Exploring the Role of Metabolites in Cancer and the Associated Nerve Crosstalk

Exploring the complex relationship between metabolomics and breast cancer early detection (Review)

An overview of metabolomics in cancer research, focusing on the identification of biomarkers, pharmacological targets and therapeutic agents, is provided in the present review. The fundamentals of metabolomics, the role of metabolites in cancer emergence and the methods used in metabolomic analysis, are reviewed. The applications of metabolomics in cancer therapy and diagnostics, as well as the challenges encountered in metabolomic research, are discussed. Finally, the potential clinical uses of metabolomics in cancer research and its future possibilities are explored, emphasising the importance of non-invasive diagnostic and monitoring techniques. The present review highlights the significance of metabolite-based metabolomics as a specialised tool for illuminating disease processes and identifying treatment potentials. The malfunctioning of metabolomic pathways and metabolite accumulation or depletion is caused by metabolomics abnormalities. Metabolite signatures close to a subject's phenotypic informative dimension can be used to monitor therapies and disease prediction diagnosis and prognosis. Non-invasive diagnostic and monitoring techniques with high specificity and selectivity are urgently needed. Metabolite-based metabolomics is a specialised metabolic biomarker and pathway-analysis technique, illuminating the putative processes of numerous human illnesses and determining treatment potentials. Locating biochemical pathway modifications that are early warning signs of pathological malfunction and illness is possible by identifying functional biomarkers linked to phenotypic variance. Scientists generated numerous metabolomics profiles to disclose the underlying processes and metabolomics networks for therapeutic target research in biomedicine. The metabolomic analysis of the potential utility of metabolites as biomarkers for clinical events is summarised in the present review. The significance of metabolite-based metabolomics as a specialised tool for illuminating disease processes and identifying treatment potentials is highlighted.

Unveiling Microbiota Profiles in Saliva and Pancreatic Tissues of Patients with Pancreatic Cancer

The pancreas, previously considered a sterile organ, has recently been shown to harbor its own microbiota that may influence tumor biology and patient outcomes. Despite increasing interest in the impact of the microbiome on cancer, the relationship between pancreatic tissue and oral microbiomes in pancreatic ductal adenocarcinoma (PDAC) remains limited. In this study, the oral and pancreas tissue microbiomes of patients with PDAC were compared to patients with other periampullary cancers (DC/AC) and a healthy control group using 16S rRNA gene sequence analysis. The results showed a significant reduction in microbial diversity in the saliva of cancer patients compared to healthy controls, while the PDAC patients exhibited a distinct microbial profile in their pancreatic tissues, consisting predominantly of Firmicutes, Proteobacteria, and Actinobacter, after filtering the microbiome of the indoor environment. Notably, the presence of oral bacteria such as Anoxybacillus, Clostridium, and Bacillus in pancreatic tissues suggests potential translocation from the oral cavity. This study emphasizes the importance of understanding the role of body fluid and tissue microbiota in pancreatic cancer, proposing that oral dysbiosis may contribute to disease progression. Moreover, the results suggest that the microbiome of the indoor environment in which samples are collected and analyzed is also important in microbiota analysis studies.

Causality of genetically determined metabolites on susceptibility to prevalent urological cancers: a two-sample Mendelian randomization study and meta-analysis

Background

Prevalent urological cancers, including kidney, prostate, bladder, and testicular cancers, contribute significantly to global cancer incidence and mortality. Metabolomics, focusing on small-molecule intermediates, has emerged as a tool to understand cancer etiology. Given the knowledge gap in this field, we employ a two-sample Mendelian randomization (MR) analysis to investigate the causal relationships between genetically determined metabolites (GDMs) and the susceptibility to four common urological cancers.

Methods

The study employs genome-wide association studies (GWAS) data from European populations, featuring the most extensive case count available for both blood metabolites and four prevalent urological cancers. Preliminary and secondary MR analyses were separately conducted, employing inverse variance weighted (IVW) as the primary method. Multiple statistical analyses, including the MR-Steiger test, Cochran's Q test, leave-one-out analysis, MR-Egger intercept analysis, and MR-PRESSO analysis, were executed to ensure robustness. Additionally, a meta-analysis was carried out to consolidate findings. The weighted median (WM) method was utilized for a relatively lenient correction (PWM < 0.05).

Results

After rigorous genetic variation filtering, 645 out of 1,400 metabolites were included in both preliminary and secondary MR analyses. Preliminary MR analysis identified 96 potential causal associations between 94 distinct metabolites and four urological cancers. Secondary analysis based on Finnish outcome data revealed 93 potential causal associations. Cross-database meta-analysis identified 68 blood metabolites associated with four urological cancers. Notably, 31 metabolites remained significant after using WM for correction, with additional 37 suggestive causal relationships. Reverse MR analysis revealed a significant causal association between genetically predicted prostate cancer and elevated 4-hydroxychlorothalonil levels (IVW, combined OR: 1.039, 95% CI 1.014-1.064, p = 0.002; WM, combined OR: 1.052, 95% CI 1.010-1.095, p = 0.014).

Conclusion

This comprehensive MR study provides insights into the causal relationships between blood metabolites and urological cancers, revealing potential biomarkers and therapeutic targets, thereby addressing gaps in understanding and laying the foundation for targeted interventions in urological cancer research and treatment.

