Elevated serum CA19-9 indicates severe liver inflammation and worse survival after curative resection in hepatitis B-related hepatocellular carcinoma

Sialylated glycoproteins and sialyltransferases in digestive cancers: Mechanisms, diagnostic biomarkers, and therapeutic targets

Sialic acid (SA), as the ultimate epitope of polysaccharides, can act as a cap at the end of polysaccharide chains to prevent their overextension. Sialylation is the enzymatic process of transferring SA residues onto polysaccharides and is catalyzed by a group of enzymes known as sialyltransferases (SiaTs). It is noteworthy that the sialylation level of glycoproteins is significantly altered when digestive cancer occurs. And this alteration exhibits a close correlation with the progression of these cancers. In this review, from the perspective of altered SiaTs expression levels and changed glycoprotein sialylation patterns, we summarize the pathogenesis of gastric cancer (GC), colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), and hepatocellular carcinoma (HCC). Furthermore, we propose potential early diagnostic biomarkers and prognostic indicators for different digestive cancers. Finally, we summarize the therapeutic value of sialylation in digestive system cancers.

A Retrospective Study on Predicting Recurrence of Intermediate-Stage Hepatocellular Carcinoma After Radical Therapy

Purpose

This study aimed to investigate the potential benefits of radical therapy in patients with stage B disease.

Patients and Methods

A retrospective analysis was conducted on a cohort of 437 patients diagnosed with stage B hepatocellular carcinoma, who underwent either hepatic resection (HR) or radiofrequency ablation (RFA) at the Cancer Institute and Hospital of Tianjin Medical University from May 2011 to May 2022. Multivariate COX regression analysis was performed to identify the independent prognostic factors related to recurrence-free survival (RFS). The performance of the developed nomogram was evaluated using various statistical measures, including the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

Multivariate analysis revealed that tumor diameter, number of tumors, number of involved liver segments, alpha-fetoprotein (AFP), carbohydrate antigen 19-9 (CA19-9), lactate dehydrogenase (LDH), and systemic immune inflammation index (SII) were independent prognostic factors influencing patients' RFS, and these factors were incorporated into the nomogram. The C-index of the nomogram in the training cohort was 0.721, and the AUC at 2 and 3 years was 0.772 and 0.790, respectively. These values were appreciably higher than commonly used clinic staging systems and other predictive models. The calibration curve and DCA demonstrated good calibration and net benefit. Survival analysis comparing stage B patients who received radical treatment with stage A patients with multiple lesions did not reveal a significant difference in Kaplan-Meier survival curves (P=0.91).

Conclusion

The nomogram provided a precise prediction of the recurrence for stage B hepatocellular carcinoma patients undergoing radical treatment. Furthermore, certain stage B patients may benefit from radical treatment.

Clinical role of serum tumor markers SCC, NSE, CA 125, CA 19-9, and CYFRA 21-1 in patients with lung cancer

OBJECTIVE

The aim of the study was to assess the diagnostic value of tumor markers in discriminating between lung cancer and benign chest diseases (BCDs).

METHODS

There were 322 patients enrolled in this investigation including 180 cases of lung cancer and 142 cases of BCD. Serum neuron-specific enolase (NSE), cancer antigen 125, cancer antigen 19-9, squamous cell carcinoma-related antigen, and cytokeratin fragment 19 (CYFRA 21-1) were compared between different populations, cancer stages, and before and after treatment. Logistic regression and receiver operating characteristic curves were used to evaluate the diagnostic markers.

RESULTS

Both NSE and CYFRA 21-1 were significantly associated with lung cancer. The CYFRA 21-1 showed the best performance, as well as its combinations, for lung cancer diagnosis. It also showed significant change 6 months after radical surgery in lung cancer patients.

CONCLUSION

The marker CYFRA 21-1 could be developed as an adjuvant marker for the early diagnosis of lung cancer and as a prognostic marker for lung cancer treatment.

