Esophageal squamous cell carcinoma (ESCC) is the primary histological subtype of esophageal carcinoma, yet research on environmental exposure risks and associated metabolic alterations preceding ESCC is limited. In a nested case-control cohort of 396 adults (199 diagnosed with ESCC and 197 healthy controls (HC)), we combined exposomics and metabolomics to assess circulating chemical residues and early serum metabolic changes linked to ESCC risk. A cell experiment further evaluated the proliferative impact of 1H,1H,2H,2H-perfluorooctanesulfonic acid (6:2 FTS), identifying it as a risk factor for ESCC, primarily through lipid metabolism-related chronic inflammation. Significant metabolic disruptions were observed in ESCC cases, characterized by increased carnitines, phosphatidylcholines (PCs), and triglycerides (TGs) alongside reduced lysophosphatidylcholines (LPCs) and ether lysophosphatidylcholines (LPC-Os). An early-warning biomarker panel, including glutamic acid, methionine, choline, LPC-O 18:0, TG (14:0_18:2_20:5), and PC (18:0_20:4)/LPC 18:0, showed improved predictive capacity when combined with 6:2 FTS. Metabolome-exposome-wide association studies largely confirmed 6:2 FTS as a potential ESCC risk factor through lipid mediation. This study offers novel insights for ESCC prevention and early diagnosis through a combined biomarker panel integrating metabolic and environmental risk indicators.

Serum total bile acid (TBA) levels are frequently assessed in clinical routine for the early detection of hepatobiliary dysfunction. However, the comparability of current 5th-generation TBA cycle assays based on 3α-hydroxysteroid dehydrogenase (3α-HSD) and their ability to quantify individual bile acids has not been systematically addressed.

Patient serum samples (n=60) across the diagnostically relevant TBA range (1-200 μmol/L) were analyzed using five TBA routine assays from Abbott, DiaSys, Diazyme, Beijing Strong (BSBE) and Randox on the same analyzer (BioMajesty® JCA-BM6010/C). The assays were compared using Passing-Bablok regression and the recovery of 11 individual BAs was evaluated against RP-HPLC-MS/MS as non-enzymatic reference method.

Despite excellent correlation (Spearman r ≥0.99), the assays showed proportional differences (slope) ranging from 0.99 (BSBE/Randox) to 1.24 (Abbott/DiaSys). The assays showed considerable deviation in the recovery of competitor's calibrators and controls, and large heterogeneity in the recovery of individual BAs, with mean deviations from reference value between 13 % (DiaSys) and 42 % (Abbott). CA and TCA were measured most accurately and consistently, whereas GCA, CDCA, DCA, UDCA, and conjugates were over- or undermeasured to varying degrees.

The linear relationship and constant proportional bias between all five routine assays enable the harmonization of TBA measurements up to 60 μmol/L. However, for patient samples with high TBA levels and disease-specific overrepresentation of individual BAs, harmonization will require: i) optimized reaction conditions to equalize substrate specificity, and ii) calibration to a common, commutable reference material with well-defined BA composition instead of internal standards spiked with different BAs.

Esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer, characterized by low five-year survival rate, and concurrent chemoradiotherapy (CCRT) has been proposed to treat ESCC, while potential biomarkers for prognostic monitoring after optimized CCRT remains unknown.

Serum samples from 45 patients with ESCC were collected and categorized into three groups: Control (pre-CCRT), CCRT (during CCRT), and CCRT-1 M (one-month post-CCRT). The therapeutic effect was evaluated using CT imaging and established evaluation criteria. Untargeted metabolomic analysis was performed on the serum samples to identify differential metabolites caused by CCRT treatment, assessing their potential for prognostic monitoring.

CCRT had significant therapeutic efficacy in patients with ESCC, as indicated by CT imaging and RECIST 1.1 solid tumor evaluation criteria. Notably, several metabolic markers were identified through non-targeted metabolomic analysis, highlighting changes following CCRT treatment. These differential metabolites are involved in the dysregulation of phenylalanine, tyrosine, and tryptophan biosynthesis, as well as histidine, arginine, and proline metabolism, and glycine, serine, and threonine metabolism, suggesting a reduction in glucose metabolism in patients with ESCC after CCRT. Additionally, ROC analysis indicated that the AUC of these metabolites exceeded 0.661, underscoring their diagnostic value for assessing CCRT efficacy and their potential use in prognostic monitoring. Comparative metabolomic analysis identified L-phenylalanine and lysine as promising serum biomarkers for predicting therapeutic outcomes.

CCRT shows considerable therapeutic benefit in patients with ESCC, with observed reductions in glucose metabolism post-treatment. L-phenylalanine and lysine may serve as potential serum biomarkers to predict CCRT efficacy.

