Aug 2026· Frontiers in Immunology· 0 citations· 53 references
TL;DR
Widespread disturbances in amino acid metabolism were identified via untargeted metabolomics in HCC patients with metastasis, closely governing inflammation-related metabolic remodeling and oxidative stress responses.
Abstract
Metastasis is the primary cause of treatment failure and adverse prognosis in hepatocellular carcinoma (HCC), and the molecular basis of HCC metastasis remains poorly defined. This work investigated the potential mechanisms underlying HCC metastasis through integrated multi-omics analysis of metabolomics and proteomics.
This retrospective study included 105 individuals with HCC, with comparative analysis between metastatic and non-metastatic cases. We further evaluated the effects of metastasis on serum metabolomics and proteomics in HCC patients.
Widespread disturbances in amino acid metabolism were identified via untargeted metabolomics in HCC patients with metastasis, closely governing inflammation-related metabolic remodeling and oxidative stress responses. Specifically, we identified 91 and 59 distinct differential metabolites capable of indicating HCC metastasis, with the screening criteria set as log
2
fold change > 1.5, adjusted
P
value < 0.05, and VIP > 1.5 in positive and negative modes, respectively. The alanine, aspartate and glutamate metabolism pathway correlated with HCC-associated lung metastasis, while the gluconeogenesis pathway was linked to HCC-associated bone metastasis. Compared with HCC (non-metastatic hepatocellular carcinoma), the key molecular alterations in the multi-omics network of HCC_M (HCC with metastasis) are implicated in inflammatory metabolic reprogramming, oxidative stress response, gluconeogenesis, glycolysis, and the tricarboxylic acid (TCA) cycle. Twenty-five proteins, including PKM2, PERCK, ALDH2, CPS1, GLS1, GLUD1, GOT1, and SLC38A2, were identified as potential biomarkers for HCC metastasis.
By integrating untargeted metabolomic and proteomic profiling, we identified distinct metabolic and proteomic changes linked to HCC metastasis. This work also characterized the pathological characteristics and core pathways underlying HCC metastasis, while identifying potential therapeutic candidates.
Hepatocellular carcinoma (HCC) poses a significant global health burden with limited therapeutic options, particularly for non-viral etiologies. The mitochondrial solute carrier SLC25A43 is implicated in cellular redox homeostasis, yet its role in HCC remains unclear. This study aimed to comprehensively investigate the expression pattern, clinical significance, biological function, and potential mechanisms of SLC25A43 in HCC. Utilizing multi-omics data from public databases (TCGA-LIHC, GEO, and HPA), we performed integrated bioinformatic analyses. SLC25A43 was consistently upregulated in HCC tissues compared with non-tumorous liver tissues and demonstrated strong diagnostic value (AUC = 0.861). High SLC25A43 expression was significantly associated with advanced tumor stage, metastasis, and adverse clinicopathological features. Survival analyses identified SLC25A43 as an independent prognostic risk factor for overall survival, progression-free interval, and disease-specific survival. Functional enrichment analyses suggested that SLC25A43 is involved in mitochondrial oxidative phosphorylation, energy metabolism, and immune-related pathways. Immune infiltration analyses using ssGSEA, xCell, and TIMER consistently revealed negative correlations between SLC25A43 expression and multiple antitumor immune cell populations, particularly CD8 + T cells. Experimental validation confirmed that SLC25A43 was significantly upregulated in HCC tissues at both mRNA and protein levels. Functional assays in Huh-7, Hep-LM3, MHCC97H, and LO2 cells demonstrated that SLC25A43 knockdown inhibited, whereas overexpression promoted, cell proliferation and migration. Rescue experiments further verified the specificity of these effects. Mechanistically, SLC25A43 regulated intracellular ATP production, ROS accumulation, and glutathione metabolism, indicating a role in redox homeostasis and energy metabolism. In addition, PBMC co-culture experiments showed that SLC25A43 suppressed CD8 + T-cell cytotoxic activity by reducing Granzyme B expression. A prognostic nomogram incorporating SLC25A43 exhibited favorable predictive performance and was successfully validated in two independent GEO cohorts. SLC25A43 is a novel diagnostic and prognostic biomarker for HCC. Its upregulation promotes tumor progression through metabolic reprogramming, redox homeostasis remodeling, and suppression of antitumor immune responses. These findings highlight SLC25A43 as a promising therapeutic target and provide new insights into the metabolic-immune regulatory network in hepatocellular carcinoma.
Dunzhen Chen, Li Yu, Xichang Zhou et al.· Scientific Reports· 0 citations
To analyze differences in serum metabolic profiles between patients with interstitial lung disease (ILD) and healthy controls in northern China using untargeted metabolomics techniques, to identify differentially expressed metabolites and key dysregulated pathways, and to identify potential metabolic biomarkers.
