Serum metabolites as early detection markers of non-muscle invasive bladder cancer in Chinese patients

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Metabolic changes preceding bladder cancer occurrence among Korean men: a nested case-control study from the KCPS-II cohort

BACKGROUND

Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker.

METHODS

We designed two nested case-control studies using Korean Cancer Prevention Study (KCPS)-II. Only males aged 35-69 were randomly selected and divided into two sets by recruitment organizations [set 1, BLCA (n = 35) vs. control (n = 35); set 2, BLCA (n = 31) vs. control (n = 31)]. Baseline serum samples were analyzed by non-targeted metabolomics profiling, and OPLS-DA and network analysis were performed. Calculated genetic risk score (GRS) for BLCA from all KCPS participants was utilized for interpreting metabolomics data.

RESULTS

Critical metabolic signatures shown in the BLCA group were dysregulation of lysine metabolism and tryptophan-indole metabolism. Furthermore, the prediction model consisting of metabolites (lysine, tryptophan, indole, indoleacrylic acid, and indoleacetaldehyde) reflecting these metabolic signatures showed mighty BLCA predictive power (AUC: 0.959 [0.929-0.989]). The results of metabolic differences between GRS-high and GRS-low groups in BLCA indicated that the pathogenesis of BLCA is associated with a genetic predisposition. Besides, the predictive ability for BLCA on the model using GRS and five significant metabolites was powerful (AUC: 0.990 [0.980-1.000]).

CONCLUSION

Metabolic signatures shown in the present research may be closely associated with BLCA pathogenesis. Metabolites involved in these could be predictive biomarkers for BLCA. It could be utilized for early diagnosis, prognostic diagnosis, and therapeutic targets for BLCA.

Roles of non-coding RNAs in the metabolism and pathogenesis of bladder cancer

Bladder cancer (BC) is featured as the second most common malignancy of the urinary tract worldwide with few treatments leading to high incidence and mortality. It stayed a virtually intractable disease, and efforts to identify innovative and effective therapies are urgently needed. At present, more and more evidence shows the importance of non-coding RNA (ncRNA) for disease-related study, diagnosis, and treatment of diverse types of malignancies. Recent evidence suggests that dysregulated functions of ncRNAs are closely associated with the pathogenesis of numerous cancers including BC. The detailed mechanisms underlying the dysregulated role of ncRNAs in cancer progression are still not fully understood. This review mainly summarizes recent findings on regulatory mechanisms of the ncRNAs, long non-coding RNAs, microRNAs, and circular RNAs, in cancer progression or suppression and focuses on the predictive values of ncRNAs-related signatures in BC clinical outcomes. A deeper understanding of the ncRNA interactive network could be compelling framework for developing biomarker-guided clinical trials.