Hepatectomy combined with apatinib and camrelizumab for CNLC stage IIIb hepatocellular carcinoma: a phase II trial protocol

INTRODUCTION

Current clinical guidelines recommend systematic antitumour therapy as the primary treatment option for patients with stage IIIb hepatocellular carcinoma (HCC) based on the China liver cancer (CNLC) staging criteria. Several different targeted therapeutics have been applied in combination with immunotherapeutic regimens to date in patients with advanced HCC. The present study was developed to evaluate the relative safety and efficacy of hepatectomy of HCC in combination with targeted apatinib treatment and immunotherapeutic camrelizumab treatment CNLC-IIIb stage HCC patients with the goal of providing evidence regarding the potential value of this therapeutic regimen in individuals diagnosed with advanced HCC.

METHODS AND ANALYSIS

This is a multicentre phase II trial with single-arm in which patients undergo hepatectomy in combination with targeted treatment (apatinib) and immunotherapy (camrelizumab). Patients will undergo follow-up every 2-3 months following treatment initiation to record any evidence of disease progression and adverse event incidence for a minimum of 24 months following the discontinuation of treatment until reaching study endpoint events or trial termination. The primary endpoint for this study is patient mortality.

ETHICS AND DISSEMINATION

This study protocol was approved by the Ethics Committee of the Guangxi Medical University Cancer Hospital (KS2022[124]). The results of this study will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT05062837.

Perioperative predictors of outcome of hepatectomy for HBV-related hepatocellular carcinoma

Hepatitis B virus (HBV) is identified as a major risk factor for hepatocellular carcinoma (HCC), resulting in so-called hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). Hepatectomy for HCC is acknowledged as an efficient treatment strategy, especially for early HCC. Furthermore, patients with advanced HCC can still obtain survival benefits through surgical treatment combined with neoadjuvant therapy, adjuvant therapy, transcatheter arterial chemoembolization, and radiofrequency ablation. Therefore, preoperative and postoperative predictors of HBV-related HCC have crucial indicative functions for the follow-up treatment of patients with feasible hepatectomy. This review covers a variety of research results on preoperative and postoperative predictors of hepatectomy for HBV-related HCC over the past decade and in previous landmark studies. The relevant contents of Hepatitis C virus-related HCC, non-HBV non-HCV HCC, and the artificial intelligence application in this field are briefly addressed in the extended content. Through the integration of this review, a large number of preoperative and postoperative factors can predict the prognosis of HBV-related HCC, while most of the predictors have no standardized thresholds. According to the characteristics, detection methods, and application of predictors, the predictors can be divided into the following categories: 1. serological and hematological predictors, 2. genetic, pathological predictors, 3. imaging predictors, 4. other predictors, 5. analysis models and indexes. Similar results appear in HCV-related HCC, non-HBV non-HCV HCC. Predictions based on AI and big biological data are actively being applied. A reasonable prediction model should be established based on the economic, health, and other levels in specific countries and regions.

Tumor burden score-AFP-albumin-bilirubin grade score predicts the survival of patients with hepatocellular carcinoma after liver resection

PURPOSE

There is little information regarding the overall survival (OS) predictive ability of the combination of tumor burden score (TBS), α-fetoprotein (AFP), and albumin-bilirubin (ALBI) grade for patients with hepatocellular carcinoma (HCC). Here, we aimed to develop a model including TBS, AFP, and ALBI grade to predict HCC patient OS following liver resection.

METHODS

Patients (N = 1556) from six centers were randomly divided 1:1 into training and validation sets. The X-Tile software was used to determine the optimal cutoff values. The time-dependent area under the receiver operating characteristic curve (AUROC) was calculated to assess the prognostic ability of the different models.

