Long-term outcome and eligibility of radiofrequency ablation for hepatocellular carcinoma over 3.0 cm in diameter

Biological Activity of Natural and Synthetic Peptides as Anticancer Agents

Cancer is one of the leading causes of morbidity and death worldwide, making it a serious global health concern. Chemotherapy, radiotherapy, and surgical treatment are the most used conventional therapeutic approaches, although they show several side effects that limit their effectiveness. For these reasons, the discovery of new effective alternative therapies still represents an enormous challenge for the treatment of tumour diseases. Recently, anticancer peptides (ACPs) have gained attention for cancer diagnosis and treatment. ACPs are small bioactive molecules which selectively induce cancer cell death through a variety of mechanisms such as apoptosis, membrane disruption, DNA damage, immunomodulation, as well as inhibition of angiogenesis, cell survival, and proliferation pathways. ACPs can also be employed for the targeted delivery of drugs into cancer cells. With over 1000 clinical trials using ACPs, their potential for application in cancer therapy seems promising. Peptides can also be utilized in conjunction with imaging agents and molecular imaging methods, such as MRI, PET, CT, and NIR, improving the detection and the classification of cancer, and monitoring the treatment response. In this review we will provide an overview of the biological activity of some natural and synthetic peptides for the treatment of the most common and malignant tumours affecting people around the world.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy

Introduction

This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).

Methods

The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.

Conclusions

The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.