Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/8278
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dc.contributor.authorLi, Hailien_US
dc.contributor.authorZheng, Xubinen_US
dc.contributor.authorZhang, Ningen_US
dc.contributor.authorGao, Jingen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorWong, Man-Honen_US
dc.contributor.authorYang, Shuen_US
dc.contributor.authorLiu, Yakunen_US
dc.contributor.authorDong, Mingen_US
dc.contributor.authorBai, Huiminen_US
dc.contributor.authorYe, Xiufengen_US
dc.contributor.authorCheng, Lixinen_US
dc.date.accessioned2023-10-17T04:36:49Z-
dc.date.available2023-10-17T04:36:49Z-
dc.date.issued2022-
dc.identifier.citationbioRxiv, 2022.en_US
dc.identifier.issn2692-8205-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/8278-
dc.description.abstractThe non-coding RNA (ncRNA) regulation apprears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). Hence, we profiled a whole transcriptome of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). We identified the mRNA–ncRNA–mRNA motifs in the ceRNA network named the non-coding RNA’s competing endogenous gene pairs (ceGPs), through the denoised individualized pair analysis of gene expression (deiPAGE) proposed in this study. 18 cricRNA’s ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. It was found that the index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression.en_US
dc.language.isoenen_US
dc.relation.ispartofbioRxiven_US
dc.titleCircular RNA’s competing endogenous gene pair as motif in serous ovarian canceren_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doihttps://doi.org/10.1101/2022.04.04.486923-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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