Differentially expressed AC077690.1, AL049874.3 and AP001037.1 lncRNAs in prostate cancer
DOI:
https://doi.org/10.2298/ABS221025034LKeywords:
Prostate cancer, Long non-coding RNA, Biomarker, Signaling pathway, Candidate therapeutic targetsAbstract
Paper description:
- Three long non-coding RNAs (lncRNAs) in prostate cancer were filtered out by high-throughput sequencing data and bioinformatics analysis as follows: AC077690.1, AL049874.3 and AP001037.1.
- A lncRNA regulatory network was constructed and differentially expressed mRNA interactions were used to predict the function of the selected lncRNAs.
- Functional enrichment analysis and PCR verification of these three lncRNAs revealed that they were closely associated with the PI3K-Akt-mTOR and forkhead box protein (FOXO) signaling pathways involved in prostate cancer.
- These lncRNAs are potential therapeutic targets in prostate cancer.
Abstract: Prostate cancer (PCa) is a common type of cancer worldwide. The incidence of PCa increases with age and it is the most common malignant tumor in men. Tissue biopsy and the serum prostate-specific antigen are still the standards for diagnosing suspected PCa. Long non-coding RNA (lncRNA) contributes to the progression of PCa by recruiting transcriptional regulators. We utilized high-throughput sequencing data and bioinformatics analysis to identify specifically expressed lncRNAs in PCa and filtered out three specific lncRNAs for further analysis: AC077690.1, AL049874.3 and AP001037.1. We constructed a lncRNA regulatory network and used differentially expressed mRNA interactions to predict the functions of the selected lncRNAs. Functional enrichment analysis and PCR verification of these three lncRNAs revealed that they were closely related to well-known PI3K-Akt-mTOR and the forkhead box protein (FOXO) signaling pathways involved in PCa. By understanding the related interactions between these molecules and signaling pathways, the lncRNAs could be potential candidates for therapeutic targets in PCa.
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Copyright (c) 2022 Li Hexin, Tang Xiaokun, Sun Gaoyuan, Xu Siyuan, Wang Luyao, Zhang Yaqun, Su Fei, Zhang Lili, Zhang Wei
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