Metabolic alterations and mitochondrial dysfunction underlie hepatocellular carcinoma cell death induced by a glycogen metabolic inhibitor

Interventing mitochondrial PD-L1 suppressed IFN-γ-induced cancer stemness in hepatocellular carcinoma by sensitizing sorafenib-induced ferroptosis

Evidence recently showed that pleiotropic cytokine interferon-gamma (IFN-γ) in the tumor microenvironment (TME) plays a positive role in hepatocellular carcinoma (HCC) progression through the regulation of liver cancer stem cells (LCSCs) in HCC. The present study explored the role and potential mechanism of mitochondrial programmed cell death-ligand 1 (PD-L1) and its regulation of ferroptosis in modulating the cancer stemness of LCSCs. It was shown that mimicking TME IFN-γ exposure increased the LCSCs ratio and cancer stemness phenotypes in HCC cells. IFN-γ exposure inhibited sorafenib (Sora)-induced ferroptosis by enhancing glutathione peroxidase 4 (GPX4) expression as well reactive oxygen species (ROS) and lipid peroxidation (LPO) generation in LCSCs. Furthermore, IFN-γ exposure upregulated PD-L1 expression and its mitochondrial translocation, inducing dynamin-related protein 1 (Drp1)-dependent mitochondrial fission and correlating with glycolytic metabolism reprogramming in LCSCs. The genetic intervention of PD-L1 promoted ferroptosis-dependent anti-tumor effects of Sora, reduced glycolytic metabolism reprogramming, and inhibited cancer stemness of HCC in vitro and in vivo. Our results revealed a novel mechanism that IFN-γ exposure-induced mitochondrial translocation of PD-L1 enhanced glycolytic reprogramming to mediate the GPX4-dependent ferroptosis resistance and cancer stemness in LCSCs. This study provided new insights into the role of mitochondrial PD-L1-Drp1-GPX4 signal axis in regulating IFN-γ exposure-associated cancer stemness in LCSCs and verified that PD-L1-targeted intervention in combination with Sora might achieve promising synergistic anti-HCC effects.

CHIR-98014, a GSK 3β Inhibitor, Protects Against Triptolide/Lipopolysaccharide-Induced Hepatotoxicity by Mitochondria-Dependent Apoptosis Inhibition

Triptolide (TP) is a remarkable anti-inflammatory and immunosuppressive component separated from Tripterygium wilfordii Hook. F. However, its hepatotoxicity limits its application in the clinical. Our group has proposed a new perspective on TP-induced hepatotoxicity, in which TP enhances liver hypersensitivity upon lipopolysaccharide (LPS) stimulation. Because the cause of the disease is unknown, there is currently no uniform treatment available. In this study, we attempted to determine whether the GSK-3β-JNK pathway affects liver damage and its regulatory mechanism in response to TP/LPS costimulation. In addition, we investigated the effect of CsA or the GSK 3β inhibitor CHIR-98014 on TP/LPS-induced hepatotoxicity. The results showed that the TP/LPS cotreatment mice exhibited obvious hepatotoxicity, as indicated by a remarkable increase in the serum ALT and AST levels, glycogen depletion, GSK 3β-JNK upregulation, and increased apoptosis. Instead of the specific knockdown of JNK1, the specific knockdown of JNK2 had a protective effect. Additionally, 40 mg/kg of CsA and 30 mg/kg of CHIR-98014 might provide protection. In summary, CHIR-98014 could protect against TP/LPS- or TP/TNF-α-induced activation of the GSK 3β-JNK pathway and mitochondria-dependent apoptosis, improving the indirect hepatotoxicity induced by TP.

Recent advancement in targeted therapy and role of emerging technologies to treat cancer

Cancer is a complex disease that causes abnormal cell growth and spread. DNA mutations, chemical or environmental exposure, viral infections, chronic inflammation, hormone abnormalities, etc., are underlying factors that can cause cancer. Drug resistance and toxicity complicate cancer treatment. Additionally, the variability of cancer makes it difficult to establish universal treatment guidelines. Next-generation sequencing has made genetic testing inexpensive. This uncovers genetic mutations that can be treated with specialty drugs. AI (artificial intelligence), machine learning, biopsy, next-generation sequencing, and digital pathology provide personalized cancer treatment. This allows for patient-specific biological targets and cancer treatment. Monoclonal antibodies, CAR-T, and cancer vaccines are promising cancer treatments. Recent trial data incorporating these therapies have shown superiority in clinical outcomes and drug tolerability over conventional chemotherapies. Combinations of these therapies with new technology can change cancer treatment and help many. This review discusses the development and challenges of targeted therapies like monoclonal antibodies (mAbs), bispecific antibodies (BsAbs), bispecific T cell engagers (BiTEs), dual variable domain (DVD) antibodies, CAR-T therapy, cancer vaccines, oncolytic viruses, lipid nanoparticle-based mRNA cancer vaccines, and their clinical outcomes in various cancers. We will also study how artificial intelligence and machine learning help find new cancer treatment targets.

The Expectation and Reality of the HepG2 Core Metabolic Profile

To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.