A Potential Serum N-glycan Biomarker for Hepatitis C Virus-Related Early-Stage Hepatocellular Carcinoma with Liver Cirrhosis

Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is widely recognized as the most lethal gynecological malignancy; however, its early-stage detection remains a considerable clinical challenge. To address this, we have introduced a new method, named Comprehensive Serum Glycopeptide Spectral Analysis (CSGSA), which detects early-stage cancer by combining glycan alterations in serum glycoproteins with tumor markers. We detected 1712 glycopeptides using liquid chromatography-mass spectrometry from the sera obtained from 564 patients with EOC and 1149 controls across 13 institutions. Furthermore, we used a convolutional neural network to analyze the expression patterns of the glycopeptides and tumor markers. Using this approach, we successfully differentiated early-stage EOC (Stage I) from non-EOC, with an area under the curve (AUC) of 0.924 in receiver operating characteristic (ROC) analysis. This method markedly outperforms conventional tumor markers, including cancer antigen 125 (CA125, 0.842) and human epididymis protein 4 (HE4, 0.717). Notably, our method exhibited remarkable efficacy in differentiating early-stage ovarian clear cell carcinoma from endometrioma, achieving a ROC-AUC of 0.808, outperforming CA125 (0.538) and HE4 (0.557). Our study presents a promising breakthrough in the early detection of EOC through the innovative CSGSA method. The integration of glycan alterations with cancer-related tumor markers has demonstrated exceptional diagnostic potential.

Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent cancers and accounts for a significant proportion of cancer-associated deaths worldwide. This disease, marked by multifaceted etiology, often poses diagnostic challenges. Finding a reliable and non-invasive diagnostic method seems to be necessary. In this study, we analyzed the gene expression profiles of 20 HCC patients, 12 individuals with chronic hepatitis, and 15 healthy controls. Enrichment analysis revealed that platelet aggregation, secretory granule lumen, and G-protein-coupled purinergic nucleotide receptor activity were common biological processes, cellular components, and molecular function in HCC and chronic hepatitis B (CHB) compared to healthy controls, respectively. Furthermore, pathway analysis demonstrated that "estrogen response" was involved in the pathogenesis of HCC and CHB conditions, while, "apoptosis" and "coagulation" pathways were specific for HCC. Employing computational feature selection and logistic regression classification, we identified candidate genes pivotal for diagnostic panel development and evaluated the performance of these panels. Subsequent machine learning evaluations assessed these panels' performance in an independent cohort. Remarkably, a 3-marker panel, comprising RANSE2, TNF-α, and MAP3K7, demonstrated the best performance in qRT-PCR-validated experimental data, achieving 98.4% accuracy and an area under the curve of 1. Our findings highlight this panel's promising potential as a non-invasive approach not only for detecting HCC but also for distinguishing HCC from CHB patients.

Systematic review: Glycomics as diagnostic markers for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer with one of the highest cancer-related mortality rates worldwide. Early diagnosis is crucial for improving the therapeutic options and reducing the disease-related mortality.

AIM

To investigate serum N-glycomics as diagnostic markers for HCC.

METHODS

We performed a comprehensive search in PubMed, EMBASE, Web of Science and Scopus through August 17, 2023. Eligible studies assessed the potential use of serum N-glycomics as diagnostic biomarkers for HCC. Study selection, data extraction and quality assessment were performed by two independent reviewers.

RESULTS

Of the 48 articles included, 11 evaluated the utility of N-glycomics for the diagnosis of HCC in whole serum while the remaining articles focused on specific protein glycoforms or protein levels. Of these specific proteins, haptoglobin, alpha-fetoprotein (AFP), kininogen (Kin), α-1-antitrypsin and Golgi protein 73 (GP73) were the most frequently studied. Increased levels of fucosylation and branching presented as the most prevalent post-translational modifications of glycoproteins in patients with HCC compared to controls. Notably, glycomics-based biomarkers may provide a clinical benefit for the diagnosis of early HCC, as several algorithms achieved AUCs between 0.92-0.97. However, these were based on single studies with limited sample sizes and should therefore be validated.

CONCLUSIONS

Alterations in serum N-glycomics, characterised by increased levels of fucosylation and branching, have potential as diagnostic biomarkers for HCC. Optimisation of study design, patient selection and analysing techniques are needed before clinical implementation will be possible.

Comprehensive serum N-glycan profiling identifies a biomarker panel for early diagnosis of non-small-cell lung cancer

Aberrant serum N-glycan profiles have been observed in multiple cancers including non-small-cell lung cancer (NSCLC), yet the potential of N-glycans in the early diagnosis of NSCLC remains to be determined. In this study, serum N-glycan profiles of 275 NSCLC patients and 309 healthy controls were characterized by MALDI-TOF-MS. The levels of serum N-glycans and N-glycosylation patterns were compared between NSCLC and control groups. In addition, a panel of N-glycan biomarkers for NSCLC diagnosis was established and validated using machine learning algorithms. As a result, a total of 54 N-glycan structures were identified in human serum. Compared with healthy controls, 29 serum N-glycans were increased or decreased in NSCLC patients. N-glycan abundance in different histological types or clinical stages of NSCLC presented differentiated changes. Furthermore, an optimal biomarker panel of eight N-glycans was constructed based on logistic regression, with an AUC of 0.86 in the validation set. Notably, this model also showed a desirable capacity in distinguishing early-stage patients from healthy controls (AUC = 0.88). In conclusion, our work highlights the abnormal N-glycan profiles in NSCLC and provides supports potential application of N-glycan biomarker panel in clinical NSCLC detection.

