Analysis of serum alpha-fetoprotein (AFP) and AFP-L3 levels by protein microarray

Hsa_circ_0005397 promotes hepatocellular carcinoma progression through EIF4A3

PURPOSE

The purpose of this study was to explore the expression and potential mechanism of hsa_circ_0005397 in hepatocellular carcinoma progression.

METHODS

Quantitative reverse transcription-polymerase chain reaction(qRT-PCR) was used to measure the expression level of hsa_circ_0005397 and EIF4A3 from paired HCC tissues and cell lines. Western Blot (WB) and immunohistochemistry (IHC) were used to verify the protein level of EIF4A3. The specificity of primers was confirmed by agarose gel electrophoresis. Receiver Operating Characteristic (ROC) Curve was drawn to analyze diagnostic value. Actinomycin D and nuclear and cytoplasmic extraction assays were utilized to evaluate the characteristics of hsa_circ_0005397. Cell Counting kit-8 (CCK-8) and colony formation assays were performed to detect cell proliferation. Flow cytometry analysis was used to detect the cell cycle. Transwell assay was performed to determine migration and invasion ability. RNA-binding proteins (RBPs) of hsa_circ_0005397 in HCC were explored using bioinformatics websites. The relationship between hsa_circ_0005397 and Eukaryotic Translation Initiation Factor 4A3 (EIF4A3) was verified by RNA Binding Protein Immunoprecipitation (RIP) assays, correlation and rescue experiments.

RESULTS

In this study, hsa_circ_0005397 was found to be significantly upregulated in HCC, and the good diagnostic sensitivity and specificity shown a potential diagnostic capability. Upregulated expression of hsa_circ_0005397 was significantly related to tumor size and stage. Hsa_circ_0005397 was circular structure which more stable than liner mRNA, and mostly distributed in the cytoplasm. Upregulation of hsa_circ_0005397 generally resulted in stronger proliferative ability, clonality, and metastatic potency of HCC cells; its downregulation yielded the opposite results. EIF4A3 is an RNA-binding protein of hsa_circ_0005397, which overexpressed in paired HCC tissues and cell lines. In addition, expression of hsa_circ_0005397 decreased equally when EIF4A3 was depleted. RIP assays and correlation assay estimated that EIF4A3 could interacted with hsa_circ_0005397. Knockdown of EIF4A3 could reverse hsa_circ_0005397 function in HCC progression.

CONCLUSIONS

Hsa_circ_0005397 promotes progression of hepatocellular carcinoma through EIF4A3. These research findings may provide novel clinical value for hepatocellular carcinoma.

Value of XGBoost machine learning model for diagnosis of hepatitis B cirrhosis

BACKGROUND

The progression of chronic hepatitis B into cirrhosis is slow and easily ignored, and the construction of a noninvasive diagnostic model for cirrhosis based on routine clinical indicators has become a hot research topic. However, there is still a lack of machine learning models regarding the early diagnosis of cirrhosis.

AIM

To investigate the performance of the extreme gradient boosting (XGBoost) machine model in the diagnosis of hepatitis B cirrhosis.

METHODS

A retrospective analysis was performed on 1087 patients with chronic hepatitis B virus infection (CHBV) diagnosed for the first time at the Department of Infection, The First/Second Affiliated Hospital of Anhui Medical University from 2010 to 2018. The patients were divided into training and validation sets in a 3:1 ratio according to the randomization principle. Clinical data of all study participants were collected and prediction models were constructed using XGBoost machine learning model. Meanwhile, the aspartate aminotransferase/platelet ratio index (APRI) and fibrosis-4 index (FIB-4) scores were calculated and compared with the XGBoost machine learning model. Area under the curve (AUC) was used to assess the model discrimination, and calibration curve (CA) and decision curve analysis (DCA) were used to assess the model calibration and benefit.

