Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/8279
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dc.contributor.authorCheng, Lixinen_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.authorKang, Linen_US
dc.contributor.authorLi, Hailien_US
dc.date.accessioned2023-10-17T06:05:58Z-
dc.date.available2023-10-17T06:05:58Z-
dc.date.issued2022-
dc.identifier.citationbioRxiv, 2022.en_US
dc.identifier.issn2692-8205-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/8279-
dc.description.abstractUnderstanding the regulatory mechanisms in serous ovarian carcinoma (SOC) is critical for its diagnosis and targeted therapy. However, some critical motifs in the competing endogenous RNA (ceRNA) network in SOC were still undiscovered. We profiled a whole transcriptome of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, lncRNAs, and circRNAs. We hypothesized the noncoding RNA’s competing endogenous gene pairs (ceGPs) relationship for the mRNA–ncRNA–mRNA motifs in the ceRNA network. Then, we proposed the denoised individualized pair analysis of gene expression (deiPAGE) to identify mRNA–ncRNA–mRNA motifs from integrated multi-cohorts. 18 cricRNA’s ceGPs (cceGPs) were identified and fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. 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.titleNoncoding 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-
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