Serum N-glycan fingerprint helps to discriminate intrahepatic cholangiocarcinoma from hepatocellular carcinoma

Site-specific N-glycan alterations on haptoglobin as potential biomarkers for distinguishing intrahepatic cholangiocarcinoma from hepatocellular carcinoma

Intrahepatic cholangiocellular carcinoma (ICC) is a challenging malignancy marked by subtle early symptoms and a high mortality rate, making effective diagnostic markers crucial for early detection and improved patient outcomes. Currently, the conventional diagnosis of ICC is not easily distinguishable from Hepatocellular Carcinoma (HCC) and lacks highly specific and sensitive diagnostic markers. Protein glycosylation, pivotal in biological processes, shows promise for cancer biomarkers due to its association with disease progression. This study aims to develop a novel biomarker discovery framework for ICC utilizing site-specific quantitative N-glycoproteomics to overcome the limitations of existing diagnostic approaches. Employing a tandem mass tag (TMT)-based quantitative analysis, we profiled serum glycoproteins from ICC, HCC, and control cohorts at site-specific glycosylation level. The identified markers underwent further validation in an independent cohort using label-free quantitative methods. Ultimately, we identified five site-specific N-glycans on haptoglobin (HP) as potential biomarkers (AUC > 0.9) for distinguishing ICC from HCC. This finding represents a considerable advance over traditional biomarkers, highlighting the significance of protein glycosylation alterations in ICC pathogenesis. This research, therefore, sets a new precedent for biomarker discovery in ICC, with potential applications in other cancers characterized by glycosylation abnormalities.

Exploring serum N-glycome patterns as candidate non-invasive biomarkers in inguinal hernia

Introduction

Although inguinal hernia (IH) is prevalent in elderly males, research on its specific diagnostic biomarkers is limited. Protein N-glycosylation is one of the most important and ubiquitous post-translational modifications and often results in a remarkable heterogeneity of protein glycoforms. Protein N-glycosylation often changes in a disease and holds great potential for discovering non-invasive biomarkers. This study aimed to gain insights into total serum protein N-glycosylation of IH to identify candidate non-invasive biomarkers for diagnosis and subtype classification of IH.

Methods

Linkage-specific sialylation derivatization combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry detection was used to analyze serum protein N-glycosylation patterns in IH patients and healthy controls.

Results

IH patients had abnormal glycan fucosylation and sialylation compared to healthy controls (HC), of which two glycan traits representing linkage-specific sialylation within monoantennary glycans showed high potential as diagnostic biomarkers for IH with an area under the curve (AUC) of 0.75. Additionally, serum N-glycans were different between indirect IH and direct IH in glycosylation features, namely complexity, fucosylation, galactosylation, sialylation, and α2,6-linked sialylation. Four distinctive glycans between the two subtypes showed good performance with AUC >0.8, suggesting that these glycan traits have potential as biomarkers for subtype classification.

Conclusions

We first reported the serum N-glycomic features of IH patients. Furthermore, we identified several potential biomarkers for the diagnosis and subtype classification of IH. These findings can deepen the understanding of IH.

N-glycan signatures identified in the serum from biliary tract cancer patients: Association with clinical diagnosis and prognosis

BACKGROUND

Changes in the expression of genes related to glycosyltransferases may lead to alterations in N-glycan structure abundance, potentially acting as markers for diagnosis and prognosis in biliary tract cancer (BTC).

METHODS

This study was divided into cross-sectional and longitudinal approaches. The cross-sectional study included 316 BTC and 301 non-BTC. Propensity score matching was applied to adjust for sex and age differences between BTC and non-BTC. Univariate and multivariate logistic regression identified independent risk factors for BTC and constructed the BTC-G model. The ROC curve was used to validate the diagnostic performance of BTC-G. Longitudinal follow-up studies included postoperative (N = 50) and immunotherapy (N = 43) follow-up cohorts. Cox regression analysis identified N-glycan structures impacting BTC prognosis postoperative and immunotherapy, with further confirmation through Kaplan-Meier curves.

RESULTS

Univariate and multivariate analyses identified Peak3 (OR: 0.790, 95% CI: 0.658-0.949), Peak9 (OR: 1.646, 95% CI: 1.409-1.922), and Peak9p (OR: 2.467, 95% CI: 1.267-4.804) as independent BTC risk factors, leading to the creation of the BTC-G. The ROC curve confirmed that BTC-G performed well in training (AUC: 0.753, 95% CI: 0.703-0.799), validation (AUC: 0.811, 95% CI: 0.740-0.870), and CA19-9 negative cohorts (AUC: 0.717, 95% CI: 0.664-0.767). Cox regression analysis and Kaplan-Meier curves established that Peak12 (HR: 5.578, 95% CI: 1.145-27.170) and Peak11 (HR: 1.104, 95% CI: 0.611-1.994) are independent risk factors for BTC prognosis following surgery and immunotherapy, respectively.

