INTEGRATING LIPIDOMICS AND PROTEOMICS FOR INCREASED DIAGNOSTIC ACCURACY IN PROSTATE CANCER

Significance of the Topic

Prostate cancer remains one of the leading malignancies in men, and early diagnosis and staging are crucial for optimal treatment. Conventional markers such as PSA lack specificity and cannot reliably distinguish disease stages. Integrating lipidomic and proteomic analyses can capture complementary molecular signatures, enhancing diagnostic precision while offering insights into underlying disease mechanisms.

Study Objectives and Overview

This study aimed to develop combined lipidomic–proteomic biomarker panels to differentiate healthy controls from patients with mild or advanced prostate cancer. Serum samples from 205 diagnosed patients and 126 age‐matched healthy individuals were analyzed to generate diagnostic models and to identify pathway alterations associated with disease progression.

Methods and Instrumentation

The experimental workflow included:

• Lipidomic profiling using the LipidQuan™ platform: protein precipitation with IPA, UPLC separation on ACQUITY I-Class, Xevo™ TQ-XS MS acquisition; data processed via Symphony™, TargetLynx™, and Skyline.
• Proteomic analysis via tryptic digestion, ACQUITY Premier I-Class UPLC with CSH™ column, SYNAPT™ XS MS; data processed in Progenesis™ QI.
• Data integration, z‐score normalization, feature selection with Boruta and Random Forest, unsupervised PCA/UMAP, hierarchical clustering, cross-validation to minimize overfitting.

Main Findings and Discussion

• A total of 1,115 combined protein and lipid features were evaluated. Random Forest ranked 59 significant biomarkers for mild prostate cancer and an expanded set of lipid features for advanced disease.
• Receiver operating characteristic analyses yielded area under curve (AUC) values of 0.955 for mild and 0.966 for advanced prostate cancer when using integrated omics signatures.
• The top five biomarkers achieved cumulative AUCs of 0.940 (mild) and 0.955 (advanced), underscoring potential for a concise panel.
• Pathway enrichment highlighted acute phase response signaling in both stages, with stage‐specific metabolic and signaling pathways also identified.
• Data quality was ensured by low missingness (<5%) and minimal BMI confounding, as cohort BMI distributions were comparable.

Benefits and Practical Applications

• Enhanced diagnostic accuracy over single‐marker assays, enabling clearer differentiation between healthy, mild, and advanced prostate cancer states.
• Deep biological insight into molecular alterations, supporting translational research and potential therapeutic target identification.
• A streamlined panel of top biomarkers could facilitate development of blood-based clinical assays.

Future Trends and Opportunities

• Validation in larger, multi-center cohorts to confirm robustness and generalizability.
• Integration of additional omics layers (e.g., metabolomics, transcriptomics) and advanced machine learning for refined predictive models.
• Adoption in personalized medicine pipelines, guiding risk stratification and treatment decisions.
• Development of automated platforms for routine clinical deployment.

Conclusion

The combined lipidomic and proteomic approach demonstrated high diagnostic performance for prostate cancer staging, with robust biomarker panels and pathway insights. This integrative strategy holds promise for translating into clinically meaningful assays that improve early detection and patient management.

Reference

1. Distler U, Kuharev J, Navarro P, et al. Label-free quantification in ion mobility–enhanced data-independent acquisition proteomics. Nat Protoc. 2016;11:795–812.
2. J Proteome Res. 2020 Apr 3;19(4):1447-1458. doi:10.1021/acs.jproteome.9b00640.