RESULTS

In the training set, tumor differentiation, TBS, AFP, ALBI grade, and Barcelona Clinic Liver Cancer (BCLC) stage were independently related to OS. According to the coefficient values of TBS, AFP, and ALBI grade, we developed the TBS-AFP-ALBI (TAA) score using a simplified point system (0, 2 for low/high TBS, 0, 1 for low/high AFP and 0,1 for ALBI grade 1/2). Patients were further divided into low TAA (TAA ≤ 1), medium TAA (TAA = 2-3), and high TAA (TAA= 4) groups. TAA scores (low: referent; medium, HR = 1.994, 95% CI = 1.492-2.666; high, HR = 2.413, 95% CI = 1.630-3.573) were independently associated with patient survival in the validation set. The TAA scores showed higher AUROCs than BCLC stage for the prediction of 1-, 3-, and 5-year OS in both the training and validation sets.

CONCLUSION

TAA is a simple score that has better OS prediction performance than the BCLC stage in predicting OS for HCC patients after liver resection.

Machine learning algorithms to predict atypical metastasis of colorectal cancer patients after surgical resection

Background

The prognosis of colorectal cancer with atypical metastasis is poor. However, atypical metastasis was less common and under-appreciated.

Methods

In this study we attempted to present the first machine learning models to predict the risk of atypical metastasis in colorectal cancer patients. We evaluated the differences between metastasis and non-metastasis groups, assessed factors associated with atypical metastasis using univariate and multivariate logistic regression analyses, and preliminarily developed the multiple machine learning models to predict atypical metastasis.

Results

168 patients were included. Prognostic Nutritional Index (PNI) [OR =  0.998; P = 0.030], Cancer antigen 19-9 (CA19-9) [OR = 1.011; P = 0.043] and MR-Distance [-mid OR = 0.289; P = 0.009] [-high OR = 0.248; P = 0.021] were shown to be independent risk factors for the atypical metastasis via multivariate analysis. Furthermore, the machine learning model based on AdaBoost algorithm (AUC: 0736) has better predictive performance comparing to Logistic Regression (AUC: 0.671) and KNeighbors Classifier (AUC: 0.618) by area under the curve (AUC) in the validation cohorts. The accuracy, sensitivity, and specificity of the model trained using the Adaboost method in the validation set are 0.786, 0.776 and 0.700, while 0.601, 0.933, 0.508 using Logistic Regression and 0.743, 0.390, 0.831 using KNeighbors Classifier.

Conclusion

Machine-learning approaches containing PNI, CA19-9 and MR-Distance show great potentials in atypical metastasis prediction.

Predictive Value of Preoperative Serum AFP, CEA, and CA19-9 Levels in Patients with Single Small Hepatocellular Carcinoma: Retrospective Study

Purpose

This study aimed to explore the relationship between the tumor marker score (TMS) and the postoperative recurrence of single small hepatocellular carcinoma (HCC).

Patients and Methods

A total of 409 patients with one resectable HCC with a diameter of 3 cm or less who visited Tianjin Medical University Cancer Institute & Hospital from January 2010 to December 2014 were included in this study. Their alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9) levels were classified into low and high groups using X-tile software. Each patients' TMS was calculated as the sum of each tumor marker (low = 0; high = 1).

Results

A total of 142 patients were classified as TMS0, 171 as TMS1, and 96 as TMS2. Kaplan–Meier analysis illustrated that TMS could divide the patients into groups with remarkably different prognoses, and the patients with high TMS had worse recurrence-free survival (RFS) than those with low TMS. Multivariate analysis showed that TMS, age, and HBeAg positive were the independent predictors of RFS rate. Subgroup analysis revealed that high TMS was a stable risk factor relative to TMS0. Receiver operating curves showed that the 1-, 3-, and 5-year area under curve (AUC) values of TMS were 0.698, 0.662, and 0.673, respectively. The AUC of TMS was higher than that of other common prognostic models in time-dependent receiver operating curve.

Conclusion

TMS was an independent prognostic factor for the postoperative recurrence of a single small HCC and can provide a well-discriminated risk stratification, thus contributing to prognostic prediction and adjuvant therapeutic development.