Manjang Kalifa, Yli-Harja Olli, Dehmer Matthias, Emmert-Streib Frank
Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Computational Systems Biology, Tampere University, Tampere, Finland.
Front Genet. 2021 Jul 22;12:649429. doi: 10.3389/fgene.2021.649429. eCollection 2021.
High-throughput technologies do not only provide novel means for basic biological research but also for clinical applications in hospitals. For instance, the usage of gene expression profiles as prognostic biomarkers for predicting cancer progression has found widespread interest. Aside from predicting the progression of patients, it is generally believed that such prognostic biomarkers also provide valuable information about disease mechanisms and the underlying molecular processes that are causal for a disorder. However, the latter assumption has been challenged. In this paper, we study this problem for prostate cancer. Specifically, we investigate a large number of previously published prognostic signatures of prostate cancer based on gene expression profiles and show that none of these can provide unique information about the underlying disease etiology of prostate cancer. Hence, our analysis reveals that none of the studied signatures has a sensible biological meaning. Overall, this shows that all studied prognostic signatures are merely black-box models allowing sensible predictions of prostate cancer outcome but are not capable of providing causal explanations to enhance the understanding of prostate cancer.
高通量技术不仅为基础生物学研究提供了新方法，也为医院的临床应用提供了新途径。例如，将基因表达谱用作预测癌症进展的预后生物标志物已引起广泛关注。除了预测患者的病情进展外，人们普遍认为此类预后生物标志物还能提供有关疾病机制以及导致疾病的潜在分子过程的有价值信息。然而，后一种假设受到了挑战。在本文中，我们针对前列腺癌研究了这个问题。具体而言，我们研究了大量先前发表的基于基因表达谱的前列腺癌预后特征，结果表明这些特征均无法提供有关前列腺癌潜在疾病病因的独特信息。因此，我们的分析表明，所研究的特征均没有合理的生物学意义。总体而言，这表明所有研究的预后特征都只是黑箱模型，能够合理预测前列腺癌的结果，但无法提供因果解释以增进对前列腺癌的理解。
