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The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.<\/jats:p>","DOI":"10.3390\/s23031432","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:01:18Z","timestamp":1675044078000},"page":"1432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue"],"prefix":"10.3390","volume":"23","author":[{"given":"Tian","family":"Mou","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China"}]},{"given":"Jianwen","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China"}]},{"given":"Trung Nghia","family":"Vu","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-1611","authenticated-orcid":false,"given":"Mu","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China"}]},{"given":"Yi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S37","DOI":"10.1074\/mcp.RA118.001232","article-title":"Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer","volume":"18","author":"Zhan","year":"2019","journal-title":"Mol. 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