Role of FLT3 in the proliferation and aggressiveness of hepatocellular carcinoma

Constructing and validating pan-apoptosis-related features for predicting prognosis and immunotherapy response in hepatocellular carcinoma

The study aimed to develop a prognostic model for Hepatocellular Carcinoma (HCC) based on pan-apoptosis-related genes, a novel inflammatory programmed cell death form intricately linked to HCC progression. Utilizing transcriptome sequencing and clinical data from the TCGA database, we identified six crucial pan-apoptosis-related genes through statistical analyses. These genes were then employed to construct a prognostic model that accurately predicts overall survival rates in HCC patients. Our findings revealed a strong correlation between the model's risk scores and tumor microenvironment (TME) status, immune cell infiltration, and immune checkpoint expression. Furthermore, we screened for drugs with potential therapeutic efficacy in high- and low-risk HCC groups. Notably, PPP2R5B gene knockdown was found to inhibit HCC cell proliferation and clonogenic capacity, suggesting its role in HCC progression. In conclusion, this study presents a novel pan-apoptosis gene-based prognostic risk model for HCC, providing valuable insights into patient TME status and guiding the selection of targeted therapies and immunotherapies.

Drug Repurposing Approach to Identify Candidate Drug Molecules for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, with a high mortality rate due to the limited therapeutic options. Systemic drug treatments improve the patient's life expectancy by only a few months. Furthermore, the development of novel small molecule chemotherapeutics is time-consuming and costly. Drug repurposing has been a successful strategy for identifying and utilizing new therapeutic options for diseases with limited treatment options. This study aims to identify candidate drug molecules for HCC treatment through repurposing existing compounds, leveraging the machine learning tool MDeePred. The Open Targets Platform, UniProt, ChEMBL, and Expasy databases were used to create a dataset for drug target interaction (DTI) predictions by MDeePred. Enrichment analyses of DTIs were conducted, leading to the selection of 6 out of 380 DTIs identified by MDeePred for further analyses. The physicochemical properties, lipophilicity, water solubility, drug-likeness, and medicinal chemistry properties of the candidate compounds and approved drugs for advanced stage HCC (lenvatinib, regorafenib, and sorafenib) were analyzed in detail. Drug candidates exhibited drug-like properties and demonstrated significant target docking properties. Our findings indicated the binding efficacy of the selected drug compounds to their designated targets associated with HCC. In conclusion, we identified small molecules that can be further exploited experimentally in HCC therapeutics. Our study also demonstrated the use of the MDeePred deep learning tool in in silico drug repurposing efforts for cancer therapeutics.

Identification and Validation of Prognosis-Related Necroptosis Genes for Prognostic Prediction in Hepatocellular Carcinoma

Background

The prediction of hepatocellular carcinoma (HCC) survival is challenging because of its rapid progression. In recent years, necroptosis was found to be involved in the progression of multiple cancer types. However, the role of necroptosis in HCC remains unclear.

Methods

Clinicopathological parameters and transcriptomic data of 370 HCC patients were obtained from TCGA-LIHC dataset. Prognosis-related necroptosis genes (PRNGs) were identified and utilized to construct a LASSO risk model. The GEO cohorts (GSE54236 and GSE14520) were used for external validation. We evaluated the distribution of HCC patients, the difference in prognosis, and the accuracy of the prognostic prediction of the LASSO risk model. The immune microenvironment and functional enrichment of different risk groups were further clarified. Finally, we performed a drug sensitivity analysis on the PRNGs that constructed the LASSO model and verified their mRNA expression levels in vitro. Results: A total of 48 differentially expressed genes were identified, 23 of which were PRNGs. We constructed the LASSO risk model using nine genes: SQSTM1, FLT3, HAT1, PLK1, MYCN, KLF9, HSP90AA1, TARDBP, and TNFRSF21. The outcomes of low-risk patients were considerably better than those of high-risk patients in both the training and validation cohorts. In addition, stronger bile acid metabolism, xenobiotic metabolism, and more active immune cells and immune functions were observed in low-risk patients, and high expressions of TARDBP, PLK1, and FLT3 were associated with greater drug sensitivity. With the exception of FLT3, the mRNA expression of the other eight genes was verified in Huh7 and 97H cells. Conclusions. The PRNG signature provides a novel and effective method for predicting the outcome of HCC as well as potential targets for further research.

Novel FLT3/AURK multikinase inhibitor is efficacious against sorafenib-refractory and sorafenib-resistant hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and has a high mortality rate worldwide. Sorafenib is the only systemic treatment demonstrating a statistically significant but modest overall survival benefit. We previously have identified the aurora kinases (AURKs) and FMS-like tyrosine kinase 3 (FLT3) multikinase inhibitor DBPR114 exhibiting broad spectrum anti-tumor effects in both leukemia and solid tumors. The purpose of this study was to evaluate the therapeutic potential of DBPR114 in the treatment of advanced HCC.

Methods

Human HCC cell lines with histopathology/genetic background similar to human HCC tumors were used for in vitro and in vivo studies. Human umbilical vein endothelial cells (HUVEC) were used to evaluate the drug effect on endothelial tube formation. Western blotting, immunohistochemical staining, and mRNA sequencing were employed to investigate the mechanisms of drug action. Xenograft models of sorafenib-refractory and sorafenib-acquired resistant HCC were used to evaluate the tumor response to DBPR114.

Results

DBPR114 was active against HCC tumor cell proliferation independent of p53 alteration status and tumor grade in vitro. DBPR114-mediated growth inhibition in HCC cells was associated with apoptosis induction, cell cycle arrest, and polyploidy formation. Further analysis indicated that DBPR114 reduced the phosphorylation levels of AURKs and its substrate histone H3. Moreover, the levels of several active-state receptor tyrosine kinases were downregulated by DBPR114, verifying the mechanisms of DBPR114 action as a multikinase inhibitor in HCC cells. DBPR114 also exhibited anti-angiogenic effect, as demonstrated by inhibiting tumor formation in HUVEC cells. In vivo, DBPR114 induced statistically significant tumor growth inhibition compared with the vehicle control in multiple HCC tumor xenograft models. Histologic analysis revealed that the DBPR114 treatment reduced cell proliferation, and induced apoptotic cell death and multinucleated cell formation. Consistent with the histological findings, gene expression analysis revealed that DBPR114-modulated genes were mostly related to the G2/M checkpoint and mitotic spindle assembly. DBPR114 was efficacious against sorafenib-intrinsic and -acquired resistant HCC tumors. Notably, DBPR114 significantly delayed posttreatment tumor regrowth and prolonged survival compared with the regorafenib group.

Conclusion

Our results indicated that targeting AURK signaling could be a new effective molecular-targeted agent in the treatment of patients with HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12929-022-00788-0.