OLink proteomic biomarker to predict clinical efficacy of Macaranga sinensis Müll.Arg: A nested case-control study

Despite the fact that herbal medicine has been used for a long time, their clinical application is challenged by unclear active ingredients and poorly understood mechanisms of action, resulting in considerable heterogeneity in therapeutic outcomes among patients.

To explore the use of OLink-based biomarkers to predict the efficacy of Macaranga sinensis Müll.Arg in individuals with long COVID-related fatigue.

This nested case-control study recruited long COVID participants who had routinely taken Macaranga sinensis Müll.Arg for more than three months. Case (response) and control (non-response) group were defined based on the change in Brief Fatigue Inventory (BFI) score. Plasma samples were analyzed using OLink. Mann-Whitney U test, Lasso and Ridge regression model, Random Forest, and Support Vector Machine (SVM) were used for differential expression analysis, feature selection, and biomarker identification, respectively.

A total of 55 long COVID fatigue patients was included in this study (27 in case group and 28 in control group). Differential expression analysis filtered in a total of 13 potential biomarkers (log₂ fold change >0.5; p < 0.05). Feature selection further selected 11 biomarkers, including uPA, TRAIL, IL-10, IL-18R1, CX3CL1, EPHB4, COL1A1, Flt3L, EGFR, IL-1RT2, and IFN-γ (all p < 1e-6). Random Forest and SVM identified the key biomarker of CX3CL1 (F1-score of 0.72).

Our exploratory analysis suggests that CX3CL1 is the candidate biomarker for predicting the efficacy of Macaranga sinensis Müll.Arg, warranting further investigation in larger studies. Our work highlights the translational potential of integrating statistical modeling and machine learning approaches with proteomic data to identify predictive biomarkers for herbal medicine efficacy in clinical settings.