Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls. Int J Mol Sci. 2018;19:pii: E756. [PMID: 29518931 PMCID: PMC5877617 DOI: 10.3390/ijms19030756]

Uncovering de novo polyamine biosynthesis in the gut microbiome and its alteration in inflammatory bowel disease

Polyamines are important gut microbial metabolites known to affect host physiology, yet the mechanisms behind their microbial production remain incompletely understood. In this study, we developed a stable isotope-resolved metabolomic (SIRM) approach to track polyamine biosynthesis in the gut microbiome. Viable microbial cells were extracted from fresh human and mouse feces and incubated anaerobically with [U-13C]-labeled inulin (tracer). Liquid chromatography-high resolution mass spectrometry analysis revealed distinct 13C enrichment profiles for spermidine (SPD) and putrescine (PUT), indicating that the arginine-agmatine-SPD pathway contributes to SPD biosynthesis in addition to the well-known spermidine synthase pathway (PUT aminopropylation). Species differences were observed in the 13C enrichments of polyamines and related metabolites between the human and mouse microbiome. By analyzing the fecal metabolomics and metatranscriptomic data from an inflammatory bowel disease (IBD) cohort, we found significantly higher polyamine levels in IBD patients compared to healthy controls. Further investigations using single-strain SIRM and in silico analyses identified Bacteroides spp. as key contributors to polyamine biosynthesis, harboring essential genes for this process and potentially driving the upregulation of polyamines in IBD. Taken together, this study expands our understanding of polyamine biosynthesis in the gut microbiome and will facilitate the development of precision therapies to target polyamine-associated diseases.

Omics technologies as powerful approaches to unravel colorectal cancer complexity and improve its management

Colorectal cancer (CRC) continues to rank among the deadliest and most prevalent cancers worldwide, necessitating an innovative and comprehensive approach that addresses this serious health challenge at various stages, from screening and diagnosis to treatment and prognosis. As CRC research progresses, the adoption of an omics-centered approach holds transformative potential to revolutionize the management of this disease. Advances in omics technologies encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics allow to unravel the oncogenic alterations at these levels, elucidating the intricacies and the heterogeneous nature of CRC. By providing a comprehensive molecular landscape of CRC, omics technologies enable the discovery of potential biomarkers for early non-invasive detection of CRC, definition of CRC subtypes, prediction of its staging, prognosis, and overall survival of CRC patients. They also allow the identification of potential therapeutic targets, prediction of drug response, tracking treatment efficacy, detection of residual disease and cancer relapse, and deciphering the mechanisms of drug resistance. Moreover, they allow the distinction of non-metastatic CRC patients from metastatic ones as well as the stratification of metastatic risk. Importantly, omics technologies open up new opportunities to establish molecular-based criteria to guide the selection of effective treatment paving the way for the personalization of therapy for CRC patients. This review consolidates current knowledge on the omics-based preclinical discoveries in CRC research emphasizing the significant potential of these technologies to improve CRC screening, diagnosis, and prognosis and promote the implementation of personalized medicine to ultimately reduce CRC prevalence and mortality.

Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics

Background

Accumulating evidence indicates that elevated polyamine levels are closely linked to tumor initiation and progression. However, the precise role of polyamine metabolism in hepatocellular carcinoma (HCC) remains poorly understood.

Methods

We conducted differential expression analysis on bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify 65 polyamine metabolism-related genes. By employing unsupervised consensus clustering, AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network analysis (WGCNA), we identified polyamine metabolism-related genes at both the bulk RNA-seq and single-cell RNA-seq (scRNA-seq) levels. Utilizing 101 machine learning algorithms, we constructed a polyamine metabolism-related signature (PMRS) and validated its predictive power across training, testing, and external validation cohorts. Additionally, we developed a prognostic nomogram model by integrating PMRS with clinical variables. To explore immune treatment sensitivity, we assessed tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, mutation frequency, and immune checkpoint genes expression. Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes.

Results

Using 101 machine learning algorithms, we established a polyamine metabolism-related signature comprising 9 genes, which exhibited strong prognostic value for HCC patients. Further analysis revealed significant differences in clinical features, biological functions, mutation profiles, and immune cell infiltration between high-risk and low-risk groups. Notably, TIDE analysis and immune phenotype scoring (IPS) demonstrated distinct immune treatment sensitivities between the two risk groups. RT-qPCR validation confirmed that these 9 genes were highly expressed in normal cells but significantly downregulated in tumor cells.

Conclusions

Our study developed a polyamine metabolism-based prognostic risk signature for HCC, which may provide valuable insights for personalized treatment strategies in HCC patients.

Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data

IntroductionEarly diagnosis of colorectal cancer (CRC) poses a significant clinical challenge. This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data.MethodsThis retrospective, single-center study analyzed laboratory examination data from healthy controls (HC), polyp patients (Polyp), and CRC patients between 2013 and 2023. Five ML algorithms, including adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), decision tree (DT), logistic regression (LR), and random forest (RF), were employed to classify subjects into HC vs Polyp vs CRC, HC vs CRC, and Polyp vs CRC, respectively.ResultsThis study included 31 539 subjects: 11 793 HCs, 10 125 polyp patients, and 9621 CRC patients. The XGBoost model achieved the highest AUCs of 0.966 for differentiating HC from CRC and 0.881 for Polyp from CRC, outperforming carcino-embryonic antigen (CEA) and fecal occult blood testing (FOBT) tests. This model could also identify CEA-negative or FOBT-negative CRC patients. Incorporating stool miR-92a detection into the model further improved diagnostic performance. Shapley additive explanations (SHAP) plots indicated that FOBT, CEA, lymphocyte percentage (LYMPH%), and hematocrit (HCT) were the most significant features contributing to CRC diagnosis. Additionally, a computational tool for predicting CRC risk based on the optimal model was developed, designed for researchers with programming experience.ConclusionFive ML models for CRC diagnosis, based on ten routine laboratory test items, were developed, achieving higher diagnostic accuracies than traditional CRC biomarkers. The diagnostic capabilities of these ML models can be further enhanced by including stool miR-92a levels.

Stage-Specific Plasma Metabolomic Profiles in Colorectal Cancer

Background/Objectives: The objective of this study was to investigate the metabolomic profiles of patients with colorectal cancer (CRC) across various stages of the disease. Methods: The plasma samples were obtained from 255 subjects, including patients with CRC in stages I-IV, polyps, and controls. We employed capillary electrophoresis time-of-flight mass spectrometry and liquid chromatography triple quadrupole mass spectrometry to analyze hydrophilic metabolites comprehensively. The data were randomly divided into two groups, and consistent differences observed in both groups were analyzed. Results: Acetylated polyamines, such as N1-acetylspermine and N1, N12-diacetylspermine, consistently showed elevated concentrations in stage IV compared to stages I-III. Non-acetylated polyamines, including spermine and spermidine, exhibited increasing trends from polyp to stage IV. Other metabolites, such as histidine and o-acetylcarnitine, showed decreasing trends across stages. While acetylated polyamines have been reported as CRC detection markers, our findings suggest that they also possess diagnostic potential for distinguishing stage IV from other stages. Conclusions: This study showed stage-specific changes in metabolic profiles, including polyamines, of colorectal cancer.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

PURPOSE

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach.

METHODS

Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles.

RESULTS

Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points.

CONCLUSION

The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer

Background

Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown.

Methods

The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the "limma" package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model.

Results

We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients' outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy.

Conclusions

In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism-based targeted therapies for KIRC.

Fatty acid binding protein 4 has prognostic value in peripheral artery disease

OBJECTIVE

Peripheral artery disease (PAD) remains undertreated, despite its association with major amputation and mortality. This is partly due to a lack of available disease biomarkers. The intracellular protein fatty acid binding protein 4 (FABP4) is implicated in diabetes, obesity, and metabolic syndrome. Given that these risk factors are strong contributors to vascular disease, we assessed the prognostic ability of FABP4 in predicting PAD-related adverse limb events.

METHODS

This was a prospective case-control study with 3 years of follow-up. Baseline serum FABP4 concentrations were measured in patients with PAD (n = 569) and without PAD (n = 279). The primary outcome was major adverse limb event (MALE; defined as a composite of vascular intervention or major amputation). The secondary outcome was worsening PAD status (drop in ankle-brachial index ≥0.15). Kaplan-Meier and Cox proportional hazards analyses adjusted for baseline characteristics were conducted to assess the ability of FABP4 to predict MALE and worsening PAD status.

RESULTS

Patients with PAD were older and more likely to have cardiovascular risk factors compared with those without PAD. Over the study period, MALE and worsening PAD status occurred in 162 (19%) and 92 (11%) patients, respectively. Higher FABP4 levels were significantly associated with 3-year MALE (unadjusted hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.04-1.27; adjusted HR, 1.18; 95% CI, 1.03-1.27; P = .022) and worsening PAD status (unadjusted HR, 1.18; 95% CI, 1.13-1.31; adjusted HR, 1.17; 95% CI, 1.12-1.28; P < .001). Three-year Kaplan-Meier survival analysis demonstrated that patients with high FABP4 levels had a decreased freedom from MALE (75% vs 88%; log rank = 22.6; P < .001), vascular intervention (77% vs 89%; log rank = 20.8; P < .001), and worsening PAD status (87% vs 91%; log rank = 6.16; P = .013).

CONCLUSIONS

Individuals with higher serum concentrations of FABP4 are more likely to develop PAD-related adverse limb events. FABP4 has prognostic value in risk-stratifying patients for further vascular evaluation and management.

Microbial Metabolite Dysbiosis and Colorectal Cancer

The global burden of colorectal cancer (CRC) is expected to continuously increase. Through research performed in the past decades, the effects of various environmental factors on CRC development have been well identified. Diet, the gut microbiota and their metabolites are key environmental factors that profoundly affect CRC development. Major microbial metabolites with a relevance for CRC prevention and pathogenesis include dietary fiber-derived short-chain fatty acids, bile acid derivatives, indole metabolites, polyamines, trimethylamine-N-oxide, formate, and hydrogen sulfide. These metabolites regulate various cell types in the intestine, leading to an altered intestinal barrier, immunity, chronic inflammation, and tumorigenesis. The physical, chemical, and metabolic properties of these metabolites along with their distinct functions to trigger host receptors appear to largely determine their effects in regulating CRC development. In this review, we will discuss the current advances in our understanding of the major CRC-regulating microbial metabolites, focusing on their production and interactive effects on immune responses and tumorigenesis in the colon.