Construction and Validation of a Prognostic Risk Prediction Model for Lactate Metabolism-Related lncRNA in Endometrial Cancer

Endometrial cancer (EC) is a common group of malignant epithelial tumors that mainly occur in the female endometrium. Lactate is a key regulator of signal pathways in normal and malignant tissues. However, there is still no research on lactate metabolism-related lncRNA in EC. Here, we intended to establish a prognostic risk model for EC based on lactate metabolism-related lncRNA to forecast the prognosis of EC patients. First, we found that 38 lactate metabolism-associated lncRNAs were significantly overall survival through univariate Cox regression analysis. Using minimum absolute contraction and selection operator (LASSO) regression analysis and multivariate Cox regression analysis, six lactate metabolism-related lncRNAs were established as independent predictor in EC patients and were used to establish a prognostic risk signature. We next used multifactorial COX regression analysis and receiver operating characteristic (ROC) curve analysis to confirm that risk score was an independent prognostic factor of overall patient survival. The survival time of patients with EC in different high-risk populations was obviously related to clinicopathological factors. In addition, lactate metabolism-related lncRNA in high-risk population participated in multiple aspects of EC malignant progress through Gene Set Enrichment Analysis, Genomes pathway and Kyoto Encyclopedia of Genes and Gene Ontology. And risk scores were strongly associated with tumor mutation burden, immunotherapy response and microsatellite instability. Finally, we chose a lncRNA SRP14-AS1 to validate the model we have constructed. Interestingly, we observed that the expression degree of SRP14-AS1 was lower in tumor tissues of EC patients than in normal tissues, which was consistent with our findings in the TCGA database. In conclusion, our study constructed a prognostic risk model through lactate metabolism-related lncRNA and validated the model, confirming that the model can be used to predict the prognosis of EC patients and providing a molecular analysis of potential prognostic lncRNA for EC.

Metabolic Signatures: Pioneering the Frontier of Rectal Cancer Diagnosis and Response to Neoadjuvant Treatment with Biomarkers-A Systematic Review

Colorectal cancer (CRC) is one of the most aggressive, heterogenous, and fatal types of human cancer for which screening, and more effective therapeutic drugs are urgently needed. Early-stage detection and treatment greatly improve the 5-year survival rate. In the era of targeted therapies for all types of cancer, a complete metabolomic profile is mandatory before neoadjuvant therapy to assign the correct drugs and check the response to the treatment given. The aim of this study is to discover specific metabolic biomarkers or a sequence of metabolomic indicators that possess precise diagnostic capabilities in predicting the efficacy of neoadjuvant therapy. After searching the keywords, a total of 108 articles were identified during a timeframe of 10 years (2013-2023). Within this set, one article was excluded due to the use of non-English language. Six scientific papers were qualified for this investigation after eliminating all duplicates, publications not referring to the subject matter, open access restriction papers, and those not applicable to humans. Biomolecular analysis found a correlation between metabolomic analysis of colorectal cancer samples and poor progression-free survival rates. Biomarkers are instrumental in predicting a patient's response to specific treatments, guiding the selection of targeted therapies, and indicating resistance to certain drugs.

Targeting cancer lactate metabolism with synergistic combinations of synthetic catalysts and monocarboxylate transporter inhibitors

Synthetic anticancer catalysts offer potential for low-dose therapy and the targeting of biochemical pathways in novel ways. Chiral organo-osmium complexes, for example, can catalyse the asymmetric transfer hydrogenation of pyruvate, a key substrate for energy generation, in cells. However, small-molecule synthetic catalysts are readily poisoned and there is a need to optimise their activity before this occurs, or to avoid this occurring. We show that the activity of the synthetic organometallic redox catalyst [Os(p-cymene)(TsDPEN)] (1), which can reduce pyruvate to un-natural D-lactate in MCF7 breast cancer cells using formate as a hydride source, is significantly increased in combination with the monocarboxylate transporter (MCT) inhibitor AZD3965. AZD3965, a drug currently in clinical trials, also significantly lowers the intracellular level of glutathione and increases mitochondrial metabolism. These synergistic mechanisms of reductive stress induced by 1, blockade of lactate efflux, and oxidative stress induced by AZD3965 provide a strategy for low-dose combination therapy with novel mechanisms of action.

The connection between innervation and metabolic rearrangements in pancreatic cancer through serine

Pancreatic cancer is a kind of aggressive tumor famous for its lethality and intractability, and pancreatic ductal adenocarcinoma is the most common type. Patients with pancreatic cancer often suffer a rapid loss of weight and abdominal neuropathic pain in their early stages and then go through cachexia in the advanced stage. These features of patients are considered to be related to metabolic reprogramming of pancreatic cancer and abundant nerve innervation responsible for the pain. With increasing literature certifying the relationship between nerves and pancreatic ductal adenocarcinoma (PDAC), more evidence point out that innervation's role is not limited to neuropathic pain but explore its anti/pro-tumor functions in PDAC, especially the neural-metabolic crosstalks. This review aims to unite pancreatic cancer's innervation and metabolic rearrangements with terminated published articles. Hopefully, this article could explore the pathogenesis of PDAC and further promote promising detecting or therapeutic measurements for PDAC according to the lavish innervation in PDAC.

scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver.