Urinary Metabotyping of Hepatocellular Carcinoma in a UK Cohort Using Proton Nuclear Magnetic Resonance Spectroscopy

Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.

Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

Circulating Biomarkers for the Early Diagnosis and Management of Hepatocellular Carcinoma with Potential Application in Resource-Limited Settings

Hepatocellular carcinoma (HCC) is among the world's third most lethal cancers. In resource-limited settings (RLS), up to 70% of HCCs are diagnosed with limited curative treatments at an advanced symptomatic stage. Even when HCC is detected early and resection surgery is offered, the post-operative recurrence rate after resection exceeds 70% in five years, of which about 50% occur within two years of surgery. There are no specific biomarkers addressing the surveillance of HCC recurrence due to the limited sensitivity of the available methods. The primary goal in the early diagnosis and management of HCC is to cure disease and improve survival, respectively. Circulating biomarkers can be used as screening, diagnostic, prognostic, and predictive biomarkers to achieve the primary goal of HCC. In this review, we highlighted key circulating blood- or urine-based HCC biomarkers and considered their potential applications in resource-limited settings, where the unmet medical needs of HCC are disproportionately highly significant.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Metabolomic biomarkers for hepatocellular carcinoma: A systematic review

Hepatocellular carcinoma (HCC) is a highly malignant cancer which lack of effective diagnosis and prognosis biomarkers, therefore surging studies focused on the metabolite candidates for HCC. The current study was designed to systematically review the metabolic studies for HCC, summarize the current available evidence and provide implication for further studies within this area. By systematically screening Pubmed and Embase, and eligibility assessment, we eventually included 55 pieces of studies. After summarized their characteristics, we reviewed them by 3 parts, regarding to the different biofluid they carried out the experiments. By collecting the candidates from all the included studies, we carried out pathway enrichment to see the representative of the reported candidates, as expected the pathway consistent with the current knowledge of HCC. Next, we conduct quality assessment on the included studies. Only 36% of the current evidence grouped as high quality, indicating the quality of metabolic studies needs further improvement.

Salivary Metabolites are Promising Non-Invasive Biomarkers of Hepatocellular Carcinoma and Chronic Liver Disease

Background

Hepatocellular carcinoma (HCC) is a leading causes of cancer mortality worldwide. Improved tools are needed for detecting HCC so that treatment can begin as early as possible. Current diagnostic approaches and existing biomarkers, such as alpha-fetoprotein (AFP) lack sensitivity, resulting in too many false negative diagnoses. Machine-learning may be able to identify combinations of biomarkers that provide more robust predictions and improve sensitivity for detecting HCC. We sought to evaluate whether metabolites in patient saliva could distinguish those with HCC, cirrhosis, and those with no documented liver disease.

Methods and Results

We tested 125 salivary metabolites from 110 individuals (43 healthy, 37 HCC, 30 cirrhosis) and identified 4 metabolites that displayed significantly different abundance between groups (FDR P <.2). We also developed four tree-based, machine-learning models, optimized to include different numbers of metabolites, that were trained using cross-validation on 99 patients and validated on a withheld test set of 11 patients. A model using 12 metabolites -octadecanol, acetophenone, lauric acid, 1-monopalmitin, dodecanol, salicylaldehyde, glycyl-proline, 1-monostearin, creatinine, glutamine, serine and 4-hydroxybutyric acid- had a cross-validated sensitivity of 84.8%, specificity of 92.4% and correctly classified 90% of the HCC patients in the test cohort. This model outperformed previously reported sensitivities and specificities for AFP (20-100ng/ml) (61%, 86%) and AFP plus ultrasound (62%, 88%).

Conclusions and Impact

Metabolites detectable in saliva may represent products of disease pathology or a breakdown in liver function. Notably, combinations of salivary metabolites derived from machine-learning may serve as promising non-invasive biomarkers for the detection of HCC.

Comprehensive physiopathology and serum metabolomics for the evaluation of the influence mechanism of qi deficiency on xenograft mouse models of liver cancer

Traditional Chinese medicine believes that qi deficiency is important pathogenesis and syndrome of liver cancer and thus is crucial in related research. However, the effect of qi deficiency on the occurrence and development of liver cancer is still unclear. This study aimed to establish a liver cancer model of qi deficiency through the swimming exhaustion and xenograft of human hepatoma HepG2 cells. The effects of qi deficiency on the occurrence and development of liver cancer were investigated by analyzing tumor development, blood routine, histopathology, and serum metabolomics. Results showed that qi deficiency greatly affected the physiology and tumor growth of xenograft mice. Eight potential biomarkers were identified by metabolomics based on ultra-high performance liquid chromatography and tandem quadrupole time-of-flight mass spectrometry. Their main pathways were arachidonic acid metabolism, phenylalanine metabolism, purine metabolism, glycerolipid metabolism, steroid biosynthesis, sphingomyelin metabolism, and fatty acid metabolism pathway. Finally, the effects of qi deficiency on the occurrence and development of liver cancer were comprehensively analyzed, and the mechanism of this process was preliminarily clarified.

