Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer.,Laboratory Investigation

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Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer.
Laboratory Investigation ( IF 5.1 ) Pub Date : 2020-03-06 , DOI: 10.1038/s41374-020-0413-8
Evelien Schaafsma ₁ , Yanding Zhao ₁ , Yue Wang ₁ , Frederick S Varn ₁ , Kenneth Zhu ₁ , Huan Yang ₂ , Chao Cheng _{1,

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Affiliation

Developing prognostic biomarkers for specific cancer types that accurately predict patient survival is increasingly important in clinical research and practice. Despite the enormous potential of prognostic signatures, proposed models have found limited implementations in routine clinical practice. Herein, we propose a generic, RNA sequencing platform independent, statistical framework named whole transcriptome signature for prognostic prediction to generate prognostic gene signatures. Using ovarian cancer and lung adenocarcinoma as examples, we provide evidence that our prognostic signatures overperform previous reported signatures, capture prognostic features not explained by clinical variables, and expose biologically relevant prognostic pathways, including those involved in the immune system and cell cycle. Our approach demonstrates a robust method for developing prognostic gene expression signatures. In conclusion, our statistical framework can be generally applied to all cancer types for prognostic prediction and might be extended to other human diseases. The proposed method is implemented as an R package (PanCancerSig) and is freely available on GitHub (https://github.com/Cheng-Lab-GitHub/PanCancer_Signature).

中文翻译：

用于预后预测的全转录组特征 (WTSPP)：全转录组特征在癌症预后预测中的应用。

开发针对特定癌症类型的预后生物标志物以准确预测患者的生存期在临床研究和实践中越来越重要。尽管预后特征具有巨大潜力，但所提出的模型在常规临床实践中的应用有限。在此，我们提出了一个通用的、独立于 RNA 测序平台的统计框架，命名为用于预后预测的全转录组特征，以生成预后基因特征。以卵巢癌和肺腺癌为例，我们提供的证据表明我们的预后特征优于之前报道的特征，捕捉了临床变量无法解释的预后特征，并揭示了生物学相关的预后通路，包括那些与免疫系统和细胞周期有关的通路。我们的方法展示了一种用于开发预后基因表达特征的稳健方法。总之，我们的统计框架可以普遍应用于所有癌症类型的预后预测，并可能扩展到其他人类疾病。所提出的方法作为 R 包 (PanCancerSig) 实现，可在 GitHub (https://github.com/Cheng-Lab-GitHub/PanCancer_Signature) 上免费获得。

更新日期：2020-04-24

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