Evidence map›Paper›PMID 36460278›Full record

ArticleInfection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2022

The risk of COVID-19 can be predicted by a nomogram based on m6A-related genes.

Lingling Lu, Yijing Li, Xiulan Ao, Jiaofeng Huang, Bang Liu, Liqing Wu, Dongliang Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Targeting G9a-miScience · 2025
    Article
  2. Article
  3. mCell death discovery · 2023
    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

7 authors.

Lingling LuFuzong Clinical Medical College of Fujian Medical University, The 900th hospital. No.156 Xierhuan Road, Fuzhou, Fujian 350025, China; Department of Hepatobiliary Disease, 900th Hospital of Joint Logistics Support Force, No.156 Xierhuan Road, Fuzhou, Fujian 350025, China.
Yijing LiFuzhou First Hospital Affiliated to Fujian Medical University, Fuzhou, Fujian, China.
Xiulan AoDepartment of Hepatobiliary Disease, 900th Hospital of Joint Logistics Support Force, No.156 Xierhuan Road, Fuzhou, Fujian 350025, China.
Jiaofeng HuangFuzong Clinical Medical College of Fujian Medical University, The 900th hospital. No.156 Xierhuan Road, Fuzhou, Fujian 350025, China.
Bang LiuFuzong Clinical Medical College of Fujian Medical University, The 900th hospital. No.156 Xierhuan Road, Fuzhou, Fujian 350025, China; Department of Hepatobiliary Disease, 900th Hospital of Joint Logistics Support Force, No.156 Xierhuan Road, Fuzhou, Fujian 350025, China.
Liqing WuDepartment of Hepatobiliary Disease, 900th Hospital of Joint Logistics Support Force, No.156 Xierhuan Road, Fuzhou, Fujian 350025, China.
Dongliang LiFuzong Clinical Medical College of Fujian Medical University, The 900th hospital. No.156 Xierhuan Road, Fuzhou, Fujian 350025, China; Department of Hepatobiliary Disease, 900th Hospital of Joint Logistics Support Force, No.156 Xierhuan Road, Fuzhou, Fujian 350025, China. Electronic address: ldliang900@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe expression of m6A-related genes and their significance in COVID-19 patients are still unknown.

methodsThe GSE177477 and GSE157103 datasets of the Gene Expression Omnibus were used to extract RNA-seq data. The expression of 26 m6A-related genes and immune cell infiltration in COVID-19 patients were analyzed. Finally, we built and validated a nomogram model to predict the risk of COVID-19 infection.

resultsThere were significant differences in 11 m6A regulatory factors between patients with COVID-19 and healthy individuals. The classification of disease subtypes based on m6A-related gene levels can be distinguished. COVID-19 patients in GSE177477 were classified into two categories based on m6A-related genes. The patients in cluster A were all symptomatic, while those in cluster B were asymptomatic. A significant correlation was also found between immune cells and m6A-related genes. Finally, seven m6A-related disease-characteristic genes, HNRNPA2B1, ELAVL1, RBM15, RBM15B, YTHDC1, HNRNPC, and WTAP, were screened to construct a nomogram model for predicting risk. The calibration curve, decision curve analysis, and clinical impact curve analysis were used to show that the nomogram model was effective and had a high net efficacy for risk prediction.

conclusionsm6A-related genes were correlated with immune cells. The nomogram model effectively predicted COVID-19 risk. Moreover, m6A-related genes may be associated with the presence or absence of symptoms in COVID-19 patients.

Indexed as

COVID-19Health StatusHumansRNA-SeqCOVID-19ImmuneN6-methyladenosineRisk

Identifiers

PMID36460278
PMCPMC9707050

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.