Homoharringtonine inhibits the progression of hepatocellular carcinoma by suppressing the PI3K/AKT/GSK3ß/Slug signaling pathway.

Homoharringtonine (HHT), was first isolated from the bark of Cephalotaxus harringtonia (Knight ex J. Forbes) K. Koch and Cephalotaxus fortunei Hook trees. The bark extract is used to treat leukemia and in recent years has also been used in traditional Chinese medicine (TCM) to treat solid tumors. However, the inhibitory mechanism of HHT in the progression of hepatocellular carcinoma (HCC) is rarely studied. We aimed to evaluate the antitumor efficacy of HHT on HCC in vitro and in vivo and elucidate the underlying molecular mechanism(s). HCC cell lines, including HCCLM3, HepG2, and Huh7, were used to evaluate the antitumor efficacy of HHT in vitro. Cytotoxicity and proliferative ability were evaluated by MTT and colony formation assays. Cell cycle progression and apoptosis in HHT-treated HCC cells were evaluated by flow cytometry. To determine the migration and invasion abilities of HCC cells, wound-healing and Transwell assays were used. Finally, western blot analysis was used to reveal the proteins involved. We also established a xenograft nude mouse model for in vivo assessments of the preclinical efficacy of HHT, mainly using hematoxylin and eosin staining, immunohistochemistry, ultrasound imaging (USI), and magnetic resonance imaging (MRI). HHT suppressed the proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of HCC cells, and induced cell cycle arrest at the G2 phase and apoptosis. In the HCC xenograft model, HHT showed an obvious tumor-suppressive effect. Surprisingly, Slug expression was also decreased by HHT via the PI3K/AKT/GSK3ß signaling pathway at least partially suppressed the growth of HCC via the PI3K/AKT/GSK3ß/Slug signaling pathway.

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients.

BACKGROUND: This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints. METHOD: LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature. RESULTS: Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes. CONCLUSIONS: The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.