The lipid droplet in cancer: From being a tumor-supporting hallmark to clinical therapy.

INTRODUCTION: Abnormal lipid metabolism, one of the hallmarks in cancer, has gradually emerged as a novel target for cancer treatment. As organelles that store and release excess lipids, lipid droplets (LDs) resemble "gears" and facilitate cancer development in the body. AIM: This review discusses the life cycle of LDs, the relationship between abnormal LDs and cancer hallmarks, and the application of LDs in theragnostic and clinical contexts to provide a contemporary understanding of the role of LDs in cancer. METHODS: A systematic literature search was conducted in PubMed and SPORTDiscus. Retrieve and summarize clinical trials of drugs that target proteins associated with LD formation using the Clinical Trials website. Create a schematic diagram of lipid droplets in the tumor microenvironment using Adobe Illustrator. CONCLUSION: As one of the top ten hallmarks of cancer, abnormal lipid metabolism caused by excessive generation of LDs interrelates with other hallmarks. The crosstalk between excessive LDs and intracellular free fatty acids (FFAs) promotes an inflammatory environment that supports tumor growth. Moreover, LDs contribute to cancer metastasis and cell death resistance in vivo. Statins, as HMGCR inhibitors, are promising to be the pioneering commercially available anti-cancer drugs that target LD formation.

The oral microbiome of pregnant women facilitates gestational diabetes discrimination.

The oral microbiota plays an important role in the development of various diseases, whereas its association with gestational diabetes mellitus (GDM) remains largely unclear. The aim of this study is to identify biomarkers from the oral microbiota of GDM patients by analyzing the microbiome of the saliva and dental plaque samples of 111 pregnant women. We find that the microbiota of both types of oral samples in GDM patients exhibits differences and significantly varies from that of patients with periodontitis or dental caries. Using bacterial biomarkers from the oral microbiota, GDM classification models based on support vector machine and random forest algorithms are constructed. The area under curve (AUC) value of the classification model constructed by combination of Lautropia and Neisseria in dental plaque and Streptococcus in saliva reaches 0.83, and the value achieves a maximum value of 0.89 by adding clinical features. These findings suggest that certain bacteria in either saliva or dental plaque can effectively distinguish women with GDM from healthy pregnant women, which provides evidence of oral microbiome as an informative source for developing noninvasive biomarkers of GDM.