CONCLUSIONS

Our NGFP technology supplements BTC diagnostics, distinguishing survival and recurrence subtypes for postoperative and immunotherapy, thereby supporting the development of treatment strategies.

Practical considerations for pathological diagnosis and molecular profiling of cholangiocarcinoma: an expert review for best practices

INTRODUCTION

Advances in precision medicine have expanded access to targeted therapies and demand for molecular profiling of cholangiocarcinoma (CCA) patients in routine clinical practice. However, pathologists face challenges in establishing a definitive intrahepatic CCA (iCCA) diagnosis while preserving sufficient tissue for molecular profiling. Additionally, they frequently face challenges in optimal tissue handling to preserve nucleic acid integrity.

AREAS COVERED

This article first identifies the challenges in establishing a definitive diagnosis of iCCA in a lesional liver biopsy while preserving sufficient tissue for molecular profiling. Then, the authors explore the clinical value of molecular profiling, the basic principles of single gene and next-generation sequencing (NGS) techniques, and the challenges in tissue sampling for genomic testing. They also propose an algorithm for best practice in tissue management for molecular profiling of CCA.

EXPERT OPINION

Several practical challenges face pathologists during tissue sampling and processing for molecular profiling. Optimized tissue processing, careful tissue handling, and selection of appropriate approaches to molecular testing are essential to ensure that the highest possible quality of diagnostic information is provided in the greatest proportion of cases.

Serum N-glycomic profiling identifies candidate biomarker panels for assessing coronary artery stenosis severity

Stenosis severity may escalate over the course of coronary artery disease (CAD), increasing the risk of death for the patient. Conventionally, the assessment of stenosis degree relies on invasive coronary angiography (ICA), an invasive examination unsuitable for patients in poor physical condition or those with contrast allergies and one that imposes a psychological burden on patients. Although abnormal serum N-glycan profiles have exhibited robust associations with various cardiovascular diseases, including CAD, their potential in diagnosing CAD stenosis remains to be determined. In this study, we performed a comprehensive analysis of serum N-glycome from 132 patients who underwent ICA and 27 healthy controls using MALDI-TOF-mass spectrometry. The patients who underwent ICA examination were categorized into four groups based on stenosis severity: no/mild/moderate/severe stenosis. Twenty-seven N-glycans were directly quantified, and 47 derived glycan traits were obtained. Notably, among these 74 glycan features, 18 exhibited variations across the study groups. Using a combination of least absolute shrinkage and selection operator and logistic regression analyses, we developed five diagnostic models for recognizing stenosis degree. Our results suggested that alterations in serum N-glycosylation modifications might be valuable for identifying stenosis degree and monitoring disease progression in individuals with CAD. It is expected to offer a noninvasive alternative for those who could not undergo ICA because of various reasons. However, the diagnostic potential of serum N-glycan panels as biomarkers requires multicenter, large cohort validation in the future.

Risks and Clinical Predictors of Hepatocellular Carcinoma in Chinese Populations: A Real-World Study of 10,359 Patients in Six Medical Centers

Purpose

Early detection of hepatocellular carcinoma (HCC) through surveillance could reduce this cancer-associated mortality. We aimed to develop and validate algorithms using panel serum biomarkers to identify HCC in a real-world multi-center study in China.

Patients and Methods

A total of 10,359 eligible subjects, including HCCs and benign liver diseases (BLDs), were recruited from six Chinese medical centers. The three nomograms were built using logistic regression and their sensitivities and specificities were carefully assessed in training and validation cohorts. HCC patients after surgical resection were followed to determine the prognostic values of these algorithms. Prospective surveillance performance was assessed in a cohort of chronic hepatitis B patients during 144 weeks follow-up.

Results

Independent risk factors such as alpha-fetoprotein (AFP), lens cuinaris agglutinin-reactive fraction of AFP (AFP-L3), des-gamma-carboxy prothrombin (DCP), albumin (ALB), and total bilirubin (TBIL) obtained from train cohort were used to construct three nomograms (LAD, C-GALAD, and TAGALAD) using logistic regression. In the training and two validation cohorts, their AUCs were all over 0.900, and the higher AUCs appeared in TAGALAD and C-GALAD. Furthermore, the three nomograms could effectively stratify HCC into two groups with different survival and recurrence outcomes in follow-up validation. Notably, TAGALAD could predict HCC up to 48 weeks (AUC: 0.984) and 24 weeks (AUC: 0.900) before clinical diagnosis.

Conclusion

The proposed nomograms generated from real-world Chinese populations are effective and easy-to use for HCC surveillance, diagnosis, as well as prognostic evaluation in various clinical scenarios based on data feasibility.

Characterization of circulating miRNAs in the treatment of primary liver tumors

BACKGROUND AND AIM

Circulating micro RNAs (miRNAs) indicate clinical pathologies such as inflammation and carcinogenesis. In this study, we aimed to investigate whether miRNA expression level patterns in could be used to diagnose hepatocellular carcinoma (HCC) and biliary tract cancer (BTC), and the relationship miRNA expression patterns and cancer etiology.

