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Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism. [ABSTRACT FROM AUTHOR]

Extracellular vesicles represent a group of structures with the capacity to communicate with different cells and organs. This complex network of interactions can regulate multiple physiological processes in the organism. Very importantly, these processes can be altered during the appearance of different diseases including cancer, metabolic diseases, etc. In addition, these extracellular vesicles can transport different cargoes, altering the initiation of the disease, driving the progression, or even accelerating the pathogenesis. Then, we have explored the implication of these structures in different alterations such as pancreatic cancer, and in different metabolic alterations such as diabetes and its complications and non-alcoholic fatty liver disease. Finally, we have explored in more detail the communication between the liver and the pancreas. In summary, extracellular vesicles represent a very efficient system for the communication among different tissues and permit an efficient system as biomarkers of the disease, as well as being involved in the extracellular-vesicle-mediated transport of molecules, serving as a potential therapy for different diseases. [ABSTRACT FROM AUTHOR]

Prostate cancer, as one of the most prevalent malignancies in males, exhibits an approximate 5-year survival rate of 95% in advanced stages. A myriad of molecular events and mutations, including the accumulation of oncometabolites, underpin the genesis and progression of this cancer type. Despite growing research demonstrating the pivotal role of oncometabolites in supporting various cancers, including prostate cancer, the root causes of their accumulation, especially in the absence of enzymatic mutations, remain elusive. Consequently, identifying a tangible therapeutic target poses a formidable challenge. In this review, we aim to delve deeper into the implications of oncometabolite accumulation in prostate cancer. We center our focus on the consequential epigenetic alterations and impacts on cancer stem cells, with the ultimate goal of outlining novel therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis. Methods: A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity). Results: Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B. Conclusions: The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC. [ABSTRACT FROM AUTHOR]

Recent studies demonstrated that short open reading frames (sORFs, <100 codons) can encode the peptides or microproteins that perform important functions in prokaryotic and eukaryotic cells. It was established that the products of sORF translation are involved in the regulation of many processes; for example, they modulate the activity of the mitochondrial respiratory chain or the activity of muscle cells in mammals. However, the identification and subsequent functional analysis of the peptides or microproteins encoded by sORF is a nontrivial task that requires the use of special approaches. The discovery of partner proteins for the studied peptide is one of the critical stages in functional analysis. This review considers the peculiarities of the analysis of the interactome of sORF-encoded microproteins and describes the approaches currently used for such studies. [ABSTRACT FROM AUTHOR]

Aims The plasticity of vascular smooth muscle cells (VSMCs) enables them to alter phenotypes under various physiological and pathological stimuli. The alteration of VSMC phenotype is a key step in vascular diseases, including atherosclerosis. Although the transcriptome shift during VSMC phenotype alteration has been intensively investigated, uncovering multiple key regulatory signalling pathways, the translatome dynamics in this cellular process, remain largely unknown. Here, we explored the genome-wide regulation at the translational level of human VSMCs during phenotype alteration. Methods and results We generated nucleotide-resolution translatome and transcriptome data from human VSMCs undergoing phenotype alteration. Deep sequencing of ribosome-protected fragments (Ribo-seq) revealed alterations in protein synthesis independent of changes in messenger ribonucleicacid levels. Increased translational efficiency of many translational machinery components, including ribosomal proteins, eukaryotic translation elongation factors and initiation factors were observed during the phenotype alteration of VSMCs. In addition, hundreds of candidates for short open reading frame-encoded polypeptides (SEPs), a class of peptides containing 200 amino acids or less, were identified in a combined analysis of translatome and transcriptome data with a high positive rate in validating their coding capability. Three evolutionarily conserved SEPs were further detected endogenously by customized antibodies and suggested to participate in the pathogenesis of atherosclerosis by analysing the transcriptome and single cell RNA-seq data from patient atherosclerotic artery samples. Gain- and loss-of-function studies in human VSMCs and genetically engineered mice showed that these SEPs modulate the alteration of VSMC phenotype through different signalling pathways, including the mitogen-activated protein kinase pathway and p53 pathway. Conclusion Our study indicates that an increase in the capacity of translation, which is attributable to an increased quantity of translational machinery components, mainly controls alterations of VSMC phenotype at the level of translational regulation. In addition, SEPs could function as important regulators in the phenotype alteration of human VSMCs. [ABSTRACT FROM AUTHOR]