Thirty patients with interstitial lung disease (ILD) and 30 healthy controls were enrolled. Untargeted metabolomics analysis was performed using LC-MS/MS in both positive and negative ion modes. Differentially expressed metabolites were identified using the criteria VIP > 1.0, FC > 1.2 or FC < 0.833 and
P
-value < 0.05. Pathway enrichment analysis was conducted using the KEGG database, and diagnostic performance was evaluated using ROC curves.
The global metabolic profile showed significant separation between the ILD group and the control group. Pathway enrichment analysis revealed significant dysregulation in lipid metabolism (particularly linoleic acid metabolism), amino acid biosynthesis, and steroid hormone metabolism. GSEA analysis further confirmed the suppression of overall metabolic activity and the specific enrichment of tryptophan metabolism. Among the differentially expressed metabolites, DG(15:0/18:2(9Z,12Z)/0:0) (AUC = 0.914), Medroxalol (AUC = 0.912), estriol 3-sulfate (AUC = 0.890) and DHEA-S (AUC = 0.877) demonstrated high diagnostic value.
Untargeted metabolomics has revealed significant systemic metabolic dysregulation in ILD. The biomarkers and “metabolic-immune-endocrine” interaction patterns identified offer potential leads for early diagnosis and targeted treatment, which require validation in larger cohorts.
Lu Liu, Xinyi Wang, Jinling Xiao et al.· Frontiers in Medicine· 0 citations
Background: Gastric cancer remains a leading cause of cancer-related deaths worldwide. Although significant progress has been made in clinical diagnosis and treatment, the molecular mechanisms underlying gastric cancer have not yet been fully elucidated. To address this, this study employs a multi-omics approach to systematically analyze the molecular characteristics of gastric cancer. Methods: This case–control study enrolled 218 GC patients and 218 healthy controls, and adopted a multi-omics strategy combining inductively coupled plasma mass spectrometry (ICP-MS), element-related genome-wide association study (eGWAS), and untargeted metabolomics to explore the element-gene-metabolite regulatory axis in GC. Results: A total of nine plasma differential elements associated with gastric cancer were identified, with a combined diagnostic accuracy of 0.918. Specifically, elements such as Fe, Co, and Li showed significant correlations with 63 genes involved in key signaling pathways, including MAPK, SMAD, and Wnt. Genome-wide association studies (GWAS) revealed that gastric cancer-related genes were significantly enriched in cancer-associated pathways and signaling cascades such as Rap1. Metabolomic analysis further demonstrated that 20 elements in the gastric cancer cohort correlated with 94 metabolites, predominantly enriched in pyrimidine and glutathione metabolism pathways. Conclusions: These nine plasma differential elements showed high combined diagnostic efficacy and were associated with genes and metabolites enriched in cancer-related signaling, metabolic reprogramming, and DNA damage response pathways. Together, these findings suggest potential multi-level associations among plasma elemental alterations, genetic variation, and metabolic dysregulation in GC, providing candidate circulating biomarkers and mechanistic clues for future investigation.
BACKGROUND
Nasopharyngeal carcinoma (NPC) is a subtype of head and neck squamous cell carcinoma characterized by high recurrence and metastasis rates and poor prognosis. Although immune checkpoint inhibitors have emerged as a promising treatment strategy for recurrent/metastatic nasopharyngeal carcinoma (R/M NPC), only a few patients have benefitted significantly from them. Lipid metabolism reprogramming plays a crucial role in NPC progression and its interaction with the immune microenvironment. This study aims to establish a prognostic model for NPC based on lipid metabolism-related factors, further explore its association with tumor immunity, and investigate the potential for immunotherapy.
METHODS
Collect GEO datasets(GSE53819 and GSE102349) for differential expression analysis and least absolute shrinkage and selection operator (LASSO) regression to identify prognostic genes and construct a fatty-acid-metabolism-related prognostic model. Survival analyses and time-dependent receiver operating characteristic (ROC) curves were applied to evaluate the predictive performance of the constructed prognostic markers. Furthermore, associations between the derived risk scores and immunological characteristics were systematically evaluated. Following the identification of ABCC1 as a key gene, its expression was validated through RT-qPCR, immunoblotting, and immunohistochemistry (IHC). Its functional role was further investigated using in vitro functional assays, multiplex immunohistochemistry (mIHC), co-culture experiments with CD8⁺ T cells, and in vivo xenograft tumor models in nude mice.
