Upregulation of LPGAT1 Enhances Lung Adenocarcinoma Proliferation

Phospholipid Acyltransferases: Characterization and Involvement of the Enzymes in Metabolic and Cancer Diseases

This review delves into the enzymatic processes governing the initial stages of glycerophospholipid (phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine) and triacylglycerol synthesis. The key enzymes under scrutiny include GPAT and AGPAT. Additionally, as most AGPATs exhibit LPLAT activity, enzymes participating in the Lands cycle with similar functions are also covered. The review begins by discussing the properties of these enzymes, emphasizing their specificity in enzymatic reactions, notably the incorporation of polyunsaturated fatty acids (PUFAs) such as arachidonic acid and docosahexaenoic acid (DHA) into phospholipids. The paper sheds light on the intricate involvement of these enzymes in various diseases, including obesity, insulin resistance, and cancer. To underscore the relevance of these enzymes in cancer processes, a bioinformatics analysis was conducted. The expression levels of the described enzymes were correlated with the overall survival of patients across 33 different types of cancer using the GEPIA portal. This review further explores the potential therapeutic implications of inhibiting these enzymes in the treatment of metabolic diseases and cancer. By elucidating the intricate enzymatic pathways involved in lipid synthesis and their impact on various pathological conditions, this paper contributes to a comprehensive understanding of these processes and their potential as therapeutic targets.

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

BACKGROUND

The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

METHODS

Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

RESULTS

Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

CONCLUSION

The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.

A risk score model based on lipid metabolism-related genes could predict response to immunotherapy and prognosis of lung adenocarcinoma: a multi-dataset study and cytological validation

BACKGROUND

Lipid metabolism is a key factor in tumorigenesis and drug resistance, and models related to lipid metabolism have shown potential to predict survival and curative effects of adjuvant therapy in various cancers. However, the relationship between lipid metabolism and prognosis and treatment response of lung adenocarcinoma (LUAD) are still unclear.

METHODS

We enrolled seven bulk RNA-sequence datasets (GSE37745, GSE19188, GSE30219, GSE31547, GSE41271, GSE42127, and GSE72094) from the GEO database and one single-cell RNA-sequencing dataset (GSE117570) from the TISCH2 database. Non-negative matrix factorization (NMF) was utilized to construct the risk score model based on lipid score calculated by GSVA algorithm. Phs000452.v3, PMID: 26359337, PMID: 32472114, PRJEB23709 datasets were used to test the response to immunotherapy. Drug sensitivity analysis was assessed according to the GDSC database, and immunotherapy response was evaluated using the Wilcoxon test. Cellular function assays including clone formation, EDU assays and flow cytometry were implemented to explore the phenotype alteration caused by the knockdown of PTDSS1, which is one of key gene in risk score model.

RESULTS

We analyzed both bulk and single-cell RNA sequencing data to establish and validate a risk score model based on 18 lipid metabolism-related genes with significant impact on prognosis. After divided the patients into two groups according to risk score, we identified differences in lipid-related metabolic processes and a detailed portrait of the immune landscapes of high- and low-risk groups. Moreover, we investigated the potentials of our risk score in predicting response to immunotherapy and drug sensitivity. In addition, we silenced PTDSS1 in LUAD cell lines, and found that the proliferation of the cells was weakened, and the apoptosis of the cells was increased.

CONCLUSION

Our study highlights the crucial roles of lipid metabolism in LUAD and provides a reliable risk score model, which can aid in predicting prognosis and response to immunotherapy. Furthermore, we investigated the roles of PTDSS1 in LUAD carcinogenesis, which showed that PTDSS1 regulated proliferation and apoptosis of LUAD cells.