Aberrant expression of long non-coding RNAs (lncRNAs) plays pivotal roles in tumorigenesis of human malignant cancers, including esophageal squamous cell carcinoma (ESCC). However, the specific role of lncRNA NRSN2-AS1 in ESCC has not been investigated. Our analysis of clinical data revealed that NRSN2-AS1 was upregulated in ESCC tissues and negatively correlated with patient survival. Luciferase reporter assays and chromatin immunoprecipitation assays demonstrated that NRSN2-AS1 is transcribed by SOX2. In vitro functional experiments showed that NRSN2-AS1 can promote ESCC cell proliferation, migration, and invasion. Furthermore, NRSN2-AS1-binding proteins were detected using RNA pull-down assays and mass spectrometry. Mechanistically, NRSN2-AS1 can bind to phosphoglycerate kinase 1 (PGK1) and upregulate its protein levels by inhibiting its ubiquitination. Knockdown of PGK1 in part abolished the NRSN2-AS1 overexpression-induced effects on ESCC cell proliferation, migration, invasion, and epithelial‑mesenchymal transition (EMT). Thus, NRSN2-AS1 may be a diagnostic biomarker or treatment target for ESCC.
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http://dx.doi.org/10.1007/s10585-022-10174-7 | DOI Listing |
World J Gastroenterol
January 2025
Department of Oncology Surgery, Cell Therapy and Organ Transplantation, Institute of Biomedicine of Seville, Virgen del Rocio University Hospital, Seville 41013, Spain.
Background: Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer with varied incidence and epidemiology worldwide. Sorafenib is still a recommended treatment for a large proportion of patients with advanced HCC. Different patterns of treatment responsiveness have been identified in differentiated hepatoblastoma HepG2 cells and metastatic HCC SNU449 cells.
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January 2025
Department of Oncology, Georgetown University Medical Center, Washington, DC, United States.
Cancer's epigenetic landscape, a labyrinthine tapestry of molecular modifications, has long captivated researchers with its profound influence on gene expression and cellular fate. This review discusses the intricate mechanisms underlying cancer epigenetics, unraveling the complex interplay between DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs. We navigate through the tumultuous seas of epigenetic dysregulation, exploring how these processes conspire to silence tumor suppressors and unleash oncogenic potential.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Hebei Key Laboratory of Laboratory Animal Science, Hebei Medical University, No. 361 East Zhongshan Road, Changan District, Shijiazhuang, 050017, China.
Objective: This study aimed to investigate the role of the autophagy-related long noncoding RNA (lncRNA) MIR210HG in hepatocellular carcinoma and its potential as a therapeutic target.
Methods: LncRNA MIR210HG expression and its correlation with survival outcomes in hepatocellular carcinoma patients were analyzed using data from The Cancer Genome Atlas (TCGA). Kaplan-Meier and Cox regression analyses were conducted to assess survival correlations.
Acta Pharmacol Sin
January 2025
Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
Dysregulation of long non-coding RNAs (lncRNAs) is common in colorectal cancer liver metastasis (CRLM). Emerging evidence links lncRNAs to multiple stages of metastasis from initial migration to colonization of distant organs. In this study we investigated the role of lncRNAs in metabolic reprogramming during CRLM using patient-derived organoid (PDO) models.
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January 2025
National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, China; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China. Electronic address:
The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding the function of lncRNAs. Since the traditional biological experimental methods are time-consuming and some existing computational methods rely on high computing power, we are committed to finding a simple and easy-to-implement method to achieve more efficient prediction of the subcellular localization of lncRNAs. In this work, we proposed a model based on multi-source features and two-stage voting strategy for predicting the subcellular localization of lncRNAs (MVSLLnc).
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