Evidence map›Paper›PMID 34168096›Full record

ArticleAging2021

Song Hu, Zheyu Hu, Jiajia Qin, Chuwen Lin, Xuan Jiang

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. 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

5 authors at 2 institutions in 1 country.

Song HuMolecular Cancer Research Center, School of Medicine, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China.
Zheyu HuMolecular Cancer Research Center, School of Medicine, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China.
Jiajia QinMolecular Cancer Research Center, School of Medicine, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China.
Chuwen LinMolecular Cancer Research Center, School of Medicine, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China.
Xuan JiangMolecular Cancer Research Center, School of Medicine, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), and is highly contagious and pathogenic. TMPRSS2 and Neuropilin-1, the key components that facilitate SARS-CoV-2 infection, are potential targets for treatment of COVID-19. Here we performed a comprehensive analysis on NRP1 and TMPRSS2 in lung to provide information for treating comorbidity of COVID-19 with lung cancer. NRP1 is widely expressed across all the human tissues while TMPRSS2 is expressed in a restricted pattern. High level of NRP1 associates with worse prognosis in multiple cancers, while high level of TMPRSS2 is associated with better survival of Lung Adenocarcinoma (LUAD). Moreover, NRP1 positively correlates with the oncogenic Cancer Associated Fibroblast (CAF), macrophage and endothelial cells infiltration, negatively correlates with infiltration of CD8

Indexed as

Gene Expression Regulation, NeoplasticAdenocarcinoma of LungCancer-Associated FibroblastsCD8-Positive T-LymphocytesComputer SimulationCOVID-19Endothelial CellsHumansLung NeoplasmsMacrophagesNeuropilin-1RNA-SeqSARS-CoV-2Serine EndopeptidasesNeuropilin-1NRP1 protein, humanSerine EndopeptidasesTMPRSS2 protein, humanCOVID-19immune infiltrationmiRNANRP1SARS-CoV-2TMPRSS2

Identifiers

PMID34168096
PMCPMC8266340
OpenAlexW3175892853

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.