Evidence map›Paper›PMID 38656671›Full record

ArticleBiochemical genetics2025

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.

Huan Li, Jin Zhao, Yan Xing, Jia Chen, Ziying Wen, Rui Ma, Fengxia Han, Boyong Huang, Hao Wang, Cui Li and 2 more

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Article in Biochemical genetics, 2025. 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.

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4 · The record

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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

12 authors.

Huan Li *Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Jin Zhao *Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Yan XingDepartment of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Jia ChenXi'an Medical University, Xi'an, China.
Ziying WenXi'an Medical University, Xi'an, China.
Rui MaDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Fengxia HanDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Boyong HuangDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Hao WangDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Cui LiDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Yang ChenDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China.
Xiaoxuan NingDepartment of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang le West Road, Xi'an, 710032, Shaanxi, China. ningxx01@fmmu.edu.cn.

Funding

Health Research Project of Shaanxi Province No. 2021E010Key Research and Development Program of Shaanxi Province No.2023-ZDLSF-15National Natural Science Foundation of China No. 82270715
6 · The paper itself

Abstract

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection. COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology functional enrichment analysis, Gene Set Enrichment Analysis, machine learning, and immune cell infiltration analysis. GO and KEGG enrichment analyses revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Three-degree algorithm (LASSO, SVM-RFE, KNN) and correlation analysis showed that the five DEGs up-regulated the immune cells of macrophages M0/M1, memory B cells, gamma delta T cell, dendritic cell resting, and master cell resisting. Our study identified five hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

Indexed as

AgingCOVID-19Machine LearningAgedAge FactorsDatabases, GeneticGene Expression ProfilingGene OntologyHumansSARS-CoV-2AgingCOVID-19Differentially expressed genesImmune cell infiltrationMachine learning

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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.