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bioRxiv · 10.1101/2023.03.02.530740

Integrating weighted correlation network analysis and machine learning identifies common trajectories of prostate cancer

Egger, G.·Sheibani-Tezerji, R.·Perez Malla, C. U.·Malzer, A.·Misura, K.·Kalla, J.·Tran, L.·Wasinger, G.
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Abstract

BackgroundProstate cancer diagnosis and prognosis is currently limited by the availability of sensitive and specific biomarkers. There is an urgent need to develop molecular biomarkers that allow for the distinction of indolent from aggressive disease, the sensitive detection of heterogeneous tumors, or the evaluation of micro-metastases. The availability of multi-omics datasets in publicly accessible databases provides a valuable foundation to develop computational workflows for the identification of suitable biomarkers for clinical management of cancer patients. ResultsWe combined transcriptomic data of primary localized and advanced prostate cancer from two cancer databases. Transcriptomic analysis of metastatic tumors unveiled a distinct overexpression pattern of genes encoding cell surface proteins intricately associated with cell-matrix components and chemokine signaling pathways. Utilizing an integrated approach combining machine learning and weighted gene correlation network modules, we identified the EZH2-TROAP axis as the main trajectory from initial tumor development to lethal metastatic disease. In addition, we identified and independently validated 58 promising biomarkers that were specifically upregulated in primary localized or metastatic disease. Among those biomarkers, 22 were highly significant for predicting biochemical recurrence. Notably, we confirmed TPX2 upregulation at the protein level in an independent cohort of primary prostate cancer and matched lymph node metastases. ConclusionsThis study demonstrates the effectiveness of using advanced bioinformatics approaches to identify the biological factors that drive prostate cancer progression. Furthermore, the targets identified show promise as prognostic biomarkers in clinical settings. Thus, integrative bioinformatics methods provide both deeper understanding of disease dynamics and open the doors for future personalized interventions.

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Egger, G., Sheibani-Tezerji, R., Perez Malla, C. U., Malzer, A., Misura, K., Kalla, J., Tran, L., Wasinger, G.. 2023-03-02. Integrating weighted correlation network analysis and machine learning identifies common trajectories of prostate cancer. https://doi.org/10.1101/2023.03.02.530740

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