Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Metabolomics in cirrhosis: Recent advances and opportunities

Liver cirrhosis (LC) represents a significant hepatic disorder that persistently commands the attention of the scientific community, especially concerning its pathogenesis and therapeutic approaches. Metabolomics, the comprehensive profiling of an organism's metabolome, has been increasingly applied in the research of cirrhosis over the past decade. This review summarizes the recent advancements and applications of metabolomics within the context of LC research, in recent five years. It highlights the role of metabolomics in the diagnosis of LC, the assessment of prognostic markers, and the evaluation of therapeutic outcomes. The discussion focuses on the potential and challenges of metabolomics in LC research, including the evolution of analytical technologies, advancements in bioinformatics, and the challenges impeding clinical implementation. Additionally, the review anticipates the forthcoming developments in metabolomics related to LC research, with the objective of facilitating innovative approaches for early detection and intervention in LC.

Targeted Metabolomic Biomarkers for Stroke Subtyping

BACKGROUND AND PURPOSE

Ischemic stroke is a heterogeneous disease with various etiologies. The current subtyping process is complicated, time-consuming, and costly. Metabolite-based biomarkers have the potential to improve classification and deliver optimal treatments. We here aimed to identify novel, targeted metabolomics-based biomarkers to discriminate between large-artery atherosclerosis (LAA) and cardioembolic (CE) stroke.

METHODS

We acquired serum samples and clinical data from a hospital-based acute stroke registry (ischemic stroke within 3 days from symptom onset). We included 346 participants (169 LAA, 147 CE, and 30 healthy older adults) and divided them into training and test sets. Targeted metabolomic analysis was performed using quantitative and quality-controlled liquid chromatography with tandem mass spectrometry. A multivariate regression model using metabolomic signatures was created that could independently distinguish between LAA and CE strokes.

RESULTS

The training set (n = 193) identified metabolomic signatures that were different in patients with LAA and CE strokes. Six metabolomic biomarkers, i.e., lysine, serine, threonine, kynurenine, putrescine, and lysophosphatidylcholine acyl C16:0, could discriminate between LAA and CE stroke after adjusting for sex, age, body mass index, stroke severity, and comorbidities. The enhanced diagnostic power of key metabolite combinations for discriminating between LAA and CE stroke was validated using the test set (n = 123).

CONCLUSIONS

We observed significant differences in metabolite profiles in LAA and CE strokes. Targeted metabolomics may provide enhanced diagnostic yield for stroke subtypes. The pathophysiological pathways of the identified metabolites should be explored in future studies.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study

BACKGROUND

Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.

METHODS

In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables.

RESULTS

The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.

CONCLUSIONS

The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

The role of serum acylcarnitine profiling for the detection of multiple solid tumors in humans

Metabolic reprogramming is an essential hallmark of cancer. Several studies have reported the dysregulation of acylcarnitine (ACar) metabolism in tumor cells, suggesting that changes in the blood ACar may be related to tumor growth. Accordingly, this study aimed to understand the alteration of serum ACar profiles in various solid tumors and explore the potential of differential serum ACars as diagnostic biomarkers. A series of 69 relatively abundant ACars were identified via untargeted analysis. Then, targeted metabolomics was used to describe the metabolic alterations in ACars between normal controls and patients with six types of solid tumors. The results suggested that changes in ACars correlated with their carbon chain length and saturation. The six tumor types had highly similar ACar metabolic profiles, indicating similar fatty acid oxidation (FAO) metabolic pathways. Moreover, the receiver operating curve analysis of differential ACars showed that 16 ACars (C8-C14) had high diagnostic capability towards the studied solid tumors. Specifically, the area under the curve of ACar 10:2 isomer2 and ACar 12:2 isomer2 was greater than 0.95. In conclusion, the marked decrease in the levels of medium- and long-chain ACars (C8-C18) in the six solid tumors suggests that they may have similar FAO-based metabolic pathways, which could afford a common target for cancer therapy. Additionally, 16 ACars (C8-C14) were identified as potential biomarkers for diagnosing six types of solid tumors.

Targeted Analysis of Glycerophospholipids and Mono-, Di-, or Tri-Acylglycerides in Liver Cancer

The metabolic rearrangements of hepatic metabolism associated with liver cancer are still incompletely understood. There is an ongoing need to identify novel and more efficient diagnostic biomarkers and therapeutic targets based on the metabolic mechanisms of these diseases. In comparison to traditional diagnostic biomarkers, metabolomics is a comprehensive technique for discovering chemical signatures for liver cancer screening, prediction, and earlier diagnosis. Lipids are a large and diverse group of complex biomolecules that are at the heart of liver physiology and play an important role in the development and progression of cancer. In this chapter, we described two detailed protocols for targeted lipids analysis: glycerophospholipids and mono, di, tri-acylglycerides, both by Flow Injection Analysis (FIA) HPLC coupled to a SelexIon/QTRAP 6500+ system. These approaches provide a targeted lipidomic metabolomic signature of dissimilar metabolic disorders affecting liver cancers.

Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis

Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.

Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.

Long non-coding RNA CASC7 is a promising serum biomarker for hepatocellular carcinoma

BACKGROUND

At present, a large number of studies have found that long non-coding RNAs (lncRNAs) can be used as biomarkers for diagnosis and monitoring prognosis of hepatocellular carcinoma (HCC). The expression of lncRNA cancer susceptibility candidate 7 (CASC7) in HCC has rarely been studied. The purpose of this study was to explore the expression of CASC7 and its correlation with clinical features, and to further analyze its diagnostic value in HCC.

