Lipid Metabolism in Tumor-Associated Natural Killer Cells

Applying Untargeted Lipidomics to Evaluate the Efficacy of Combined Neoadjuvant Chemotherapy and Immunotherapy for Esophageal Squamous Carcinoma Treatment

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignant tumor with a poor prognosis due to insidious symptoms that make early diagnosis difficult. Despite the combination of multiple treatment modalities, the recurrence and mortality rates of ESCC remain high. Neoadjuvant chemotherapy combined with immunotherapy is an emerging treatment modality that improves the prognosis of patients with ESCC. However, owing to the presence of hyperprogression and pseudoprogression, the currently used methods cannot accurately evaluate the efficacy of this therapy in patients, thus creating an evaluation bias and depriving these patients of the opportunity to benefit. We used untargeted lipidomics to identify the differences in lipid composition between cancer specimens and normal tissue specimens in the neoadjuvant chemotherapy combined with the immunotherapy group and the surgery-alone group of esophageal cancer patients and constructed a prediction model based on sphingomyelin 12:1;2O/30:0 and triglyceride (TG) 60:3 | TG 18:0_24:1_18 using a machine learning approach, which helps to better evaluate the neoadjuvant efficacy of combination therapy and better guide the treatment of ESCC.

Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy

Tumor metabolism and tumor immunity are inextricably linked. Targeting the metabolism of tumors is a point worth studying in tumor immunotherapy. Recently, the influence of the metabolism of tumors and immune cells on the occurrence, proliferation, metastasis, and prognosis of tumors has attracted more attention. Tumor tissue forms a specific tumor microenvironment (TME). In addition to tumor cells, there are also immune cells, stromal cells, and other cells in TME. To adapt to the environment, tumor cells go through the metabolism reprogramming of various substances. The metabolism reprogramming of tumor cells may further affect the formation of the tumor microenvironment and the function of a variety of cells, especially immune cells, eventually promoting tumor development. Therefore, it is necessary to study the metabolism of tumor cells and its effects on immune cells to guide tumor immunotherapy. Inhibiting tumor metabolism may restore immune balance and promote the immune response in tumors. This article will describe glucose metabolism, lipid metabolism, amino acid metabolism, and immune cells in tumors. Besides, the impact of metabolism on the immune cells in TME is also discussed for analyzing and exploring tumor immunotherapy.

Lipid metabolism and tumor immunotherapy

In recent years, the relationship between lipid metabolism and tumour immunotherapy has been thoroughly investigated. An increasing number of studies have shown that abnormal gene expression and ectopic levels of metabolites related to fatty acid synthesis or fatty acid oxidation affect tumour metastasis, recurrence, and drug resistance. Tumour immunotherapy that aims to promote an antitumour immune response has greatly improved the outcomes for tumour patients. However, lipid metabolism reprogramming in tumour cells or tumour microenvironment-infiltrating immune cells can influence the antitumour response of immune cells and induce tumor cell immune evasion. The recent increase in the prevalence of obesity-related cancers has drawn attention to the fact that obesity increases fatty acid oxidation in cancer cells and suppresses the activation of immune cells, thereby weakening antitumour immunity. This article reviews the changes in lipid metabolism in cells in the tumour microenvironment and describes the relationship between lipid metabolism reprogramming in multiple cell types and tumour immunotherapy.

Lipid metabolism in tumor immunology and immunotherapy

Lipids are a diverse class of biomolecules that have been implicated in cancer pathophysiology and in an array of immune responses, making them potential targets for improving immune responsiveness. Lipid and lipid oxidation also can affect tumor progression and response to treatment. Although their importance in cellular functions and their potential as cancer biomarkers have been explored, lipids have yet to be extensively investigated as a possible form of cancer therapy. This review explores the role of lipids in cancer pathophysiology and describes how further understanding of these macromolecules could prompt novel treatments for cancer.

Unique metabolism and protein expression signature in human decidual NK cells

Human decidual natural killer (dNK) cells are a unique type of tissue-resident NK cells at the maternal-fetal interface. dNK cells are likely to have pivotal roles during pregnancy, including in maternal-fetal immune tolerance, trophoblast invasion, and fetal development. However, detailed insights into these cells are still lacking. In this study, we performed metabolomic and proteomic analyses on human NK cells derived from decidua and peripheral blood. We found that 77 metabolites were significantly changed in dNK cells. Notably, compared to peripheral blood NK (pNK) cells, 29 metabolites involved in glycerophospholipid and glutathione metabolism were significantly decreased in dNK cells. Moreover, we found that 394 proteins were differentially expressed in dNK cells. Pathway analyses and network enrichment analyses identified 110 differentially expressed proteins involved in focal adhesion, cytoskeleton remodeling, oxidoreductase activity, and fatty acid metabolism in dNK cells. The integrated proteomic and metabolomic analyses revealed significant downregulation in glutathione metabolism in dNK cells compared to pNK cells. Our data indicate that human dNK cells have unique metabolism and protein-expression features, likely regulating their function in pregnancy and immunity.