Salivary Polyamines Help Detect High-Risk Patients with Pancreatic Cancer: A Prospective Validation Study

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Identification of a polyamine-related signature and six novel prognostic biomarkers in oral squamous cell carcinoma

Elevated polyamine levels are required for tumor transformation and development; however, expression patterns of polyamines and their diagnostic potential have not been investigated in oral squamous cell carcinoma (OSCC), and its impact on prognosis has yet to be determined. A total of 440 OSCC samples and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Consensus clustering was conducted to classify OSCC patients into two subgroups based on the expression of the 17 polyamine regulators. Polyamine-related differentially expressed genes (PARDEGs) among distinct polyamine clusters were determined. To create a prognostic model, PARDEGs were examined in the training cohorts using univariate-Lasso-multivariate Cox regression analyses. Six prognostic genes, namely, "CKS2," "RIMS3," "TRAC," "FMOD," CALML5," and "SPINK7," were identified and applied to develop a predictive model for OSCC. According to the median risk score, the patients were split into high-risk and low-risk groups. The predictive performance of the six gene models was proven by the ROC curve analysis of the training and validation cohorts. Kaplan-Meier curves revealed that the high-risk group had poorer prognosis. Furthermore, the low-risk group was more susceptible to four chemotherapy drugs according to the IC50 of the samples computed by the "pRRophetic" package. The correlation between the risk scores and the proportion of immune cells was calculated. Meanwhile, the tumor mutational burden (TMB) value of the high-risk group was higher. Real-time quantitative polymerase chain reaction was applied to verify the genes constructing the model. The possible connections of the six genes with various immune cell infiltration and therapeutic markers were anticipated. In conclusion, we identified a polyamine-related prognostic signature, and six novel biomarkers in OSCC, which may provide insights to identify new treatment targets for OSCC.

Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings

BACKGROUND

African colorectal cancer (CRC) rates are rising rapidly. A low-cost CRC screening approach is needed to identify CRC from non-CRC patients who should be sent for colonoscopy (a scarcity in Africa).

OBJECTIVE

To identify urinary metabolite biomarkers that, combined with easy-to-measure clinical variables, would identify patients that should be further screened for CRC by colonoscopy. Ideal metabolites would be water-soluble and easily translated into a sensitive, low-cost point-of-care (POC) test.

METHODS

Liquid-chromatography mass spectrometry (LC-MS/MS) was used to quantify 142 metabolites in spot urine samples from 514 Nigerian CRC patients and healthy controls. Metabolite concentration data and clinical characteristics were used to determine optimal sets of biomarkers for identifying CRC from non-CRC subjects.

RESULTS

Our statistical analysis identified N1, N12-diacetylspermine, hippurate, p-hydroxyhippurate, and glutamate as the best metabolites to discriminate CRC patients via POC screening. Logistic regression modeling using these metabolites plus clinical data achieved an area under the receiver-operator characteristic (AUCs) curves of 89.2% for the discovery set, and 89.7% for a separate validation set.

CONCLUSIONS

Effective urinary biomarkers for CRC screening do exist. These results could be transferred into a simple, POC urinary test for screening CRC patients in Africa.

Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.

Metabolomics Analysis of Blood, Urine, and Saliva Samples Based on Capillary Electrophoresis-Mass Spectrometry

Capillary electrophoresis-mass spectrometry (CE-MS) is an ideal method for analyzing various metabolites in biological samples. CE-MS can simultaneously identify and quantify hundreds of charged metabolites using only two acquisition methods for positively and negatively charged metabolites. Furthermore, CE-MS is commonly used for analyzing biological samples to understand the pathology of diseases at the metabolic level and biofluid samples, such as blood and urine, to explore biomarkers. Here, we introduce a protocol that delineates the handling of clinical samples to ensure that the CE-MS analysis yields reproducible quantified data. We have focused on sample collection, storage, processing, and measurement. Although the implementation of rigorous standard operating protocols is preferred for enhancing the quality of the samples, various limitations in an actual clinical setting make it difficult to adhere to strict rules. Therefore, the effect of each process on the quantified metabolites needs to be evaluated to design a protocol with acceptable tolerances. Furthermore, quality controls and assessments to handle clinical samples are introduced.

Quantitative Metabolomics to Explore the Role of Plasma Polyamines in Colorectal Cancer

Colorectal cancer (CRC) is one of the major public health and socio-economic problems, which management demands the development of non-invasive screening tests. Assessment of circulating polyamines could be a valuable tool, although analytical problems still preclude its clinical practice. We exploited ultra-high-resolution liquid chromatography and mass spectrometry, as a highly sensitive and innovative method, to profile eleven polyamines, including spermine and spermidine with their acetylated forms. These data together with an evaluation of the inflammatory indexes might represent suitable biomarkers for the identification of CRC patients. The statistical models revealed good discrimination in distinguishing CRC patients from healthy subjects. The plasma assessment of ornithine and acetylspermine, as well as lymphocyte/platelet ratio, revealed helpful information on the progression of CRC. The combined profiles of circulating polyamines and inflammatory indexes, together with the application of an innovative technology, could represent a valuable tool for discriminating patients from different clinical groups.