RESULTS
Four genes (ABCC1, CD1D, CYP4B1, and DPEP2) were identified to constr uct a prognostic model associated with fatty acid metabolism. This model revealed significant distinctions in immune infiltration patterns between high-risk and low-risk groups. Specifically, the high-risk group displayed immunosuppressive characteristics, marked by reduced infiltration of CD8⁺T cells. Functional studies demonstrated that ABCC1 promoted NPC cell proliferation, migration, invasion, ROS accumulation, and lipid metabolic reprogramming. Mechanistically, ABCC1 was epigenetically upregulated by the histone acetyltransferase P300 and contributed to CD8⁺ T cell dysfunction and MEK/ERK pathway activation, thereby driving tumor progression.
CONCLUSION
In summary, we established a novel fatty-acid-metabolism-related prognostic model for assessing the prognosis and potential immunotherapy response of NPC patients, as well as for characterizing the immunological features of the tumor microenvironment (TME). Furthermore, ABCC1 emerged as a promising prognostic biomarker associated with immunotherapeutic responsiveness in NPC, warranting further validation.
CLINICAL TRIAL NUMBER
Not applicable.
Yang Xu, Liru Zhu, Qingqing Zhang et al.· Biology Direct· 0 citations
To elucidate the molecular characteristics of synergistic interactions across the clinical stages of coronary heart disease (CHD)—specifically stable angina pectoris (SAP), unstable angina pectoris (UAP), and acute myocardial infarction (AMI)—through integrated metabolomic and proteomic analyses. Based on a cohort including SAP, UAP, AMI, and healthy controls, metabolomic and proteomic analyses were performed to identify differentially expressed molecules, followed by KEGG pathway enrichment analysis. Pathways co-enriched across both omics platforms were selected to construct metabolite-protein interaction networks. The number of pathways co-enriched in both metabolomic and proteomic analyses increased markedly with disease stage. Only two pathways (histidine metabolism and arginine and proline metabolism) were identified in the SAP stage; this number increased to five in the UAP stage (including ferroptosis and efferocytosis) and expanded to 25 in the AMI stage, encompassing three major functional modules: immune inflammation, metabolic reprogramming, and cell signaling. The core network exhibited a stepwise increase in connectivity, shifting from a sparse structure in the SAP stage to a highly interconnected architecture in the AMI stage, with L-glutamate and KNG1 identified as the central hubs in this cross-sectional network. In addition, CNDP1 exhibited a stage-dependent functional transition, shifting from downregulation in SAP to upregulation in AMI. In this cross-sectional analysis, metabolic dysregulation and immune activation exhibited stepwise increases in interconnectivity across the SAP, UAP, and AMI groups, with the most extensive crosstalk observed in the AMI stage—a network configuration consistent with a tightly coupled “molecular storm”. These findings provide novel insights into stage-associated molecular signatures of CHD and identify candidate hub molecules for stage-oriented therapeutic investigation.
Xilun Tan, Yuanxiaoxue Gao, Jia Wang et al.· Scientific Reports· 0 citations
Objective Due to the lack of specific biomarkers, patients with esophageal cancer are often diagnosed at an advanced stage, resulting in poor treatment outcomes. This study aims to identify potential diagnostic and therapeutic biomarkers for esophageal squamous cell carcinoma (ESCC) through metabolomics and to elucidate their mechanisms of action. Methods Untargeted metabolomics was employed to analyze ESCC tissue samples and matched normal esophageal tissue samples. Differentially expressed metabolites were identified using multivariate statistical analysis. MetaboAnalyst 6.0 software was used for pathway analysis. Key molecules were validated via immunohistochemistry (IHC) and Western blot (WB), and their functions were assessed through cellular functional assays. Results A total of 2,850 metabolites were identified, among which 939 were differentially regulated, including 575 upregulated and 364 downregulated metabolites. Pathway enrichment analysis revealed that these differentially expressed metabolites were predominantly enriched in amino acid metabolism-related pathways, Mechanistically, activation of the HIF-1α–iNOS–NO signaling axis drove arginine metabolic reprogramming, leading to profound metabolic disturbances that ultimately enhanced the migratory and invasive capacities of ESCC cells. Conclusion This study confirms that ESCC exhibits significant amino acid metabolic dysregulation, with arginine related metabolic pathways being significantly enriched. This disruption in arginine metabolism may be caused by hypoxia, which activates the iNOS-NO signaling axis mediated by HIF-1α, thereby promoting the migratory and invasive capabilities of ESCC. Finally, the study confirmed that NO can serve as a potential non-invasive biomarker for distinguishing ESCC.
Kaiyuan Yao, Shunshun Zhang, Siya Tang et al.· Frontiers in Molecular Biosc...· 0 citations