Protein glycosylation alterations in hepatocellular carcinoma: function and clinical implications

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. Understanding the cancer mechanisms provides novel diagnostic, prognostic, and therapeutic markers for the management of HCC disease. In addition to genomic and epigenomic regulation, post-translational modification exerts a profound influence on protein functions and plays a critical role in regulating various biological processes. Protein glycosylation is one of the most common and complex post-translational modifications of newly synthesized proteins and acts as an important regulatory mechanism that is implicated in fundamental molecular and cell biology processes. Recent studies in glycobiology suggest that aberrant protein glycosylation in hepatocytes contributes to the malignant transformation to HCC by modulating a wide range of pro-tumorigenic signaling pathways. The dysregulated protein glycosylation regulates cancer growth, metastasis, stemness, immune evasion, and therapy resistance, and is regarded as a hallmark of HCC. Changes in protein glycosylation could serve as potential diagnostic, prognostic, and therapeutic factors in HCC. In this review, we summarize the functional importance, molecular mechanism, and clinical application of protein glycosylation alterations in HCC.

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.

The diagnostic performance of contrast-enhanced CT versus extracellular contrast agent-enhanced MRI in detecting hepatocellular carcinoma: direct comparison and a meta-analysis

To compare the diagnostic value of contrast-enhanced computed tomography (CT) with extracellular contrast agent-enhanced magnetic resonance imaging (ECA-MRI) for the detection of hepatocellular carcinoma (HCC). Pubmed, Embase, Web of Science and Cochrane Library were searched (1/5/2021) for studies comparing contrast-enhanced CT with ECA-MRI in patients suspected of HCC. Studies without head-to-head comparison were excluded. The pooled sensitivity, specificity and summary area under the curve (sAUC) of contrast-enhanced CT and ECA-MRI in detecting HCC was calculated based on bivariate random effects model. Heterogeneity test included threshold effect analysis and meta-regression. Subgroup analyses were conducted according to lesion size (< 20 mm or ≥ 20 mm). Overall, 10 articles containing 1333 patients were deemed suitable for inclusion in this meta-analysis. ECA-MRI displayed increased sensitivity to contrast-enhanced CT in detecting HCC (0.77 vs. 0.63, P < 0.01). The difference in specificity between ECA-MRI and contrast-enhanced CT was not statistically significant (0.93 vs. 0.94, P = 0.25). ECA-MRI yielded higher diagnostic accuracy (sAUCs = 0.88 vs. 0.80, P < 0.01). In the subgroup analysis with a lesion size < 20 mm, ECA-MRI allowed significant gains of accuracy compared to contrast-enhanced CT (0.79 vs. 0.72, P = 0.02). ECA-MRI also outperformed contrast-enhanced CT in patients with lesion size ≥ 20 mm (sAUCs = 0.96 vs. 0.93, P = 0.04). ECA-MRI provided higher sensitivity and accuracy than contrast-enhanced CT in detecting HCC, especially lesions size < 20 mm.

GlycoFibroTyper: A Novel Method for the Glycan Analysis of IgG and the Development of a Biomarker Signature of Liver Fibrosis

Our group has recently developed the GlycoTyper assay which is a streamlined antibody capture slide array approach to directly profile N-glycans of captured serum glycoproteins including immunoglobulin G (IgG). This method needs only a few microliters of serum and utilizes a simplified processing protocol that requires no purification or sugar modifications prior to analysis. In this method, antibody captured glycoproteins are treated with peptide N-glycosidase F (PNGase F) to release N-glycans for detection by MALDI imaging mass spectrometry (IMS). As alterations in N-linked glycans have been reported for IgG from large patient cohorts with fibrosis and cirrhosis, we utilized this novel method to examine the glycosylation of total IgG, as well as IgG1, IgG2, IgG3 and IgG4, which have never been examined before, in a cohort of 106 patients with biopsy confirmed liver fibrosis. Patients were classified as either having no evidence of fibrosis (41 patients with no liver disease or stage 0 fibrosis), early stage fibrosis (10 METAVIR stage 1 and 18 METAVIR stage 2) or late stage fibrosis (6 patients with METAVIR stage 3 fibrosis and 37 patients with METAVIR stage 4 fibrosis (cirrhosis)). Several major alterations in glycosylation were observed that classify patients as having no fibrosis (sensitivity of 92% and a specificity of 90%), early fibrosis (sensitivity of 84% with 90% specificity) or significant fibrosis (sensitivity of 94% with 90% specificity).