METHODS

Serum samples were collected from 80 patients with HCC, 80 patients with chronic hepatitis B (CHB), and 80 healthy people. The expression level of serum CASC7 was detected by droplet digital PCR. Appropriate parametric and nonparametric tests were used for data analysis.

RESULTS

The results showed that the expression of CASC7 in serum of patients with HCC was significantly higher than that of patients with CHB (median: 8.8 versus 2.2 copies/µl, p < 0.001) and healthy controls (median: 8.8 versus 3.8 copies/µl, p < 0.001). High expression of serum CASC7 was significantly correlated with tumor number (p = 0.005), intrahepatic metastasis (IM) (p < 0.001), tumor size (p = 0.007) and tumor-node-metastasis (TNM) stage (p = 0.008). The area under the curve (AUC) of CASC7 to distinguish HCC patients from CHB patients and healthy controls was 0.808 (95% CI: 0.742-0.874) at the cut-off value of 7.24 copies/µl with 63.8% sensitivity and 95.2% specificity.

CONCLUSIONS

This study suggested that CASC7 was significantly up-regulated in serum of patients with HCC and closely related to tumor number, IM, tumor size and TNM stage, which may serve as a promising diagnostic biomarker.

Baseline Tyrosine Level Is Associated with Dynamic Changes in FAST Score in NAFLD Patients under Lifestyle Modification

Noninvasive risk stratification is a challenging issue in the management of patients with nonalcoholic fatty liver disease (NAFLD). This study aimed to identify multiomics-based predictors of NAFLD progression, as assessed by changes in serial FibroScan-aspartate aminotransferase (FAST) scores during lifestyle modification. A total of 266 patients with available metabolomics and genotyping data were included. The follow-up sub-cohort included patients with paired laboratory and transient elastography results (n = 160). The baseline median FAST score was 0.37. The PNPLA3 rs738409 genotype was significantly associated with a FAST score > 0.35. Circulating metabolomics significantly associated with a FAST score > 0.35 included SM C24:0 (odds ratio [OR] = 0.642; 95% confidence interval [CI], 0.463-0.891), PC ae C40:6 (OR = 0.477; 95% CI, 0.340-0.669), lysoPC a C18:2 (OR = 0.570; 95% CI, 0.417-0.779), and tyrosine (OR = 2.743; 95% CI, 1.875-4.014). A combination of these metabolites and PNPLA3 genotype yielded a c-index = 0.948 for predicting a FAST score > 0.35. In the follow-up sub-cohort (median follow-up = 23.7 months), 47/76 patients (61.8%) with a baseline FAST score > 0.35 had a follow-up FAST score ≤ 0.35. An improved FAST score at follow-up was significantly associated with age, serum alanine aminotransferase, and tyrosine. In conclusion, baseline risk stratification in NAFLD patients may be assisted using a multiomics-based model. Particularly, patients with increased tyrosine may benefit from an earlier switch to pharmacologic approaches.

Focusing on discoidin domain receptors in premalignant and malignant liver diseases

Discoidin domain receptors (DDRs) are receptor tyrosine kinases on the membrane surface that bind to extracellular collagens, but they are rarely expressed in normal liver tissues. Recent studies have demonstrated that DDRs participate in and influence the processes underlying premalignant and malignant liver diseases. A brief overview of the potential roles of DDR1 and DDR2 in premalignant and malignant liver diseases is presented. DDR1 has proinflammatory and profibrotic benefits and promotes the invasion, migration and liver metastasis of tumour cells. However, DDR2 may play a pathogenic role in early-stage liver injury (prefibrotic stage) and a different role in chronic liver fibrosis and in metastatic liver cancer. These views are critically significant and first described in detail in this review. The main purpose of this review was to describe how DDRs act in premalignant and malignant liver diseases and their potential mechanisms through an in-depth summary of preclinical in vitro and in vivo studies. Our work aims to provide new ideas for cancer treatment and accelerate translation from bench to bedside.

Biomarker discovery and application-An opportunity to resolve the challenge of liver cancer diagnosis and treatment

Liver cancer is one of the most common malignancies, with severe morbidity and mortality. While considerable progress has been made in liver cancer treatment, the 5-year overall survival (OS) of patients has not improved significantly. Reasons include the inadequate capability of early screening and diagnosis, a high incidence of recurrence and metastasis, a high degree of tumor heterogeneity, and an immunosuppressive tumor microenvironment. Therefore, the identification and validation of specific and robust liver cancer biomarkers are of major importance for early screening, timely diagnosis, accurate prognosis, and the prevention of tumor progression. In this review, we highlight some of the latest research progress and potential applications of liver cancer biomarkers, describing hotspots and prospective directions in biomarker discovery.

Metabolomic profiling in kidney cells treated with a sodium glucose-cotransporter 2 inhibitor

We aimed to determine the metabolomic profile of kidney cells under high glucose conditions and following sodium-glucose cotransporter 2 (SGLT2) inhibitor treatment. Targeted metabolomics using the Absolute IDQ-p180 kit was applied to quantify metabolites in kidney cells stimulated with high glucose (25 and 50 mM) and treated with SGLT2 inhibitor, dapagliflozin (2 µM). Primary cultured human tubular epithelial cells and podocytes were used to identify the metabolomic profile in high glucose conditions following dapagliflozin treatment. The levels of asparagine, PC ae C34:1, and PC ae C36:2 were elevated in tubular epithelial cells stimulated with 50 mM glucose and were significantly decreased after 2 µM dapagliflozin treatment. The level of PC aa C32:0 was significantly decreased after 50 mM glucose treatment compared with the control, and its level was significantly increased after dapagliflozin treatment in podocytes. The metabolism of glutathione, asparagine and proline was significantly changed in tubular epithelial cells under high-glucose stimulation. And the pathway analysis showed that aminoacyl-tRNA biosynthesis, arginine and proline metabolism, glutathione metabolism, valine, leucine and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, beta-alanine metabolism, phenylalanine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, glycine, serine and threonine metabolism were altered in tubular epithelial cells after dapagliflozin treatment following 50 mM glucose compared to those treated with 50 mM glucose.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Circulating Metabolic Markers Related to the Diagnosis of Hepatocellular Carcinoma