Hepatocellular carcinoma (HCC) is the third deadliest malignant tumor worldwide. Most patients are initially diagnosed as HCC at advanced stages and are too late for radical treatment by surgery, resulting in poor prognosis. Over 50% of the HCC patients are caused by hepatitis B virus (HBV) infection. Therefore, effective early identification of HCC in the high-risk population with HBV infection is crucial for early intervention of HCC.

We employed plasma lipidomics to identify critical lipid classes associated with tumorigenesis in the high-risk population with HBV infection. Potential regulatory mechanisms are validated at multi-omic levels. A machine learning algorithm is used for feature selection and diagnostic modelling, and performance of the models is evaluated by ROC curves.

We unveiled varied profiles of plasma lipid metabolites in a cohort of 57 HBV-related HCC subjects, 57 HBV-related liver cirrhosis (LC) subjects and 61 chronic hepatitis B (CHB) subjects with matched age, sex and HBV status. We identified a correlation of the ether phosphatidylcholine (PC) synthesis with hepatocarcinogenesis in patients with HBV-related liver diseases. The diagnostic models achieved an area under ROC curve (AUC) of 0.849 for discriminating HCC from CHB and an AUC of 0.829 for discriminating HCC from LC.

We illustrate the role of ether PC in hepatocarcinogenesis upon HBV infection and provide novel effective markers for early detection of HCC in a cohort with HBV infection.

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Background: Even though many metabolic liver diseases can now be diagnosed using blood tests and diagnostic imaging, early diagnosis remains difficult. Understanding mechanisms contributing to the progression from Metabolic Dysfunction-Associated Steatohepatitis (MASH) and Alcoholic Hepatitis (AH) to cirrhosis is critical to reduce the burden of end-stage liver disease. Monitoring individual bile acids has been proposed as a way to distinguish various liver disorders. Methods: This study explored bile acid profiles in patients with MASH and AH. Plasma samples from patients with MASH, AH, and a control group were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify bile acid concentrations. Targeted metabolomic analysis was performed to compare bile acid levels between the hepatitis and control groups. Results: Concentrations of ursodeoxycholic acid (UDCA), chenodeoxycholic acid (CDCA), taurocholic acid (TCA), tauroursodeoxycholic acid (TUDCA), taurochenodeoxycholic acid (TCDCA), glycoursodeoxycholic acid (GUDCA), glycochenodeoxycholic acid (GCDCA), and glycocholic acid (GCA) were significantly elevated in the hepatitis group. Correlation analysis revealed strong positive relationships between the total and direct bilirubin levels and TUDCA and GCDCA. Aspartate aminotransferase (AST) showed strong positive correlations with TCDCA and GCDCA. Child-Pugh score, Fibrosis-4 index, and non-alcoholic fatty liver disease fibrosis score were positively correlated with GCA, whereas the aspartate aminotransferase-to-platelet ratio correlated with TCA, TCDCA, and GCA. The model for end-stage liver disease (MELD) score showed a strong positive correlation with GCDCA. Implications: GCDCA may serve as a predictive biomarker for liver damage, potentially enabling early diagnosis and targeted intervention in patients with MASH and AH.

Background: Giant cell tumor of bone (GCTB) is a locally aggressive tumor. It accounts for only 5% of all bony tumors. Early diagnosis, and follow-up for recurrence is often difficult due to a lack of biogenetic markers. Giant cells are multinucleated epithelioid cells derived from macrophages. Histologically, giant cells are also present in other pathologies of bone, e.g., aneurysmal bone cyst, chondroblastoma, giant cell granuloma, and malignant giant cell tumor, etc. Similarly, radiographic findings overlap with other osteolytic lesions, making the diagnosis and prognosis of giant cell tumor very challenging. Aims and Objectives: The purpose of this study was to explore biological and genetic markers which can be used for detection, differentiation, recurrence, and prognosis of GCTB. This will help to better understand the clinical outcome of GCTB and minimize the need for interventions. Methods: We conducted a literature search using Google, Google Scholar, PubMed, Wiley Library, Medline, Clinical trials.org, and Web of Science. Our search strategy included MeSH terms and key words for giant cell tumor and biogenetic markers from date of inception to September 2020. After excluding review articles, 246 duplicates, and non-relevant articles, we included 24 articles out of 1568 articles, summarizing the role of biogenetic markers in the prognosis of GCT. Results: P63 is 98.6% sensitive and relatively specific for GCT as compared to other multinucleated giant cells containing neoplasms. MDM2 (mouse double minute 2 homolog), IGF1 (insulin-like growth factor 1), STAT1 (signal transducer and activator of transcription 1), and RAC1 (Ras-related C3 botulinum toxin substrate 1) are associated with GCTB recurrence, and might serve as biomarkers for it. Increased expression of the proteins STAT5B, GRB2, and OXSR1 was related to a higher probability of metastasis. H3F3A and H3F3B mutation analysis appears to be a highly specific, although less sensitive, diagnostic tool for the distinction of giant cell tumor of bone (GCTB) and chondroblastoma from other giant cell-containing tumors. A neutrophil to lymphocyte ratio (NLR) > 2.70, platelet to lymphocyte ratio (PLR) > 215.80, lymphocyte to monocyte ratio (LMR) ≤ 2.80, and albumin to globulin ratio (AGR) < 1.50 were significantly associated with decreased disease-free survival (DFS) (p < 0.05). Large amounts of osteoclast-related mRNA (cathepsin K, tartrate-resistant acid phosphatase, and matrix metalloproteinase9) in GCTs (p < 0.05) are associated with the grade of bone resorption. We propose that subarticular primary malignant bone sarcomas with H3.3 mutations represent true malignant GCTB, even in the absence of a benign GCTB component. IMP3 and IGF2 might be potential biomarkers for GCT of the spine in regulating the angiogenesis of giant cell tumor of bone and predicting patients' prognosis. Conclusions: This review study shows serological markers, genetic factors, cell membrane receptor markers, predictive markers for malignancy, and prognostic protein markers which are highly sensitive for GCT and relatively specific for giant cell tumor. MDM2, IGF1, STAT1, RAC1 are important makers in determining recurrence, while P63 and H3F3A differentiate GCT from other giant cell-containing tumors. STAT5B, GRB2, and OXSR1 are significant in determining the prognosis of GCT. Apart from using radiological and histological parameters, we can add them to tumor work-up for definitive diagnosis and prognosis.

