A meta-analysis and of clinical values of 11 blood biomarkers, such as AFP, DCP, and GP73 for diagnosis of hepatocellular carcinoma

Multifunctional fluorescence/photoacoustic bimodal imaging of γ-glutamyltranspeptidase in liver disorders under different triggering conditions

Hepatocellular carcinoma (HCC) seriously threatens the human health. Previous investigations revealed that γ-glutamyltranspeptidase (GGT) was tightly associated with the chronic injury, hepatic fibrosis, and the development of HCC, therefore might act as a potential indicator for monitoring the HCC-related processes. Herein, with the contribution of a structurally optimized probe ETYZE-GGT, the bimodal imaging in both far red fluorescence (FL) and photoacoustic (PA) modes has been achieved in multiple HCC-related models. To our knowledge, this work covered the most comprehensive models including the fibrosis and developed HCC processes as well as the premonitory induction stages (autoimmune hepatitis, drug-induced liver injury, non-alcoholic fatty liver disease). ETYZE-GGT exhibited steady and practical monitoring performances on reporting the HCC stages via visualizing the GGT dynamics. The two modes exhibited working consistency and complementarity with high spatial resolution, precise apparatus and desirable biocompatibility. In cooperation with the existing techniques including testing serum indexes and conducting pathological staining, ETYZE-GGT basically realized the universal application for the accurate pre-clinical diagnosis of as many HCC stages as possible. By deeply exploring the mechanically correlation between GGT and the HCC process, especially during the premonitory induction stages, we may further raise the efficacy for the early diagnosis and treatment of HCC.

Clinical potential of serum prostaglandin A2 as a novel diagnostic biomarker for hepatocellular cancer

BACKGROUND

Hepatocellular cancer (HCC) is one of the most harmful tumors to human health. Currently, there is still a lack of highly sensitive and specific HCC biomarkers in clinical practice. In this study, we aimed to explore the diagnostic performance of prostaglandin A2 (PGA2) for the early detection of HCC.

METHODS

Untargeted metabolomic analyses on normal control (NC) and HCC participants in the discovery cohort were performed, and PGA2 was identified to be dysregulated in HCC. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for detecting serum PGA2 was established and applied to validate the dysregulation of PGA2 in another independent validation cohort. Receiver operating characteristic (ROC), decision curve analysis (DCA) and some other statistical analyses were performed to evaluate the diagnostic performance of PGA2 for HCC.

RESULTS

At first, PGA2 was found to be dysregulated in HCC in untargeted metabolomic analyses. Then a precise quantitative LC-MS/MS method for PGA2 has been established and has passed rigorous method validation. Targeted PGA2 analyses confirmed that serum PGA2 was decreased in HCC compared to normal-risk NC and high-risk cirrhosis group. Subsequently, PGA2 was identified as a novel biomarker for the diagnosis of HCC, with an area under the ROC curve (AUC) of 0.911 for differentiating HCC from the combined NC + cirrhosis groups. In addition, PGA2 exhibited high performance for differentiating small-size (AUC = 0.924), early-stage (AUC = 0.917) and AFP (-) HCC (AUC = 0.909) from the control groups. The combination of PGA2 and AFP might be useful in the surveillance of risk population for HCC and early diagnosis of HCC.

CONCLUSION

This study establishes that PGA2 might be a novel diagnostic biomarker for HCC.

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.

Development of a diagnostic nomogram for alpha-fetoprotein-negative hepatocellular carcinoma based on serological biomarkers

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Serum biomarkers play an important role in the early diagnosis and prognosis of HCC. Because a certain percentage of HCC patients are negative for alpha-fetoprotein (AFP), the diagnosis of AFP-negative HCC is essential to improve the detection rate of HCC.

AIM

To establish an effective model for diagnosing AFP-negative HCC based on serum tumour biomarkers.

METHODS

A total of 180 HCC patients were enrolled in this study. The expression levels of GP73, des-γ-carboxyprothrombin (DCP), CK18-M65, and CK18-M30 were detected by a fully automated chemiluminescence analyser. The variables were selected by logistic regression analysis. Several models were constructed using stepwise backward logistic regression. The performance of the models was compared using the C statistic, integrated discrimination improvement, net reclassification improvement, and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The results showed that the expression levels of GP73, DCP, CK18-M65, and CK18-M30 were significantly greater in AFP-negative HCC patients than in healthy controls (P < 0.001). Multivariate logistic regression analysis revealed that GP73, DCP, and CK18-M65 were independent factors for diagnosing AFP-negative HCC. By comparing the diagnostic performance of multiple models, we included GP73 and CK18-M65 as the model variables, and the model had good discrimination ability (area under the curve = 0.946) and good goodness of fit. The DCA curves indicated the good clinical utility of the nomogram.

