Hepatocellular carcinoma (HCC) remains a global medical problem. Programmed cell death protein 1 (PD-1) is a powerful weapon against many cancers, but it is not sensitive to some patients with HCC. We obtained datasets from the Gene Expression Omnibus (GEO) database on HCC patients and PD-1 immunotherapy to select seven intersecting DEGs. Through Lasso regression, two intersecting genes were acquired as predictors of HCC and PD-1 treatment prognosis, including HAMP and FOS. Logistic regression was performed to build a prediction model. HAMP had a better ability to diagnose HCC and predict PD1 treatment sensitivity. Further, we adapted the support vector machine (SVM) technique using HAMP to predict triple-classified outcomes after PD1 treatment in HCC patients, which had an excellent classification ability. We also performed external validation using TCGA data, which showed that HAMP was elevated in the early stage of HCC. HAMP was positively correlated with the infiltration of 18 major immune cells and the expression of 2 important immune checkpoints, PDCD1 and CTLA4. We discovered a biomarker that can be used for the early diagnosis, prognosis and PD1 immunotherapy efficacy prediction of HCC for the first time and developed a diagnostic model, prognostic model and prediction model of PD1 treatment sensitivity and treatment outcome for HCC patients accordingly.
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http://dx.doi.org/10.3390/biom13020360 | DOI Listing |
Am J Med Sci
December 2024
Department of Endocrinology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
The relationship between diabetes and hepatitis B remains unclear. We have found that there is no general correlation between the incidence of diabetes and hepatitis B, except in certain populations. Patients with co-existing diabetes and hepatitis B tend to have poorer overall prognoses, primarily evidenced by an increased risk of hepatocellular carcinoma (HCC) and all-cause mortality within this population.
View Article and Find Full Text PDFBiol Pharm Bull
December 2024
Department of Laboratory Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences.
The aim of this study was to analyze dihydrolipoyllysine-residue acetyltransferase (DLAT) expression and diagnostic ability in hepatocellular carcinoma (HCC), assess its role in HCC growth, and factors affecting it. We conducted bioinformatics analyses, examined DLAT expression and prognosis in pre-cancer, and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment studies while investigating its correlation with immunity. We also predicted regulatory factors, and detected DLAT in HCC cells using quantitative PCR (qPCR) and Western blotting, and in patient serum via enzyme-linked immunosorbent assay (ELISA).
View Article and Find Full Text PDFExp Cell Res
December 2024
NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, The Fourth Hospital of Harbin Medical University, Harbin, 150001, China; Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China. Electronic address:
Lactylation is an emerging pathogenesis of hepatocellular carcinoma (HCC). However, the underlying mechanisms and biological significance remain poorly understood. The Carbonic anhydrase III (CA3) gene, previously defined as a binding protein of SQLE and involved in the NAFLD disease, has now been identified as a novel tumor suppressor in HCC.
View Article and Find Full Text PDFJ 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.
Biomaterials
December 2024
Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing, 210009, China; National Innovation Platform for Integration of Medical Engineering Education (NMEE) (Southeast University), Nanjing, 210009, China; Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nanjing, 210009, China; State Key Laboratory of Digital Medical Engineering, Southeast University, Nanjing, 210009, China. Electronic address:
In the clinic, Lipiodol chemotherapeutic emulsions remain a main choice for patients diagnosed with hepatocellular carcinoma (HCC) via the mini-invasive transarterial chemoembolization (TACE) therapy. However, the poor stability of conventional Lipiodol chemotherapeutic emulsions would result in the fast drug diffusion and incomplete embolization, inducing systemic toxicity and impairing the efficacy of TACE therapy. Therefore, it is of great importance to construct alternative formulations based on commercial Lipiodol to achieve the improved efficacy and safety of HCC treatment.
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