AIMC Topic: Liver Neoplasms

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Identification and validation of feature genes in hepatocellular carcinoma based on bioinformatics and machine learning: An observational study.

Medicine
The incidence of hepatocellular carcinoma (HCC) has risen significantly in recent years, while current diagnostic and therapeutic approaches remain suboptimal. This study aimed to identify novel biomarkers and therapeutic targets to improve early det...

A technological convergence in hepatobiliary oncology: Evolving roles of smart surgical systems.

Bioscience trends
Cancer remains a major threat to human health, with the incidence of hepatobiliary tumors consistently high. Treatment methods for hepatobiliary tumors include surgical intervention, ablation, embolization, and pharmacological treatments, with surger...

Leveraging machine learning and single-cell RNA sequencing strategies to develop a risk prognosis scoring based on liquid-liquid phase separation feature genes in pediatric hepatoblastoma.

Computers in biology and medicine
BACKGROUND: Considerable evidence highlights the intricate association between liquid-liquid phase separation (LLPS) and tumorigenesis, progression, and therapy resistance. However, there has been limited exploration of the role of LLPS in hepatoblas...

Developing risk stratification strategies and biomarkers for recurrent hepatocellular carcinoma.

Clinical and translational medicine
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with high rates of post-resection recurrence posing significant clinical challenges. Early recurrence is largely driven by aggressive tumor biology, while late recurr...

Current Advances in Classification, Prediction and Management of Microvascular Invasion in Hepatocellular Carcinoma.

Journal of cellular and molecular medicine
Liver resection remains the mainstay curative treatment for hepatocellular carcinoma (HCC); however, the recurrence rate is reported to exceed 70% within 5 years after surgery. Microvascular invasion (MVI) has attracted great research interest in the...

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

The journal of pathology. Clinical research
The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, identifying the spatial organization of diverse cell types that significantly correlates with patient pr...

Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma.

Computers in biology and medicine
BACKGROUND: The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby provi...