Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism.

Triple-negative breast cancer (TNBC) is recognized for its aggressive nature, lack of effective diagnosis and treatment, and generally poor prognosis. The objective of this study was to investigate the metabolic changes in TNBC using metabolomics approaches and to explore underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were firstly explored between 18 TNBC and 21 healthy controls (HC) by liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly altered metabolites (DMs). Subsequently, the receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was subsequently conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, explaining the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as critical components. In summary, this study highlights metabolic biomarkers that could potentially be utilized for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data provides a validated and in-depth understanding of TNBC metabolism.

Hepatocellular carcinoma (HCC) progression involves disruption of oncogenic and tumor-suppressive signaling networks. DEPDC7 (DEP domain-containing protein 7), a liver-specific gene associated with intercellular communication, is highly expressed in normal hepatocytes but markedly downregulated in HCC. Here, we investigated the tumor-suppressive mechanisms of DEPDC7 using Huh-7 cells. Structural analysis revealed conserved DEP and RhoGAP domains, with multiple predicted post-translational modification sites suggesting regulatory potential. DEPDC7 expression was significantly reduced in HCC cells and localized to both cytoplasm and nucleus. Functionally, DEPDC7 overexpression inhibited cell proliferation and migration. RNA-seq analysis identified the JAK1/STAT3 pathway was the most suppressed upon DEPDC7 overexpression, with downregulation of JAK1 and STAT3. Molecular docking and co-immunoprecipitation confirmed direct interaction between DEPDC7 and the JAK1 kinase domain, indicating regulation through physical binding. Moreover, DEPDC7 overexpression suppressed epithelial–mesenchymal transition (EMT), increasing E-cadherin while reducing N-cadherin and vimentin. Morphological changes observed by scanning electron microscopy supported reduced migratory capacity. Collectively, DEPDC7 exerts tumor-suppressive effects by (1) promoting cell cycle arrest and apoptosis, (2) inhibiting JAK1/STAT3 signaling, and (3) attenuating EMT. These findings provide mechanistic evidence that DEPDC7 functions as a tumor suppressor in HCC, highlighting its potential as a therapeutic target.

Obstructive sleep apnea (OSA) is characterized by recurrent upper airway obstruction during sleep and is frequently accompanied by dyslipidemia. However, the molecular mechanisms linking lipid metabolism to OSA remain incompletely understood. This study aimed to investigate the association between blood lipid levels and OSA and explore potential underlying molecular pathways. Data from the National Health and Nutrition Examination Survey (NHANES) 2005-2008 and 2015-2018 were analyzed to evaluate the associations between OSA and blood lipid parameters using multivariable regression and sensitivity analyses. Additionally, OSA-related transcriptomic data (GSE135917) were obtained from the Gene Expression Omnibus (GEO), and lipid metabolism-related genes were retrieved from the Molecular Signatures Database (MSigDB). Differentially expressed lipid metabolism-related genes (DELMRGs) were identified through data integration. Machine learning approaches, protein-protein interaction network analysis, and receiver operating characteristic analysis were applied to identify key genes. Gene set enrichment analysis (GSEA) was performed to elucidate associated biological pathways, and transcription factor-gene and gene-microRNA regulatory networks were constructed using NetworkAnalyst and Cytoscape. Analysis of NHANES data showed that triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) levels were positively associated with OSA, whereas high-density lipoprotein cholesterol (HDL-C) levels was inversely associated (all p < 0.001). A nonlinear, inverted U-shaped association between TG levels and OSA risk was also observed (p for nonlinearity < 0.05). Transcriptomic analysis identified 34 DELMRGs, among which CYP3A4, CYP4A22, and MED18 emerged as key genes. GSEA revealed pathways potentially involved in lipid metabolism and OSA pathophysiology, while regulatory network analyses further supported the biological relevance of these genes. This study demonstrates that dyslipidemia characterized by elevated TG and LDL-C levels and reduced HDL-C levels is associated with an increased likelihood of OSA, and identifies three DELMRGs that may be involved in OSA pathophysiology. These findings provide exploratory mechanistic insights and offer a basis for future studies to further investigate the role of lipid metabolism in OSA.

Background Obstructive sleep apnea (OSA), which is a condition characterized by persistent upper airway blockage during sleep and is related to dyslipidemia, lacks a clear molecular explanation. This study aims to investigate lipid‒OSA correlations, identify potential biomarkers, and explore underlying molecular pathways. Methods Data collected from the National Health and Nutrition Examination Survey (NHANES) from 2005–2008 and 2015–2018 were analyzed to investigate the relationships between OSA and lipid profiles. This work was completed through multiple regression analysis and sensitivity analysis. Concurrently, we identified differentially expressed lipid metabolism-related genes (DELMRGs) by integrating lipid-related genes with OSA transcriptome data (GSE135917). Bioinformatics approaches revealed functional mechanisms via gene expression and immune infiltration profiling, revealing dyslipidemia-OSA pathogenic pathways Results According to NHANES research, TG and LDL-C levels were positively correlated with OSA incidence, whereas HDL-C was inversely related ( p < 0.001). Additionally, the relationship between TG levels and OSA risk was inverted U shaped ( p for nonlinearity < 0.05). Transcriptomics identified 34 DELMRGs, with CYP3A4, CYP4A22, and MED18 emerging as key genes via bioinformatics. Immune cell infiltration analysis revealed altered cell composition, with key DELMRGs linked to immune dysregulation. Gene set enrichment analysis revealed key pathways linked to these genes, and regulatory networks, including TF–gene and gene–miRNA networks, were constructed, suggesting their mechanistic role in OSA pathogenesis. Conclusions This study highlights the close association between dyslipidemia and OSA and identifies three key DELMRGs as potential mechanistic contributors, offering new insights and potential directions for future research.