Evidence map›Paper›PMID 39350407›Full record

ArticleCurrent gene therapy2025

Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.

YuSheng Bao, JingXin Ren, Lei Chen, Wei Guo, KaiYan Feng, Tao Huang, Yu-Dong Cai

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Article in Current gene therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

YuSheng BaoSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
JingXin RenSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
Lei ChenCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Wei GuoKey Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, China.
KaiYan FengDepartment of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China.
Tao HuangBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Yu-Dong CaiSchool of Life Sciences, Shanghai University, Shanghai 200444, China.

Funding

Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences 202002Major Project of Guangzhou National Laboratory GZNL2024A01003National Key R&D Program of China 2022YFF1203202Selfsupporting Program of Guangzhou Laboratory SRPG22-007Shandong Provincial Natural Science Foundation ZR2022MC072Strategic Priority Research Program of Chinese Academy of Sciences XDB38050200, XDA26040304
6 · The paper itself

Abstract

backgroundSignificant variations in immune profiles across different age groups manifest distinct clinical symptoms and prognoses in Coronavirus Disease 2019 (COVID-19) patients. Predominantly, severe COVID-19 cases that require hospitalization occur in the elderly, with the risk of severe illness escalating with age among young adults, children, and adolescents.

objectiveThis study aimed to delineate the unique immune characteristics of COVID-19 across various age groups and evaluate the feasibility of detecting COVID-19-induced immune alterations through peripheral blood analysis.

methodsBy employing a machine learning approach, we analyzed gene expression data from nasopharyngeal and peripheral blood samples of COVID-19 patients across different age brackets. Nasopharyngeal data reflected the immune response to COVID-19 in the upper respiratory tract, while peripheral blood samples provided insights into the overall immune system status. Both datasets encompassed COVID-19 patients and healthy controls, with patients divided into children, adolescents, and adult age groups. The analysis included the expression levels of 62,703 genes per patient. Then, 9 feature-sequencing methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, random forest, ridge regression, adaptive boosting, categorical boosting, extremely randomized trees, and extreme gradient boosting) were employed to evaluate the association of the genes with COVID-19. Key genes were then utilized to develop efficient classification models.

resultsThe findings identified specific markers: insulin-like growth factor binding protein 3 (downregulated in the peripheral blood of COVID-19 patients), interferon alpha-inducible protein 27 (upregulated), and SERPING1 (upregulated in nasopharyngeal tissues). In addition, fibulin-2 was downregulated in adolescent patients, but upregulated in the other groups, while epoxide hydrolase 3 was upregulated in healthy controls, but downregulated in children and adolescents.

conclusionThis study offers valuable insights into the local and systemic immune responses of COVID-19 patients across age groups, aiding in identifying potential therapeutic targets and formulating personalized treatment strategies.

Indexed as

COVID-19Machine LearningNasopharynxTranscriptomeAdolescentAdultAge FactorsChildChild, PreschoolFemaleGene Expression ProfilingHumansMaleSARS-CoV-2Young AdultCOVID-19cytokinegene expression.immune responsemachine learningnasopharynx

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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.