One-Carbon and Polyamine Metabolism as Cancer Therapy Targets

Cancer metabolic reprogramming is essential for maintaining cancer cell survival and rapid replication. A common target of this metabolic reprogramming is one-carbon metabolism which is notable for its function in DNA synthesis, protein and DNA methylation, and antioxidant production. Polyamines are a key output of one-carbon metabolism with widespread effects on gene expression and signaling. As a result of these functions, one-carbon and polyamine metabolism have recently drawn a lot of interest for their part in cancer malignancy. Therapeutic inhibitors that target one-carbon and polyamine metabolism have thus been trialed as anticancer medications. The significance and future possibilities of one-carbon and polyamine metabolism as a target in cancer therapy are discussed in this review.

Histone deacetylase-10 liberates spermidine to support polyamine homeostasis and tumor cell growth

Cytosolic histone deacetylase-10 (HDAC10) specifically deacetylates the modified polyamine N8-acetylspermidine (N8-AcSpd). Although intracellular concentrations of N8-AcSpd are low, extracellular sources can be abundant, particularly in the colonic lumen. Extracellular polyamines, including those from the diet and microbiota, can support tumor growth both locally and at distant sites. However, the contribution of N8-AcSpd in this context is unknown. We hypothesized that HDAC10, by converting N8- AcSpd to spermidine, may provide a source of this growth-supporting polyamine in circumstances of reduced polyamine biosynthesis, such as in polyamine-targeting anticancer therapies. Inhibitors of polyamine biosynthesis, including α-difluoromethylornithine (DFMO), inhibit tumor growth, but compensatory uptake of extracellular polyamines has limited their clinical success. Combining DFMO with inhibitors of polyamine uptake have improved the antitumor response. However, acetylated polyamines may use different transport machinery than the parent molecules. Here, we use CRISPR/Cas9-mediated HDAC10-knockout cell lines and HDAC10-specific inhibitors to investigate the contribution of HDAC10 in maintaining tumor cell proliferation. We demonstrate inhibition of cell growth by DFMO-associated polyamine depletion is successfully rescued by exogenous N8-AcSpd (at physiological concentrations), which is converted to spermidine and spermine, only in cell lines with HDAC10 activity. Furthermore, we show loss of HDAC10 prevents both restoration of polyamine levels and growth rescue, implicating HDAC10 in supporting polyamine-associated tumor growth. These data suggest the utility of HDAC10-specific inhibitors as an antitumor strategy that may have value in improving the response to polyamine-blocking therapies. Additionally, the cell-based assay developed in this study provides an inexpensive, high-throughput method of screening potentially selective HDAC10 inhibitors.

Salivary metabolomics with machine learning for colorectal cancer detection

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva‐based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)‐based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross‐validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver‐operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828‐0.891) for CV and 0.870 (95% CI: 0.837‐0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851‐0.907) and 0.870 (95% CI: 0.838‐0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Salivary Metabolomics for Oral Cancer Detection: A Narrative Review

The development of low- or non-invasive screening tests for cancer is crucial for early detection. Saliva is an ideal biofluid containing informative components for monitoring oral and systemic diseases. Metabolomics has frequently been used to identify and quantify numerous metabolites in saliva samples, serving as novel biomarkers associated with various conditions, including cancers. This review summarizes the recent applications of salivary metabolomics in biomarker discovery in oral cancers. We discussed the prevalence, epidemiologic characteristics, and risk factors of oral cancers, as well as the currently available screening programs, in India and Japan. These data imply that the development of biomarkers by itself is inadequate in cancer detection. The use of current diagnostic methods and new technologies is necessary for efficient salivary metabolomics analysis. We also discuss the gap between biomarker discovery and nationwide screening for the early detection of oral cancer and its prevention.

Polyamine Metabolites as Biomarkers in Head and Neck Cancer Biofluids

Background: Novel, non-invasive diagnostic biomarkers that facilitate early intervention in head and neck cancer are urgently needed. Polyamine metabolites have been observed to be elevated in numerous cancer types and correlated with poor prognosis. The aim of this study was to assess the concentration of polyamines in the saliva and urine from head and neck cancer (HNC) patients, compared to healthy controls. Methods: Targeted metabolomic analysis was performed on saliva and urine from 39 HNC patient samples and compared to 89 healthy controls using a quantitative, targeted liquid chromatography mass spectrometry approach. Results: The metabolites N¹-acetylspermine (ASP), N⁸-acetylspermidine (ASD) and N¹,N¹²-diacetylspermine (DAS) were detected at significantly different concentrations in the urine of HNC patients as compared to healthy controls. Only ASP was detected at elevated levels in HNC saliva as compared to healthy controls. Conclusion: These data suggest that assessment of polyamine-based metabolite biomarkers within the saliva and urine warrants further investigation as a potential diagnostic in HNC patients.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

Validation of Urinary Charged Metabolite Profiles in Colorectal Cancer Using Capillary Electrophoresis-Mass Spectrometry

