Evidence map›Paper›PMID 36772473›Full record

ArticleSensors (Basel, Switzerland)2023

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue.

Tian Mou, Jianwen Liang, Trung Nghia Vu, Mu Tian, Yi Gao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tian MouSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China.
Jianwen LiangSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China.
Trung Nghia VuDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.
Mu TianSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China.ORCID 0000-0002-8232-1611
Yi GaoSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China.

Funding

Department of Education of Guangdong Province 2017KZDXM072Key Technology Development Program of Shenzhen JSGG20210713091811036National Natural Science Foundation of China 61601302National Natural Science Foundation of China 82202246Science, Technology and Innovation Commission of Shenzhen Municipality RCBS20210609103155057Shenzhen Key Laboratory Foundation ZDSYS20200811143757022Shenzhen Peacock Plan KQTD2016053112051497the Key-Area Research and Development Program of Guangdong Province 2021B0101420005
6 · The paper itself

Abstract

The expression abundance of transcripts in nondiseased breast tissue varies among individuals. 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.

Indexed as

Breast NeoplasmsTranscriptomeFemaleGenotypeHumansPhenotypeRNASequence Analysis, RNARNAbioinformaticsgene expressionimaging genomicsnondiseased breast tissuepathological image features

Identifiers

PMID36772473
PMCPMC9921444

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.