RESULTS

A total of 1087 CHBV patients were included, including 817 in the training set and 270 in the validation set. There was no statistical difference between the training and validation sets for all predictor variables (P > 0.05). Cirrhosis occurred in 103 patients in the training set, and APRI and FIB-4 scores were significantly higher in cirrhotic patients than in non-cirrhotic patients (P < 0.05). The relative importance of platelets was the highest among all predictors. The AUCs of the model in the training and validation sets were 0.95 and 0.86 (P < 0.05), respectively, and the Kappa values were 0.78 and 0.74, which suggested that the model was reproducible. CA curve analysis indicated that the model predicted a high degree of agreement with the true situation fit. DCA of the training and validation sets implied that the developed model could result in a high degree of benefit for patients. XGBoost machine learning model was significantly more efficient for the diagnosis of cirrhosis than APRI and FIB-4 scores.

CONCLUSION

The XGBoost machine learning model constructed in this study based on common clinical information of CHBV patients has an excellent performance for the diagnosis of cirrhosis and deserves further clinical promotion.

The role of N-glycosylation modification in the pathogenesis of liver cancer

N-glycosylation is one of the most common types of protein modifications and it plays a vital role in normal physiological processes. However, aberrant N-glycan modifications are closely associated with the pathogenesis of diverse diseases, including processes such as malignant transformation and tumor progression. It is known that the N-glycan conformation of the associated glycoproteins is altered during different stages of hepatocarcinogenesis. Characterizing the heterogeneity and biological functions of glycans in liver cancer patients will facilitate a deeper understanding of the molecular mechanisms of liver injury and hepatocarcinogenesis. In this article, we review the role of N-glycosylation in hepatocarcinogenesis, focusing on epithelial-mesenchymal transition, extracellular matrix changes, and tumor microenvironment formation. We highlight the role of N-glycosylation in the pathogenesis of liver cancer and its potential applications in the treatment or diagnosis of liver cancer.

Recent Trends in Biosensing and Diagnostic Methods for Novel Cancer Biomarkers

Cancer is one of the major public health issues in the world. It has become the second leading cause of death, with approximately 75% of cancer deaths transpiring in low- or middle-income countries. It causes a heavy global economic cost estimated at more than a trillion dollars per year. The most common cancers are breast, colon, rectum, prostate, and lung cancers. Many of these cancers can be treated effectively and cured if detected at the primary stage. Nowadays, around 50% of cancers are detected at late stages, leading to serious health complications and death. Early diagnosis of cancer diseases substantially increases the efficient treatment and high chances of survival. Biosensors are one of the potential screening methodologies useful in the early screening of cancer biomarkers. This review summarizes the recent findings about novel cancer biomarkers and their advantages over traditional biomarkers, and novel biosensing and diagnostic methods for them; thus, this review may be helpful in the early recognition and monitoring of treatment response of various human cancers.

Evaluation of high-risk factors and the diagnostic value of alpha-fetoprotein in the stratification of primary liver cancer

BACKGROUND

Alpha-fetoprotein (AFP) is one of the diagnostic standards for primary liver cancer (PLC); however, AFP exhibits insufficient sensitivity and specificity for diagnosing PLC.

AIM

To evaluate the effects of high-risk factors and the diagnostic value of AFP in stratified PLC.

METHODS

In total, 289 PLC cases from 2013 to 2019 were selected for analysis. First, the contributions of high-risk factors in stratifying PLC were compared according to the following criteria: Child–Pugh score, clinical stage of liver cirrhosis, tumor size, and Barcelona Clinic Liver Cancer (BCLC) stage. Then, the diagnostic value of AFP was evaluated in different stratifications of PLC by receiver operating characteristic curves. For PLC cases in which AFP played little role, the diagnostic values of carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA 19-9), gamma-glutamyl transferase (GGT), and AFP were analyzed.

RESULTS

The roles of high-risk factors differed in stratified PLC. The incidence of smoking and drinking history was higher in PLC with Child–Pugh scores of C (P < 0.0167). The hepatitis B virus (HBV) infection rate in PLC with cirrhosis was more than in PLC without cirrhosis (P < 0.0167). Small tumors were more prone to cirrhosis than large tumors (P < 0.005). BCLC stage D PLC was more likely to be associated with HBV infection and cirrhosis (P < 0.0083). AFP levels were higher in PLC with cirrhosis, diffuse tumors, and BCLC stage D disease. In diagnosing PLC defined as Child–Pugh A, B, and C, massive hepatoma, diffuse hepatoma, BCLC stage B, C, and D, and AFP showed significant diagnostic value [all area under the curve (AUC) > 0.700]. However, these measures were meaningless (AUC < 0.600) in small hepatomas and BCLC A stage PLC, but could be replaced by the combined detection of CEA, CA 19-9, GGT, and AFP (AUC = 0.810 and 0.846, respectively).

