Work

Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study

Year: 2025

Type: article

Abstract: Overview of the study design. The participants were divided into four groups: control group (n=265), influenza group (n=225), COVID-19 group (n=358), and mixed group (n=267). Principal component analysis was used to group samples according to clinical characteristics. Selected features were analyzed based on random forest model and LASSO regression model. Proteome sequencing combined with machine learning methods were used for analysis. ROC curve analysis was used to analyze the clinical diagnostic efficacy of inflammatory factors among different groups. Risk prediction analysis was used to analyze the relationship between high expression of some inflammatory factors in COVID-19 cases and higher risk of disease. ELISA was used to detect the levels of different inflammatory factors in plasma of different populations. • Machine learning and proteomics enhance influenza diagnosis. • Bioinformatics analysis identifies key biomarkers for influenza. • SAA2 as an auxiliary diagnostic marker for influenza infection. • Accurate classification based on clinical features and protein biomarkers. Influenza viruses cause seasonal epidemics almost every year, and it is difficult to diagnose quickly and accurately. Machine learning and peripheral blood protein omics have brought new ideas to the research of clinical markers. Prediction of key molecular marker of influenza virus infection by the established machine learning model and peripheral blood protein omics. This study used the testing data of 850 patients (including influenza, COVID-19 and mixed infections) and 265 healthy individuals, to establish and validate a diagnostic prediction model for influenza infection and verified the potential value of this model in the differential diagnosis of influenza, COVID-19 and healthy people. The overall analysis showed that there were significant differences in 9 clinical features in the influenza group. Principal component analysis can effectively group samples based on these clinical features. Based on the random forest model and LASSO regression model found that the selected features are clinical indicators that can accurately distinguish influenza patients. We performed proteome sequencing combined with machine learning and found a total of 26 DEPs. Through PPI and WGCNA analysis, we identified several genes related to the proportion of monocytes. We then analyzed the correlation of these factors with immune cell proportions and found that SAA1 and SAA2 were highly correlated with various vital immunocyte. ROC curve analysis shows that SERPINA3 can distinguish influenza, COVID-19, mixed infection and healthy people; SAA1 can distinguish COVID-19, mixed infection and healthy people; SAA2 can distinguish influenza and healthy people. In influenza, high expression of SERPINA3, SAA1, and SAA2 is associated with higher risk. Finally, we used the ELISA method to confirm that SAA2 protein can be used as an auxiliary diagnostic indicator for influenza infection. Preliminary results showed that SAA2 is an important molecular marker specific to influenza infection.

Source: Journal of Advanced Research

DOI: 10.1016/j.jare.2025.03.051