Metabolic dysfunction-associated steatohepatitis (MASH) and progression to hepatocellular carcinoma (HCC) exhibits distinct molecular and immune characteristics. These traits are influenced by multiple factors, including the gut microbiome, which interacts with the liver through the "gut-liver axis". This bidirectional relationship between the gut and its microbiota and the liver plays a key role in driving various liver diseases, with microbial metabolites and immune responses being central to these processes. Our review consolidates the latest research on how gut microbiota contributes to MASH development and its progression to HCC, emphasizing new diagnostic and therapeutic possibilities. We performed a comprehensive literature review across PubMed/MedLine, Scopus, and Web of Science from January 2000 to August 2024, focusing on both preclinical and clinical studies that investigate the gut microbiota's roles in MASH and HCC. This includes research on pathogenesis, as well as diagnostic and therapeutic advancements related to the gut microbiota. This evidence emphasizes the critical role of the gut microbiome in the pathogenesis of MASH and HCC, highlighting the need for further clinical studies and trials. This is to refine diagnostic techniques and develop targeted therapies that exploit the microbiome's capabilities, aiming to enhance patient care in liver diseases.

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Schistosomiasis is still one of the most serious parasitic diseases. Evidence showed that the metabolite profile in serum can potentially act as a marker for parasitic disease diagnosis and evaluate disease progression and prognosis. However, the serum metabolome in patients with Schistosoma japonicum infection is not well defined. In this study, we investigated the metabolite profiles of patients with chronic and with advanced S. japonicum infection.

The sera of 33 chronic S. japonicum patients, 15 patients with advanced schistosomiasis and 17 healthy volunteers were collected. Samples were extracted for metabolites and analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

We observed significant differences in metabolite profiles in positive and negative ion modes between patients with advanced and chronic S. japonicum infection. In patients with chronic S. japonicum infection, 199 metabolites were significantly upregulated while 207 metabolites were downregulated in advanced infection. These differential metabolites were mainly concentrated in steroid hormone biosynthesis, cholesterol metabolism and bile secretion pathways. We also found that certain bile acid levels were significantly upregulated in the progression from chronic to advanced S. japonicum infection. In receiver operator characteristic (ROC) analysis, we identified three metabolites with area under the curve (AUC) > 0.8, including glycocholic (GCA), glycochenodeoxycholate (GCDCA) and taurochenodeoxycholic acid (TCDCA) concentrated in cholesterol metabolism, biliary secretion and primary bile acid biosynthesis.

This study provides evidence that GCA, GCDCA and TCDCA can potentially act as novel metabolite biomarkers to distinguish patients in different stages of S. japonicum infection. This study will contribute to the understanding of the metabolite mechanisms of the transition from chronic to advanced S. japonicum infection, although more studies are needed to validate this potential role and explore the underlying mechanisms.

Hepatitis B Virus (HBV) is widely recognized as a "metabolic virus" that disrupts hepatic metabolic homeostasis, rendering it one of the foremost risk factors for hepatocellular carcinoma (HCC). Except for antiviral therapy, the fundamental principles underlying HBV- and HBV+ HCC have remained unchanged, limiting HCC treatment options.