Primary liver carcinoma is the sixth most common cancer worldwide, while hepatocellular carcinoma (HCC) is the most dominant cancer type. Chronic hepatitis B and C virus infections and aflatoxin exposure are the main risk factors, while nonalcoholic fatty liver disease caused by obesity, diabetes, and metabolic syndrome are the more common risk factors for HCC. Metabolic disorders caused by these high-risk factors are closely related to the tumor microenvironment of HCC, revealing a possible cause-and-effect relationship between the two. These metabolic disorders involve many complex metabolic pathways, such as carbohydrate, lipid, lipid derivative, amino acid, and amino acid derivative metabolic processes. The resulting metabolites with significant abnormal changes in the concentration level in circulating blood may be used as biomarkers to guide the diagnosis, treatment, or prognosis of HCC. At present, there are high-throughput technologies that can quickly detect small molecular metabolites in many samples. Compared to tissue biopsy, blood samples are easier to obtain, and patients’ willingness to participate is higher, which makes it possible to study blood HCC biomarkers. Over the past few years, a substantial body of research has been performed worldwide, and other potential biomarkers have been identified. Unfortunately, due to the limitations of each study, only a few markers have been widely verified and are suitable for clinical use. This review briefly summarizes the potential blood metabolic markers related to the diagnosis of HCC, mainly focusing on amino acids and their derivative metabolism, lipids and their derivative metabolism, and other possible related metabolisms.

Urine fluorescence spectroscopy combined with machine learning for screening of hepatocellular carcinoma and liver cirrhosis

In this paper, we investigated the possibility of using urine fluorescence spectroscopy and machine learning method to identify hepatocellular carcinoma (HCC) and liver cirrhosis from healthy people. Urine fluorescence spectra of HCC (n = 62), liver cirrhosis (n = 65) and normal people (n = 60) were recorded at 405 nm excitation using a Fluorescent scan multimode reader. The normalized fluorescence spectra revealed endogenous metabolites differences associated with the disease, mainly the abnormal metabolism of porphyrin derivatives and bilirubin in the urine of patients with HCC and liver cirrhosis compared to normal people. The Support vector machine (SVM) algorithm was used to differentiate the urine fluorescence spectra of the HCC, liver cirrhosis and normal groups, and its overall diagnostic accuracy was 83.42%, the sensitivity for HCC and liver cirrhosis were 93.55% and 73.85%, and the specificity for HCC and liver cirrhosis were 88.00% and 89.34%, respectively. This exploratory work shown that the combination of urine fluorescence spectroscopy and SVM algorithm has great potential for the noninvasive screening of HCC and liver cirrhosis.

Comprehensive serum metabolomics and network analysis to reveal the mechanism of gypenosides in treating lung cancer and enhancing the pharmacological effects of cisplatin

Gypenosides (GYP) exerted anticancer activity against various cancers. However, the mechanism of GYP against lung cancer (LC) in vivo remains unclear. This study aims to reveal the potential mechanism of GYP against LC and enhancing cisplatin efficacy using a comprehensive analysis of metabolomics, network analysis. Pharmacodynamic results showed that GYP inhibited tumor growth, reduced tumor volume and tumor weight, and alleviated pathological symptoms in Lewis tumor-bearing mice, and GYP could enhance the anti-LC effects of cisplatin. Using serum metabolomics methods, 53 metabolites were found to be significantly altered in the model group, and the levels of 23 biomarkers were significantly restored after GYP treatment. GYP-related metabolic pathways involved six pathways, including alpha-linolenic acid metabolism, glutathione metabolism, sphingolipid metabolism, glycerophospholipid metabolism, tryptophan metabolism, and primary bile acid biosynthesis. 57 genes associated with differential metabolites of GYP recovery and 7 genes of 11 saponins of GYP against LC were screened by network analysis, the STRING database was used to find the association between 57 genes and 7 genes, and a compound-intersection gene-metabolite related gene-metabolite-pathway network was constructed, and STAT3, MAPK14, EGFR and TYMS might be the crucial targets of GYP against LC. Western blot results showed that GYP restored the levels of STA3, MAPK14, EGFR, and TYMS in the model group, and GYP also restored the levels of STAT3 and MAPK14 in the cisplatin group, indicating that GYP might exert anti-LC effects and enhance the pharmacological effects of cisplatin through MAPK14/STAT3 signaling pathway. Our method revealed the effect and mechanism of GYP on LC and the pharmacological effects of GYP-enhanced chemotherapeutic agent cisplatin, which provided some reference for the development of anti-cancer drugs.

Identification of novel lactate metabolism signatures and molecular subtypes for prognosis in hepatocellular carcinoma

Background: Evidence has shown that lactate, an immune signaling molecule, is associated with hepatocellular carcinoma (HCC) progression and immune suppression. Therefore, identifying lactate metabolism-related molecules is a promising therapeutic strategy to inhibit the development of HCC and overcome chemotherapy resistance. Long noncoding RNAs (lncRNAs) are related to tumorigenesis and metastasis. Hence, verifying the molecular subtypes of lncRNAs related to lactate metabolism will play a critical role in managing HCC.