This study aimed to validate and reanalyze urinary biomarkers for detecting colorectal cancers (CRCs). We previously conducted urinary metabolomic analyses using capillary electrophoresis-mass spectrometry and found a significant difference in various metabolites, especially polyamines, between patients with CRC and healthy controls (HC). We analyzed additional samples and confirmed consistency between the newly and previously analyzed data. In total, we included 36 HC, 34 adenoma (AD), and 214 CRC samples, which were used for subsequent analyses. Among the 132 quantified metabolites, 16 exhibited consistent differences in both datasets, which included polyamines, etc. Pathway analyses of the integrated data revealed significant differences in many metabolites, such as glutamine, and metabolites of the TCA (tricarboxylic acid cycle) and urea cycles. The discrimination ability of the combination of multiple metabolites among the three groups was evaluated, which yielded higher sensitivity than tumor markers. The Mann–Whitney test was employed to evaluate the prognosis predictivity of the assessed metabolites and the difference between the patients with or without recurrence, which yielded 16 significantly different metabolites. Among these 16 metabolites, 11 presented significant prognosis predictivity. These data indicated the potential of metabolite-based discrimination of patients with CRC and AD from HC and prognosis predictivity of the monitored metabolites.

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancers management for early diagnosis and treatment. Urine is a highly...

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.

High-throughput screening of salivary polyamine markers for discrimination of colorectal cancer by multisegment injection capillary electrophoresis tandem mass spectrometry

Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking. Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS), which allows sequential 40-sample injection followed by electrophoretic separation and specific mass detection. To achieve consecutive analysis of polyamine samples, 1 M formic acid was used as the background electrolyte (BGE). The BGE spacer volume had an apparent effect on peak resolution among samples, and 20 nL was selected as the optimal volume. The use of polyamine isotopomers as the internal standard enabled the correction of matrix effects in MS detection. This method is sensitive, selective and quantitative, and its utility was demonstrated by screening polyamines in 359 salivary samples within 360 min, resulting in discrimination of colorectal cancer patients from noncancer controls.

Differential Tissue Metabolic Signatures in IgG4-Related Ophthalmic Disease and Orbital Mucosa-Associated Lymphoid Tissue Lymphoma

Purpose

To identify tissue metabolomic profiles in biopsy specimens with IgG4-related ophthalmic disease (IgG4-ROD) and mucosa-associated lymphoid tissue (MALT) lymphoma and investigate their potential implication in the disease pathogenesis and biomarkers.

Methods

We conducted a comprehensive analysis of the metabolomes and lipidomes of biopsy-proven IgG4-ROD (n = 22) and orbital MALT lymphoma (n = 21) specimens and matched adjacent microscopically normal adipose tissues using liquid chromatography time-of-flight mass spectrometry. The altered metabolomic profiles were visualized by heat map and principal component analysis. Metabolic pathway analysis was performed by Metabo Analyst 4.0 using differentially expressed metabolites. The diagnostic performance of the metabolic markers was evaluated using receiver operating characteristic curves. Machine learning algorithms were implemented by random forest using the R environment. Finally, an independent set of 18 IgG4-ROD and 17 orbital MALT lymphoma specimens were used to validate the identified biomarkers.

Results

The principal component analysis showed a significant difference of both IgG4-ROD and orbital MALT lymphoma for biopsy specimens and controls. Interestingly, lesions in IgG4-ROD were uniquely enriched in arachidonic metabolism, whereas those in orbital MALT lymphoma were enriched in tricarboxylic acid cycle metabolism. We identified spermine as the best discriminator between IgG4-ROD and orbital MALT lymphoma, and the area under the receiver operating characteristic curve of the spermine to discriminate between the two diseases was 0.89 (95% confidence interval, 0.803–0.984). A random forest model incorporating a panel of five metabolites showed a high area under the receiver operating characteristic curve value of 0.983 (95% confidence interval, 0.981–0.984). The results of validation revealed that four tissue metabolites: N¹,N¹²-diacetylspermine, spermine, malate, and glycolate, had statistically significant differences between IgG4-ROD and orbital MALT lymphoma with receiver operating characteristic values from 0.708 to 0.863.

Conclusions

These data revealed the characteristic differences in metabolomic profiles between IgG4-ROD and orbital MALT lymphoma, which may be useful for developing new diagnostic biomarkers and elucidating the pathogenic mechanisms of these common orbital lymphoproliferative disorders.

Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles.

Polyamines of biological fluids of the body and the diagnostic value of their determination in clinical and laboratory researches (review of literature)

The review provides the analysis of the content of the main polyamines (PA) - spermine, spermidine and putrescine in the most important biological fluids of the human body (blood, urine, seminal fluid, etc.). The assessment of their diagnostic and prognostic value in clinical practice is carried out. The novelty and value of assessing of the level of PA metabolites as new diagnostic markers of various diseases has been shown. Among such diseases as cancer, stroke, renal failure, for which the search for early markers is especially relevant. This survey data can be of practical interest and taken into account in estimating the level of PA and its derivatives in clinical and laboratory reseaches. The literature search for the review was carried out using the Scopus, Web of Science, MedLine, RSCI databases.

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.