Tumor metabolism and associated serum metabolites define prognostic subtypes of Asian hepatocellular carcinoma

Treatment effectiveness in hepatocellular carcinoma (HCC) depends on early detection and precision-medicine-based patient stratification for targeted therapies. However, the lack of robust biomarkers, particularly a non-invasive diagnostic tool, precludes significant improvement of clinical outcomes for HCC patients. Serum metabolites are one of the best non-invasive means for determining patient prognosis, as they are stable end-products of biochemical processes in human body. In this study, we aimed to identify prognostic serum metabolites in HCC. To determine serum metabolites that were relevant and representative of the tissue status, we performed a two-step correlation analysis to first determine associations between metabolic genes and tissue metabolites, and second, between tissue metabolites and serum metabolites among 49 HCC patients, which were then validated in 408 additional Asian HCC patients with mixed etiologies. We found that certain metabolic genes, tissue metabolites and serum metabolites can independently stratify HCC patients into prognostic subgroups, which are consistent across these different data types and our previous findings. The metabolic subtypes are associated with β-oxidation process in fatty acid metabolism, where patients with worse survival outcome have dysregulated fatty acid metabolism. These serum metabolites may be used as non-invasive biomarkers to define prognostic tumor molecular subtypes for HCC.

Abdominal ultrasound and alpha-foetoprotein for the diagnosis of hepatocellular carcinoma in adults with chronic liver disease

BACKGROUND

Hepatocellular carcinoma (HCC) occurs mostly in people with chronic liver disease and ranks sixth in terms of global instances of cancer, and fourth in terms of cancer deaths for men. Despite that abdominal ultrasound (US) is used as an initial test to exclude the presence of focal liver lesions and serum alpha-foetoprotein (AFP) measurement may raise suspicion of HCC occurrence, further testing to confirm diagnosis as well as staging of HCC is required. Current guidelines recommend surveillance programme using US, with or without AFP, to detect HCC in high-risk populations despite the lack of clear benefits on overall survival. Assessing the diagnostic accuracy of US and AFP may clarify whether the absence of benefit in surveillance programmes could be related to under-diagnosis. Therefore, assessment of the accuracy of these two tests for diagnosing HCC in people with chronic liver disease, not included in surveillance programmes, is needed.

OBJECTIVES

Primary: the diagnostic accuracy of US and AFP, alone or in combination, for the diagnosis of HCC of any size and at any stage in adults with chronic liver disease, either in a surveillance programme or in a clinical setting. Secondary: to assess the diagnostic accuracy of abdominal US and AFP, alone or in combination, for the diagnosis of resectable HCC; to compare the diagnostic accuracy of the individual tests versus the combination of both tests; to investigate sources of heterogeneity in the results.

SEARCH METHODS

We searched the Cochrane Hepato-Biliary Group Controlled Trials Register, the Cochrane Hepato-Biliary Group Diagnostic-Test-Accuracy Studies Register, Cochrane Library, MEDLINE, Embase, LILACS, Science Citation Index Expanded, until 5 June 2020. We applied no language or document-type restrictions.

SELECTION CRITERIA

Studies assessing the diagnostic accuracy of US and AFP, independently or in combination, for the diagnosis of HCC in adults with chronic liver disease, with cross-sectional and case-control designs, using one of the acceptable reference standards, such as pathology of the explanted liver, histology of resected or biopsied focal liver lesion, or typical characteristics on computed tomography, or magnetic resonance imaging, all with a six-months follow-up.

DATA COLLECTION AND ANALYSIS

We independently screened studies, extracted data, and assessed the risk of bias and applicability concerns, using the QUADAS-2 checklist. We presented the results of sensitivity and specificity, using paired forest-plots, and tabulated the results. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs). We double-checked all data extractions and analyses.