Methods: Based on HCC data in The Cancer Genome Atlas (TCGA), lactate metabolic pathway-related genes were enriched by gene collection and enrichment analysis (GSEA). Lactate metabolism-related lncRNAs (LM_lncRNAs) were identified by correlation analysis, HCC molecular subtypes were determined using nonnegative matrix factorization (NMF) clustering, and the response of the three subtypes to chemotherapeutics was further evaluated using the Genomic Tumor Sensitive Cell Line (GDSC) dataset. LM_lncRNAs were examined via Lasso-Cox regression analysis to determine prognosis for patients. A Nomagram plot was used to predict patient survival time.

Results: Three molecular subtypes of HCC were identified. The survival rate of patients with C1 subtype was higher than that of those with C2 and C3. Additionally, patients with C3 subtype have higher levels of immune cell infiltration and high expression of genes related to immune checkpoints. The GDSC results indicated that patients with C3 subtypes were more sensitive to chemotherapy drugs such as sorafenib and sunitinib. The prognostic risk assessment model consisted of six risk factors (AC034229.4, AC131009.1, MYOSLID, AC008667.1, AC012073.1, AC068025.1) and two protective factors (LINC00402 and AC103858.1). Based on Kaplan-Meier analysis, low-risk HCC patients had a high survival rate, and the receiver operating characteristic curve (ROC), calibration curve, and C-index confirmed good prediction ability.

Conclusion: In this study, the molecular subtyping method and prediction model of lactate metabolism-related lncRNAs (LM_lncRNAs) were constructed for the prognosis of HCC patients. This work demonstrated the potential targets of LM_lncRNAs and provided a novel perspective and therapeutic paradigm for future clinical translation.

Microfluidic wearable electrochemical sweat sensors for health monitoring

Research on remote health monitoring through wearable sensors has attained popularity in recent decades mainly due to aging population and expensive health care services. Microfluidic wearable sweat sensors provide economical, non-invasive mode of sample collection, important physiological information, and continuous tracking of human health. Recent advances in wearable sensors focus on electrochemical monitoring of biomarkers in sweat and can be applicable in various fields like fitness monitoring, nutrition, and medical diagnosis. This review focuses on the evolution of wearable devices from benchtop electrochemical systems to microfluidic-based wearable sensors. Major classification of wearable sensors like skin contact-based and biofluidic-based sensors are discussed. Furthermore, sweat chemistry and related biomarkers are explained in addition to integration of microfluidic systems in wearable sweat sensors. At last, recent advances in wearable electrochemical sweat sensors are discussed, which includes tattoo-based, paper microfluidics, patches, wrist band, and belt-based wearable sensors.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

The Role of Chronic Liver Diseases in the Emergence and Recurrence of Hepatocellular Carcinoma: An Omics Perspective

Hepatocellular carcinoma (HCC) typically develops from a background of cirrhosis resulting from chronic inflammation. This inflammation is frequently associated with chronic liver diseases (CLD). The advent of next generation sequencing has enabled extensive analyses of molecular aberrations in HCC. However, less attention has been directed to the chronically inflamed background of the liver, prior to HCC emergence and during recurrence following surgery. Hepatocytes within chronically inflamed liver tissues present highly activated inflammatory signaling pathways and accumulation of a complex mutational landscape. In this altered environment, cells may transform in a stepwise manner toward tumorigenesis. Similarly, the chronically inflamed environment which persists after resection may impact the timing of HCC recurrence. Advances in research are allowing an extensive epigenomic, transcriptomic and proteomic characterization of CLD which define the emergence of HCC or its recurrence. The amount of data generated will enable the understanding of oncogenic mechanisms in HCC from the CLD perspective and provide the possibility to identify robust biomarkers or novel therapeutic targets for the treatment of primary and recurrent HCC. Importantly, biomarkers defined by the analysis of CLD tissue may permit the early detection or prevention of HCC emergence and recurrence. In this review, we compile the current omics based evidence of the contribution of CLD tissues to the emergence and recurrence of HCC.

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.

Kernel-based hierarchical structural component models for pathway analysis

MOTIVATION

Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex.

RESULTS

To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Liver injury in non-alcoholic fatty liver disease is associated with urea cycle enzyme dysregulation

The main aim was to evaluate changes in urea cycle enzymes in NAFLD patients and in two preclinical animal models mimicking this entity. Seventeen liver specimens from NAFLD patients were included for immunohistochemistry and gene expression analyses. Three-hundred-and-eighty-two biopsy-proven NAFLD patients were genotyped for rs1047891, a functional variant located in carbamoyl phosphate synthetase-1 (CPS1) gene. Two preclinical models were employed to analyse CPS1 by immunohistochemistry, a choline deficient high-fat diet model (CDA-HFD) and a high fat diet LDLr knockout model (LDLr -/-). A significant downregulation in mRNA was observed in CPS1 and ornithine transcarbamylase (OTC1) in simple steatosis and NASH-fibrosis patients versus controls. Further, age, obesity (BMI > 30 kg/m²), diabetes mellitus and ALT were found to be risk factors whereas A-allele from CPS1 was a protective factor from liver fibrosis. CPS1 hepatic expression was diminished in parallel with the increase of fibrosis, and its levels reverted up to normality after changing diet in CDA-HFD mice. In conclusion, liver fibrosis and steatosis were associated with a reduction in both gene and protein expression patterns of mitochondrial urea cycle enzymes. A-allele from a variant on CPS1 may protect from fibrosis development. CPS1 expression is restored in a preclinical model when the main trigger of the liver damage disappears.