In this study, we aim to identify the distinctive metabolic profile of HBV-associated HCC, with the promise of identifying novel metabolic targets that confer survival advantages and ultimately impede cancer progression.

We employed a comprehensive methodology to evaluate metabolic alterations systematically. Initially, we analyzed transcriptomic and proteomic data obtained from a public database, subsequently validating these findings within our test cohort at both the proteomic and transcriptomic levels. Additionally, we conducted a comprehensive analysis of tissue metabolomics profiles, lipidomics, and the activity of the MAPK and AKT signaling pathway to corroborate the abovementioned changes.

Our multi-omics approach revealed distinct metabolic dysfunctions associated with HBV-associated HCC. Specifically, we observed upregulated steroid hormone biosynthesis, primary bile acid metabolism, and sphingolipid metabolism in HBV-associated HCC patients' serum. Notably, metabolites involved in primary bile acid and sphingolipids can activate the MAPK/mTOR pathway. Tissue metabolomics and lipidomics analyses further validated the serum metabolic alterations, particularly alterations in lipid composition and accumulation of unsaturated fatty acids.

Our findings emphasize the pivotal role of HBV in HCC metabolism, elucidating the activation of a unique MAPK/mTOR signaling axis by primary bile acids and sphingolipids. Moreover, the hyperactive MAPK/mTOR signaling axis transduction leads to significant reprogramming in lipid metabolism within HCC cells, further triggering the activation of the MAPK/mTOR pathway in turn, thereby establishing a self-feeding circle driven by primary bile acids and sphingolipids.

Clear cell renal cell carcinoma (ccRCC) incidence has risen steadily over the last decade. Elevated lipid uptake and storage is required for ccRCC cell viability. As stored cholesterol is the most abundant component in ccRCC intracellular lipid droplets, it may also play an important role in ccRCC cellular homeostasis. In support of this hypothesis, ccRCC cells acquire exogenous cholesterol through the high-density lipoprotein receptor SCARB1, inhibition or suppression of which induces apoptosis. Here, we showed that elevated expression of 3 beta-hydroxy steroid dehydrogenase type 7 (HSD3B7), which metabolizes cholesterol-derived oxysterols in the bile acid biosynthetic pathway, is also essential for ccRCC cell survival. Development of an HSD3B7 enzymatic assay and screening for small-molecule inhibitors uncovered the compound celastrol as a potent HSD3B7 inhibitor with low micromolar activity. Repressing HSD3B7 expression genetically or treating ccRCC cells with celastrol resulted in toxic oxysterol accumulation, impaired proliferation, and increased apoptosis in vitro and in vivo. These data demonstrate that bile acid synthesis regulates cholesterol homeostasis in ccRCC and identifies HSD3B7 as a plausible therapeutic target.

The bile acid biosynthetic enzyme HSD3B7 is essential for ccRCC cell survival and can be targeted to induce accumulation of cholesterol-derived oxysterols and apoptotic cell death.

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.

Background and aims: Tyrosine kinase inhibitors (TKIs) combined with programmed cell death protein-1 (PD-1) have significantly improved survival in patients with unresectable hepatocellular carcinoma (uHCC), but effective biomarkers to predict treatment efficacy are lacking. Peripheral blood bile acids (BAs) are associated with tumor response to therapy, but their roles in HCC remain unclear. Methods: This retrospective study included HCC patients who received first-line TKIs combined with PD-1 inhibitors treatment (combination therapy) in our clinical center from November 2020 to June 2022. The aim of this study was to analyze the changes in plasma BA profiles before and after treatment in both the responding group (Res group) and the non-responding group (Non-Res group). We aimed to explore the potential role of BAs in predicting the response to combination therapy in HCC patients. Results: Fifty-six patients with HCC who underwent combination therapy were included in this study, with 28 designated as responders (Res group) and 28 as non-responders (Non-Res group). There were differences in plasma BA concentrations between the two groups before systemic therapy. Plasma taurohyocholic acid (THCA) levels in the Res group were significantly lower than those in the Non-Res group. Patients with low levels of THCA exhibited superior median progression-free survival (7.6 vs. 4.9 months, p = 0.027) and median overall survival (23.7 vs. 11.6 months, p = 0.006) compared to those of patients with high levels of THCA. Conclusion: Peripheral blood BA metabolism is significantly correlated with combination therapy response and survival in patients with HCC. Our findings emphasize the potential of plasma BAs as biomarkers for predicting combination therapy outcomes and offering novel therapeutic targets for modulating responses to systemic cancer therapy.

