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

Identification of hub genes significantly linked to tuberous sclerosis related-epilepsy and lipid metabolism via bioinformatics analysis

Background

Tuberous sclerosis complex (TSC) is one of the most common genetic causes of epilepsy. Identifying differentially expressed lipid metabolism related genes (DELMRGs) is crucial for guiding treatment decisions.

Methods

We acquired tuberous sclerosis related epilepsy (TSE) datasets, GSE16969 and GSE62019. Differential expression analysis identified 1,421 differentially expressed genes (DEGs). Intersecting these with lipid metabolism related genes (LMRGs) yielded 103 DELMRGs. DELMRGs underwent enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA) and AUCell analysis.

Results

In TSE datasets, 103 DELMRGs were identified. Four diagnostic biomarkers (ALOX12B, CBS, CPT1C, and DAGLB) showed high accuracy for epilepsy diagnosis, with an AUC value of 0.9592. Significant differences (p < 0.05) in Plasma cells, T cells regulatory (Tregs), and Macrophages M2 were observed between diagnostic groups. Microglia cells were highly correlated with lipid metabolism functions.

Conclusions

Our research unveiled potential DELMRGs (ALOX12B, CBS, CPT1C and DAGLB) in TSE, which may provide new ideas for studying the psathogenesis of epilepsy.

Low serum apolipoprotein A1 level predicts poor prognosis of patients with diffuse large B-cell lymphoma in the real world: a retrospective study

BACKGROUND

Apolipoprotein A1 (ApoA1) is a member of the apolipoprotein family with diverse functions. It is associated with the pathogenesis and prognosis of several types of tumors. However, the role of serum apolipoprotein A1 (ApoA1) in the prognosis of patients with diffuse large B-cell lymphoma (DLBCL) remains unclear. This study aimed to elucidate its influence on clinical outcomes in patients with DLBCL.

METHODS

We retrospectively analyzed a cohort of 1583 consecutive DLBCL patients admitted to the Fujian Medical University Union Hospital between January 2011 and December 2021. 949 newly diagnosed DLBCL patients who met the inclusion criteria were enrolled for statistical analysis. Receiver operating characteristic curve analysis was performed to determine the optimal cut-off value for serum ApoA1 levels for prognostic prediction among patients with DLBCL. The correlations between serum ApoA1 levels and clinical and laboratory parameters were analyzed. Prognostic significance was analyzed using univariate and multivariate Cox proportional hazards models.

RESULTS

Newly diagnosed patients with DLBCL demonstrated low serum ApoA1 levels (< 0.925 g/L), had more B symptoms, higher levels of serum lactate dehydrogenase (LDH) (>upper limit of normal), poorer performance status (Eastern Cooperative Oncology Group score of 2-4), higher percentage of advanced stage and non-germinal center B-cell (non-GCB) subtype, more cases of > 1 extranodal site, higher International Prognostic Index (IPI) score (3-5), and higher incidence of relapse or refractory diseases compared with those with high serum ApoA1 levels (≥ 0.925 g/L). Low serum ApoA1 levels were an independent adverse prognostic factor for overall survival (OS) but not progression-free survival (PFS).

CONCLUSIONS

Low serum ApoA1 levels were associated with poor treatment response and inferior survival in newly diagnosed patients with DLBCL.

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.

Identification of lipid metabolism-related biomarkers for diagnosis and molecular classification of atherosclerosis

BACKGROUND

Atherosclerosis is now the main cause of cardiac-cerebral vascular diseases around the world. Disturbances in lipid metabolism have an essential role in the development and progression of atherosclerosis. Thus, we aimed to investigate lipid metabolism-related molecular clusters and develop a diagnostic model for atherosclerosis.

METHODS

First, we used the GSE100927 and GSE43292 datasets to screen differentially expressed lipid metabolism-related genes (LMRGs). Subsequent enrichment analysis of these key genes was performed using the Metascape database. Using 101 atherosclerosis samples, we investigated the LMRG-based molecular clusters and the corresponding immune cell infiltration. After that, a diagnostic model for atherosclerosis was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a series of bioinformatics techniques, including CIBERSORT, gene set variation analysis, and single-cell data analysis, were used to analyze the potential mechanisms of the model genes in atherosclerosis.

RESULTS

A total of 29 LMRGs were found to be differentially expressed between atherosclerosis and normal samples. Functional and DisGeNET enrichment analyses indicated that 29 LMRGs are primarily engaged in cholesterol and lipid metabolism, the PPAR signaling pathway, and regulation of the inflammatory response and are also closely associated with atherosclerotic lesions. Two LMRG-related molecular clusters with significant biological functional differences are defined in atherosclerosis. A three-gene diagnostic model containing ADCY7, SCD, and CD36 was subsequently constructed. Receiver operating characteristic curves, decision curves, and an external validation dataset showed that our model exhibits good predictive performance. In addition, three model genes were found to be closely associated with immune cell infiltration, especially macrophage infiltration.

CONCLUSIONS

Our study comprehensively highlighted the intricate association between lipid metabolism and atherosclerosis and created a three-gene model for future clinical diagnosis.