Evidence map›Paper›PMID 37486005›Full record

ArticleCell cycle (Georgetown, Tex.)

COVID-19 of differing severity: from bulk to single-cell expression data analysis.

Linlin Tian, Min He, Huafeng Fan, Hongying Zhang, Xiaoxiao Dong, Mengkai Qiao, Chenyu Tang, Yan Yu, Tong Chen, Nan Zhou

Open access · greenAbstract read
In one paragraph

Article in Cell cycle (Georgetown, Tex.). 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 49% 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, 1 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

10 authors at 2 institutions in 1 country.

Linlin TianNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Min HeNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Huafeng FanNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Hongying ZhangNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Xiaoxiao DongNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Mengkai QiaoNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Chenyu TangNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Yan YuNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Tong ChenDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, P.R. China.ORCID 0000-0001-6786-0310
Nan ZhouNanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, P.R. China.
Nanjing Municipal Center for Disease Control And Prevention · CNJiangsu Province Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) is raging worldwide and causes an immense disease burden. Despite this, the biomarkers and targeting drugs of COVID-19 of differing severity remain largely unknown. Based on the GSE164805 dataset, we identified modules most critical for mild COVID-19 (mCOVID-19) and severe COVID-19 (sCOVID-19) through WGCNA, respectively. We subsequently constructed a protein-protein interaction network, and detected 16 hub genes for mCOVID-19 and 10 hub genes for sCOVID-19, followed by the prediction of upstream transcription factors (TFs) and kinases. The enrichment analysis then showed downregulation of TNFA signaling via NFKB for mCOVID-19, as well as downregulation of MYC targets V1 for sCOVID-19. Infiltration degrees of many immune cells, such as macrophages, were also sharply different between mCOVID-19 and sCOVID-19 samples. Predicted protein targeting drugs with the highest scores nearly all belong to naturally derived or synthetic glucocorticoids. For the two single-cell RNA-seq datasets, we explored the expression distribution of hub genes for mCOVID-19/sCOVID-19 in each cell type. The expression levels of PRKCA, MCM5, TYMS, RBBP4, BCL6, FLOT1, KDM6B, and TLR2 were found to be cell-type-specific. Furthermore, the expression levels of 10 hub genes for mCOVID-19 were significantly upregulated in PBMCs between eight healthy controls and eight mCOVID-19 patients at our institution. Collectively, we detected critical modules, pathways, TFs, kinases, immune cells, targeting drugs, hub genes, and their expression distributions in different cell types that may involve the pathogenesis of COVID-19 of differing severity, which may propel earlier diagnosis and more effective treatment of this intractable disease in the future.

Indexed as

COVID-19Data AnalysisDown-RegulationDrug Delivery SystemsGlucocorticoidsHumansJumonji Domain-Containing Histone DemethylasesGlucocorticoidsJumonji Domain-Containing Histone DemethylasesKDM6B protein, humanbulkCOVID-19single-cell

Identifiers

PMID37486005
PMCPMC10446813
OpenAlexW4385186248

What OpenQuestion holds

Textmetadata
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.