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant cancers worldwide. Due to the asymptomatic features of HCC at early stages, patients are often diagnosed at advanced stages and missed effective treatment. Thus, there is an urgent need to identify sensitive and specific biomarkers for HCC early diagnosis. In the present study, an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) approach was used to profile serum metabolites from HCC patients, liver cirrhosis (LC) patients, and normal controls (NC). Univariate and multivariate statistical analyses were performed to obtain the metabolomic differences of the three groups and select significantly changed metabolites that can be used as diagnostic biomarkers. In total, 757 differential metabolites were quantified among the three groups, and pathway enrichment analysis of these metabolites indicated that glycerophospholipid metabolism, pentose and glucuronate interconversions, phenylalanine, tyrosine and tryptophan biosynthesis, and linoleic acid metabolism were the most altered pathways involved in HCC development. Receiver operating characteristic (ROC) curve analysis was performed to select and evaluate the diagnostic biomarker performance. Seven metabolites were identified as potential biomarkers that can differentiate HCC from LC and NC, and LC from NC with the good diagnostic performance of area under the curve (AUC) from 0.890 to 0.990. In summary, our findings provide highly effective biomarker candidates to differentiate HCC from LC and NC, LC, and NC, which shed insight into HCC pathological mechanisms and will be helpful in better understanding and managing HCC.

Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer.

We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk.

After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI: 0.81-0.87) to 0.89 (95% CI: 0.86-0.91).

This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer.

National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].

Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine-were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.

Fasting has been grown in popularity with multiple potential benefits. However, very few studies dynamically monitor physiological and pathological changes during long-term fasting using noninvasive methods. In the present study, we recruited 37 individuals with metabolic syndrome to engage in a 5-day water-only fasting regimen, and simultaneously captured the molecular alterations through urinary proteomics and metabolomics. Our findings reveal that water-only fasting significantly mitigated metabolic syndrome-related risk markers, such as body weight, body mass index, abdominal circumference, blood pressure, and fasting blood glucose levels in metabolic syndrome patients. Indicators of liver and renal function remained within the normal range, with the exception of uric acid. Notably, inflammatory response was inhibited during the water-only fasting period, as evidenced by a decrease in the human monocyte differentiation antigen CD14. Intriguingly, glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation underwent a sex-dependent reprogramming throughout the fasting period, whereby males exhibited a greater upregulation of carbohydrate metabolism-related enzymes than females. This disparity may be attributed to evolutionary pressures. Collectively, our study sheds light on the beneficial physiological effects and novel dynamic molecular features associated with fasting in individuals with metabolic syndrome using noninvasive methods.

Lipidomics elucidates the roles of lipids in both physiological and pathological processes, intersecting with many diseases and cellular functions. The maintenance of lipid homeostasis, essential for cell health, significantly influences the survival, maturation, and functionality of sperm during fertilization. While capacitation and the acrosome reaction, key processes before fertilization, involve substantial lipidomic alterations, a comprehensive understanding of the changes in human spermatozoa's lipidomic profiles during these processes remains unknown. This study aims to explicate global lipidomic changes during capacitation and the acrosome reaction in human sperm, employing an untargeted lipidomic strategy using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

Twelve semen specimens, exceeding the WHO reference values for semen parameters, were collected. After discontinuous density gradient separation, sperm concentration was adjusted to 2 x 106 cells/ml and divided into three groups: uncapacitated, capacitated, and acrosome-reacted. UPLC-MS analysis was performed after lipid extraction from these groups. Spectral peak alignment and statistical analysis, using unsupervised principal component analysis (PCA), bidirectional orthogonal partial least squares discriminant analysis (O2PLS-DA) analysis, and supervised partial least-squares-latent structure discriminate analysis (PLS-DA), were employed to identify the most discriminative lipids.

The 1176 lipid peaks overlapped across the twelve individuals in the uncapacitated, capacitated, and acrosome-reacted groups: 1180 peaks between the uncapacitated and capacitated groups, 1184 peaks between the uncapacitated and acrosome-reacted groups, and 1178 peaks between the capacitated and acrosome-reacted groups. The count of overlapping peaks varied among individuals, ranging from 739 to 963 across sperm samples. Moreover, 137 lipids had VIP values > 1.0 and twenty-two lipids had VIP > 1.5, based on the O2PLS-DA model. Furthermore, the identified twelve lipids encompassed increases in PI 44:10, LPS 20:4, LPA 20:5, and LPE 20:4, and decreases in 16-phenyl-tetranor-PGE2, PC 40:6, PS 35:4, PA 29:1, 20-carboxy-LTB4, and 2-oxo-4-methylthio-butanoic acid.

This study has been the first time to investigate the lipidomics profiles associated with acrosome reaction and capacitation in human sperm, utilizing UPLC-MS in conjunction with multivariate data analysis. These findings corroborate earlier discoveries on lipids during the acrosome reaction and unveil new metabolites. Furthermore, this research highlights the effective utility of UPLC-MS-based lipidomics for exploring diverse physiological states in sperm. This study offers novel insights into lipidomic changes associated with capacitation and the acrosome reaction in human sperm, which are closely related to male reproduction.

Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC).

Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future.

In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease.

Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.

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.

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.

This review analyzed 21 scientific papers on the determination of amino acids in various types of cancer in saliva. Most of the studies are on oral cancer (8/21), breast cancer (4/21), gastric cancer (3/21), lung cancer (2/21), glioblastoma (2/21) and one study on colorectal, pancreatic, thyroid and liver cancer. The amino acids alanine, valine, phenylalanine, leucine and isoleucine play a leading role in the diagnosis of cancer via the saliva. In an independent version, amino acids are rarely used; the authors combine either amino acids with each other or with other metabolites, which makes it possible to obtain high values of sensitivity and specificity. Nevertheless, a logical and complete substantiation of the changes in saliva occurring in cancer, including changes in salivary amino acid levels, has not yet been formed, which makes it important to continue research in this direction.

The effect of anti-programmed cell death 1 (anti-PD-1) immunotherapy is limited in patients with hepatocellular carcinoma (HCC). Yes-associated protein 1 (YAP1) expression increased in liver tumor cells in early HCC, and Akkermansia muciniphila abundance decreased in the colon. The response to anti-PD-1 treatment is associated with A. muciniphila abundance in many tumors. However, the interaction between A. muciniphila abundance and YAP1 expression remains unclear in HCC. Here, anti-PD-1 treatment decreased A. muciniphila abundance in the colon, but increased YAP1 expression in the tumor cells by mice with liver tumors in situ. Mechanistically, hepatocyte-specific Yap1 knockout (Yap1LKO) maintained bile acid homeostasis in the liver, resulting in an increased abundance of A. muciniphila in the colon. Yap1 knockout enhanced anti-PD-1 efficacy. Therefore, YAP1 inhibition is a potential target for increasing A. muciniphila abundance to promote anti-PD-1 efficacy in liver tumors. Dihydroartemisinin (DHA), acting as YAP1 inhibitor, increased A. muciniphila abundance to sensitize anti-PD-1 therapy. A. muciniphila by gavage increased the number and activation of CD8+ T cells in liver tumor niches during DHA treatment or combination with anti-PD-1. Our findings suggested that the combination anti-PD-1 with DHA is an effective strategy for liver tumor treatment.

Bile acids (BAs) are well known to facilitate the absorption of dietary fat and fat-soluble molecules. These unique steroids also function by binding to the ubiquitous cell membranes and nuclear receptors. As chemical signals in gut-liver axis, the presence of metabolic disorders such as nonalcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM), and even tumors have been reported to be closely related to abnormal levels of BAs in the blood and fecal metabolites of patients. Thus, the gut microbiota interacting with BAs and altering BA metabolism are critical in the pathogenesis of numerous chronic diseases. This review intends to summarize the mechanistic links between metabolic disorders and BAs in gut-liver axis, and such stage-specific BA perturbation patterns may provide clues for developing new auxiliary diagnostic means.

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Hypertensive cerebral microbleeds (HCMB) may be the early stage of hypertensive intracerebral hemorrhage (HICH), which is a serious threat to health due to its high mortality and disability rates. The early clinical symptoms of HCMB may not be significant. Moreover, it is difficult to achieve early diagnosis and intervention for targeted prevention of HICH. Although hypertension (HTN) is a predisposition for HCMB, it remains unclear whether there is any difference between hypertensive patients with or without HCMB. Therefore, we carried out liquid chromatography-mass spectrometry (LC-MS) to analyze early biomarkers for HCMB in mice with hypertension and to lay the foundation for early prevention of HICH in hypertensive patients. In total, 18 C57 male mice were randomly divided into the HCMB (n = 6), HTN (n = 6), and control groups (CON, n = 6). Hematoxylin-eosin and diaminobenzidine staining were used to assess the reliability of the model. The metabolite expression level and sample category stability were tested using the displacement test of orthogonal partial least squares discriminant analysis (OPLS-DA). Significant differences in metabolites were screened out using variable importance in the projection (VIP > 1), which were determined using the OPLS-DA model and the P-value of the t-test (P < 0.05) combined with the nonparametric rank-sum test. With an area under the curve (AUC) > 0.85 and a P-value of 0.05, the receiver operating characteristic curve (ROC) was used to further screen the distinct metabolites of HCMB. Compared with the HTN and CON groups, the HCMB group had significantly higher blood pressure and lower average body weight (P < 0.05). Through untargeted LC-MS analysis, 93 distinct metabolites were identified in the HCMB (P < 0.05, VIP > 1) group. Among these potential biomarkers, six significantly decreased and eight significantly increased differential metabolites were found. Meanwhile, we found that the HCMB group had statistically distinct arginine and purine metabolism pathways (P < 0.05), and citrulline may be the most significant possible biomarker of HCMB (AUC > 0.85, P < 0.05). All of these potential biomarkers may serve as early biomarkers for HICH in hypertension.

