Multiomics Biomarkers for Differential Diagnosis of Pleural Effusion: Integration of Proteomic Markers and Single-Cell Transcriptomics.

BACKGROUND

Differential diagnosis of pleural effusion remains challenging despite medical thoracoscopy (MT). We investigated whether integrating proteomic biomarkers with single-cell transcriptomics and genomic mutation profiling could improve diagnostic accuracy and reveal mechanistic insights.

METHODS

We prospectively enrolled 564 patients with pleural effusion undergoing medical thoracoscopy. Pleural fluid biomarkers (adenosine deaminase, carcinoembryonic antigen, cytokeratin-19 fragment, neuron-specific enolase) were measured. Single-cell RNA sequencing profiled immune landscapes across disease etiologies. Driver mutation profiling was performed on malignant pleural effusion samples using targeted next-generation sequencing encompassing 15 cancer-related genes. Diagnostic performance was evaluated against histopathological diagnosis.

RESULTS

Final diagnoses included inflammatory PE (n = 95, 16.8%), tuberculous PE (n = 299, 53.0%), and malignant PE (n = 170, 30.1%). For tuberculous PE, ADA achieved AUC 0.916 (sensitivity 83.3% and specificity 89.4%). For malignant PE, combined CEA/CYFRA21-1 achieved AUC 0.957 (sensitivity 98.2% and specificity 98.7%). Single-cell analysis revealed distinct immune signatures: Tuberculous PE showed M1 macrophage polarization (M1/M2 ratio 9.48) strongly correlating with ADA levels (rho = 0.68, p < 0.001), whereas malignant PE exhibited immunosuppressive features with elevated M2 macrophages and reduced NK cells. Mutation profiling of malignant PE revealed EGFR (46.5%), TP53 (34.7%), and PIK3CA (8.2%) as the most frequently mutated genes. EGFR-mutant tumors exhibited significantly higher CEA levels (p = 0.033) and more immunosuppressive microenvironments with increased M2 macrophages (p < 0.001) and decreased CD8+ T cells (p < 0.001). The sequential multibiomarker algorithm achieved 96.5% sensitivity and 98.7% specificity for malignant PE detection, with 85.3% overall three-way classification accuracy.

CONCLUSIONS

Multiomics integration combining proteomic biomarkers with single-cell immune profiling and genomic mutation characterization achieves high diagnostic accuracy for pleural effusion while revealing disease-specific immune mechanisms and mutation-driven therapeutic opportunities.