Evidence map›Paper›PMID 38653779›Full record

ArticleScientific reports2024

Potential therapeutic targets for COVID-19 complicated with pulmonary hypertension: a bioinformatics and early validation study.

Qingbin Hou, Jinping Jiang, Kun Na, Xiaolin Zhang, Dan Liu, Quanmin Jing, Chenghui Yan, Yaling Han

Open access · goldAbstract readValidation Study
In one paragraph

Article in Scientific reports, 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
1.3field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
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 2 institutions in 1 country.

Qingbin HouState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China.
Jinping JiangDepartment of Cardiology, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.
Kun NaState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China.
Xiaolin ZhangState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China.
Dan LiuState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China.
Quanmin JingState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China.
Chenghui YanState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China. yanch1029@163.com.
Yaling HanState Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China. hanyaling@163.net.
General Hospital of Shenyang Military Region · CNChina Medical University · CN

Funding

Liaoning science and technology project 2022JH2/101300012Liaoning science and technology project 2022JH2/101300054National Natural Science Foundation of China 82270300
6 · The paper itself

Abstract

Coronavirus disease (COVID-19) and pulmonary hypertension (PH) are closely correlated. However, the mechanism is still poorly understood. In this article, we analyzed the molecular action network driving the emergence of this event. Two datasets (GSE113439 and GSE147507) from the GEO database were used for the identification of differentially expressed genes (DEGs).Common DEGs were selected by VennDiagram and their enrichment in biological pathways was analyzed. Candidate gene biomarkers were selected using three different machine-learning algorithms (SVM-RFE, LASSO, RF).The diagnostic efficacy of these foundational genes was validated using independent datasets. Eventually, we validated molecular docking and medication prediction. We found 62 common DEGs, including several ones that could be enriched for Immune Response and Inflammation. Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms. They performed well in diagnostic tests on independent datasets. In particular, we observed an upregulation of functions associated with the adaptive immune response, the leukocyte-lymphocyte-driven immunological response, and the proinflammatory response. Moreover, by ssGSEA, natural killer T cells, activated dendritic cells, activated CD4 T cells, neutrophils, and plasmacytoid dendritic cells were correlated with COVID-19 and PH, with SELE and CCL20 showing the strongest correlation with dendritic cells. Potential therapeutic compounds like FENRETI-NIDE, AFLATOXIN B1 and 1-nitropyrene were predicted. Further molecular docking and molecular dynamics simulations showed that 1-nitropyrene had the most stable binding with SELE and CCL20.The findings indicated that SELE and CCL20 were identified as novel diagnostic biomarkers for COVID-19 complicated with PH, and the target of these two key genes, FENRETI-NIDE and 1-nitropyrene, was predicted to be a potential therapeutic target, thus providing new insights into the prediction and treatment of COVID-19 complicated with PH in clinical practice.

Indexed as

Computational BiologyCOVID-19Hypertension, PulmonaryMolecular Docking SimulationBiomarkersCOVID-19 Drug TreatmentHumansMachine LearningSARS-CoV-2BiomarkersCore geneCOVID-19Differentially expressed genesImmune invasionMachine algorithmsMolecular dockingPulmonary hypertension

Identifiers

PMID38653779
PMCPMC11039624
OpenAlexW4395043610

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.