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Cancers 2019, 11(3), 361; https://doi.org/10.3390/cancers11030361

Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer

1
SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
2
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
3
Department of Biostatistics, Yale University, New Haven, CT 06520, USA
*
Authors to whom correspondence should be addressed.
Received: 12 February 2019 / Revised: 4 March 2019 / Accepted: 10 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Abstract

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. View Full-Text
Keywords: cancer prognosis; independent prognostic power; omics profiles; histopathological imaging features cancer prognosis; independent prognostic power; omics profiles; histopathological imaging features
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Zhong, T.; Wu, M.; Ma, S. Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer. Cancers 2019, 11, 361.

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