A Predictive Model Using N-Glycan Biosignatures for Clinical Diagnosis of Early Hepatocellular Carcinoma Related to Hepatitis B Virus

Functional prediction of the potential NGLY1 mutations associated with rare disease CDG

Genetic diseases are currently diagnosed by functional mutations. However, only some mutations are associated with disease. It is necessary to establish a quick prediction model for clinical screening. Pathogenic mutations in NGLY1 cause a rare autosomal recessive disease known as congenital disorder of deglycosylation (NGLY1-CDDG). Although NGLY1-CDDG can be diagnosed through gene sequencing, clinical relevance of a detected mutation in NGLY1 needs to be further confirmed. In this study, taken NGLY1-CDDG as an example, a comprehensive and practical predictive model for pathogenic mutations on NGLY1 through an NGLY1/Glycopeptide complex model was constructed, the binding sites of NGLY1 and glycopeptides were simulated, and an in vitro enzymatic assay system was established to facilitate quick clinical decisions for NGLY1-CDDG patients. The docking model covers 42 % of reported NGLY1-CDDG missense mutations (5/12). All reported mutations were subjected to in vitro enzymatic assay in which 18 mutations were dysfunctional (18/30). In addition, a full spectrum of functional R328 mutations was assayed and 11 mutations were dysfunctional (11/19). In this study, a model of NGLY1 and glycopeptides was built for potential functional mutations in NGLY1. In addition, the effect of potential regulatory compounds, including N-acetyl-l-cysteine and dithiothreitol, on NGLY1 was examined. The established in vitro assay may serve as a standard protocol to facilitate rapid diagnosis of all mutations in NGLY1-CDDG. This method could also be applied as a comprehensive and practical predictive model for the other rare genetic diseases.

N-glycan biosignatures as a potential diagnostic biomarker for early-stage pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with a 5-year survival rate of less than 10%, owing to its late-stage diagnosis. Early detection of pancreatic cancer (PC) can significantly increase survival rates.

AIM

To identify the serum biomarker signatures associated with early-stage PDAC by serum N-glycan analysis.

METHODS

An extensive patient cohort was used to determine a biomarker signature, including patients with PDAC that was well-defined at an early stage (stages I and II). The biomarker signature was derived from a case-control study using a case-cohort design consisting of 29 patients with stage I, 22 with stage II, 4 with stage III, 16 with stage IV PDAC, and 88 controls. We used multiparametric analysis to identify early-stage PDAC N-glycan signatures and developed an N-glycan signature-based diagnosis model called the "Glyco-model".

RESULTS

The biomarker signature was created to discriminate samples derived from patients with PC from those of controls, with a receiver operating characteristic area under the curve of 0.86. In addition, the biomarker signature combined with cancer antigen 19-9 could discriminate patients with PDAC from controls, with a receiver operating characteristic area under the curve of 0.919. Glyco-model demonstrated favorable diagnostic performance in all stages of PC. The diagnostic sensitivity for stage I PDAC was 89.66%.

CONCLUSION

In a prospective validation study, this serum biomarker signature may offer a viable method for detecting early-stage PDAC.

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

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

A Fast-Response AIE-Active Ratiometric Fluorescent Probe for the Detection of Carboxylesterase

Hepatocellular carcinoma (HCC) is associated with a high mortality rate worldwide. The therapeutic outcomes can be significantly improved if diagnosis and treatment are initiated earlier in the disease process. Recently, the carboxylesterase (CaE) activity/level in human plasma was reported to be a novel serological biomarker candidate for HCC. In this article, we fabricated a new fluorescent probe with AIE characteristics for the rapid detection of CaE with a more reliable ratiometric response mode. The TCFISE probe showed high sensitivity (LOD: 93.0 μU/mL) and selectivity toward CaE. Furthermore, the good pH stability, superior resistance against photobleaching, and low cytotoxicity highlight the high potential of the TCFISE probe for application in the monitoring of CaE activity in complex biological samples and in live cells, tissues, and animals.

The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection

Chronic hepatitis B (CHB) seriously threatens human health. About 820,000 deaths annually are due to related complications such as hepatitis B and hepatocellular carcinoma (HCC). Recently, the use of oral antiviral agents has significantly improved the prognosis of patients with CHB infection and reduced the risk of HCC. However, hepatitis B virus still remains a major factor in the development of HCC, raising many concerns. Therefore, numerous studies have been conducted to assess the risk of HCC in patients with CHB infection and many models have been proposed to predict the risk of developing HCC. However, as each study has different models for predicting HCC development that can be applied depending on the use of antiviral agents or the type of antiviral agents, it is necessary to properly understand characteristics of each model when using it for the evaluation of HCC in patients with CHB infection. In addition, because different variables such as host factor, viral activity, and cirrhosis are used to evaluate the risk of HCC development, it is necessary to assess the risk by carefully verifying which variables are used. Recently, studies have also evaluated the risk of HCC using risk prediction models through transient elastography and artificial intelligence (AI) system. These HCC risk predication models are also noteworthy. In this review, we aimed to compare HCC risk prediction models in patients with CHB infection reported to date to confirm variables used and specificity between each model to determine an appropriate HCC risk prediction method.

Magnetically guided theranostics: montmorillonite-based iron/platinum nanoparticles for enhancing in situ MRI contrast and hepatocellular carcinoma treatment

In Asia, including Taiwan, malignant tumors such as Hepatocellular carcinoma (HCC) one of the liver cancer is the most diagnosed subtype. Magnetic resonance imaging (MRI) has been a typical diagnostic method for accurately diagnosing HCC. When it is difficult to demonstrate non-enhanced MRI of tumors, radiologists can use contrast agents (such as Gd3+, Fe3O4, or FePt) for T1-weighted and T2-weighted imaging remain in the liver for a long time to facilitate diagnosis via MRI. However, it is sometimes difficult for T2-weighted imaging to detect small tumor lesions because the liver tissue may absorb iron ions. This makes early cancer detection a challenging goal. This challenge has prompted current research to create novel nanocomposites for enhancing the noise-to-signal ratio of MRI. To develop a method that can more efficiently diagnose and simultaneously treat HCC during MRI examination, we designed a functionalized montmorillonite (MMT) material with a porous structure to benefit related drugs, such as mitoxantrone (MIT) delivery or as a carrier for the FePt nanoparticles (FePt NPs) to introduce cancer therapy. Multifunctional FePt@MMT can simultaneously visualize HCC by enhancing MRI signals, treating various diseases, and being used as an inducer of magnetic fluid hyperthermia (MFH). After loading the drug MIT, FePt@MMT-MIT provides both MFH treatment and chemotherapy in one nanosystem. These results ultimately prove that functionalized FePt@MMT-MIT could be integrated as a versatile drugs delivery system by combining with MRI, chemotheraeutic drugs, and magnetic guide targeting.