N-glycan fingerprint predicts alpha-fetoprotein negative hepatocellular carcinoma: A large-scale multicenter study

Profiling of aberrant sialylated N-glycans in hepatocellular carcinoma by liquid chromatography mass spectrometry

BACKGROUND

Aberrant sialylation is closely associated with the tumorigenesis, progression, and metastasis, and may be of importance for disease diagnosis. However, the analysis of altered expression of sialylated glycans (SGs) in blood is particularly challenging due to the low content and poor ionization efficiency of sialylated glycans in mass spectrometry.

METHODS

An analytical strategy based on enrichment of SGs, liquid chromatography-high resolution mass spectrometric detection, and automatic glycan annotation was developed to profile the sialylated N-glycome in serum. The enrichment of sialylated glycans was accomplished using cationic cotton via electrostatic and hydrogen interaction. Using partial least squares-discriminant analysis (PLS-DA), the approach was applied for nontarget screening and profiling of aberrant sialylated N-glycans in hepatocellular carcinoma (HCC).

RESULTS

55 SGs were identified in human serum, and three important SGs (SG35, SG45, and SG46) were screened to have good diagnostic specificity for HCC. Their areas under the receiver operating characteristic (ROC) curve (AUC) were higher than α-fetoprotein (AFP)'s (AUC = 0.85), at 0.88, 0.87, and 0.91, respectively. When three SGs are combined, the diagnostic specificity for HCC may increase to 94 %. The fact that SGs biomarkers are sensitive to AFP-Negative HCC is very noteworthy.

CONCLUSIONS

The method significantly advanced the search for sialylated glycan-based cancer biomarkers. In comparison to traditional indicators like AFP and imaging tools, SGs showed a higher diagnostic sensitivity for HCC.

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.

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.

Beta2-Microglobulin as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram

Background

Accurate prognosis is crucial for improving hepatocellular carcinoma (HCC) patients, clinical management, and outcomes post-liver resection. However, the lack of reliable prognostic indicators poses a significant challenge. This study aimed to develop a user-friendly nomogram to predict HCC patients' post-resection prognosis.

Methods

We retrospectively analyzed the data from 1091 HCC patients, randomly split into training (n=767) and validation (n=324) cohorts. Receiver operating characteristic (ROC) curves determined the optimal cut-off value for alpha1-microglobulin (α1MG) and Beta2-microglobulin (β2MG). Kaplan-Meier analysis assessed microglobulin's impact on survival, followed by Cox regression to identify prognostic factors and construct a nomogram. The predictive accuracy and discriminative ability of the nomogram were measured by the concordance index (C-index), calibration curves, area under the ROC curve (AUC), and decision curve analysis (DCA), and were compared with the BCLC staging system, Edmondson grade, or BCLC stage plus Edmondson grade.

Results

Patients with high β2MG (≥2.395mg/L) had worse overall survival (OS). The nomogram integrated β2MG, BCLC stage, Edmondson grade, microvascular invasion (MVI), and serum carbohydrate antigen 199 (CA199) levels. C-index for training and validation cohorts (0.712 and 0.709) outperformed the BCLC stage (0.660 and 0.657), Edmondson grade (0.579 and 0.564), and the combination of BCLC stage with Edmondson grade (0.681 and 0.668), improving prognosis prediction. Calibration curves demonstrated good agreement between predicted and observed survival. AUC values exceeded 0.700 over time, highlighting the nomogram's discriminative ability. DCA revealed superior overall net income compared to other systems, emphasizing its clinical utility.

Conclusion

Our β2MG-based nomogram accurately predicts HCC patients' post-resection prognosis, aiding intervention and follow-up planning. Significantly, our nomogram surpasses existing prognostic indicators, including BCLC stage, Edmondson grade, and the combination of BCLC stage with Edmondson grade, by demonstrating superior predictive performance.

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.

Recent advancements in the B7/CD28 immune checkpoint families: new biology and clinical therapeutic strategies

The B7/CD28 families of immune checkpoints play vital roles in negatively or positively regulating immune cells in homeostasis and various diseases. Recent basic and clinical studies have revealed novel biology of the B7/CD28 families and new therapeutics for cancer therapy. In this review, we discuss the newly discovered KIR3DL3/TMIGD2/HHLA2 pathways, PD-1/PD-L1 and B7-H3 as metabolic regulators, the glycobiology of PD-1/PD-L1, B7x (B7-H4) and B7-H3, and the recently characterized PD-L1/B7-1 cis-interaction. We also cover the tumor-intrinsic and -extrinsic resistance mechanisms to current anti-PD-1/PD-L1 and anti-CTLA-4 immunotherapies in clinical settings. Finally, we review new immunotherapies targeting B7-H3, B7x, PD-1/PD-L1, and CTLA-4 in current clinical trials.

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.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

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.

Identifying a Novel Endoplasmic Reticulum-Related Prognostic Model for Hepatocellular Carcinomas

From the standpoint of the ER (endoplasmic reticulum), we were interested in identifying hub genes that impact clinical prognosis for HCC (hepatocellular carcinoma) patients and developing an ER-related prognostic model. Using TCGA-LIHC (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) and GSE14520 datasets, we conducted a series of analyses, which included differential gene screening, clinical prognostic analysis, Lasso regression, nomogram prediction, tumour clustering, gene functional enrichment, and tumour infiltration of immune cells. Following our screening for ER-related genes ( $n=1975$ ), we conducted a Lasso regression model to obtain five hub genes, KPNA2, FMO3, SPP1, KIF2C, and LPCAT1, using TCGA-LIHC as a training set. According to risk scores, HCC samples within either the TCGG-LIHC or GSE14520 cohort were categorized into high- and low-risk groups. Compared to the high-risk group of HCC patients, patients in the low-risk group had a better prognosis of OS (overall survival) or RFS (relapse-free survival). For TCGA-LIHC training set, with the factors of risk score, stage, age, and sex, we plotted a nomogram for 1-, 3-, and 5-year survival predictions. Our model demonstrated better clinical validity in both TCGA-LIHC and GSE14520 cohorts. Additionally, events related to biological enzyme activity, biological metabolic processes, or the cell cycle were associated with the prognostic risk of ER. Furthermore, two HCC prognosis-associated tumour clusters were identified by ER hub gene-based consensus clustering. Our findings indicated a link between ER prognostic signature-related high/low risk and tumour infiltration levels of several immune cells, such as “macrophages M2/M0” and “regulatory T cells (Tregs).” Overall, we developed a novel ER-related clinical prognostic model for HCC patients.