دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer
المؤلفون: Tingyan Zhong, Mengyun Wu, Shuangge Ma
المصدر: Cancers; Volume 11; Issue 3; Pages: 361
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2019
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: cancer prognosis, independent prognostic power, omics profiles, histopathological imaging features
الوصف: 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.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
العلاقة: https://dx.doi.org/10.3390/cancers11030361Test
DOI: 10.3390/cancers11030361
الإتاحة: https://doi.org/10.3390/cancers11030361Test
حقوق: https://creativecommons.org/licenses/by/4.0Test/
رقم الانضمام: edsbas.63596A3A
قاعدة البيانات: BASE