Identification of the metabolic signatures of prostate cancer by mass spectrometry‐based plasma and urine metabolomics analysis

Yu, Chao-wen; Lu, Luo; Li, Ting; Duan, Limei; He, Zhenting; Zhao, Yan; Zou, Lin; Wu, Xiaohou; Luo, Chunli

doi:10.1002/pros.24229

Cited by 14 publications

(13 citation statements)

References 41 publications

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“…A metabolic signature for a diagnostic prediction has been proposed [ 237 ]. A number of metabolites, including several ones related to energy production, to amino acid metabolism and to the TCA cycle, are altered in urine, as reported in independent studies [ 223 , 238 ]. Some of these metabolic variations are similar in urine and prostate cancer tissue [ 221 ].…”

Section: Metabolomementioning

confidence: 76%

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Nevedomskaya

Haendler

2022

IJMS

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Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

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Section: Metabolomementioning

confidence: 76%

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Nevedomskaya

Haendler

2022

IJMS

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“…Prostate cancer (PCa) is one of the pervasive carcinoma occurring in men and a large health burden worldwide ( 41 ). The lethality of PCa is due to the lack of treatment options that can produce a lasting response at the genetic and cellular biological levels ( 42 , 43 ). The progression and pathogenic mechanisms of PCa remain unclear.…”

Section: Discussionmentioning

confidence: 99%

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

et al. 2022

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BackgroundProstate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.MethodsHigh-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.ResultsA total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.ConclusionThis new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

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“…Importantly, Glu/Gln had high predictive power when detecting PCa patients (AUC = 0.984), with a higher sensitivity (96.6%) than PSA (94.4%). 480 In summary, these studies show the small molecule phenotype signatures offers new avenues for better understanding biological metabolic processes of diseases, and for developing new biomarkers to improve patient management in clinic.…”

Section: Exploring Phenotype Signaturesmentioning

confidence: 93%

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Qiu

Cai

et al. 2023

Sig Transduct Target Ther

228

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Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject’s phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

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Identification of the metabolic signatures of prostate cancer by mass spectrometry‐based plasma and urine metabolomics analysis

Cited by 14 publications

References 41 publications

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

Small molecule metabolites: discovery of biomarkers and therapeutic targets

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