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Results for “SLC25A45”

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PubMed2026

SLC25A45 as a prognostic biomarker promotes malignant progression via mutant p53 in hepatocellular carcinoma.

BACKGROUND: SLC25A45 belongs to the mitochondrial trimethyllysine carrier protein family. To date, its role in tumor development has not been fully elucidated. Previous studies have demonstrated its pro-tumor function in ovarian cancer; however, research on the expression characteristics, biological roles and underlying regulatory pathways of SLC25A45 in hepatocellular carcinoma (HCC) is limited. This study aims to explore the expression, functional roles, and regulatory pathways of SLC25A45 in HCC. METHODS: This study examined SLC25A45 expression and clinical relevance in HCC using publicly available transcriptomic data. SLC25A45 knockdown cell lines were constructed using PLC/PRF/5, Huh7, and Huh1 cells. Functional assays, including cell proliferation and colony formation assays, were then conducted. To investigate the effects of SLC25A45 knockdown on the malignant phenotypes of HCC cells in vitro, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses were performed to identify potential underlying mechanisms, which were subsequently validated by experimental assays. RESULTS: SLC25A45 was elevated in clinical HCC specimens, and high SLC25A45 expression was correlated with disease progression and poor overall survival (OS). In vitro functional assays demonstrated that SLC25A45 silencing reduced HCC cell proliferation and colony formation. Mechanistically, GSEA indicated that high SLC25A45 expression was significantly enriched in the p53 signaling pathways. SLC25A45 knockdown in HCC cells led to a significant increase in wild-type p53 protein expression, but a substantial decrease in mutant p53 expression. CONCLUSIONS: SLC25A45 is a potential prognostic indicator and may drive HCC tumorigenesis and progression through the differential modulation of the p53 signaling pathways.

SLC25A45↗

PubMed2025

Construction and accuracy assessment of an efferocytosis-related prognostic model for ovarian cancer: A diagnostic accuracy study.

The study aimed to investigate the prognostic significance of efferocytosis-related genes in ovarian cancer (OC) with regard to cancer development, progression, invasion, and metastasis. OC cohorts were assembled from bioinformatics repositories. Utilizing consensus clustering analysis, distinct clusters were delineated based on the intersection of OC-related genes and efferocytosis-related genes. A prognostic signature specific to efferocytosis in OC was developed using data from The Cancer Genome Atlas, validated against the gene expression omnibus database, and subjected to independent prognostic analysis. Subsequently, a nomogram model was formulated. Moreover, investigations encompassed the immune microenvironment, immunotherapy, mutation profiling, drug sensitivity assessments, drug prediction models, and molecular docking analyses. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to ascertain the mRNA expression levels of key genes. Five key genes, FCGBP, BTN3A3, WDR91, SLC25A45, and BTNL3, were identified as significantly associated with OC. Both datasets and qRT-PCR demonstrated elevated expression levels of FCGBP and WDR91 in OC. Notably, AFLATOXIN B1 exhibited strong binding affinity to SLC25A45, ciclopirox to BTN3A3, and irinotecan to WDR91. The risk score, age, and stage were identified as independent prognostic factors, with the nomogram displaying efficacy in predicting OC patient survival. Variations in the immune cell infiltration profiles, including naive B cells, and expression levels of 6 immune checkpoint genes, such as CTLA4, were notable. High tumor mutation burden scores were associated with improved survival outcomes. Additionally, significant differences in the IC50 values of 123 anticancer drugs were observed between the 2 risk groups. This findings of this study highlight the efficacy of the efferocytosis-associated risk model in predicting the survival outcomes of OC patients, thus providing a novel reference for prognostic prediction in OC patients.

Humans↗