Background: Despite the significant advances in the management of advanced prostate cancer (PCa), metastatic PCa is currently considered incurable. For further investigations in precision treatment, the development of preclinical models representing the complex prostate tumor heterogeneity are mandatory. Accordingly, we aimed to establish a resource of patient-derived xenograft (PDX) models that exemplify each phase of this multistage disease for accurate and rapid evaluation of candidate therapies. Methods: Fresh tumor samples along with normal corresponding tissues were obtained directly from patients at surgery. To ensure that the established models reproduce the main features of patient's tumor, both PDX tumors at multiple passages and patient's primary tumors, were processed for histological characteristics. STR profile analyses were also performed to confirm patient identity. Finally, the responses of the PDX models to androgen deprivation, PARP inhibitors and chemotherapy were also evaluated. Results: In this study, we described the development and characterization of 5 new PDX models of PCa. Within this collection, hormone-naïve, androgensensitive and castration-resistant (CRPC) primary tumors as well as prostate carcinoma with neuroendocrine differentiation (CRPC-NE) were represented. Interestingly, the comprehensive genomic characterization of the models identified recurrent cancer driver alterations in androgen signaling, DNA repair and PI3K, among others. Results were supported by expression patterns highlighting new potential targets among gene drivers and the metabolic pathway. In addition, in vivo results showed heterogeneity of response to androgen deprivation and chemotherapy, like the responses of patients to these treatments. Importantly, the neuroendocrine model has been shown to be responsive to PARP inhibitor. Conclusion: We have developed a biobank of 5 PDX models from hormonenaïve, androgen-sensitive to CRPC primary tumors and CRPC-NE. Increased copy-number alterations and accumulation of mutations within cancer driver genes as well as the metabolism shift are consistent with the increased resistance mechanisms to treatment. The pharmacological characterization suggested that the CRPC-NE could benefit from the PARP inhibitor treatment. Given the difficulties in developing such models, this relevant panel of PDX models of PCa will provide the scientific community with an additional resource for the further development of PDAC research. [ABSTRACT FROM AUTHOR]

There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer. [ABSTRACT FROM AUTHOR]

Prostate adenocarcinoma (PRAD), also named prostate cancer, the most common visceral malignancy, is diagnosed in male individuals. Herein, in order to obtain immune-based subtypes, we performed an integrative analysis to characterize molecular subtypes based on immune-related genes, and further discuss the potential features and differences between identified subtypes. Simultaneously, we also construct an immune-based risk model to assess cancer prognosis. Our findings showed that the two subtypes, C1 and C2, could be characterized, and the two subtypes showed different characteristics that could clearly describe the heterogeneity of immune microenvironments. The C2 subtype presented a better survival rate than that in the C1 subtype. Further, we constructed an immune-based prognostic model based on four screened abnormally expressed genes, and they were selected as predictors of the robust prognostic model (AUC = 0.968). Our studies provide reference for characterization of molecular subtypes and immunotherapeutic agents against prostate cancer, and the developed robust and useful immune-based prognostic model can contribute to cancer prognosis and provide reference for the individualized treatment plan and health resource utilization. These findings further promote the development and application of precision medicine in prostate cancer. [ABSTRACT FROM AUTHOR]

Our study aimed to assess the applicability of miR-486 in combination with soluble GP88 protein as a diagnostic and/or predictive biomarker for prostate cancer (PCa) patients. miR-486 and GP88 levels in serum samples from 136 patients undergoing MRI-guided biopsy of the prostate were assessed by qRT–PCR and ELISA, respectively. Of these, 86 patients received a histologically confirmed diagnosis of PCa. Neither marker showed an association with the diagnosis of cancer. PCa patients were separated based on (i) treatment into patients with active surveillance or patients with any type of curative treatment and (ii) age into elderly (>68 years) patients and younger patients (≤68 years). In elderly patients (N = 41) with the intention of curative treatment at optimized cut-off values, significantly higher GP88 levels (p = 0.018) and lower miR-486 levels (p = 0.014) were observed. The total PSA level and ISUP biopsy grade were used in a baseline model for predicting definitive therapy. The baseline model exhibited an area under the curve (AUC) of 0.783 (p = 0.005). The addition of the serum biomarkers miR-486 and GP88 to the baseline model yielded an improved model with an AUC of 0.808 (p = 0.002). Altogether, combined miR-486 and GP88 serum levels are associated with and are therefore suggested as supportive biomarkers for therapy decisions, particularly in elderly PCa patients. [ABSTRACT FROM AUTHOR]