Lipid Metabolism in Tumor-Associated Fibroblasts

Biomimetic Nano-Drug Delivery System: An Emerging Platform for Promoting Tumor Treatment

With the development of nanotechnology, nanoparticles (NPs) have shown broad prospects as drug delivery vehicles. However, they exhibit certain limitations, including low biocompatibility, poor physiological stability, rapid clearance from the body, and nonspecific targeting, which have hampered their clinical application. Therefore, the development of novel drug delivery systems with improved biocompatibility and high target specificity remains a major challenge. In recent years, biofilm mediated biomimetic nano-drug delivery system (BNDDS) has become a research hotspot focus in the field of life sciences. This new biomimetic platform uses bio-nanotechnology to encapsulate synthetic NPswithin biomimetic membrane, organically integrating the low immunogenicity, low toxicity, high tumor targeting, good biocompatibility of the biofilm with the adjustability and versatility of the nanocarrier, and shows promising applications in the field of precision tumor therapy. In this review, we systematically summarize the new progress in BNDDS used for optimizing drug delivery, providing a theoretical reference for optimizing drug delivery and designing safe and efficient treatment strategies to improve tumor treatment outcomes.

Dietary fat and lipid metabolism in the tumor microenvironment

Metabolic reprogramming has been considered a core hallmark of cancer, in which excessive accumulation of lipids promote cancer initiation, progression and metastasis. Lipid metabolism often includes the digestion and absorption of dietary fat, and the ways in which cancer cells utilize lipids are often influenced by the complex interactions within the tumor microenvironment. Among multiple cancer risk factors, obesity has a positive association with multiple cancer types, while diets like calorie restriction and fasting improve health and delay cancer. Impact of these diets on tumorigenesis or cancer prevention are generally studied on cancer cells, despite heterogeneity of the tumor microenvironment. Cancer cells regularly interact with these heterogeneous microenvironmental components, including immune and stromal cells, to promote cancer progression and metastasis, and there is an intricate metabolic crosstalk between these compartments. Here, we focus on discussing fat metabolism and response to dietary fat in the tumor microenvironment, focusing on both immune and stromal components and shedding light on therapeutic strategies surrounding lipid metabolic and signaling pathways.

Establishment of a prognostic model for ovarian cancer based on mitochondrial metabolism-related genes

Background

Mitochondrial metabolism and mitochondrial structure were found to be altered in high-grade serous ovarian cancer (HGSOC). The intent of this exploration was to systematically depict the relevance between mitochondrial metabolism-related genes (MMRGs) and the prognosis of HGSOC patients by bioinformatics analysis and establish a prognostic model for HGSOC.

Methods

First of all, screened differentially expressed genes (DEGs) between TCGA-HGSOC and GTEx-normal by limma, with RNA-seq related HGSOC sourced from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, expressed MMRGs (DE-MMRGs) were acquired by overlapping DEGs with MMRGs, and an enrichment analysis of DE-MMRGs was performed. Kaplan-Meier (K-M) survival analysis and Cox regression analysis were conducted to validate the genes' prognostic value, Gene Set Enrichment Analysis (GSEA) to elucidate the molecular mechanisms of the risk score, and CIBERSORT algorithm to explore the immuno landscape of HGSOC patients. Finally, a drug sensitivity analysis was made via the Drug Sensitivity in Cancer (GDSC) database.

Results

436 HGSOC-related DE-MMRGs (222 up-regulated and 214 down-regulated) were observed to participate in multiple metabolic pathways. The study structured a MMRGs-related prognostic signature on the basis of IDO1, TNFAIP8L3, GPAT4, SLC27A1, ACSM3, ECI2, PPT2, and PMVK. Risk score was the independent prognostic element for HGSOC. Highly dangerous population was characterized by significant association with mitochondria-related biological processes, lower immune cell abundance, lower expression of immune checkpoint and antigenic molecules. Besides, 54 drugs associated with eight prognostic genes were obtained. Furthermore, copy number variation was bound up with the 8 prognostic genes in expression levels.

Conclusion

We have preliminarily determined the prognostic value of MMRGs in HGSOC as well as relationship between MMRGs and the tumor immune microenvironment.

Identification of solute-carrier family 27A molecules (SCL27As) as a potential biomarker of ovarian cancer based on bioinformatics and experiments

Background

Ovarian cancer is one of the 3 major gynecological malignancies with high mortality, poor prognosis, and lack of specific diagnostic and prognostic markers. Solute-carrier family 27A molecules (SCL27As) play a crucial role in multiple malignant tumors via the regulation of long-chain fatty acid uptake and subsequent regulation of lipid metabolism. To date, the specific mechanisms and roles of SCL27As in epithelial ovarian cancer (EOC) have remained unclear.

Methods

The Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the Kaplan-Meier plotter were used to explore the differential expression and the prognostic value of SCL27As in EOC. The expression of SCL27A6 in 20 normal ovarian tissues and 120 ovarian cancer tissues was detected by immunohistochemistry (IHC). Cell Counting Kit-8 (CCK8) assay and colony-forming experiments were conducted to evaluate the role of SCL27A6 in the proliferation of ovarian cancer cells, so as to verify the clinical application value of SCL27A6 in the diagnosis and prognosis of ovarian cancer. We extracted the data of SCL27A6 for multiple bioinformatics analysis to identify the potential regulatory mechanism of SLC27A6.

Results

The expression levels of SLC27A1 and SLC27A6 were significantly decreased in ovarian cancer tissues. Prognostic analysis showed that SLC27A2, SLC27A4, SLC27A5, and SLC27A6 expression levels were significantly correlated with overall survival (OS) in EOC patients. Moreover, the expression of the SLC27A6 protein was decreased in EOC tissues, which was related to the prognosis. Additionally, knocking down the expression of SLC27A6 could significantly enhance the malignant biological behavior of ovarian cancer cells. The SLC27A6 gene may be involved in the proteasome, cell cycle, Hippo signaling pathway, and so on.

Conclusions

This study revealed the abnormal expression and prognostic value of SLC27As in EOC. In addition, it was highlighted that SLC27A6 may be a novel biomarker for the diagnosis and prognostication of EOC patients.