肺癌血液样本的血浆蛋白质组学分析揭示免疫相关炎症特征作为预后生物标志物

Background. Lung cancer presents a significant global burden of mortality. Predictive biomarkers are urgently needed to help stratify patient populations for targeted and systemic therapies. Here, we performed comprehensive proteomic profiling using the nELISA technology to identify baseline and post treatment proteomic signatures associated with clinical outcomes to immune checkpoint immunotherapy (ICI). Methods. Plasma were prepared from 66 patients who underwent ICI (63 pre-treatment samples, 19 post-treatment samples). The nELISA assay (Nomic Bio, Canada) was used to detect the abundance of 969 proteins. Data QC, Differential expression, survival modelling, and pathway enrichment was performed to understand associations with clinical endpoints. Results. Baseline blood samples from patients who experienced disease progression demonstrated coordinated activation of three interconnected pathways: coagulation, complement cascade, and IL-6/JAK/STAT3 signalling. This pattern was consistent across PFS events (p<0.001), OS events (p<0.001), and progressive disease comparisons (RECIST, PR vs PD: p=0.002). In contrast, baseline samples from patients with better clinical outcomes exhibited enrichment of type I/II interferon responses, DNA damage response and intact apoptotic machinery. Notably, patient responders maintained balanced inflammation with high interferon signalling, while patient non-responders showed high inflammatory markers paradoxically paired with low interferon activity, suggesting dysfunctional, immunosuppressive inflammation. Analysis of paired PRE/POST samples (n=19) revealed that patients who progressed exhibited marked treatment-induced increases in TNFalpha/NFκB signaling, inflammatory responses, combined with decreases in IL-2/STAT5-mediated T cell signalling. This pattern, consistent across OS (p=0.001), PFS (p=0.002) suggests treatment-related immune dysregulation or failed immune reconstitution despite checkpoint blockade. Importantly, the coagulation signature present at baseline decreased post-treatment in poor-outcome patients, possibly reflecting consumption coagulopathy during systemic inflammatory states. Post-treatment proteomic changes in deceased patients included shifts from fatty acid oxidation toward adipogenesis with concurrent decreases in myogenesis, collectively indicating cancer cachexia-associated metabolic reprogramming that may contribute to treatment intolerance and mortality. Conclusion. We identified two distinct baseline immunophenotypes predictive of immunotherapy outcomes including an “immune inflammed” and “thrombo-inflammatory.” The protein signatures warrant prospective validation as predictive biomarkers for immunotherapy patient selection and response monitoring, with potential to guide precision medicine approaches.