CONCLUSION

Our study identified GP73 and CK18-M65 as serum biomarkers with certain application value in the diagnosis of AFP-negative HCC. The diagnostic nomogram based on CK18-M65 combined with GP73 demonstrated good performance and effectively identified high-risk groups of patients with HCC.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Development of a diagnostic nomogram for alpha-fetoprotein-negative hepatocellular carcinoma based on serological biomarkers

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Serum biomarkers play an important role in the early diagnosis and prognosis of HCC. Because a certain percentage of HCC patients are negative for alpha-fetoprotein (AFP), the diagnosis of AFP-negative HCC is essential to improve the detection rate of HCC.

AIM

To establish an effective model for diagnosing AFP-negative HCC based on serum tumour biomarkers.

METHODS

A total of 180 HCC patients were enrolled in this study. The expression levels of GP73, des-γ-carboxyprothrombin (DCP), CK18-M65, and CK18-M30 were detected by a fully automated chemiluminescence analyser. The variables were selected by logistic regression analysis. Several models were constructed using stepwise backward logistic regression. The performance of the models was compared using the C statistic, integrated discrimination improvement, net reclassification improvement, and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The results showed that the expression levels of GP73, DCP, CK18-M65, and CK18-M30 were significantly greater in AFP-negative HCC patients than in healthy controls (P < 0.001). Multivariate logistic regression analysis revealed that GP73, DCP, and CK18-M65 were independent factors for diagnosing AFP-negative HCC. By comparing the diagnostic performance of multiple models, we included GP73 and CK18-M65 as the model variables, and the model had good discrimination ability (area under the curve = 0.946) and good goodness of fit. The DCA curves indicated the good clinical utility of the nomogram.

CONCLUSION

Our study identified GP73 and CK18-M65 as serum biomarkers with certain application value in the diagnosis of AFP-negative HCC. The diagnostic nomogram based on CK18-M65 combined with GP73 demonstrated good performance and effectively identified high-risk groups of patients with HCC.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Sandwich-type supersensitive electrochemical aptasensor of glypican-3 based on PrGO-Hemin-PdNP and AuNP@PoPD

A new sandwich-type electrochemical biosensing platform was developed by gold @polyphthalenediamine nanohybrids (AuNP@PoPD) as the sensing platform and phosphorus doped reduced graphene oxide-hemin-palladium nanoparticles (PrGO-Hemin-PdNP) as the signal amplifier for phosphatidylinositol proteoglycan 3 (GPC3). AuNP@PoPD, co-electrodeposited into the screen printed electrode with high conductivity and stability, is dedicated to assembling the primary GPC3 aptamer (GPC3Apt). The second GPC3Apt immobilized on the high conductivity and large surface area of PrGO-Hemin-PdNP was utilized as an electrochemical signal reporter by hemin oxidation (PrGO-Hemin-PdNP-GPC3Apt). In the range 0.001-10.0 ng/mL, the hemin oxidation current signal of the electrochemical aptasensor increased log-linearly with the concentration of GPC3, the lowest detection limit was 0.13 pg/mL, and the sensitivity was 2.073 μA/μM/cm2. The aptasensor exhibited good sensing performance in a human serum sample with the relative error of 4.31-8.07%. The sandwich sensor showed good selectivity and stability for detection GPC3 in human serum samples, providing a new efficient and sensitive method for detecting HCC markers.