Microbial Metabolites in Colorectal Cancer: Basic and Clinical Implications

Colorectal cancer (CRC) is one of the leading cancers that cause cancer-related deaths worldwide. The gut microbiota has been proved to show relevance with colorectal tumorigenesis through microbial metabolites. By decomposing various dietary residues in the intestinal tract, gut microbiota harvest energy and produce a variety of metabolites to affect the host physiology. However, some of these metabolites are oncogenic factors for CRC. With the advent of metabolomics technology, studies profiling microbiota-derived metabolites have greatly accelerated the progress in our understanding of the host-microbiota metabolism interactions in CRC. In this review, we briefly summarize the present metabolomics techniques in microbial metabolites researches and the mechanisms of microbial metabolites in CRC pathogenesis, furthermore, we discuss the potential clinical applications of microbial metabolites in cancer diagnosis and treatment.

Identification of novel neuroblastoma biomarkers in urine samples

Urine is a complex liquid containing numerous small molecular metabolites. The ability to non-invasively test for cancer biomarkers in urine is especially beneficial for screening child patients. This study attempted to identify neuroblastoma biomarkers by comprehensively analysing urinary metabolite samples from children. A total of 87 urine samples were collected from 54 participants (15 children with neuroblastoma and 39 without cancer) and used to perform a comprehensive analysis. Urine metabolites were extracted using liquid chromatography/mass spectrometry and analysed by Metabolon, Inc. Biomarker candidates were extracted using the Wilcoxon rank sum test, random forest method (RF), and orthogonal partial least squares discriminant analysis (OPLS-DA). RF identified three important metabolic pathways in 15 samples from children with neuroblastoma. One metabolite was selected from each of the three identified pathways and combined to create a biomarker candidate (3-MTS, CTN, and COR) that represented each of the three pathways; using this candidate, all 15 cases were accurately distinguishable from the control group. Two cases in which known biomarkers were negative tested positive using this new biomarker. Furthermore, the predictive value did not decrease in cases with a low therapeutic effect. This approach could be effectively applied to identify biomarkers for other cancer types.

Polyamine biomarkers as indicators of human disease

The significant increase of periodontitis, chronic kidney disease (CKD), Alzheimer's disease and cancer can be attributed to an ageing population. Each disease produces a range of biomarkers that can be indicative of disease onset and progression. Biomarkers are defined as cellular (intra/extracellular components and whole cells), biochemical (metabolites, ions and toxins) or molecular (nucleic acids, proteins and lipids) alterations which are measurable in biological media such as human tissues, cells or fluids. An interesting group of biomarkers that merit further investigation are the polyamines. Polyamines are a group of molecules consisting of cadaverine, putrescine, spermine and spermidine and have been implicated in the development of a range of systemic diseases, in part due to their production in periodontitis. Cadaverine and putrescine within the periodontal environment have demonstrated cell signalling interfering abilities, by way of leukocyte migration disruption. The polyamines spermine and spermidine in tumour cells have been shown to inhibit cellular apoptosis, effectively prolonging tumorigenesis and continuation of cancer within the host. Polyamine degradation products such as acrolein have been shown to exacerbate renal damage in CKD patients. Thus, the use of such molecules has merit to be utilized in the early indication of such diseases in patients.

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Colorectal cancer (CRC) has increasing global prevalence and poor prognostic outcomes, and the development of low- or less invasive screening tests is urgently required. Urine is an ideal biofluid that can be collected non-invasively and contains various metabolite biomarkers. To understand the metabolomic profiles of different stages of CRC, we conducted metabolomic profiling of urinary samples. Capillary electrophoresis-time-of-flight mass spectrometry was used to quantify hydrophilic metabolites in 247 subjects with stage 0 to IV CRC or polyps, and healthy controls. The 154 identified and quantified metabolites included metabolites of glycolysis, TCA cycle, amino acids, urea cycle, and polyamine pathways. The concentrations of these metabolites gradually increased with the stage, and samples of CRC stage IV especially showed a large difference compared to other stages. Polyps and CRC also showed different concentration patterns. We also assessed the differentiation ability of these metabolites. A multiple logistic regression model using three metabolites was developed with a randomly designated training dataset and validated using the remaining data to differentiate CRC and polys from healthy controls based on a panel of urinary metabolites. These data highlight the changes in metabolites from early to late stage of CRC and also the differences between CRC and polyps.

Spermidine as a target for cancer therapy

Spermidine, as a natural component from polyamine members, is originally isolated from semen and also existed in many natural plants, and can be responsible for cell growth and development in eukaryotes. The supplementation of spermidine can extend health and lifespan across species. Although the elevated levels of polyamines and the regulation of rate-limiting enzymes for polyamine metabolism have been identified as the biomarkers in many cancers, recent epidemiological data support that an increased uptake of spermidine as a caloric restriction mimic can reduce overall mortality associated with cancers. The possible mechanisms between spermidine and cancer development may be related to the precise regulation of polyamine metabolism, anti-cancer immunosurveillance, autophagy, and apoptosis. Increased intake of polyamine seems to suppress tumorigenesis, but appears to accelerate the growth of established tumors. Based on these observations and the absolute requirement for polyamines in tumor growth, spermidine could be a rational target for chemoprevention and clinical therapeutics of cancers.

