Predictive model for recurrence of renal cell carcinoma by comparing pre‐ and postoperative urinary metabolite concentrations

Abstract

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Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

“…Ratios may be considered as an internal normalization of confounders such as different diets and life styles among patients and may contribute to reduce the systematic experimental error . Models based on different combinations of plasma lipid ratios were suggested as potential biomarkers for the diagnosis and prognosis of endometrial cancer, and ratios of urine estrogen levels were suggested for breast cancer risk evaluation. , Regarding ccRCC, the ratio between urine metabolite concentrations in pre- and post-surgery (3–7 days) samples were suggested as potential biomarkers for recurrence prediction based on multiple logistic regression analysis . Although in our lipid profiling study the linear response of the detected ionic species for PC 16:0/0:0, PC 18:2/18:2, and linoleic acid was not evaluated, our results highlight that the ratio between PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid may be a useful marker for patient prognosis.…”

Elucidation and Regulation of Tyrosine Kinase Inhibitor Resistance in Renal Cell Carcinoma Cells from the Perspective of Glutamine Metabolism

“…Additionally, poor prognosis, and resistance to second-line treatment are associated with urinary glutamate levels. In our previous research, the accurate quantification of urinary glutamate reflected the malignant and recurrent status of RCC after definitive therapy [ 16 , 17 ]. Elevated glutamate levels have also been reported to be associated with poor prognosis [ 18 ].…”

Liquid biopsy at the frontier in renal cell carcinoma: recent analysis of techniques and clinical application

“…Therefore, the detection of changes in circulating metabolite profiles can reveal cancer-induced abnormalities in biometabolic pathways and associated biological behaviors, thus effectively differentiating Patients with RCC from healthy populations, distinguishing ccRCC from other subtypes of RCC, and evaluating treatment efficiency and prognosis. Currently, the analysis of metabolites mainly depends on mass spectrometer (MS)-based methods, including liquid chromatography-MS (LC–MS), gas chromatography-MS (GC–MS), matrix-assisted laser desorption/ionization-MS (MALDI), and related derivative techniques such as ultra-performance LC–MS (UPLC-MS) or headspace solid-phase microextraction coupled with GC–MS (HS–SPME–GC–MS) [ 100 – 104 ]. By accurately measuring compound masses and inferring chemical composition and structure, MS has shown high sensitivity and specificity for probing compounds, becoming the preferred method for structural identification of analytes in complex mixtures.…”

“…Compared to MS, NMR has lower sensitivity and specificity but can provide more detailed structural information. However, metabolite profiles vary widely among individuals, and although some compounds, such as 2-hydroxyglutarate (2HG), have been co-detected in several clinical trials [ 100 , 106 ], the panel of compounds used in most experiments is inconsistent.…”

“…Briefly, GRP78 can predict patient grades and metastasis [ 180 ]; SAA2 and CFB were associated with patient recurrence, where combining Fuhrman grade with CFB levels showed the best C-index to predict recurrence [ 187 ]; IL-6 and IL-27 can predict recurrence of RCC, but as with survival prediction their abilities to predict recurrence vary across tumor sizes [ 117 ]; plasma immune checkpoints, including PD-1, PD-L1, and BTN3A1, can predict metastasis, which is correlated with the number and location of metastases [ 188 ]; and urinary PLIN-2 levels can predict tumor size [ 186 ]. Compared to protein, metabolite analysis has been less reported in prognosis prediction, with only two studies in the last 5 years constructing metabolite models to predict patient grades and metastasis [ 100 , 177 ]. Finally, mtDNA in different phases of exosomes is strongly associated with RCC metastasis.…”