Hepatocellular carcinoma subtypes based on metabolic pathways reveals potential therapeutic targets

Novel phloretin-based combinations targeting glucose metabolism in hepatocellular carcinoma through GLUT2/PEPCK axis of action: in silico molecular modelling and in vivo studies

Hepatocellular carcinoma (HCC) is commonly associated with disturbances in glucose metabolism and enhanced glycolysis. However, a controversial role for gluconeogenesis was reported to be tumor-promoting and tumor-suppressive. We investigated novel anti-HCC treatments through either the simultaneous inhibition of glycolysis and gluconeogenesis by "phloretin" and "sodium meta-arsenite", respectively (Combination 1); or the concurrent inhibition of glycolysis and induction of gluconeogenesis by phloretin and dexamethasone, respectively, (combination 2). A total of 110 Swiss albino mice were divided into eleven groups, HCC was induced by N, N-dimethyl-4-aminoazobenzene. We have measured the expression of the glucose transporter 2 (GLUT2), Phosphoenolpyruvate carboxykinases (PEPCK), Caspase-3, Beclin 1, Cyclin D1, and cytokeratin 18 genes; blood glucose and ATP levels; alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities. Furthermore, in silico molecular docking was performed to investigate the potential drug-receptor interactions. Histologically, the phloretin-based combinations resulted in a significant regression of malignant tissue compared to various treatments. GLUT2 and PEPCK mRNA analysis indicated successful off/on modulation of glycolysis and gluconeogenesis. Docking confirmed the potent binding between phloretin, sodium meta-arsenite, and dexamethasone with GLUT2, PEPCK, and Retinoid X Receptor Alpha, respectively. Molecularly, Combination 2 resulted in the highest reduction in cyclin D1, cytokeratin 18, and Beclin 1 expression contemporaneously with the upregulation in Caspase-3 levels. Biochemically, both combinations caused a significant reduction in ATP levels, ALT, and AST activity compared to the other groups. In conclusion, we propose two novel phloretin-based combinations that can be used in treating HCC through the regulation of glucose metabolism and ATP production.

Construction of a hepatocytes-related and protein kinase-related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase-related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

Single-cell transcriptome analysis reveals the clinical implications of myeloid-derived suppressor cells in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is the most common malignant tumor that arises in the epithelium of the head and neck regions. Myeloid-derived suppressor cells (MDSCs) are one of the tumor-infiltrating immune cell populations, which play a powerful role in inhibiting anti-tumor immune response. Herein, we employed a single-cell RNA sequencing (scRNA-seq) dataset to dissect the heterogeneity of myeloid cells. We found that SPP1 ⁺ tumor-associated macrophages (TAMs) and MDSCs were the most abundant myeloid cells in the microenvironment. By cell cluster deconvolution from bulk RNA-seq datasets of larger patient groups, we observed that highly-infiltrated MDSC was a poor prognostic marker for patients' overall survival (OS) probabilities. To better apply the MDSC OS prediction values, we identified a set of six MDSC-related genes (ALDOA, CD52, FTH1, RTN4, SLC2A3, and TNFAIP6) as the prognostic signature. In both training and test cohorts, MDSC-related prognostic signature showed a promising value for predicting patients' prognosis outcomes. Further parsing the ligand-receptor pairs of intercellular communications by CellChat, we found that MDSCs could frequently interact with cytotoxic CD8 ⁺ T cells, SPP1 ⁺ TAMs, and endothelial cells. These interactions likely contributed to the establishment of an immunosuppressive microenvironment and the promotion of tumor angiogenesis. Our findings suggest that targeting MDSCs may serve as an alternative and promising target for the immunotherapy of HNSC.