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Metabolic dysregulation is a widely acknowledged contributor for the development and tumorigenesis of colorectal cancer (CRC), highlighting the need for reliable prognostic biomarkers in this malignancy. Herein, we identified key genes relevant to CRC metabolism through a comprehensive analysis of lactate metabolism-related genes from GSEA MsigDB, employing univariate Cox regression analysis and random forest algorithms. Clinical prognostic analysis was performed following identification of three key genes, and consistent clustering enabled the classification of public datasets into three patterns with significant prognostic differences. The molecular pathways and tumor microenvironment (TME) of these patterns were then investigated through correlation analyses. Quantitative PCR was employed to quantify the mRNA expression levels of the three pivotal genes in CRC tissue. Single-cell RNA sequencing data and fluorescent multiplex immunohistochemistry were utilized to analyze relevant T cells and validate the correlation between key genes and CD4 T cells. Our analysis revealed that MPC1, COQ2, and ADAMTS13 significantly stratify the cohort into three patterns with distinct prognoses. Additionally, the immune infiltration and molecular pathways were significantly different for each pattern. Among the key genes, MPC1 and COQ2 were positively associated with good prognosis, whereas ADAMTS13 was negatively associated with good prognosis. Single-cell RNA sequencing (scRNA-seq) data illustrated that the relationship between three key genes and T cells, which was further confirmed by the results of fluorescent multiplex immunohistochemistry demonstrating a positive correlation between MPC1 and COQ2 with CD4 T cells and a negative correlation between ADAMTS13 and CD4 T cells. These findings suggest that the three key lactate metabolism genes, MPC1, COQ2, and ADAMTS13, may serve as effective prognostic biomarkers and support the link between lactate metabolism and the immune microenvironment in CRC.
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http://dx.doi.org/10.3389/fcell.2023.1173803 | DOI Listing |
Anim Sci J
January 2025
Department of Animal Science, Bangladesh Agricultural University, Mymensingh, Bangladesh.
This study investigates the effects of L-carnitine on nuclear maturation and fertilization in cattle and goat oocytes. Ovaries were collected from females with poor reproductive efficiency in the tropical climate, and cumulus-oocyte complexes (COCs) were retrieved from large antral follicles. COCs were cultured with varying concentrations of L-carnitine (0, 0.
View Article and Find Full Text PDFGenome Biol
September 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
Background: Soil salinization represents a critical global challenge to agricultural productivity, profoundly impacting crop yields and threatening food security. Plant salt-responsive is complex and dynamic, making it challenging to fully elucidate salt tolerance mechanism and leading to gaps in our understanding of how plants adapt to and mitigate salt stress.
Results: Here, we conduct high-resolution time-series transcriptomic and metabolomic profiling of the extremely salt-tolerant maize inbred line, HLZY, and the salt-sensitive elite line, JI853.
Virchows Arch
September 2025
Department of Oral Surgery and Pathology, School of Dentistry, Universidade Federal de Minas Gerais, Minas Gerais, Av. Antônio Carlos, Pampulha, Belo Horizonte, 31270-901, Brazil.
Plasmablastic lymphoma (PBL) is a rare and aggressive non-Hodgkin lymphoma with a poor prognosis and short survival rates. It is classified as a large B-cell lymphoma subtype, but carries a plasmacytic immunophenotype. Therefore, PBL has pathogenetic overlaps with diffuse large B-cell lymphoma not otherwise specified (DLBCL NOS) and plasma cell neoplasms (PCNs).
View Article and Find Full Text PDFImmunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.
Biochem Genet
September 2025
Department of Medical Biology, Cerrahpasa Faculty of Medicine, Istanbul University Cerrahpasa, Kocamustafapasa, 34098, Istanbul, Turkey.
Glioblastoma is the most aggressive and malignant tumor of the central nervous system. Current treatment options, including surgical excision, radiotherapy, and chemotherapy, have Limited efficacy, with a median survival rate of approximately 15 months. To develop novel therapeutics, it is crucial to understand the underlying molecular mechanisms driving glioblastoma.
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