Serum concentration of intercellular adhesion molecule-1 in patients with hepatocellular carcinoma is a marker of the disease progression and prognosis

Plasma sICAM-1 correlates with tumor volume before primary radiochemotherapy of head and neck squamous cell carcinoma patients

BACKGROUND

Biomarkers are of major interest to optimize diagnosis, prognosis and to guide treatment in head and neck cancer patients. Especially blood-based biomarkers appear promising as they can be easily collected and repeatedly analyzed during the course of radiochemotherapy.

PATIENTS AND METHODS

At first, for a broad overview, multiple immune markers were evaluated in six plasma samples of three head and neck squamous cell carcinoma (HNSCC) patients at the beginning and the end of radio-chemotherapy. In this pre-selection, the soluble Intercellular Adhesion Molecule 1 (sICAM-1) appeared most promising. Thus, this marker was measured in multiple samples (n = 86) during treatment and follow-up in a cohort of eleven patients and correlated with tumor features and clinical data.

RESULTS

We found a strong correlation between the initial levels of sICAM-1 in the plasma and the gross tumor volumes of the primary tumor and the involved lymph nodes. However, during the course of treatment no systematic dynamics could be identified. Toxicity or infections did not seem to influence sICAM-1 concentrations.

CONCLUSIONS

sICAM-1 appears to reflect the pre-treatment total tumor burden (primary tumor and involved lymph nodes) in head and neck tumor patients. However, it does not seem to be a dynamic marker reflecting response during radiochemotherapy. Thus, if our findings are confirmed in future, sICAM-1 could be used as a staging marker: if high sICAM-1 levels but low tumor burden are found it might be reasonable to intensify staging investigations to rule out further, yet undetected, tumor sites.

Lysis of cold-storage-induced microvascular obstructions for ex vivo revitalization of marginal human kidneys

Thousands of kidneys from higher-risk donors are discarded annually because of the increased likelihood of complications posttransplant. Given the severe organ shortage, there is a critical need to improve utilization of these organs. To this end, normothermic machine perfusion (NMP) has emerged as a platform for ex vivo assessment and potential repair of marginal organs. In a recent study of 8 transplant-declined human kidneys on NMP, we discovered microvascular obstructions that impaired microvascular blood flow. However, the nature and physiologic impact of these lesions were unknown. Here, in a study of 39 human kidneys, we have identified that prolonged cold storage of human kidneys induces accumulation of fibrinogen within tubular epithelium. Restoration of normoxic conditions-either ex vivo during NMP or in vivo following transplant-triggered intravascular release of fibrinogen correlating with red blood cell aggregation and microvascular plugging. Combined delivery of plasminogen and tissue plasminogen activator during NMP lysed the plugs leading to a significant reduction in markers of renal injury, improvement in indicators of renal function, and improved delivery of vascular-targeted nanoparticles. Our study suggests a new mechanism of cold storage injury in marginal organs and provides a simple treatment with immediate translational potential.

Prognostic impact of soluble intercellular adhesion molecule-1 in hepatocellular carcinoma

The identification of novel biomarkers for hepatocellular carcinoma (HCC) is of great importance in improving the outcome of patients with HCC. The present study aimed to determine the prognostic significance of the soluble intercellular adhesion molecule (sICAM)-1 in patients with HCC. The present study prospectively collected clinicopathological data from 36 patients with HCC who had undergone successful hepatectomy. An analysis using a receiver operating characteristic (ROC) curve was performed to determine the cut-off value for predicting prognosis. Overall survival (OS), recurrence-free survival (RFS) and potential prognostic factors were analyzed. The ROC curve analysis revealed a sICAM-1 cut-off value of 440 ng/ml. HCC patients with sICAM-1 ≥440 ng/ml exhibited a poorer OS and RFS than those with sICAM-1 <440 ng/ml (P=0.002). sICAM-1 ≥440 ng/ml (hazard ratio=3.623; 95% confidence interval: 1.145-11.458; P=0.028) and Child B (hazard ratio=1.514; 95% confidence interval: 1.066-2.150; P=0.021) were independent prognostic factors for OS, and sICAM-1 ≥440 ng/ml was an independent prognostic factor for RFS (hazard ratio=3.625; 95% confidence interval: 1.233-10.659; P=0.019). Serum sICAM-1 may be a promising predictor for the overall and recurrence-free survival of patients with HCC.

Early detection of hepatocellular carcinoma co-occurring with hepatitis C virus infection: A mathematical model

AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP).

METHODS: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-RII), proteasome, and β-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data. Predictive power was used to evaluate the performance of the model. Receiver operating characteristic curve analysis over pairs of groups was used to identify the best cutoffs differentiating the different groups.

RESULTS: We revealed mathematical models, based on a binary classifier, made up of a unique panel of serum proteins that improved the individual performance of AFP in discriminating HCC patients from patients with chronic liver disease either with or without cirrhosis. We discriminated the HCC group from the cirrhotic liver group using a mathematical model (-11.3 + 7.38 × Prot + 0.00108 × sICAM + 0.2574 ×β-catenin + 0.01597 × AFP) with a cutoff of 0.6552, which achieved 98.8% specificity and 89.1% sensitivity. For the discrimination of the HCC group from the CHC group, we used a mathematical model [-10.40 + 1.416 × proteasome + 0.002024 × IL + 0.004096 × sICAM-1 + (4.251 × 10^-4) × sTNF + 0.02567 ×β-catenin + 0.02442 × AFP] with a cutoff 0.744 and achieved 96.8% specificity and 89.7% sensitivity. Additionally, we derived an algorithm, based on a binary classifier, for resolving the multi-class classification problem by using three successive mathematical model predictions of liver disease status.

CONCLUSION: Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.