Fatty acids secreted from head and neck cancer induce M2-like Macrophages

A 3D spheroid model of quadruple cell co-culture with improved liver functions for hepatotoxicity prediction

Drug-induced liver injury (DILI) is one of the major concerns during drug development. Wide acceptance of the 3 R principles and the innovation of in-vitro techniques have introduced various novel model options, among which the three-dimensional (3D) cell spheroid cultures have shown a promising prospect in DILI prediction. The present study developed a 3D quadruple cell co-culture liver spheroid model for DILI prediction via self-assembly. Induction by phorbol 12-myristate 13-acetate at the concentration of 15.42 ng/mL for 48 hours with a following 24-hour rest period was used for THP-1 cell differentiation, resulting in credible macrophagic phenotypes. HepG2 cells, PUMC-HUVEC-T1 cells, THP-1-originated macrophages, and human hepatic stellate cells were selected as the components, which exhibited adaptability in the designated spheroid culture conditions. Following establishment, the characterization demonstrated the competence of the model in long-term stability reflected by the maintenance of morphology, viability, cellular integration, and cell-cell junctions for at least six days, as well as the reliable liver-specific functions including superior albumin and urea secretion, improved drug metabolic enzyme expression and CYP3A4 activity, and the expression of MRP2, BSEP, and P-GP accompanied by the bile acid efflux transport function. In the comparative testing using 22 DILI-positive and 5 DILI-negative compounds among the novel 3D co-culture model, 3D HepG2 spheroids, and 2D HepG2 monolayers, the 3D culture method significantly enhanced the model sensitivity to compound cytotoxicity compared to the 2D form. The novel co-culture liver spheroid model exhibited higher overall predictive power with margin of safety as the classifying tool. In addition, the non-parenchymal cell components could amplify the toxicity of isoniazid in the 3D model, suggesting their potential mediating role in immune-mediated toxicity. The proof-of-concept experiments demonstrated the capability of the model in replicating drug-induced lipid dysregulation, bile acid efflux inhibition, and α-SMA upregulation, which are the key features of liver steatosis and phospholipidosis, cholestasis, and fibrosis, respectively. Overall, the novel 3D quadruple cell co-culture spheroid model is a reliable and readily available option for DILI prediction.

Fatty acid metabolism of immune cells: a new target of tumour immunotherapy

Metabolic competition between tumour cells and immune cells for limited nutrients is an important feature of the tumour microenvironment (TME) and is closely related to the outcome of tumour immune escape. A large number of studies have proven that tumour cells need metabolic reprogramming to cope with acidification and hypoxia in the TME while increasing energy uptake to support their survival. Among them, synthesis, oxidation and uptake of fatty acids (FAs) in the TME are important manifestations of lipid metabolic adaptation. Although different immune cell subsets often show different metabolic characteristics, various immune cell functions are closely related to fatty acids, including providing energy, providing synthetic materials and transmitting signals. In the face of the current situation of poor therapeutic effects of tumour immunotherapy, combined application of targeted immune cell fatty acid metabolism seems to have good therapeutic potential, which is blocked at immune checkpoints. Combined application of adoptive cell therapy and cancer vaccines is reflected. Therefore, it is of great interest to explore the role of fatty acid metabolism in immune cells to discover new strategies for tumour immunotherapy and improve anti-tumour immunity.

Targeting lipid biosynthesis on the basis of conventional treatments for clear cell renal cell carcinoma: A promising therapeutic approach

A variety of cancer cells exhibit dysregulated lipid metabolism, characterized by excessive intracellular lipid accumulation, and clear cell renal cell carcinoma (ccRCC) is the most typical disease with these characteristics. As the most common malignancy of all renal cell carcinomas (RCCs), ccRCC is typically characterized by a large accumulation of lipids and glycogen in the cytoplasm and a nucleus that is squeezed by the accumulated lipid droplets and localized to the marginal areas within the cytoplasm. This lipid accumulation has been found to be critically involved in the maintenance of malignant features observed in various cancers. Firstly, it maintains the persistent proliferative and metastasis properties of cancer cells. Secondly, it acts as a buffer against lipid peroxidation, preventing lipid peroxidation-induced ferroptosis. Moreover, lipids can diminish the sensitivity of cancer cells to radiotherapy. As ccRCC is a type of cancer with high lipid synthesis, targeting lipid synthesis-related genes in cancer cells may be a promising therapeutic modality for single treatment or in combination with radiotherapy, chemotherapy, and immunotherapy. This may revolutionize the choice of treatment modality for ccRCC patients. In this review, we concentrate on the current status and progress of research on lipid biosynthesis in ccRCC and the potential applications of targeting lipid synthesis to treat ccRCC. At last, we propose perspective and future research directions for targeting inhibition of lipid biosynthesis in combination with conventional therapeutic approaches for the treatment of ccRCC, which will help to evolve the therapeutic model.

Identification of genetic mechanisms underlying lipid metabolism-mediated tumor immunity in head and neck squamous cell carcinoma

OBJECTIVE

To identify the genetic mechanisms underlying lipid metabolism-mediated tumor immunity in head and neck squamous carcinoma (HNSC).

MATERIALS AND METHODS

RNA sequencing data and clinical characteristics of HNSC patients were procured from The Cancer Genome Atlas (TCGA) database. Lipid metabolism-related genes were collected from KEGG and MSigDB databases. Immune cells and immune-related genes were obtained from the TISIDB database. The differentially expressed genes (DEGs) in HNSC were identified and weighted correlation network analysis (WGCNA) was performed to identify the significant gene modules. Lasso regression analysis was performed to identify hub genes. The differential gene expression pattern, diagnostic values, relationships with clinical features, prognostic values, relationships with tumor mutation burden (TMB), and signaling pathways involved, were each investigated.

RESULTS

One thousand six hundred sixty-eight DEGs were identified as dysregulated between HNSC tumor samples and healthy control head and neck samples. WGCNA analysis and Lasso regression analysis identified 8 hub genes, including 3 immune-related genes (PLA2G2D, TNFAIP8L2 and CYP27A1) and 5 lipid metabolism-related genes (FOXP3, IL21R, ITGAL, TRAF1 and WIPF1). Except CYP27A1, the other hub genes were upregulated in HNSC as compared with healthy control samples, and a low expression of these hub genes indicated a higher risk of death in HNSC. Except PLA2G2D, all other hub genes were significantly and negatively related with TMB in HNSC. The hub genes were implicated in several immune-related signaling pathways including T cell receptor signaling, Th17 cell differentiation, and natural killer (NK) cell mediated cytotoxicity.

CONCLUSION

Three immune genes (PLA2G2D, TNFAIP8L2, and CYP27A1) and immune-related pathways (T cell receptor signaling, Th17 cell differentiation, and natural killer (NK) cell mediated cytotoxicity) were predicted to play significant roles in the lipid metabolism-mediated tumor immunity in HNSC.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.