The Dark Side of Lipid Metabolism in Prostate and Renal Carcinoma: Novel Insights into Molecular Diagnostic and Biomarker Discovery

Nicola Antonio di Meo,Francesco Lasorsa,Monica Rutigliano,Martina Milella,Matteo Ferro,Michele Battaglia,Pasquale Ditonno,Giuseppe Lucarelli

Expert review of molecular diagnostics（2023）SCI 3区SCI 2区

Polytechnic University of Bari | European Inst Oncol

Cited 38|Views6

Abstract

IntroductionLipidomics focuses on the in-depth analysis of lipids, which are crucial macromolecules involved in a wide range of metabolic pathways. The increased intracellular accumulation of different classes of lipids in renal cell carcinoma (RCC) and prostate cancer (PCa) cells may be caused by elevated absorption or by increased de novo lipogenesis as a consequence of lipid metabolism reprogramming. The involvement of cholesterol metabolism in cancer's aberrant pathways has also been demonstrated.Areas coveredThis review provides an update on the most important lipidomics studies and applications in RCC and PCa, with a particular focus on how knowledge of aberrant lipid pathways may be used to identify biomarkers and novel therapeutic targets. In addition, the application of this methodologies have led to novel cancer subtypes identification and patient's risk stratification. Tracking tumor progression using specific biofluid metabolite profiles offers a huge translational opportunity for urological malignancies.Expert opinionLipidomics is a promising branch of 'omics' approach and should include in next decade new standardized analysis methods and randomized clinical trials in order to reach the aim to use this high-throughput technique in patient-tailored therapy perspective.

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Key words

Biomarker,lipidomics,metabolomics,metabolism,prostate cancer,renal cell carcinoma

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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