Integrated Analysis of miRNA and mRNA Expression Profiles Reveals the Molecular Mechanism of Posttraumatic Stress Disorder and Therapeutic Drugs

Purpose

Post-traumatic stress disorder (PTSD) is a result of trauma exposure and is related to psychological suffering as a long-lasting health issue. Further analysis of the networks and genes involved in PTSD are critical to the molecular mechanisms of PTSD.

Methods

In this study, we aimed to identify key genes and molecular interaction networks involved in the pathogenesis of PTSD by integrating mRNA and miRNA data.

Results

By integrating three high-throughput datasets, 5606 differentially expressed genes (DEGs) were detected, including five differentially expressed miRNAs (DEmiRNAs) and 5525 differentially expressed mRNAs (DEmRNAs). Nineteen upregulated and 46 downregulated DEmRNAs were identified in both GSE64813 and GSE89866 datasets, while five upregulated DEmiRNAs were found in the GSE87768 dataset. Functional annotations of these DEmRNAs indicated that they were mainly enriched in blood coagulation, cell adhesion, platelet activation, and extracellular matrix (ECM)-receptor interaction. Integrated protein-protein and miRNA-protein interaction networks among the DEGs were established with the help of 65 nodes and 121 interactions. Finally, 286 small molecules were obtained based on the Drug-Gene Interaction database (DGIdb). Three genes, prostaglandin-endoperoxide synthase 1 (PTGS1), beta-tubulin gene (TUBB1), and cyclin-dependent kinase inhibitor 1A (CDKN1A), were the most promising targets for PTSD therapy. Additionally, the present study also provided a higher performance diagnostic model for PTSD based on 17 DEmRNAs, which was validated in two independent datasets, GSE109409 and GSE63878.

Conclusion

Our data provides a new molecular aspect that ECM-receptor interaction and the platelet activation process could be the potential molecular mechanism of PTSD, and the genes involved in this process may be promising therapeutic targets. A higher-performance diagnostic model for PTSD has also been identified.

A metabolomics strategy to identify potential biomarkers associated with human laryngeal cancer based on dried blood spot mass spectrometry approach

Laryngeal cancer (LC) as one of common malignant tumors in the head and neck region accounted for 1% to 5% of new cancer cases and was ranked as the third otolaryngology cancer. However, some patients with LC were diagnosed at the advanced stage, which can cause delayed diagnosis and treatment. It is an urgent task to seek effective biomarkers for the early diagnosis of LC aimed at alleviating suffering.A combination of dried blood spot sampling and direct infusion mass spectrometry technology was applied to 39 patients with LC and 53 healthy individuals. Multiple algorithms towards 93 metabolites including amino acids and carnitine/acylcarnitines were run for selecting differential metabolites. Furthermore, leave-one-out cross-validation method was used to evaluate diagnostic performance of selected metabolite biomarkers.A biomarker panel consisting of arginine, proline, hexacosanoic carnitine, ornithine /citrulline, and 3-hydroxy-octadecenoylcarnitine exhibited potential to distinguish patients with LC from healthy individuals, with a sensitivity of 0.8974 and a specificity of 0.8302 in leave-one-out cross-validation model.The metabolomic analysis of LC patients is beneficial to screen disease-associated biomarkers and develop new diagnostic approaches.

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.

Critical biomarkers of hepatocellular carcinoma in body fluids and gut microbiota

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer and one of the major causes of cancer-related death. The development of specific non-invasive or diagnostic markers from blood, urine and feces may represent a valuable tool for detecting HCC at an early stage. Biomarkers are considered novel potential targets for therapeutic intervention. It helps in the prediction of prognosis or recurrence of HCC, and also assist in the selection of appropriate treatment modality. We summarize the most relevant existing data about various biomarkers that play a key role in the progression of HCC.

Hepatocellular carcinoma biomarkers, an imminent need

Hepatocellular carcinoma (HCC) is the most common malignant neoplasm of the liver and one of the deadliest cancers worldwide. The identification of novel, highly specific and more sensitive biomarkers for HCC is crucial because existing ones are deficient and non-confirmatory without histological biopsy or imaging techniques.

Specific Gut Microbiome and Serum Metabolome Changes in Lung Cancer Patients

Background

Lung cancer (LC) is one of the most aggressive, prevalent and fatal malignancies. Gut microbes and their associated metabolites are thought to cause and modulate LC development, albeit influenced by the host genetic make-up and environment. Herein, we identified and classified gut microbiota and serum metabolites associated with LC.

Methods

Stool samples were collected from 41 LC patients and 40 healthy volunteers. The gut microbiota was analyzed using 16S rRNA gene sequencing. Serum samples were collected from the same LC patients (n=30) and healthy volunteers (n=30) and serum metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS). Microbiome and metabolome data were analyzed separately and integrated for combined analysis using various bioinformatics methods.

Results

Serum metabolomics uncovered 870 metabolites regulated in 76 metabolic pathways in both groups. Microbial diversity analyses identified 15967 operational taxonomic units (OTUs) in groups. Of these, the abundance of 232 OTUs was significantly different between HC and LC groups. Also, serum levels of glycerophospholipids (LysoPE 18:3, LysoPC 14:0, LysoPC 18:3), Imidazopyrimidines (Hypoxanthine), AcylGlcADG 66:18; AcylGlcADG (22:6/22:6/22:6) and Acylcarnitine 11:0 were substantially different between HC and LC groups. Combined analysis correlated LC-associated microbes with metabolites, such as Erysipelotrichaceae_UCG_003, Clostridium and Synergistes with glycerophospholipids.

Conclusions

There is an intricate relationship between gut microbiome and levels of several metabolites such as glycerophospholipids and imidazopyrimidines. Microbial-associated metabolites are potential diagnostic biomarkers and therapeutic targets for LC.

