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鑒定預測卵巢癌生存的潛在預后TF相關lncRNAs

二維碼

發表時間：2024-09-10 17:06

2018年2月，哈爾濱醫科大學**附屬醫院；哈爾濱醫科大學生物信息科學與技術學院(The First Affiliated Hospital, Harbin Medical University, Harbin,China；College of Bioinformatics Science and Technology, Harbin Medical University,Harbin, China) Guangmei Zhang老師研究團隊在《Journal of Cellular and Molecular Medicine》上發表論文：

“Identification of potential prognostic TF‐associated lncRNAs for predicting survival in ovarian cancer”

“鑒定預測卵巢癌生存的潛在預后TF相關lncRNAs”

Abstract：

The pathophysiology of ovarian cancer (OV) is complex and depends on multiple biological processes and pathways. Therefore, there is an urgent need to identify reliable prognostic biomarkers for predicting clinical outcomes and helping personalize treatment of OV. A long non-coding RNA (lncRNA)-based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in other independent cohorts from Gene Expression Omnibus. Time-dependent receiver operating characteristic curves were used to evaluate the survival prediction performance in comparison with other clinical and molecular variables. Our results revealed that the top-ranked TF-associating lncRNAs were significantly associated with overall survival, progression-free survival and disease-free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR2A-associating lncRNAs. In comparison, the signature of POLR2A-associating lncRNAs was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR2A-associating lncRNAs may be involved in known biological processes and pathways of OV. Our findings revealed that the lncRNA-based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.

摘要：

卵巢癌(OV)的病理生理是復雜的，依賴于多種生物過程和途徑。因此，迫切需要確定可靠的預后生物標志物來預測臨床結果并幫助個體化治療OV。基于癌癥基因組圖譜的OV數據集，構建了基于長鏈非編碼RNA (lncRNA)的風險評分模型來推斷轉錄因子(tf)的預后療效。風險評分模型在Gene Expression Omnibus的其他獨立隊列中進一步驗證。與其他臨床和分子變量相比，使用時間相關的受試者工作特征曲線來評估生存預測性能。科研人員的研究結果顯示，排名靠前的tf相關lncrna與總生存期、無進展生存期和無病生存期顯著相關。根據臨床變量進行分層分析顯示polr2a相關lncrna對預后的獨立性。相比之下，與polr2a相關的lncRNAs的特征比現有的臨床和分子特征更敏感和特異性。功能和實驗分析表明，polr2a相關lncrna可能參與OV的已知生物學過程和途徑。科研人員的研究結果表明，基于lncrna的風險評分模型可以為OV預后分層和發現治療性生物標志物提供有用的信息。