Metabolic reprogramming is one of the hallmarks of cancer. Hepatocellular carcinoma (HCC) is one of the most lethal malignancy camcer, but the early diagnosis of HCC remains difficult. In this study, we searched for potential plasma metabolite biomarkers of HCC.

A total of plasma samples of 104 HCC, 76 cirrhosis and 10 healthy subjects were assessed and validated through Gas chromatography-Mass spectrometry. Receiver-operating characteristic curves (ROC) combined with multivariate statistical analyses were used to assess the diagnostic performance of metabolites and combinations.

10 metabolites in screening cohort were significantly changed in the plasma of HCC patients. Multivariate logistic regression analysis of candidate metabolites in validation cohort showed that N-formylglycine, oxoglutaric acid, citrulline and heptaethylene glycol could distinguish HCC from cirrhosis. The combination of these four metabolites showed a better performance than AFP with the Area Under the Curve (AUC), sensitivity, specificity as 0.940, 84.00%, 97.56%, respectively. In further, the panel of N-formylglycine, heptaethylene glycol and citrulline can more effectively discriminate early stage HCC from cirrhosis than AFP (AUC: 0.835 vs. 0.634). Finally, heptaethylene glycol could significantly inhibit the proliferation, migration and invasion of HCC cells in vitro.

The combination of plasma N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol can be an efficient novel diagnostic biomarker for HCC.

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.

Cardiovascular disease (CVD) and cancer are collectively responsible for tens of millions of global deaths each year. These rates are projected to intensify as the COVID-19 pandemic has caused delays in individualized diagnostics, or exacerbated prevalence due to Post Acute Coronavirus (COVID-19) Syndrome. Wastewater-based epidemiology (WBE) has successfully been employed as a useful tool for generating population-level health assessments, and was examined here in this systematic scoping literature review to (i) identify endogenous human biomarkers reported to indicate CVD or cancer in clinical practice, (ii) assess specificity to the indicated diseases, (iii) evaluate the utility for estimating population-level disease prevalence in community wastewater, and (iv) contextualize the obtained information for monitoring CVD and cancer presence via WBE. A total of 48 peer-reviewed papers were critically examined identifying five urinary protein biomarkers: cardiac troponin I (cTnI) (heart attack/heart failure), cystatin C (atherosclerosis), normetanephrine (tumor presence), α-fetoprotein (prostate and liver cancer), and microtubule assisted serine/threonine kinase 4 (MAST4) (breast cancer). Next, urinary excretion information was utilized to predict biomarker concentrations extant in community wastewater, resulting in average healthy concentrations ranging from 0.02 to 1159 ng/L, and disease-indicating thresholds from 0.16 to 3041 ng/L. Finally, estimating prevalence-adjusted wastewater measurements was explored in order to assess community-level CVD and cancer presence utilizing U.S. reported prevalence rates. Results obtained suggest that WBE can serve as a viable tool in support of current methods for CVD and cancer assessment to reduce morbidities and mortalities worldwide.

Bile acids (BAs) are pivotal signaling molecules that regulate energy metabolism and inflammation. Recent epidemiological studies have reported specific alterations in circulating BA profiles in certain disease states, including obesity, type 2 diabetes mellitus (T2DM), nonalcoholic fatty liver disease (NAFLD), and Alzheimer disease (AD). In the past decade, breakthroughs have been made regarding the translation of BA profiling into clinical use for disease prediction. In this review, we summarize and synthesize recent data on variation in circulating BA profiles in patients with various diseases to evaluate the value of these biomarkers in human plasma for early diagnosis.

This review is based on a collection of primary and review literature gathered from a PubMed search for BAs, obesity, T2DM, insulin resistance (IR), NAFLD, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), colon cancer, and AD, among other keywords.

Individuals with obesity, T2DM, HCC, CCA, or AD showed specific alterations in circulating BA profiles. These alterations may have existed long before the initial diagnosis of these diseases. The intricate relationship between obesity, IR, and NAFLD complicates the establishment of clear and independent associations between BA profiles and nonalcoholic steatohepatitis. Alterations in the levels of total BAs and several BA species were seen across the entire spectrum of NAFLD, demonstrating significant increases with the worsening of histological features.

Aberrant circulating BA profiles are an early event in the onset and progression of obesity, T2DM, HCC, and AD. The pleiotropic effects of BAs explain these broad connections. Circulating BA profiles could provide a basis for the development of biomarkers for the diagnosis and prevention of a wide range of diseases.

Hepatocellular carcinoma (HCC) is the most dangerous complication of chronic liver disease. It is a multifactorial complicated disease. Hepatitis C and hepatitis B viruses (HCV and HBV, respectively) represent the main causes of HCC in Egypt. Early diagnosis is very important to aid in early intervention.