Biomarkers for hepatocellular carcinoma based on body fluids and feces

Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma (HCC). Overwhelming evidence from published studies on HCC has documented that multiple molecular biomarkers detected in body fluids and feces can be utilized in early-diagnosis, predicting responses to specific therapies, evaluating prognosis before or after therapy, as well as serving as novel therapeutic targets. Detection and analysis of proteins, metabolites, circulating nucleic acids, circulating tumor cells, and extracellular vesicles in body fluids (e.g., blood and urine) and gut microbiota (e.g., in feces) have excellent capabilities to improve different aspects of management of HCC. Numerous studies have been devoted in identifying more promising candidate biomarkers and therapeutic targets for diagnosis, treatment, and monitoring responses of HCC to conventional therapies, most of which may improve diagnosis and management of HCC in the future. This review aimed to summarize recent advances in utilizing these biomarkers in HCC and discuss their clinical significance.

Metabolic Fingerprinting Uncovers the Distinction Between the Phenotypes of Tuberculosis Associated COPD and Smoking-Induced COPD

Background: Although smoking is considered the main cause of chronic obstructive pulmonary disease (COPD), several other risk factors, including pulmonary tuberculosis (TB), contribute significantly to disease causation, particularly in developing countries. However, the underlying pathogenesis of TB-associated COPD (T-COPD) is unclear. Moreover, the need for prompt diagnosis and treatment of T-COPD to decrease the future burden of inflammation is underestimated. This study aimed to identify distinctive endogenous metabotypes of T-COPD, compared to smoking-associated COPD (S-COPD). Methods: Cross-sectional metabolomic analyses and clinical examinations of serum samples were performed for three groups of 168 male subjects: T-COPD (n = 59), S-COPD (n = 70), and healthy normal controls (n = 39). To retain a broad spectrum of metabolites, we performed technically distinct analyses (global metabolomic profiling using LC-QTOFMS and targeted analyses using LC-MS/MS). Results: Higher levels of IL-6 and C-reactive protein and St. George Respiratory Questionnaire scores were seen in the T-COPD group, compared to those in the S-COPD group. Global metabolomic profiling showed elevated metabolites, including arachidonic and eicosanoic acids, in the T-COPD group. Typical changes in tryptophan catabolism were observed through targeted profiling. Additionally, in the T-COPD group, kynurenine was elevated, and serotonin levels were reduced; therefore, indoleamine dioxygenase (IDO)/tryptophan hydroxylase (TPH) activities were dysregulated. Correlation analyses showed that changes in oxylipins were positively correlated with serum levels of IL-6 and C-reactive protein. Conclusion: Patients with TB-related COPD have enhanced inflammatory responses that may be linked to fatty acid pathways and tryptophan catabolism, which could be novel therapeutic targets for T-COPD.

Anthracyclins Increase PUFAs: Potential Implications in ER Stress and Cell Death

Metabolic and personalized interventions in cancer treatment require a better understanding of the relationship between the induction of cell death and metabolism. Consequently, we treated three primary liver cancer cell lines with two anthracyclins (doxorubicin and idarubin) and studied the changes in the lipidome. We found that both anthracyclins in the three cell lines increased the levels of polyunsaturated fatty acids (PUFAs) and alkylacylglycerophosphoethanolamines (etherPEs) with PUFAs. As PUFAs and alkylacylglycerophospholipids with PUFAs are fundamental in lipid peroxidation during ferroptotic cell death, our results suggest supplementation with PUFAs and/or etherPEs with PUFAs as a potential general adjuvant of anthracyclins. In contrast, neither the markers of de novo lipogenesis nor cholesterol lipids presented the same trend in all cell lines and treatments. In agreement with previous research, this suggests that modulation of the metabolism of cholesterol could be considered a specific adjuvant of anthracyclins depending on the type of tumor and the individual. Finally, in agreement with previous research, we found a relationship across the different cell types between: (i) the change in endoplasmic reticulum (ER) stress, and (ii) the imbalance between PUFAs and cholesterol and saturated lipids. In the light of previous research, this imbalance partially explains the sensitivity to anthracyclins of the different cells. In conclusion, our results suggest that the modulation of different lipid metabolic pathways may be considered for generalized and personalized metabochemotherapies.

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.

Early diagnosis and therapeutic strategies for hepatocellular carcinoma: From bench to bedside

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths worldwide. The prognosis of patients with HCC remains poor largely due to the late diagnosis and lack of effective treatments. Despite being widely used, alpha-fetoprotein serology and ultrasonography have limited diagnostic performance for early-stage HCC. The emergence of omics strategies has contributed to significant advances in the development of non-invasive biomarkers for the early diagnosis of HCC including proteins, metabolites, circulating tumor deoxyribonucleic acid, and circulating non-coding ribonucleic acid. Early diagnosis is beneficial to patients as it increases the proportion who can be treated with curative treatment, thus prolonging survival outcomes. Currently, multiple clinical trials involving locoregional, systemic therapies, and combinations of these modalities are changing therapeutic strategies for different stage HCC. Success in several preclinical trials that involve immunotherapeutic innovations has created the potential to complement and enforce other treatment strategies in the future. This review summarizes the most recent advances in non-invasive early molecular detection, current therapy strategies, and potential immunotherapeutic innovations of HCC.