Spermine synthesis inhibitor blocks 25-hydroxycholesterol-induced- apoptosis via SREBP2 upregulation in DLD-1 cell spheroids

The oxysterol 25-hydroxycholesterol (25-HC) has diverse physiological activities, including the ability to inhibit anchorage-independent growth of colorectal cancer cells. Here, we found that a polyamine synthesis inhibitor, DFMO, prevented 25-HC-induced apoptosis in non-anchored colorectal cancer DLD-1 cells. Additionally, we found that the spermine synthesis inhibitor APCHA also inhibited 25-HC-induced apoptosis in DLD-1 spheroids. Inhibiting the maturation of SREBP2, a critical regulator of cholesterol synthesis, reversed the effects of APCHA. SREBP2 knockdown also abolished the ability of APCHA to counteract 25-HC activity. Furthermore, APCHA induced SREBP2 maturation and upregulated its transcriptional activity, indicating that altered polyamine metabolism can increase SREBP2 activity and block 25-HC-induced apoptosis in spheroids. These results suggest that crosstalk between polyamine metabolism and cholesterol synthetic pathways via SREBP2 governs the proliferative and malignant properties of colorectal cancer cells.

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DFMO inhibits 25-HC-induced apoptosis in non-anchored DLD-1 cells.

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APCHA inhibits 25-HC-induced apoptosis in DLD-1 spheroids.

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APCHA induces maturation and upregulation of SREBP2.

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Chemical or genetic inhibition of SREBP2 abrogates anti-apoptotic activity.

Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics

Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a 10 year history supporting six major instrument vendors. Designed initially for proteomics analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring, high-resolution mass spectrometry, and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence-centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline, such as peak picking, chromatographic alignment, and transition selection, have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable instrument vendor software tool, and present additional real-world applications. Further, we include step-by-step instructions on using Skyline for small molecule quantitative method development and data analysis on data acquired with a variety of mass spectrometers from multiple instrument vendors.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study

BACKGROUND

Population-based screening programs are credited with earlier colorectal cancer diagnoses and treatment initiation, which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory as they are invasive, are resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomic-based biomarker panel for the detection of colorectal cancer that has the potential for global population-based screening.

METHODS

Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 342 participants (colorectal cancer, 171; healthy controls, 171) from two study sites (Canada, United States) were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for colorectal cancer were identified by comparing the metabolomic profiles from colorectal cancer versus controls. Multiple models were constructed leading to a good separation of colorectal cancer from controls.

RESULTS

A panel of 17 metabolites was identified as possible biomarkers for colorectal cancer. Using only two of the selected metabolites, namely diacetylspermine and kynurenine, a predictor for detecting colorectal cancer was developed with an AUC of 0.864, a specificity of 80.0%, and a sensitivity of 80.0%.

CONCLUSIONS

We present a potentially "universal" metabolomic biomarker panel for colorectal cancer independent of cohort clinical features based on a North American population. Further research is needed to confirm the utility of the profile in a prospective, population-based colorectal cancer screening trial.

IMPACT

A urinary metabolomic biomarker panel was identified for colorectal cancer with the potential of clinical application.

Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination

PURPOSE

The aim of this study is to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls.

METHODS

Saliva samples were collected after 9 h fasting and were immediately stored at - 80 °C. Capillary electrophoresis and liquid chromatography with mass spectrometry were used to quantify hundreds of hydrophilic metabolites. Conventional statistical analyses and artificial intelligence-based methods were used to assess the discrimination abilities of the quantified metabolites. A multiple logistic regression (MLR) model and an alternative decision tree (ADTree)-based machine learning method were used. The generalization abilities of these mathematical models were validated in various computational tests, such as cross-validation and resampling methods.

RESULTS

One hundred sixty-six unstimulated saliva samples were collected from 101 patients with invasive carcinoma of the breast (IC), 23 patients with ductal carcinoma in situ (DCIS), and 42 healthy controls (C). Of the 260 quantified metabolites, polyamines were significantly elevated in the saliva of patients with breast cancer. Spermine showed the highest area under the receiver operating characteristic curves [0.766; 95% confidence interval (CI) 0.671-0.840, P < 0.0001] to discriminate IC from C. In addition to spermine, polyamines and their acetylated forms were elevated in IC only. Two hundred each of two-fold, five-fold, and ten-fold cross-validation using different random values were conducted and the MLR model had slightly better accuracy. The ADTree with an ensemble approach showed higher accuracy (0.912; 95% CI 0.838-0.961, P < 0.0001). These prediction models also included spermine as a predictive factor.

CONCLUSIONS

These data indicated that combinations of salivary metabolomics with the ADTree-based machine learning methods show potential for non-invasive screening of breast cancer.

Deciphering the complex interplay between microbiota, HPV, inflammation and cancer through cervicovaginal metabolic profiling

BACKGROUND

Dysbiotic vaginal microbiota have been implicated as contributors to persistent HPV-mediated cervical carcinogenesis and genital inflammation with mechanisms unknown. Given that cancer is a metabolic disease, metabolic profiling of the cervicovaginal microenvironment has the potential to reveal the functional interplay between the host and microbes in HPV persistence and progression to cancer.

METHODS

Our study design included HPV-negative/positive controls, women with low-grade and high-grade cervical dysplasia, or cervical cancer (n = 78). Metabolic fingerprints were profiled using liquid chromatography-mass spectrometry. Vaginal microbiota and genital inflammation were analysed using 16S rRNA gene sequencing and immunoassays, respectively. We used an integrative bioinformatic pipeline to reveal host and microbe contributions to the metabolome and to comprehensively assess the link between HPV, microbiota, inflammation and cervical disease.