The goal of this research is to evaluate the metabolic role of different amino acids as non-invasive biomarkers over the course of HCC.

This study included 302 participants with 97 diagnosed, untreated HCC patients, 81 chronic HCV patients, 56 chronic HBV patients, 18 co-infected patients, and a control group of 50 normal age and gender-matched individuals. All participants provided complete medical histories and underwent complete clinical examinations, abdominal ultrasonography and/or computed tomography, routine laboratory investigations, estimation of serum α-fetoprotein, and determination of amino acid levels using ultra-performance liquid chromatography (UPLC MS/MS).

This work revealed a decline in branched chain amino acids (BCAA) and increase in aromatic amino acids (AAA) among infected groups (HCC, HBV, HCV, and co-infected patients) compared to control subjects and a marked change in Fisher's and the BCAAs/tyrosine molar concentration ratios (BTR) between controls and infected groups.

Different amino acids could be used as non-invasive markers to discriminate and follow chronic hepatitis patients to predict the course of HCC.

The nucleoside inosine is an essential metabolite for purine biosynthesis and degradation; it also acts as a bioactive molecule that regulates RNA editing, metabolic enzyme activity, and signaling pathways. As a result, inosine is emerging as a highly versatile bioactive compound and second messenger of signal transduction in cells with diverse functional abilities in different pathological states. Gut microbiota remodeling is closely associated with human disease pathogenesis and responses to dietary and medical supplementation. Recent studies have revealed a critical link between inosine and gut microbiota impacting anti-tumor, anti-inflammatory, and antimicrobial responses in a context-dependent manner. In this review, we summarize the latest progress in our understanding of the mechanistic function of inosine, to unravel its immunomodulatory actions in pathological settings such as cancer, infection, inflammation, and cardiovascular and neurological diseases. We also highlight the role of gut microbiota in connection with inosine metabolism in different pathophysiological conditions. A more thorough understanding of the mechanistic roles of inosine and how it regulates disease pathologies will pave the way for future development of therapeutic and preventive modalities for various human diseases.

A metabolomic study of hepatolithiasis has yet to be performed. The purpose of the present study was to characterize the metabolite profile and identify potential biomarkers of hepatolithiasis using a metabolomic approach.

We comprehensively analyzed the serum metabolites from 30 patients with hepatolithiasis and 20 healthy individuals using ultra-high performance liquid chromatography-tandem mass spectrometry operated in negative and positive ionization modes. Statistical analyses were performed using univariate (Student's t-test) and multivariate (orthogonal partial least-squares discriminant analysis) statistics and R language. Receiver operator characteristic (ROC) curve analysis was performed to identify potential predictors of hepatolithiasis.

We identified 277 metabolites that were significantly different between hepatolithiasis serum group and healthy control serum group. These metabolites were principally lipids and lipid-like molecules and amino acid metabolites. The steroid hormone biosynthesis pathway was enriched in hepatolithiasis serum group. In all specific metabolites, 75 metabolites were over-expressed in hepatolithiasis serum group. The AUC values for 60 metabolites exceeded 0.70, 4 metabolites including 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) exceeded 0.90.

We have identified serum metabolites that are associated with hepatolithiasis for the first time. 60 potential metabolic biomarkers were identified, 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) may have the potential clinical utility in hepatolithiasis.

Esophageal squamous cell carcinoma (ESCC) is a major histological subtype of esophageal cancer with a poor prognosis. Although several serum metabolomic investigations have been reported, ESCC tumor-associated metabolic alterations and predictive biomarkers in sera have not been defined. Here, we enrolled 34 treatment-naive patients with ESCC and collected their pre- and post-esophagectomy sera together with the sera from 34 healthy volunteers for a metabolomic survey. Our comprehensive analysis identified ESCC tumor-associated metabolic alterations as represented by a panel of 12 serum metabolites. Notably, postoperative abrosia and parenteral nutrition substantially perturbed the serum metabolome. Furthermore, we performed an examination using sera from carcinogen-induced mice at the dysplasia and ESCC stages and identified three ESCC tumor-associated metabolites conserved between mice and humans. Notably, among these metabolites, the level of pipecolic acid was observed to be progressively increased in mouse sera from dysplasia to cancerization, and it could be used to accurately discriminate between mice at the dysplasia stage and healthy control mice. Furthermore, this metabolite is essential for ESCC cells to restrain oxidative stress-induced DNA damage and cell proliferation arrest. Together, this study revealed a panel of 12 ESCC tumor-associated serum metabolites with potential for monitoring therapeutic efficacy and disease relapse, presented evidence for refining parenteral nutrition composition, and highlighted serum pipecolic acid as an attractive biomarker for predicting ESCC tumorigenesis.