CONCLUSION

Stratification of PLC was essential for precise diagnoses and benefited from evaluating AFP levels.

Comparison of Fucose-Specific Lectins to Improve Quantitative AFP-L3 Assay for Diagnosing Hepatocellular Carcinoma Using Mass Spectrometry

Glycoproteins have many important biological functions. In particular, aberrant glycosylation has been observed in various cancers, such as liver cancer. A well-known glycoprotein biomarker is α-fetoprotein (AFP), a surveillance biomarker for hepatocellular carcinoma (HCC) that contains a glycosylation site at asparagine 251. The low diagnostic sensitivity of AFP led researchers to focus on AFP-L3, which has the same sequence as conventional AFP but contains a fucosylated glycan. AFP-L3 has high affinity for Lens culinaris agglutinin (LCA) lectin, prompting many groups to use it for detecting AFP-L3. However, a few studies have identified more effective lectins for fractionating AFP-L3. In this study, we compared the amounts of enriched AFP-L3 with five fucose-specific lectins─LCA, Lotus tetragonolobus lectin (LTL), Ulex europaeus agglutinin I (UEA I), Aleuria aurantia lectin (AAL), and Aspergillus oryzae lectin (AOL)─to identify better lectins and improve HCC diagnostic assays using mass spectrometry (MS). Our results indicate that LTL was the most effective lectin for capturing AFP-L3 species, yielding approximately 3-fold more AFP-L3 than LCA from the same pool of HCC serum samples. Thus, we recommend the use of LTL for AFP-L3 assays, given its potential to improve the diagnostic sensitivity in patients having limited results by conventional LCA assay. The MS data have been deposited to the PeptideAtlas (PASS01752).

Reusable, Noninvasive, and Sensitive Fluorescence Enhanced ZnO-Nanorod-Based Microarrays for Quantitative Detection of AFP in Human Serum

The fabrication of sensitive protein microarrays such as PCR used in DNA microarray is challenging due to lack of signal amplification. The development of microarrays is utilized to improve the sensitivity and limitations of detection towards primal cancer detection. The sensitivity is enhanced by the use of ZnO-nanorods and is investigated as a substrate which enhance the florescent signal to diagnose the hepatocellular carcinoma (HCC) at early stages. The substrate for deposition of ZnO-nanorods is prepared by the conventional chemical bath deposition method. The resultant highly dense ZnO-nanorods enhance the fluorescent signal 7.2 times as compared to the substrate without ZnO-nanorods. The microarray showed sensitivity of 1504.7 ng ml⁻¹ and limit of detection of 0.1 pg ml⁻¹ in wide dynamic range of 0.05 pg-10 μg ml⁻¹ for alpha fetoprotein (AFP) detection in 10% human serum. This immunoassay was successfully applied for human serum samples to detect tumor marker with good recoveries. The ZnO-nanorod substrate is a simple protein microarray which showed a great promise for developing a low-cost, sensitive, and high-throughput protein assay platform for several applications in both fundamental research and clinical diagnosis.

Biomarkers Determination Based on Surface-Enhanced Raman Scattering

An overview of noteworthy new methods of biomarker determination based on surface-enhanced Raman scattering (SERS) is presented. Biomarkers can be used to identify the occurrence and development of diseases, which furthers the understanding of biological processes in the body. Accurate detection of a disease-specific biomarker is helpful for the identification, early diagnosis and prevention of a disease and for monitoring during treatment. The search for and discovery of valuable biomarkers have become important research hotspots. Different diseases have different biomarkers, some of which are involved in metabolic processes. Therefore, the fingerprint characteristics and band intensities in SERS spectra have been used to identify metabolites and analyze markers. As a promising technique, SERS has been widely used for the quantitative and qualitative determination of different types of biomarkers for different diseases. SERS techniques provide new technologies for the diagnosis of disease-related markers and determining the basis for clinical treatment. Herein, several SERS-based methods with excellent sensitivity and selectivity for the determination of biomarkers for tumors, viruses, Alzheimer’s disease, cardiac muscle tissue injury, and cell activity are highlighted.