Lipid metabolism-related gene signature predicts prognosis and depicts tumor microenvironment immune landscape in gliomas

Background

Glioma is the most common primary brain tumor in adults and accounts for more than 70% of brain malignancies. Lipids are crucial components of biological membranes and other structures in cells. Accumulating evidence has supported the role of lipid metabolism in reshaping the tumor immune microenvironment (TME). However, the relationship between the immune TME of glioma and lipid metabolism remain poorly described.

Materials and methods

The RNA-seq data and clinicopathological information of primary glioma patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). An independent RNA-seq dataset from the West China Hospital (WCH) also included in the study. Univariate Cox regression and LASSO Cox regression model was first to determine the prognostic gene signature from lipid metabolism-related genes (LMRGs). Then a risk score named LMRGs-related risk score (LRS) was established and patients were stratified into high and low risk groups according to LRS. The prognostic value of the LRS was further demonstrated by construction of a glioma risk nomogram. ESTIMATE and CIBERSORTx were used to depicted the TME immune landscape. Tumor Immune Dysfunction and Exclusion (TIDE) was utilized to predict the therapeutic response of immune checkpoint blockades (ICB) among glioma patients.

Results

A total of 144 LMRGs were differentially expressed between gliomas and brain tissue. Finally, 11 prognostic LMRGs were included in the construction of LRS. The LRS was demonstrated to be an independent prognostic predictor for glioma patients, and a nomogram consisting of the LRS, IDH mutational status, WHO grade, and radiotherapy showed a C-index of 0.852. LRS values were significantly associated with stromal score, immune score, and ESTIMATE score. CIBERSORTx indicated remarkable differences in the abundance of TME immune cells between patients with high and low LRS risk levels. Based on the results of TIDE algorithm, we speculated that the high-risk group had a greater chance of benefiting from immunotherapy.

Conclusion

The risk model based upon LMRGs could effectively predict prognosis in patients with glioma. Risk score also divided glioma patients into different groups with distinct TME immune characteristics. Immunotherapy is potentially beneficial to glioma patients with certain lipid metabolism profiles.

The role of lipid metabolism in tumor immune microenvironment and potential therapeutic strategies

Aberrant lipid metabolism is nonnegligible for tumor cells to adapt to the tumor microenvironment (TME). It plays a significant role in the amount and function of immune cells, including tumor-associated macrophages, T cells, dendritic cells and marrow-derived suppressor cells. It is well-known that the immune response in TME is suppressed and lipid metabolism is closely involved in this process. Immunotherapy, containing anti-PD1/PDL1 therapy and adoptive T cell therapy, is a crucial clinical cancer therapeutic strategy nowadays, but they display a low-sensibility in certain cancers. In this review, we mainly discussed the importance of lipid metabolism in the formation of immunosuppressive TME, and explored the effectiveness and sensitivity of immunotherapy treatment by regulating the lipid metabolism.

A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

Background

Breast cancer (BRCA) is the most common tumor in women, and lipid metabolism involvement has been demonstrated in its tumorigenesis and development. However, the role of lipid metabolism-associated genes (LMAGs) in the immune microenvironment and prognosis of BRCA remains unclear.

Methods

A total of 1076 patients with BRCA were extracted from The Cancer Genome Atlas database and randomly assigned to the training cohort (n = 760) or validation cohort (n = 316). Kaplan–Meier analysis was used to assess differences in survival. Consensus clustering was performed to categorize the patients with BRCA into subtypes. Using multivariate Cox regression analysis, an LMAG-based prognostic risk model was constructed from the training cohort and validated using the validation cohort. The immune microenvironment was evaluated using the ESTIMATE and tumor immune estimation resource algorithms, CIBERSORT, and single sample gene set enrichment analyses.

Results

Consensus clustering classified the patients with BRCA into two subgroups with significantly different overall survival rates and immune microenvironments. Better prognosis was associated with high immune infiltration. The prognostic risk model, based on four LMAGs (MED10, PLA2G2D, CYP4F11, and GPS2), successfully stratified the patients into high- and low-risk groups in both the training and validation sets. High risk scores predicted poor prognosis and indicated low immune status. Subgroup analysis suggested that the risk model was an independent predictor of prognosis in BRCA.

Conclusion

This study demonstrated, for the first time, that LMAG expression plays a crucial role in BRCA. The LMAG-based risk model successfully predicted the prognosis and indicated the immune microenvironment of patients with BRCA. Our study may provide inspiration for further research on BRCA pathomechanisms.