LC/MS-Based Global Metabolomic Identification of Serum Biomarkers Differentiating Hepatocellular Carcinoma from Chronic Hepatitis B and Liver Cirrhosis

Chronic hepatitis B virus (CHB) infection is one of the primary risk factors associated with the development of hepatocellular carcinoma (HCC). Despite having been extensively studied, diagnosing early-stage HCC remains challenging, and diagnosed patients have a poor (3-5%) survival rate. Identifying new approaches to detect changes in the serum metabolic profiles of patients with CHB and liver cirrhosis (LC) may provide a valuable approach to better detect HCC at an early stage when it is still amenable to treatment, thereby improving patient prognosis and survival. In the present study, we, therefore, employed a liquid chromatography-mass spectrometry (LC-MS)-based approach to evaluate the serum metabolic profiles of 30 CHB patients, 29 LC patients, and 30 HCC patients. We then employed appropriate statistical methods to identify those metabolites that were best able to distinguish HCC cases from LC and CHB controls. A mass-based database was then used to putatively identify these metabolites. We then confirmed the identities of a subset of these metabolites through comparisons with the MS/MS fragmentation patterns and retention times of reference standards. The serum samples were then reanalyzed to quantify the levels of these selected metabolites and of other metabolites that have previously been identified as potential HCC biomarkers. Through this approach, we observed clear differences in the metabolite profiles of the CHB, LC, and HCC patient groups in both positive- and negative-ion modes. We found that the levels of taurodeoxy cholic acid (TCA) and 1,2-diacyl-3-β-d-galactosyl-sn-glycerol rose with the progression from CHB to LC to HCC, whereas levels of 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid were gradually reduced with liver disease progression in these groups. The ROC analysis showed that taurodeoxy cholic acid (TCA), 1,2-diacyl-3-β-d-galactosyl-sn-glycerol, 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid had a diagnosis performance with liver disease progression. These four metabolites have a significant correlation with alpha fetal protein (AFP) level and age. Our results highlight novel metabolic biomarkers that have the potential to be used for differentiating between CHB, LC, and HCC patients, thereby facilitating the identification and treatment of patients with early-stage HCC.

Diagnostic value of 5 serum biomarkers for hepatocellular carcinoma with different epidemiological backgrounds: A large-scale, retrospective study

Objective:

Hepatocellular carcinoma (HCC) is a lethal global disease that requires an accurate diagnosis. We assessed the potential of 5 serum biomarkers (AFP, AFU, GGT-II, GPC3, and HGF) in the diagnosis of HCC.

Methods:

In this retrospective study, we measured the serum levels of each biomarker using ELISAs in 921 participants, including 298 patients with HCC, 154 patients with chronic hepatitis (CH), 122 patients with liver cirrhosis (LC), and 347 healthy controls from 3 hospitals. Patients negative for hepatitis B surface antigen and hepatitis C antibody (called “NBNC-HCC”) and patients positive for the above indices (called “HBV-HCC and HCV-HCC”) were enrolled. The selected diagnostic model was constructed using a training cohort (n = 468), and a validation cohort (n = 453) was used to validate our results. Receiver operating characteristic analysis was used to evaluate the diagnostic accuracy.

Results:

The α-L-fucosidase (AFU)/α-fetoprotein (AFP) combination was best able to distinguish NBNC-HCC [area under the curve: 0.986 (95% confidence interval: 0.958–0.997), sensitivity: 92.6%, specificity: 98.9%] from healthy controls in the test cohort. For screening populations at risk of developing HCC (CH and LC), the AFP/AFU combination improved the diagnostic specificity for early-stage HCC [area under the curve: 0.776 (0.712–0.831), sensitivity: 52.5%, specificity: 91.6% in the test group]. In all-stage HBV-HCC and HCV-HCC, AFU was also the best candidate biomarker combined with AFP [area under the curve: 0.835 (0.784–0.877), sensitivity 69.1%, specificity: 87.4% in the test group]. All results were verified in the validation group.

Conclusions:

The AFP/AFU combination could be used to identify NBNC-HCC from healthy controls and hepatitis-related HCC from at-risk patients.

Human Hepatocellular Carcinoma (HCC)

Hepatocellular carcinoma (HCC) is the fifth most common cancer and accounts for 8 [...].

Cancer Alters the Metabolic Fingerprint of Extracellular Vesicles

Cancer alters cell metabolism. How these changes are manifested in the metabolite cargo of cancer-derived extracellular vesicles (EVs) remains poorly understood. To explore these changes, EVs from prostate, cutaneous T-cell lymphoma (CTCL), colon cancer cell lines, and control EVs from their noncancerous counterparts were isolated by differential ultracentrifugation and analyzed by nanoparticle tracking analysis (NTA), electron microscopy (EM), Western blotting, and liquid chromatography-mass spectrometry (LC-MS). Although minor differences between the cancerous and non-cancerous cell-derived EVs were observed by NTA and Western blotting, the largest differences were detected in their metabolite cargo. Compared to EVs from noncancerous cells, cancer EVs contained elevated levels of soluble metabolites, e.g., amino acids and B vitamins. Two metabolites, proline and succinate, were elevated in the EV samples of all three cancer types. In addition, folate and creatinine were elevated in the EVs from prostate and CTCL cancer cell lines. In conclusion, we present the first evidence in vitro that the altered metabolism of different cancer cells is reflected in common metabolite changes in their EVs. These results warrant further studies on the significance and usability of this metabolic fingerprint in cancer.