Alpha-fetoprotein: Past, present, and future

Alpha-fetoprotein (AFP) is a glycoprotein that plays an important role in immune regulation with critical involvement in early human development and maintaining the immune balance during pregnancy. Postfetal development, the regulatory mechanisms controlling AFP undergo a shift and AFP gene transcription is suppressed. Instead, these enhancers refocus their activity to maintain albumin gene transcription throughout adulthood. During the postnatal period, AFP expression can increase in the setting of hepatocyte injury, regeneration, and malignant transformation. It is the first oncoprotein discovered and is routinely used as part of a screening strategy for HCC. AFP has been shown to be a powerful prognostic biomarker, and multiple HCC prognosis models confirmed the independent prognostic utility of AFP. AFP is also a useful predictive biomarker for monitoring the treatment response of HCC. In addition to its role as a biomarker, AFP plays important roles in immune modulation to promote tumorigenesis and thus has been investigated as a therapeutic target in HCC. In this review article, we aim to provide an overview of AFP, encompassing the discovery, biological role, and utility as an HCC biomarker in combination with other biomarkers and how it impacts clinical practice and future direction.

Serum Aldo-Keto Reductase Family 1 Member B10 (AKR1B10) as a Potential Biomarker for Diagnosis of Hepatocellular Carcinoma

Objective

To evaluate the diagnostic performance of aldo-keto reductase family 1 member B10 (AKR1B10) in a Beijing cohort with hepatocellular carcinoma (HCC).

Methods

This study included 521 subjects who visited Peking Union Medical College Hospital from June 2017 to May 2023, including 109 cases of HCC, 165 cases of healthy controls, 106 cases of benign liver diseases, and 141 cases of other cancers. Serum AKR1B10 levels were measured and compared across various groups. Diagnostic performances of serum AKR1B10 and other tumor markers were assessed using receiver operator characteristic (ROC) curves. In addition, a subset of HCC patients who underwent surgical resection were recruited for clinical follow-up study.

Results

We found that serum AKR1B10 expression was higher in patients with HCC relative to other control groups. The association between serum AKR1B10 and clinical features of HCC was not observed. Serum AKR1B10 showed a high diagnostic performance for HCC, and when combined with AFP, the diagnostic effectiveness was significantly improved. Specifically, serum AKR1B10 showed superior diagnostic effectiveness for AFP-negative HCC. The clinical follow-up study indicated a gradual decrease in serum AKR1B10 after surgery.

Conclusion

Our study demonstrated that serum AKR1B10 is a promising biomarker for HCC, and when used in combination with AFP can significantly improve the detection rate of HCC.

Nomogram for Predicting Postoperative Pulmonary Metastasis in Hepatocellular Carcinoma Based on Inflammatory Markers

BACKGROUND

Uncertainty surrounds the usefulness of inflammatory markers in hepatocellular carcinoma (HCC) patients for predicting postoperative pulmonary metastasis (PM). The purpose of this study was to assess the predictive value of inflammatory markers as well as to create a new nomogram model for predicting PM.

METHODS

Cox regression was utilized to identify independent prognostic variables and to create a nomogram that predicted PM for comparison with a validation cohort and other prediction systems. We retrospectively analyzed a total of 1109 cases with HCC were included.

RESULTS

The systemic inflammatory response index (SIRI) and aspartate aminotransferase-to-platelet ratio index (APRI) were independent risk factors for PM, with a concordance index of .78 (95% CI: .74-.81) for the nomogram. The areas under the curve of the nomograms for PM predicted at 1-, 3-, and 5-year were .82 (95% CI: .77-.87), .82 (95% CI: .78-.87) and .81 (95% CI: .75-.86), respectively, which were better than those of Barcelona Clinic Liver Cancer and China liver cancer stage. Decision curve analyses demonstrated a broader range of nomogram threshold probabilities.

CONCLUSION

A nomogram based on SIRI and APRI can accurately predict postoperative PM in HCC.

α-Fetoprotein, α-Fetoprotein-L3, and Des-γ-Carboxy Prothrombin Stratify Hepatocellular Carcinoma Treatment Response and Progression Risk

Background and Aims

Assessing aggressive biology at early-stage hepatocellular carcinoma (HCC) diagnosis remains challenging. Alpha-fetoprotein (AFP) is the only clinical biomarker of aggressive HCC. In this study, AFP, Lens culinaris agglutinin-reactive AFP (AFP-L3), and des-γ-carboxy prothrombin (DCP) were measured at diagnosis prior to transplant evaluation and first cycle liver-directed therapy (LDT).

Methods

The prospective cohort included 207 patients who received LDT as a bridge/downstage to transplant or definitive treatment plan between 2016 and 2022. Plasma AFP, AFP-L3, and DCP levels were measured at diagnosis and analyzed with other factors associated with treatment response and time-to-progression.

