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As one of the main characteristics of neoplasia, metabolic reprogramming provides nutrition and energy to enhance cell proliferation and maintain environment homeostasis. Glycolysis is one of the most important components of cancer metabolism and the Warburg effect contributes to the competitive advantages of cancer cells in the threatened microenvironment. Studies show strong links between N-methyladenosine (mA) modification and metabolic recombination of cancer cells. As the most abundant modification in eukaryotic RNA, mA methylation plays important roles in regulating RNA processing, including splicing, stability, transportation, translation and degradation. The aberration of mA modification can be observed in a variety of diseases such as diabetes, neurological diseases and cancers. This review describes the mechanisms of mA on cancer glycolysis and their applications in cancer therapy and prognosis evaluation, aiming to emphasize the importance of targeting mA in modulating cancer metabolism.
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http://dx.doi.org/10.2174/1566523222666220830150446 | DOI Listing |
Front Immunol
September 2025
Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
Cancer is a multifaceted disease driven by a complex interplay of genetic predisposition, environmental factors and lifestyle habits. With the accelerating pace of cancer research, the gut microbiome has emerged as a critical modulator of human health and immunity. Disruption in the gut microbial populations and diversity, known as dysbiosis, has been linked with the development of chronic inflammation, oncogenesis, angiogenesis and metastasis.
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September 2025
Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
Background: People living with HIV(PLWH) are a high-risk population for cancer. We conducted a pioneering study on the gut microbiota of PLWH with various types of cancer, revealing key microbiota.
Methods: We collected stool samples from 54 PLWH who have cancer (PLWH-C), including Kaposi's sarcoma (KS, n=7), lymphoma (L, n=22), lung cancer (LC, n=12), and colorectal cancer (CRC, n=13), 55 PLWH who do not have cancer (PLWH-NC), and 49 people living without HIV (Ctrl).
Front Immunol
September 2025
Institute of Pulmonary Medicine, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
Neutrophil extracellular traps (NETs) are DNA-protein structures released during a form of programmed neutrophil death known as NETosis. While NETs have been implicated in both tumor inhibition and promotion, their functional role in cancer remains ambiguous. In this study, we compared the NET-forming capacity and functional effects of NETs derived from lung cancer (LC) patients and healthy donors (H).
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September 2025
Department of Thoracic Surgery, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, China.
Background: Lung cancer remains the leading cause of cancer-related mortality globally, primarily due to late-stage diagnosis, molecular heterogeneity, and therapy resistance. Key biomarkers such as EGFR, ALK, KRAS, and PD-1 have revolutionized precision oncology; however, comprehensive structural and clinical validation of these targets is crucial to enhance therapeutic efficacy.
Methods: Protein sequences for EGFR, ALK, KRAS, and PD-1 were retrieved from UniProt and modeled using SWISS-MODEL to generate high-confidence 3D structures.
Front Immunol
September 2025
Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.
Background: Metabolic reprogramming is an important hallmark of cervical cancer (CC), and extensive studies have provided important information for translational and clinical oncology. Here we sought to determine metabolic association with molecular aberrations, telomere maintenance and outcomes in CC.
Methods: RNA sequencing data from TCGA cohort of CC was analyzed for their metabolic gene expression profile and consensus clustering was then performed to classify tumors into different groups/subtypes.