A large database of 1773 HCC patients in Turkey was examined. 41.9% had alpha-fetoprotein (AFP) levels <20 IU/ml and an additional 16.123% had values between 20-100 IU/ml. This 58% of the cohort (<100 IU/ml AFP levels) was examined in detail. 66% of patients with small (<5 cm) HCCs had low AFP, compared to 49% of patients with larger (>5 cm) HCCs. The mean diameter (MTD) of larger MTD, low AFP tumors was 8.4cm. Therefore, factors other than AFP must contribute to HCC tumor growth. Larger tumors in low AFP patients had both higher platelet levels and increased PVT percent. Linear regression analysis for both MTD and multifocality showed that platelet numbers and presence of PVT were significant variables; whereas for PVT, significant variables were albumin, alkaline phosphatase and MTD. Comparisons between patients with AFP levels <20, 20-<100, 100-<1000 and >1000 IU/ml showed the most significant tumor finding was an increase in PVT percent between each group, and to a lesser extent, MTD. Thus, low- or normal-AFP HCCs constitute the majority of patients and have slightly lower MTD and much lower PVT percent than HCCs associated with elevated blood AFP levels. New, non-AFP markers are thus needed, especially for small HCCs.
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http://dx.doi.org/10.4172/clinical-practice.1000393 | DOI Listing |
J Hepatol
December 2024
Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea; Inocras Inc., San Diego, CA, USA. Electronic address:
Background & Aims: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.
Methods: An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software.
J Transl Med
December 2024
Institute of Immunopharmaceutical Sciences, School of Pharmaceutical Sciences, Shandong University, Jinan, China.
Background: JAK/STAT3 is one of the critical signaling pathways involved in the occurrence and development of hepatocellular carcinoma (HCC). BBI608 (Napabucasin), as a novel small molecule inhibitor of STAT3, has shown previously excellent anti-HCC effects in vitro and in mouse models. However, low bioavailability, high cytotoxicity and other shortcomings limit its clinical application.
View Article and Find Full Text PDFMol Med
December 2024
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, with the characteristics of high mortality and low 5-year survival rate. The potential role of BTF3 and PDCD2L in HCC remains unclear. Our study found that BTF3 expression was upregulated in hepatocellular carcinoma tissues, and its high expression was associated with poor prognosis.
View Article and Find Full Text PDFFuture Oncol
December 2024
Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with high rates of postoperative recurrence. Identifying reliable biomarkers for predicting recurrence is critical for improving patient outcomes. This study investigates the predictive value of m6A methylation-related genes, METTL4 and METTL5, on HCC recurrence after surgery.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China. Electronic address:
Current chemotherapeutic efficacy is limited by the rapid development of multidrug resistance (MDR) in hepatocellular carcinoma (HCC). In this study, 66 MDR-related hub genes in drug-resistant HCC were identified through combined analysis of differential expressed genes (DEGs), gene functional enrichment, Cox proportional regression, weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network construction. A prognostic risk model was established through the LASSO-Cox regression analysis.
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