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Comprehensive analysis of lncRNA-mRNA co-expression patterns identifies immune-associated lncRNA biomarkers in ovarian cancer malignant progression

Ovarian cancer (OV) is the most common and lethal gynecological tumor with a poor prognosis for women; however, the regulatory roles of the long non-coding RNAs (lncRNAs) in ovarian malignant progression are insufficiently understood. Here, we investigated the expression patterns of lncRNAs and mRNA...

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Detalles Bibliográficos
Autores principales: Guo, Qiuyan, Cheng, Yan, Liang, Tian, He, Yanan, Ren, Chengcheng, Sun, Liyuan, Zhang, Guangmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668366/
https://www.ncbi.nlm.nih.gov/pubmed/26631459
http://dx.doi.org/10.1038/srep17683
Descripción
Sumario:Ovarian cancer (OV) is the most common and lethal gynecological tumor with a poor prognosis for women; however, the regulatory roles of the long non-coding RNAs (lncRNAs) in ovarian malignant progression are insufficiently understood. Here, we investigated the expression patterns of lncRNAs and mRNAs in the high-throughput molecular profiles of 399 OV patients and constructed a functional lncRNA-mRNA co-expression network across OV malignant progression. We found that two protective lncRNAs, RP11-284N8.3.1 and AC104699.1.1, were not only differentially expressed throughout the progression of malignant OV but were also independently predictive of the survival of patients with different OV stages. A functional analysis of the two lncRNAs predicted their roles in immune system activation and other anti-tumor processes in the OV microenvironment. Integrating these two lncRNAs into an OV risk model was able to significantly stratify patients into different risk groups. Overall, our analysis effectively provides insights into the lncRNA association with malignant OV progression. The two-lncRNA signature is a candidate biomarker for the prognosis of patients with OV and may enable a more accurate prediction of survival.