METHODS

Patients with HCC and BTC with indications for surgery were selected for the study. Total RNA was extracted from the extracellular vesicle (EV)-rich fraction of the serum and analyzed using Toray miRNA microarray. Samples were divided into two cohorts in order of collection, the first 85 HCC were analyzed using a microarray based on miRBase ver.2.0 (hereafter v20 cohort), and the second 177 HCC and 43 BTC were analyzed using a microarray based on miRBase ver.21 (hereafter v21 cohort).

RESULTS

Using miRNA expression patterns, we found that HCC and BTC could be identified with an area under curve (AUC) 0.754 (v21 cohort). Patients with anti-hepatitis C virus (HCV) treatment (SVR-HCC) and without antiviral treatment (HCV-HCC) could be distinguished by an AUC 0.811 (v20 cohort) and AUC 0.798 (v21 cohort), respectively.

CONCLUSIONS

In this study, we could diagnose primary hepatic malignant tumor using miRNA expression patterns. Moreover, the difference of miRNA expression in SVR-HCC and HCV-HCC can be important information for enclosing cases that are prone to carcinogenesis after being cured with antiviral agents, but also for uncovering the mechanism for some carcinogenic potential remains even after persistent virus infection has disappeared.

Comprehensive Analysis Identified Glycosyltransferase Signature to Predict Glioma Prognosis and TAM Phenotype

Glycosylation is the most common posttranslational modification of proteins. Glycosyltransferase gene differential expression dictates the glycosylation model and is epigenetically regulating glioma progression and immunity. This study is aimed at identifying the glycosyltransferase gene signature to predict the prognosis and immune characteristics of glioma. The glycosyltransferase gene signature of glioma was identified in the TCGA database and validated in the CGGA database. Glioma patients were then divided into high- and low-risk groups based on risk scores to compare survival differences and predictive capacity. Subsequently, validation of glycosyltransferase gene signature merits by comparing with other signatures and utility in clinical judgment. The immune cell infiltration, immune pathways, and immune checkpoint expression level were also analyzed and compared in the high- and low-risk groups. Finally, the signature and its gene function were tested in our cohort and in vitro experiments. Eight glycosyltransferase genes were identified to establish the glycosyltransferase signature to predict the prognosis of glioma patients. The survival time was shorter in the high-risk group compared to the low-risk group based on glycosyltransferase signature and was confirmed in an independent external cohort. The glycosyltransferase signature displayed outstanding predictive capacity than other signatures in the TCGA and CGGA database cohorts. Furthermore, patients in the high-risk group were positively correlated with TAM infiltration, immune checkpoint expression level, and protumor immune pathways in TCGA cohorts. Validation of clinical tissue specimens revealed that the high-risk group was closely associated with infiltration of M2 TAMs. High-risk genes in the signature promote glioma proliferation, invasion, and macrophage recruitment in an in vitro validation of U87 and U251 cell lines. This carefully constructed that glycosyltransferase signature can predict the prognosis and immune profile of gliomas and help us evaluate subsequent macrophage-targeted therapies as well as other immune microenvironment modulation therapeutic strategies.

Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.

An Efficient Nomogram for Discriminating Intrahepatic Cholangiocarcinoma From Hepatocellular Carcinoma: A Retrospective Study

Objective

This study aims to establish a nomogram and provide an effective method to distinguish between intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC).

Methods

A total of 1,591 patients with HCC or ICC hospitalized at Shandong Provincial Hospital between January 2016 and August 2021 were included and randomly divided into development and validation groups in a ratio of 3:1. Univariate and multivariate analyses were performed to determine the independent differential factors between HCC and ICC patients in the development cohort. By combining these independent differential factors, the nomogram was established for discriminating ICC from HCC. The accuracy of the nomogram was estimated by using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Furthermore, the predictive nomogram was assessed in the internal testing set.

Results

Through multivariate analysis, independent differential factors between HCC and ICC involved hepatitis B virus (HBV), logarithm of alpha-fetoprotein (Log AFP), logarithm of protein induced by vitamin K absence or antagonist-II (Log PIVKA-II), logarithm of carbohydrate antigen 199 (Log CA199), and logarithm of carbohydrate antigen 125 (Log CA125). A nomogram was finally established by incorporating these five independent differential factors. Comparing a model of conventional tumor biomarkers including AFP and CA199, the nomogram showed a better distinction between ICC and HCC. The area under the ROC curve (AUC) of ICC diagnosis was 0.951 (95% CI, 0.938–0.964) for the nomogram. The results were consistent in the validation cohort with an AUC of 0.958 (95% CI, 0.938–0.978). After integrating patient preferences into the analysis, the DCA showed that using this nomogram to distinguish ICC and HCC increased more benefit compared with the conventional model.

Conclusion

An efficient nomogram has been established for the differential diagnosis between ICC and HCC, which may facilitate the detection and diagnosis of ICC. Further use of the nomogram in multicenter investigations will confirm the practicality of the tool for future clinical application.