Results

Biomarker phenotyping revealed 41% were triple negative, 30% expressed multiple biomarkers, and 12% express all 3 biomarkers. The biomarker profile was associated with target/overall response rate and time-to-progression (P < .001). Profiling stratified 1-year progression risk in nontransplant candidates, driven by coexpression of AFP and DCP in multivariate analysis controlling for tumor burden and staging.

Conclusion

The biomarker panel at diagnosis established prognosis for LDT response and stratified 1-year HCC progression risk. AFP, AFP-L3, and DCP profiling isolated aggressive HCC biology at diagnosis and may have important implications in post-LDT surveillance and transplant wait time.

Ferroptosis: a new promising target for hepatocellular carcinoma therapy

Hepatocellular carcinoma (HCC) is the sixed most common malignant tumor in the world. The study for HCC is mired in the predicament confronted with the difficulty of early diagnosis and high drug resistance, the survival rate of patients with HCC being low. Ferroptosis, an iron-dependent cell death, has been discovered in recent years as a cell death means with tremendous potential to fight against cancer. The in-depth researches for iron metabolism, lipid peroxidation and dysregulation of antioxidant defense have brought about tangible progress in the firmament of ferroptosis with more and more results showing close connections between ferroptosis and HCC. The potential role of ferroptosis has been widely used in chemotherapy, immunotherapy, radiotherapy, and nanotherapy, with the development of various new drugs significantly improving the prognosis of patients. Based on the characteristics and mechanisms of ferroptosis, this article further focuses on the main signaling pathways and promising treatments of HCC, envisioning that existing problems in regard with ferroptosis and HCC could be grappled with in the foreseeable future.

The diagnostic performance of AFP and PIVKA-II models for non-B non-C hepatocellular carcinoma

OBJECTIVE

This study aims to describe the diagnostic performance of alpha-fetoprotein (AFP), alpha-fetoprotein L3 isoform (AFP-L3), protein induced by vitamin K absence II (PIVKA-II), and combined biomarkers for non-B non-C hepatocellular carcinoma (NBNC-HCC).

RESULTS

A total of 681 newly-diagnosed primary liver disease subjects (385 non-HCC, 296 HCC) who tested negativity for the hepatitis B surface antigen (HBsAg) and hepatitis C antibody (anti-HCV) enrolled in this study. At the cut-off point of 3.8 ng/mL, AFP helps to discriminate HCC from non-HCC with an area under the curve (AUC) value of 0.817 (95% confidence interval [CI]: 0.785-0.849). These values of AFP-L3 (cut-off 0.9%) and PIVKA-II (cut-off 57.7 mAU/mL) were 0.758 (95%CI: 0.725-0.791) and 0.866 (95%CI: 0.836-0.896), respectively. The Bayesian Model Averaging (BMA) statistic identified the optimal model, including patients' age, aspartate aminotransferase, AFP, and PIVKA-II combination, which helps to classify HCC with better performance (AUC = 0.896, 95%CI: 0.872-0.920, P < 0.001). The sensitivity and specificity of the optimal model reached 81.1% (95%CI: 76.1-85.4) and 83.2% (95%CI: 78.9-86.9), respectively. Further analyses indicated that AFP and PIVKA-II markers and combined models have good-to-excellent performance detecting curative resected HCC, separating HCC from chronic hepatitis, dysplastic, and hyperplasia nodules.

Imaging Investigation of Hepatocellular Carcinoma Progress via Monitoring γ-Glutamyltranspeptidase Level with a Near-Infrared Fluorescence/Photoacoustic Bimodal Probe

Hepatocellular carcinoma (HCC) is one of the main principal causes of cancer death, and the late definite diagnosis limits therapeutic approaches in time. The early diagnosis of HCC is essential, and the previous investigations on the biomarkers inferred that the γ-glutamyltranspeptidase (GGT) level could indicate the HCC process. Herein, a near-infrared fluorescence/photoacoustic (NIRF/PA) bimodal probe, CySO3-GGT, was developed for monitoring the GGT level and thus to image the HCC process. After the in-solution tests, the bimodal response was convinced. The various HCC processes were imaged by CySO3-GGT at the cellular level. Then, the CCl4-induced HCC (both induction and treatment) and the subcutaneous and orthotopic xenograft mice models were selected. All throughout the tests, CySO3-GGT achieved NIRF and PA bimodal imaging of the HCC process. In particular, CySO3-GGT could effectively realize 3D imaging of the HCC nodule by visualizing the boundary between the tumor and the normal tissue. The information here might offer significant guidance for the dynamic monitoring of HCC in the near future.

