Evidence map›Paper›PMID 37896902›Full record

ArticleViruses2023

Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration.

Tao Huang, Xueqi Wang, Yuqian Mi, Tiezhu Liu, Yang Li, Ruixue Zhang, Zhen Qian, Yanhan Wen, Boyang Li, Lina Sun and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Viruses, 2023. 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
–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

1 citing paper in PubMed.

  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

14 authors.

Tao HuangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.ORCID 0000-0002-9304-0567
Xueqi WangCapital Institute of Pediatrics, Beijing 100020, China.
Yuqian MiShanxi Academy of Advanced Research and Innovation, Taiyuan 030032, China.
Tiezhu LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Yang LiChongqing Research Institute of Big Data, Peking University, Chongqing 400039, China.
Ruixue ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Zhen QianNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Yanhan WenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Boyang LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Lina SunNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Wei WuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Jiandong LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Shiwen WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.
Mifang LiangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe Fever with thrombocytopenia syndrome (SFTS) is a highly fatal viral infectious disease that poses a significant threat to public health. Currently, the phase and pathogenesis of SFTS are not well understood, and there are no specific vaccines or effective treatment available. Therefore, it is crucial to identify biomarkers for diagnosing acute SFTS, which has a high mortality rate. In this study, we conducted differentially expressed genes (DEGs) analysis and WGCNA module analysis on the GSE144358 dataset, comparing the acute phase of SFTSV-infected patients with healthy individuals. Through the LASSO-Cox and random forest algorithms, a total of 2128 genes were analyzed, leading to the identification of four genes: ADIPOR1, CENPO, E2F2, and H2AC17. The GSEA analysis of these four genes demonstrated a significant correlation with immune cell function and cell cycle, aligning with the functional enrichment findings of DEGs. Furthermore, we also utilized CIBERSORT to analyze the immune cell infiltration and its correlation with characteristic genes. The results indicate that the combination of ADIPOR1, CENPO, E2F2, and H2AC17 genes has the potential as characteristic genes for diagnosing and studying the acute phase of SFTS virus (SFTSV) infection.

Indexed as

Bunyaviridae InfectionsPhlebovirusSevere Fever with Thrombocytopenia SyndromeCyclic N-OxidesEthylnitrosoureaHumans1-(2-chloroethyl)-3-(1-oxyl-2,2,6,6-tetramethylpiperidinyl)-1-nitrosoureaCyclic N-OxidesEthylnitrosoureaimmune cells infiltrationLASSO–Coxmachine learningSFTSSFTS acute phase

Identifiers

PMID37896902
PMCPMC10612101

What Socratic 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 Socratic graph.