FINDINGS

Metabolic analysis yielded 475 metabolites with known identities. Unique metabolic fingerprints discriminated patient groups from healthy controls. Three-hydroxybutyrate, eicosenoate, and oleate/vaccenate discriminated (with excellent capacity) between cancer patients versus the healthy participants. Sphingolipids, plasmalogens, and linoleate positively correlated with genital inflammation. Non-Lactobacillus dominant communities, particularly in high-grade dysplasia, perturbed amino acid and nucleotide metabolisms. Adenosine and cytosine correlated positively with Lactobacillus abundance and negatively with genital inflammation. Glycochenodeoxycholate and carnitine metabolisms connected non-Lactobacillus dominance to genital inflammation.

INTERPRETATION

Cervicovaginal metabolic profiles were driven by cancer followed by genital inflammation, HPV infection, and vaginal microbiota. This study provides evidence for metabolite-driven complex host-microbe interactions as hallmarks of cervical cancer with future translational potential. FUND: Flinn Foundation (#1974), Banner Foundation Obstetrics/Gynecology, and NIH NCI (P30-CA023074).

Current state and future direction of screening tool for colorectal cancer

As the second-most-common cause of cancer death, colorectal cancer (CRC) has been recognized as one of the biggest health concerns in advanced countries. The 5-year survival rate for patients with early-stage CRC is significantly better than that for patients with CRC detected at a late stage. The primary target for CRC screening and prevention is advanced neoplasia, which includes both CRC itself, as well as benign but histologically advanced adenomas that are at increased risk for progression to malignancy. Prevention of CRC through detection of advanced adenomas is important. It is, therefore, necessary to develop more efficient detection methods to enable earlier detection and therefore better prognosis. Although a number of CRC diagnostic methods are currently used for early detection, including stool-based tests, traditional colonoscopy, etc., they have not shown optimal results due to several limitations. Hence, development of more reliable screening methods is required in order to detect the disease at an early stage. New screening tools also need to be able to accurately diagnose CRC and advanced adenoma, help guide treatment, and predict the prognosis along with being relatively simple and non-invasive. As part of such efforts, many proposals for the early detection of colorectal neoplasms have been introduced. For example, metabolomics, referring to the scientific study of the metabolism of living organisms, has been shown to be a possible approach for discovering CRC-related biomarkers. In addition, a growing number of high-performance screening methodologies could facilitate biomarker identification. In the present, evidence-based review, the authors summarize the current state as recognized by the recent guideline recommendation from the American Cancer Society, US Preventive Services Task Force and the United States Multi-Society Task Force and discuss future direction of screening tools for colorectal cancer. Further, we highlight the most interesting publications on new screening tools, like molecular biomarkers and metabolomics, and discuss these in detail.

Polyamine metabolism and cancer: treatments, challenges and opportunities

Advances in our understanding of the metabolism and molecular functions of polyamines and their alterations in cancer have led to resurgence in the interest of targeting polyamine metabolism as an anticancer strategy. Increasing knowledge of the interplay between polyamine metabolism and other cancer-driving pathways, including the PTEN-PI3K-mTOR complex 1 (mTORC1), WNT signalling and RAS pathways, suggests potential combination therapies that will have considerable clinical promise. Additionally, an expanding number of promising clinical trials with agents targeting polyamines for both therapy and prevention are ongoing. New insights into molecular mechanisms linking dysregulated polyamine catabolism and carcinogenesis suggest additional strategies that can be used for cancer prevention in at-risk individuals. In addition, polyamine blocking therapy, a strategy that combines the inhibition of polyamine biosynthesis with the simultaneous blockade of polyamine transport, can be more effective than therapies based on polyamine depletion alone and may involve an antitumour immune response. These findings open up new avenues of research into exploiting aberrant polyamine metabolism for anticancer therapy.

Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review

BACKGROUND

Several approaches have been suggested to be useful in the early detection of colorectal neoplasms. Since metabolites are closely related to the phenotype and are available from different human bio-fluids, metabolomics are candidates for non-invasive early detection of colorectal neoplasms.

OBJECTIVES

We aimed to summarize current knowledge on performance characteristics of metabolomics biomarkers that are potentially applicable in a screening setting for the early detection of colorectal neoplasms.

DESIGN

We conducted a systematic literature search in PubMed and Web of Science and searched for biomarkers for the early detection of colorectal neoplasms in easy-to-collect human bio-fluids. Information on study design and performance characteristics for diagnostic accuracy was extracted.

RESULTS

Finally, we included 41 studies in our analysis investigating biomarkers in different bio-fluids (blood, urine, and feces). Although single metabolites mostly had limited ability to distinguish people with and without colorectal neoplasms, promising results were reported for metabolite panels, especially amino acid panels in blood samples, as well as nucleosides in urine samples in several studies. However, validation of the results is limited.

CONCLUSIONS

Panels of metabolites consisting of amino acids in blood and nucleosides in urinary samples might be useful biomarkers for early detection of advanced colorectal neoplasms. However, to make metabolomic biomarkers clinically applicable, future research in larger studies and external validation of the results is required.