Proteomic approaches to investigate gammaherpesvirus biology and associated tumorigenesis

The DNA viruses, Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV), are members of the gammaherpesvirus subfamily, a group of viruses whose infection is associated with multiple malignancies, including cancer. The primary host for these viruses is humans and, like all herpesviruses, infection with these pathogens is lifelong. Due to the persistence of gammaherpesvirus infection and the potential for cancer formation in infected individuals, there is a driving need to understand not only the biology of these viruses and how they remain undetected in host cells but also the mechanism(s) by which tumorigenesis occurs. One of the methods that has provided much insight into these processes is proteomics. Proteomics is the study of all the proteins that are encoded by a genome and allows for (i) identification of existing and novel proteins derived from a given genome, (ii) interrogation of protein-protein interactions within a system, and (iii) discovery of druggable targets for the treatment of malignancies. In this chapter, we explore how proteomics has contributed to our current understanding of gammaherpesvirus biology and their oncogenic processes, as well as the clinical applications of proteomics for the detection and treatment of gammaherpesvirus-associated cancers.

Evaluation of serum AFP and DCP levels in the diagnosis of early-stage HBV-related HCC under different backgrounds

OBJECTIVE

This study compared the diagnostic performance of alpha-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP) in early-stage hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) under different backgrounds.

METHODS

Patients were enrolled and divided in four groups: chronic HBV infection (CHB), liver cirrhosis (LC), early-stage CHB-HCC, and early-stage LC-HCC. Serum AFP and DCP levels were measured. Receiver-operating characteristic (ROC) curve and area under the curve (AUC) analyses were applied to compare the diagnostic performance of DCP and AFP for HCC.

RESULTS

In total, 200 patients were enrolled, including 48, 64, 33, and 55 patients with CHB, LC, CHB-HCC, and LC-HCC, respectively. ROC curve analysis revealed that the AUCs of AFP, DCP, and their combination in differentiating early-stage LC-HCC from LC were 0.776, 0.758, and 0.786, respectively. The values of these markers in discriminating early-stage CHB-HCC from CHB were 0.828, 0.731, and 0.862, respectively.

CONCLUSIONS

DCP was inferior to AFP in differentiating early-stage CHB-HCC from CHB. However, AFP and DCP displayed similar performance in distinguishing early-stage LC-HCC and LC.

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

In recent years, there has been a plethora of attempts to discover biomarkers that are more reliable than α-fetoprotein for the early prediction and prognosis of hepatocellular carcinoma (HCC). Efforts have involved such fields as genomics, transcriptomics, epigenetics, microRNA, exosomes, proteomics, glycoproteomics, and metabolomics. HCC arises against a background of inflammation, steatosis, and cirrhosis, due mainly to hepatic insults caused by alcohol abuse, hepatitis B and C virus infection, adiposity, and diabetes. Metabolomics offers an opportunity, without recourse to liver biopsy, to discover biomarkers for premalignant liver disease, thereby alerting the potential of impending HCC. We have reviewed metabolomic studies in alcoholic liver disease (ALD), cholestasis, fibrosis, cirrhosis, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH). Specificity was our major criterion in proposing clinical evaluation of indole-3-lactic acid, phenyllactic acid, N-lauroylglycine, decatrienoate, N-acetyltaurine for ALD, urinary sulfated bile acids for cholestasis, cervonoyl ethanolamide for fibrosis, 16α-hydroxyestrone for cirrhosis, and the pattern of acyl carnitines for NAFL and NASH. These examples derive from a large body of published metabolomic observations in various liver diseases in adults, adolescents, and children, together with animal models. Many other options have been tabulated. Metabolomic biomarkers for premalignant liver disease may help reduce the incidence of HCC.