Evidence map›Paper›PMID 36949176›Full record

ArticleScientific reports2023

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing.

Md Ariful Islam, Md Kaderi Kibria, Md Bayazid Hossen, Md Selim Reza, Samme Amena Tasmia, Khanis Farhana Tuly, Md Parvez Mosharof, Syed Rashel Kabir, Md Hadiul Kabir, Md Nurul Haque Mollah

Open access · goldFull text read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

10 citing papers in PubMed, 13 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

10 authors at 2 institutions in 2 countries.

Md Ariful IslamBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Kaderi KibriaBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Bayazid HossenBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Selim RezaBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Samme Amena TasmiaBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Khanis Farhana TulyBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Parvez MosharofBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Syed Rashel KabirDepartment of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Hadiul KabirBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Nurul Haque MollahBioinformatics Lab(Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. mollah.stat.bio@ru.ac.bd.
University of Rajshahi · BDUniversity of Southern Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Indexed as

COVID-19Idiopathic Pulmonary FibrosisComputational BiologyDrug RepositioningHumansMolecular Docking SimulationSARS-CoV-2

Identifiers

PMID36949176
PMCPMC10031699
OpenAlexW4353072337

What Socratic holds

Textfull text, public
LicenceCC BY
reference markers read5
measurements read14
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 Socratic graph.