Evidence map›Paper›PMID 38596062›Full record

ArticleHeliyon2024

Discovering common pathogenetic processes between tuberculosis and COVID-19 by bioinformatics and system biology approach.

Xin Liu, Haoran Li, Yilin Wang, Shanshan Li, Weicong Ren, Jinfeng Yuan, Yu Pang

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 95% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 1 institution in 1 country.

Xin LiuDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Haoran LiDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Yilin WangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Shanshan LiDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Weicong RenDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Jinfeng YuanDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Yu PangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: SARS-CoV-2, the cause of the COVID-19 pandemic, poses a significant threat to humanity. Individuals with pulmonary tuberculosis (PTB) are at increased risk of developing severe COVID-19, due to long-term lung damage that heightens their susceptibility to full-blown disease. Methods: Three COVID-19 datasets (GSE157103, GSE166253, and GSE171110) and one PTB dataset (GSE83456) were obtained from the Gene Expression Omnibus databases. Subsequently, data were subjected to weighted gene co-expression network analysis(WGCNA)followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. These analyses revealed two overlapping disease-specific modules, each comprising co-regulated genes with potentially related biological functions. Using Cytoscape, we visualised the interaction network containing common disease-related genes found within the intersection between modules and predicted transcription factors (TFs). Real-time qPCR was conducted to quantify expression levels of these genes in blood samples from COVID-19 and PTB patients. Finally, DisGeNET and the Drug Signatures database were employed to analyze these common genes, unveiling their connections to clinical disease features and potential drug treatments. Results: Examination of the overlap between COVID-19 and PTB gene modules unveiled 11 common genes. Functional enrichment analyses using KEGG and GO shed light on potential functional relationships among these genes, providing insights into their potential roles in the heightened mortality of PTB patients due to SARS-CoV-2 infection. Furthermore, results of various bioinformatics-based analyses of common TFs and target genes led to identification of shared pathways and therapeutic targets for PTB patients with COVID-19, along with potential drug treatments for these patients. Conclusion: Our results unveiled a potential biological connection between COVID-19 and PTB, as supported by results of functional enrichment analysis that highlighted potential biological processes and signaling pathways shared by both diseases. Building on these findings, we propose potential drug treatments for PTB patients with COVID-19, pending verification of drug safety and efficacy through laboratory and multicentre studies before clinical use.

Indexed as

Common geneDrug moleculeSARS-CoV-2TF regulatory networkTuberculosis

Identifiers

PMID38596062
PMCPMC11002586
OpenAlexW4393226558

What Socratic 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 Socratic graph.