Zhong Tingyan, Wu Mengyun, Ma Shuangge
SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China.
Cancers (Basel). 2019 Mar 13;11(3):361. doi: 10.3390/cancers11030361.
Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the "connectedness" between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling.
癌症预后至关重要，人们已经进行了广泛的研究来寻找具有预后能力的生物标志物。最近的研究表明，组学特征和组织病理学影像特征都具有预后能力。也有研究探索将这两种测量方法结合用于预后建模。然而，缺乏研究严格检验组学测量在组织病理学影像特征条件下是否具有独立的预后能力，反之亦然。在本文中，我们采用了严格的统计检验框架，检验个体基因表达测量在高维影像特征条件下是否能改善预后建模，并对基因表达和影像特征的作用进行了反向的平行分析。在对癌症基因组图谱（TCGA）肺腺癌和肝细胞癌数据的分析中，发现多个个体基因在影像特征条件下可显著改善预后建模；然而，个体影像特征在基因表达条件下仅具有有限的预后能力。作为首批检验独立预后能力的研究之一，本研究可能有助于更好地理解组学特征与组织病理学影像特征之间的“关联性”，并为癌症建模中的数据整合提供重要见解。
