IL7R relate to activated NK cells and resting memory CD4 cells respectively in IRs. A diagnostic model was constructed using multiple logistic regression and nine small molecules were identified as possible drugs. Conclusion: In this study, we suggested that the process of PIR might be related to TNF, CXCR4, TFRC, CD48, and IL7R. And these IRGs play roles in regulating immune-competent cells. And our constructed diagnostic model has excellent effectiveness. Moreover, some small-molecule drugs are screened to alleviate PIR.

Indexed as

bioinformaticsHIVimmune cellsimmune-related genesimmunological non-responders

Identifiers

PMID36514742
PMCPMC9741855
OpenAlexW4310869659

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.