The use of minimally invasive biomarkers for the diagnosis and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common cause of cancer-related deaths worldwide. Despite advances in systemic therapies, patient survival remains low due to late diagnosis and frequent underlying liver diseases. HCC diagnosis generally relies on imaging and liver tissue biopsy. Liver biopsy presents limitations because it is invasive, potentially risky for patients and it frequently misrepresents tumour heterogeneity. Recently, liquid biopsy has emerged as a way to monitor cancer progression in a non-invasive manner. Tumours shed content into the bloodstream, such as circulating tumour cells (CTCs), circulating nucleic acids, extracellular vesicles and proteins, that can be isolated from biological fluids of patients with HCC. These biomarkers provide knowledge regarding the genetic landscape of tumours and might be used for diagnostic or prognostic purposes. In this review, we summarize recent literature on circulating biomarkers for HCC, namely CTCs, circulating tumour DNA (ctDNA), RNA, extracellular vesicles and proteins, and their clinical relevance in HCC.

Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host's phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways.

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941–0.972; p < 0.001) with a sensitivity of 90.91%, a specificity of 92.97%. The SP.eHCC model performed well for predicting the overall survival risk of HCC patients (HR = 10.79; 95% CI: 6.16–18.89; p < 0.001). The oncogenesis of early HCC was revealed mainly involving in pathways associated with cell proliferation and tumor microenvironment. And the transcription factors including EZH2, EGR1, and SOX17 were screened in the genome networks as the promising targets used for precise treatment in patients with HCC. Our findings provide robust models for the early diagnosis and prognosis of HCC, and are crucial for the development of novel targets applied in the precision therapy of HCC.

Omics-derived hepatocellular carcinoma risk biomarkers for precision care of chronic liver diseases

Precise hepatocellular carcinoma (HCC) risk prediction will play increasingly important roles with the contemporary HCC etiologies, that is, non-alcoholic fatty liver disease and resolved hepatitis C virus infection. Because the HCC incidence rate in this emerging patient population is relatively low (~1% per year), identification of a subset of patients at the highest risk is critical to concentrate the effort and resources of regular HCC screening to those who most need it. Omics profiling has been derived using several candidate HCC risk biomarkers, which could refine HCC screening by enabling individual risk-based personalized or risk-stratified patient management. Various types of biomolecules have been explored as sources of information to predict HCC risk at various time horizons. Germline DNA polymorphisms likely reflect race/ethnicity- and/or etiology-specific susceptibility to HCC development or chronic liver disease progression toward carcinogenesis. Transcriptomic dysregulations in the diseased liver capture functional molecular status supporting oncogenesis such as inflammatory pathway and myofibroblast activation. Circulating nucleic acids, proteins, and metabolites could serve as less-invasive measures of molecular HCC risk. Characterization of gut microbiota could also inform HCC risk estimation. Each biomarker could have its niche of clinical application depending on logistics of use, performance, and costs with a goal to eventually improve patient prognosis as a part of the whole algorithm of chronic liver disease management.

Development and validation of a prognostic classifier based on HIF-1 signaling for hepatocellular carcinoma

HIF-1 (hypoxia-inducible factor 1) signaling played a vital role in HCC (hepatocellular carcinoma) prognosis. We aimed to establish an accurate risk scoring system for HCC prognosis prediction and treatment guidance. 424 samples from TCGA (The Cancer Genome Atlas) and 445 samples from GSE14520 dataset were included as the derivation and validation cohort, respectively. In the derivation cohort, prognostic relevant signatures were selected from sixteen HIF-1 related genes and LASSO regression was adopted for model construction. Tumor-infiltrating immune cells were calculated using CIBERSORT algorithm. HIF-1 signaling significantly increased in HCC samples compared with normal tissues. Scoring system based on SLC2A1, ENO1, LDHA and GAPDH exhibited a continuous predictive ability for OS (overall survival) in HCC patients. PCA and t-SNE analysis confirmed a reliable clustering ability of risk score in both cohorts. Patients were classified into high-risk and low-risk groups and the survival outcomes between the two groups showed significant differences. In the derivation cohort, Cox regression indicated the scoring system was an independent predictor for OS, which was validated in the validation cohort. Different infiltrating immune cells fraction and immune scores were also observed in different groups. Herein, a novel integrated scoring system was developed based on HIF-1 related genes, which would be conducive to the precise treatment of patients.

Colorectal cancer diagnostic model utilizing metagenomic and metabolomic data of stool microbial extracellular vesicles

Colorectal cancer (CRC) is the most common type cancers in the world. CRC occurs sporadically in the majority of cases, indicating the predominant cause of the disease are environmental factors. Diet-induced changes in gut-microbiome are recently supposed to contribute on epidemics of CRC. This study was aimed to investigate the association of metagenomics and metabolomics in gut extracellular vesicles (EVs) of CRC and healthy subjects. A total of 40 healthy volunteers and 32 patients with CRC were enrolled in this study. Metagenomic profiling by sequencing 16 S rDNA was performed for assessing microbial codiversity. We explored the small molecule metabolites using gas chromatography-time-of-flight mass spectrometry. In total, stool EVs were prepared from 40 healthy volunteers and 32 patients with CRC. Metagenomic profiling demonstrated that bacterial phyla, particularly of Firmicutes and Proteobacteria, were significantly altered in patients with colorectal cancer. Through metabolomics profiling, we determined seven amino acids, four carboxylic acids, and four fatty acids; including short-chain to long chain fatty acids that altered in the disease group. Binary logistic regression was further tested to evaluate the diagnostic performance. In summary, the present findings suggest that gut flora dysbiosis may result in alternation of amino acid metabolism, which may be correlated with the pathogenesis of CRC.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Virus-Induced Interferon Regulates the Urea Cycle

Integrating transcriptomic, proteomic, and metabolomic data, Lercher et al. show in a mouse model of LCMV infection that type I interferon alters the expression and function of key enzymes of the urea cycle in hepatocytes. This results in altered systemic metabolism, attenuating antiviral T cell responses and ameliorating liver injury.