Methylated SEPT9 combined with AFP and PIVKA-II is effective for the detection of HCC in high-risk population

BACKGROUND

The methylation SEPT9 (mSEPT9) appeared to be effective for hepatocellular carcinoma (HCC) detection. However, its performance in high-risk population has not been validated. We designed a pilot study and aimed to investigate the performance of mSEPT9, AFP, PIVKA-II and their combination in hepatic cirrhosis (HC) population.

METHODS

A training cohort was established including 103 HCC and 114 HC patients. 10 ml blood was collected from each patient with K₂EDTA tubes, and 3-4 ml plasma was extracted for subsequent tests. The performance of mSEPT9, AFP, PIVKA-II and their combination was optimized by the training cohort. Test performance was prospectively validated with a validation cohort, including 51 HCC and 121 HC patients.

RESULTS

At the optimal thresholds in the training cohort, the sensitivity, specificity and area under curve (AUC) was 72.82%, 89.47%, 0.84, and 48.57%, 89.92%, 0.79, and 63.64%, 95.95%, 0.79 for mSEPT9, AFP and PIVKA-II, respectively. The combined test significantly increased the sensitivity to 84.47% (P < 0.05) at the specificity of 86.84% with an AUC of 0.91. Stage-dependent performance was observed with all single markers and their combination in plasma marker levels, positive detection rate (PDR) and AUC. Moderate correlation was found between mSEPT9 and AFP plasma levels (r = 0.527, P < 0.0001). Good complementarity was found between any two of the three markers, providing optimal sensitivity in HCC detection when used in combination. Subsequent validation achieved a sensitivity, specificity and AUC of 65.31%, 92.86%, 0.80, and 44.24%, 89.26%, 0.75, and 62.22%, 95.27%, 0.78 for mSEPT9, AFP and PIVKA-II, respectively. The combined test yielded a significantly increased sensitivity of 84.00% (P < 0.05) at 85.57% specificity, with an AUC at 0.89.

CONCLUSIONS

The performance was optimal by the combination of mSEPT9, AFP, PIVKA-II compared with any single marker, and the combination may be effective for HCC opportunistic screening in HC population.

A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers

Various statistical models predict the probability of developing hepatocellular carcinoma (HCC) in patients with cirrhosis, with GALAD being one of the most extensively studied scores. Biomarkers like alpha-fetoprotein (AFP), AFP-L3, and des-g-carboxyprothrombin (DCP) are widely used alone or in conjunction with ultrasound to screen for HCC. Our study aimed to compare the effectiveness of Cytokeratin 19 (CK19) and Glypican-3 (GPC3) as standalone biomarkers and in a statistical model to predict the likelihood of HCC. We conducted a monocentric prospective study involving 154 participants with previously diagnosed liver cirrhosis, divided into two groups: 95 patients with confirmed HCC based on clinical, biological, and imaging features and 59 patients without HCC. We measured the levels of AFP, AFP-L3, DCP, GPC3, and CK19 in both groups. We used univariate and multivariate statistical analyses to evaluate the ability of GPC3 and CK19 to predict the presence of HCC and incorporated them into a statistical model—the GALKA score—which was then compared to the GALAD score. AFP performed better than AFP-F3, DCP, GPC3, and CK19 in predicting the presence of HCC in our cohort. Additionally, GPC3 outperformed CK19. We used multivariate analysis to compute the GALKA score to predict the presence of HCC. Using these predictors, the following score was formulated: 0.005*AFP-L3 + 0.00069*AFP + 0.000066*GPC3 + 0.01*CK19 + 0.235*Serum Albumin—0.277. The optimal cutoff was >0.32 (AUROC = 0.98, sensitivity: 96.8%, specificity: 93%, positive predictive value—95.8%, negative predictive value—94.8%). The GALKA score had a similar predictive value to the GALAD score for the presence of HCC. In conclusion, AFP, AFP-L3, and DCP were the best biomarkers for predicting the likelihood of HCC. Our score performed well overall and was comparable to the GALAD score.