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Metabolomic analysis of biological material using LC-MS in the quest for urinary system cancer biomarkers - review

Szuberla Wiktoria, Wrona Maria

Chemical Technology & Biotechnology

2024

Vol. 4

70--75

Renal cell carcinoma (RCC) and bladder cancer (BC) are among the most frequently diagnosed urinary system cancers worldwide. They are characterized by high mortality and recurrence rates. In response to the rising incidence and mortality rates, scientists are exploring innovative diagnostic and therapeutic methods. Metabolomics, which analyzes metabolite levels, may enable early diagnosis and monitoring of therapy progress. Compared to other omics technologies, it focuses on the outcomes of metabolite activity, providing a unique perspective on processes occurring in cancer cells. Metabolomic analyses utilize techniques such as mass spectrometry. These methods allow the identification of biomarkers and precise determination of the chemical composition of biological samples. However, the most commonly used method is liquid chromatography-mass spectrometry (LC-MS), which enables the most comprehensive screening of cancer metabolomes. Recent studies show significant progress in recognizing characteristic metabolites associated with urological cancers, although this area remains partially unexplored. Research on circulating metabolites, especially those present in easily accessible samples like blood or urine, demonstrates promising potential in clinical practice. Study results reveal differences in metabolic profiles between various stages of cancer development, which may have clinical significance. The future of this field involves an increasing number of clinical cohorts, standardization of sample preparation, and further improvements in instrument sensitivity and speed. LC-MS-based metabolomics has the potential to contribute to the improvement of diagnostics, therapy, and the quality of life of patients with some urological cancers. However, challenges, such as the lack of uniform methodologies and understanding of metabolite determinants, require further research and innovation.

Multi-feature ensemble system in the renal tumour classification task

Osowska-Kurczab Aleksandra Maria, Markiewicz Tomasz, Dziekiewicz Miroslaw, Lorent Malgorzata

Bulletin of the Polish Academy of Sciences. Technical Sciences

2021

Vol. 69, nr 3

art. no. e136749

Recently, the analysis of medical imaging is gaining substantial research interest, due to advancements in the computer vision field. Automation of medical image analysis can significantly improve the diagnosis process and lead to better prioritization of patients waiting for medical consultation. This research is dedicated to building a multi-feature ensemble model which associates two independent methods of image description: textural features and deep learning. Different algorithms of classification were applied to single-phase computed tomography images containing 8 subtypes of renal neoplastic lesions. The final ensemble includes a textural description combined with a support vector machine and various configurations of Convolutional Neural Networks. Results of experimental tests have proved that such a model can achieve 93.6% of weighted F1-score (tested in 10-fold cross validation mode). Improvement of performance of the best individual predictor totalled 3.5 percentage points.