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Transcriptomic and miRNA Signatures of ChAdOx1 nCoV-19 Vaccine Response Using Machine Learning.

Jinting Lin¹, Qinglan Ma¹, Lei Chen²

¹School of Life Sciences, Shanghai University, Shanghai 200444, China.

Life (Basel, Switzerland)

|June 26, 2025

Summary

This study identified key genes associated with ChAdOx1 nCoV-19 vaccine efficacy using machine learning on transcriptomic data. It found specific gene expression changes in vaccinated individuals, aiding understanding of immune responses to COVID-19 vaccines.

Keywords:

ChAdOx1 nCoV-19 vaccination gene signature machine learning miRNA signature

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

Area of Science:

Immunology
Genomics
Bioinformatics

Background:

The COVID-19 pandemic necessitates effective vaccines like ChAdOx1 nCoV-19.
Understanding vaccine-induced immune responses, particularly gene expression changes, is crucial for optimizing vaccine efficacy.
Previous studies have explored omics data, but comprehensive analysis of ChAdOx1 nCoV-19 induced transcriptomic changes is needed.

Purpose of the Study:

To identify immune-related genes associated with ChAdOx1 nCoV-19 vaccine efficacy.
To develop a machine learning classifier for distinguishing vaccination and infection statuses based on transcriptomic data.
To investigate gene expression patterns related to ChAdOx1 nCoV-19 vaccination and SARS-CoV-2 infection.

Main Methods:

Transcriptomic data analysis using feature ranking and incremental feature selection algorithms.
Application of classification algorithms to analyze RNA and small RNA features from vaccinated and control groups.
Comparative analysis of gene expression profiles across different time points and infection statuses.

Main Results:

Identification of key genes (IGHG1, FOXM1, CASP10) linked to ChAdOx1 nCoV-19 vaccine efficacy.
Development of a classifier capable of distinguishing between vaccination and infection groups.
Discovery of specific gene expression patterns: HIST1H3G upregulation and CASP10 downregulation in ChAdOx1 nCoV-19 vaccinated and SARS-CoV-2 infected subjects.
Machine learning enabled faster and more comprehensive analysis of transcriptome data.

Conclusions:

The study successfully identified potential biomarkers for ChAdOx1 nCoV-19 vaccine efficacy and response.
Machine learning approaches provide an efficient method for analyzing complex transcriptomic data in vaccine research.
Findings enhance understanding of immune and inflammatory responses to the ChAdOx1 nCoV-19 vaccine, offering insights for future vaccine improvements.