MAIN RESULTS

We included 373 studies. The index-test was AFP (326 studies, 144,570 participants); US (39 studies, 18,792 participants); and a combination of AFP and US (eight studies, 5454 participants). We judged at high-risk of bias all but one study. Most studies used different reference standards, often inappropriate to exclude the presence of the target condition, and the time-interval between the index test and the reference standard was rarely defined. Most studies with AFP had a case-control design. We also had major concerns for the applicability due to the characteristics of the participants. As the primary studies with AFP used different cut-offs, we performed a meta-analysis using the hierarchical-summary-receiver-operating-characteristic model, then we carried out two meta-analyses including only studies reporting the most used cut-offs: around 20 ng/mL or 200 ng/mL. AFP cut-off 20 ng/mL: for HCC (147 studies) sensitivity 60% (95% CI 58% to 62%), specificity 84% (95% CI 82% to 86%); for resectable HCC (six studies) sensitivity 65% (95% CI 62% to 68%), specificity 80% (95% CI 59% to 91%). AFP cut-off 200 ng/mL: for HCC (56 studies) sensitivity 36% (95% CI 31% to 41%), specificity 99% (95% CI 98% to 99%); for resectable HCC (two studies) one with sensitivity 4% (95% CI 0% to 19%), specificity 100% (95% CI 96% to 100%), and one with sensitivity 8% (95% CI 3% to 18%), specificity 100% (95% CI 97% to 100%). US: for HCC (39 studies) sensitivity 72% (95% CI 63% to 79%), specificity 94% (95% CI 91% to 96%); for resectable HCC (seven studies) sensitivity 53% (95% CI 38% to 67%), specificity 96% (95% CI 94% to 97%). Combination of AFP (cut-off of 20 ng/mL) and US: for HCC (six studies) sensitivity 96% (95% CI 88% to 98%), specificity 85% (95% CI 73% to 93%); for resectable HCC (two studies) one with sensitivity 89% (95% CI 73% to 97%), specificity of 83% (95% CI 76% to 88%), and one with sensitivity 79% (95% CI 54% to 94%), specificity 87% (95% CI 79% to 94%). The observed heterogeneity in the results remains mostly unexplained, and only in part referable to different cut-offs or settings (surveillance programme compared to clinical series). The sensitivity analyses, excluding studies published as abstracts, or with case-control design, showed no variation in the results. We compared the accuracy obtained from studies with AFP (cut-off around 20 ng/mL) and US: a direct comparison in 11 studies (6674 participants) showed a higher sensitivity of US (81%, 95% CI 66% to 90%) versus AFP (64%, 95% CI 56% to 71%) with similar specificity: US 92% (95% CI 83% to 97%) versus AFP 89% (95% CI 79% to 94%). A direct comparison of six studies (5044 participants) showed a higher sensitivity (96%, 95% CI 88% to 98%) of the combination of AFP and US versus US (76%, 95% CI 56% to 89%) with similar specificity: AFP and US 85% (95% CI 73% to 92%) versus US 93% (95% CI 80% to 98%).

AUTHORS' CONCLUSIONS

In the clinical pathway for the diagnosis of HCC in adults, AFP and US, singularly or in combination, have the role of triage-tests. We found that using AFP, with 20 ng/mL as a cut-off, about 40% of HCC occurrences would be missed, and with US alone, more than a quarter. The combination of the two tests showed the highest sensitivity and less than 5% of HCC occurrences would be missed with about 15% of false-positive results. The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.

Serum metabolites may be useful markers to assess vascular invasion and identify normal alpha-fetoprotein in hepatocellular carcinoma undergoing liver resection: a pilot study

Purpose

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a dismal prognosis. Vascular invasion, among others, is the most robust indicator of postoperative recurrence and overall survival after liver resection for HCC. Few studies to date have attempted to search for effective markers to predict vascular invasion before the operation. The current study would examine the plasma metabolic profiling via ¹H-NMR of HCC patients undergoing liver resection and aim to search for potential biomarkers in the early detection of HCC with normal alpha-fetoprotein (AFP) and the diagnosis of vascular invasion preoperatively.

Materials and methods

HCC patients scheduled to receive liver resections for their HCC were recruited and divided into two separate groups, investigation cohort and validation cohort. Their preoperative blood samples were collected and subjected to a comprehensive metabolomic profiling using ¹H-nuclear magnetic resonance spectroscopy (NMR).

Results

There were 35 HCC patients in the investigation group and 22 patients in the validation group. Chronic hepatitis B remained the most common etiology of HCC, followed by chronic HCV infection. The two study cohorts were essentially comparable in terms of major clinicopathological variables. After ¹H-nuclear NMR analysis, we found in the investigation cohort that HCC with normal alpha-fetoprotein (AFP < 15 ng/mL) had significantly higher serum level of O-acetylcarnitine than those with higher AFP (AFP ≥ 15 ng/mL, P = 0.025). In addition, HCC with microscopic vascular invasion (VI) had significantly higher preoperative serum level of formate than HCC without microscopic VI (P = 0.023). These findings were similar in the validation cohort.

Conclusion

A comprehensive metabolomic profiling of HCC demonstrated that serum metabolites may be utilized to assist the early diagnosis of AFP-negative HCC patients and recognition of microvascular invasion in order to facilitate preoperative surgical planning and postoperative follow-up. Further, larger scale prospective studies are warranted to consolidate our findings.

Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways

Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.