Evidence map›Paper›PMID 39465460›Full record

ArticleJournal of applied genetics2025

Experimental and in silico analysis of LINC01279 expression in tumor of patients with breast cancer.

Negar Mokhtari, Najmeh Ahmadi, Sahar Moradi, Shiva Farmani, Elham Kheyrani, Nasrin Fattahi Dolatabadi

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Article in Journal of applied genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Negar MokhtariDepartment of Cellular and Molecular Biology, Islamic Azad University, Najafabad Branch, Isfahan, Iran.
Najmeh AhmadiDepartmant of Medical Laboratory Sciences, School of Paramedical Sciences, Gerash University of Medical Sciences, Gerash, Iran.
Sahar MoradiDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Isfahan, Iran.
Shiva FarmaniDepartment of Biology, Faculty of Basic Sciences, Yazd University, Yazd, Iran.
Elham KheyraniBiotechcell Lab, Isfahan, Iran.
Nasrin Fattahi DolatabadiDepartment of Genetics, Facultyof Basic Sciences, Shahrekord University, Shahrekord, Iran. na71fattahi@gmail.com.

Funding

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6 · The paper itself

Abstract

Breast cancer (BC) is characterized by the increase of malignant cells in the breast. The malignant cells begin in the lining of the breast milk glands or ducts (ductal epithelium). BC is the most frequent cancer in women, but it may also occur in males. Long non-coding RNAs (lncRNA) have been demonstrated to control the development and incidence of cancer. However, some lncRNAs experience potential changes in BC, but their role has not been well studied. LINC01279 is known as a valuable biomarker in gastric cancer but has not yet been studied in BC. Changes in LINC01279 expression levels in BC samples were investigated by microarray. Q-PCR was also used to evaluate the expression of LINC01279 in the tumor and normal adjacent samples of 30 BC patients. The LINC01279 co-expressed gene module was discovered using weighted gene correlation network analysis (WGCNA) on the relevant dataset. The top ten hub genes were determined using gene ontology (GO) functional enrichments on the co-expressed gene module. The results of the bioinformatics study showed an increase in LINC01279 expression levels (log2FC = 3.228749561, adj.P.Val = 1.69E - 12) in tumor samples compared to normal marginal tissue. Q-PCR results also showed a significant increase in LINC01279 expression (P-value = 0.0005) in tumor samples. WGCNA analysis identified that the black module is the LINC01279 co-expressed module, and functional annotation analysis of black module genes enriched in significant cancer-related pathways and processes, including cell growth and/or maintenance, regulation of immune response, regulation of cell proliferation, and epithelial-to-mesenchymal transition (EMT). Regarding the real-time PCR results, the analysis of expression patterns has illuminated a distinct association between the heightened expression levels of LINC01279, and the stages of cancer progression as well as the metastatic potential of tumors. However, intriguingly, our observations have failed to reveal any statistically significant correlations between the relative expression of LINC01279 and tumor grade classification, or the presence of ER, PR, and HER2 biomarkers. The present study could provide a new perspective on the molecular regulatory. Processes associated with BC pathogenic mechanisms are linked to the LINC01279, although further research is needed on the possible role of this lncRNA in BC.

Indexed as

Biomarkers, TumorBreast NeoplasmsGene Expression Regulation, NeoplasticRNA, Long NoncodingComputational BiologyComputer SimulationFemaleGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansMiddle AgedBiomarkers, TumorRNA, Long NoncodingBioinformaticsBreast cancerHub geneLINC01279LncRNAMicroarrayQ-PCRWGCNA

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.