ArticleFrontiers in endocrinology2022
Identification of the alpha linolenic acid metabolism-related signature associated with prognosis and the immune microenvironment in nasopharyngeal carcinoma.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 18 citations in OpenAlex.
- A gene signature associated with alpha-linolenic acid metabolism predicts clinical prognosis in hepatocellular carcinoma.European journal of medical research · 2026Article
- Association between methylation quantitative trait loci and colorectal cancer risk, survival and cancer recurrence.British journal of cancer · 2025Article
- A novel mitochondrial quality regulation gene signature for anticipating prognosis, TME, and therapeutic response in LUAD by multi-omics analysis and experimental verification.Cancer cell international · 2025Article
- Molecular characterization of EBV-associated primary pulmonary lymphoepithelial carcinoma by multiomics analysis.BMC cancer · 2025Article
- Multiomics Analysis of Exportin Family Reveals XPO1 as a Novel Target for Clear Cell Renal Cell Carcinoma.International journal of genomics · 2025Article
- Fatty acid metabolism: A new target for nasopharyngeal carcinoma therapy.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- Impact of immunosuppressants on tumor pulmonary metastasis: new insight into transplantation for hepatocellular carcinoma.Cancer biology & medicine · 2024Review
- A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.British journal of cancer · 2024Article
- Systematic proteome-wide Mendelian randomization using the human plasma proteome to identify therapeutic targets for lung adenocarcinoma.Journal of translational medicine · 2024Article
- The Antitumor Effects of α-Linolenic Acid.Journal of personalized medicine · 2024Review
- Correlation of serum IL-2 and IFN-γ levels with clinical prognosis of nasopharyngeal carcinoma patients and analysis of risk factors.Journal of medical biochemistry · 2024Article
- Uncovering the Mechanisms of Active Components from Toad Venom against Hepatocellular Carcinoma Using Untargeted Metabolomics.Molecules (Basel, Switzerland) · 2022Article
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4 authors at 2 institutions in 1 country.
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Abstract
Background: Tumor metabolism is important for cancer progression. Nevertheless, the role of the metabolism pathway and related molecules in nasopharyngeal carcinoma (NPC) is limited. Methods: Open-accessed data was downloaded from The Cancer Genome Atlas database. All the analysis was performed using the R software and the package in R environments. Results: In our study, we firstly explored the role of 21 metabolism-related pathways in NPC patients. We found that the steroid biosynthesis and biosynthesis of unsaturated fatty acids were risk factors, while the alpha linolenic acid metabolism was a protective factor. Then, the alpha linolenic acid metabolism aroused our interest. A total of 128 differentially expressed genes (DEGs) were identified, including 71 downregulated and 57 upregulated genes identified between high and low alpha linolenic acid metabolism level. Based on these DEGs, we constructed a prognosis model including DEFB4B, FOXL2NB, MDGA2, RTL1, SLURP2, TMEM151B and TSPAN19, which showed great prediction efficiency in both training and validation cohorts. Clinical correlation analysis showed that high-risk patients might have worse clinical pathology parameters. Pathway enrichment analysis showed that riskscore was positively correlated with angiogenesis, DNA repair, G2/M checkpoints, IL6/JAK/STAT3 signaling, KRAS signaling up, WNT beta-catenin signaling, PI3K/AKT/mTOR signaling, yet positively correlated with inflammatory response, xenobiotic metabolism, TNF-α signaling Conclusions: In all, our study identified the genes associated with alpha linolenic acid metabolism and constructed an effective prognosis model which could robustly predict NPC patients prognosis.
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