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Breast cancer gene expression datasets do not reflect the disease at the population level.

ABSTRACT: Publicly available tumor gene expression datasets are widely reanalyzed, but it is unclear how representative they are of clinical populations. Estimations of molecular subtype classification and prognostic gene signatures were calculated for 16,130 patients from 70 breast cancer datasets. Collated patient demographics and clinical characteristics were sparse for many studies. Considerable variations were observed in dataset size, patient/tumor characteristics, and molecular composition. Results were compared with Surveillance, Epidemiology, and End Results Program (SEER) figures. The proportion of basal subtype tumors ranged from 4 to 59%. Date of diagnosis ranged from 1977 to 2013, originating from 20 countries across five continents although European ancestry dominated. Publicly available breast cancer gene expression datasets are a great resource, but caution is required as they tend to be enriched for high grade, ER-negative tumors from European-ancestry patients. These results emphasize the need to derive more representative and annotated molecular datasets from diverse populations.

SUBMITTER: Xie Y

PROVIDER: S-EPMC7447772 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I-III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Project description:BackgroundTo understand how health care delays may affect breast cancer detection, the authors quantified changes in breast-related preventive and diagnostic care during the coronavirus disease 2019 (COVID-19) pandemic.MethodsEligible women (N = 39,444) were aged ≥18 years and received a screening mammogram, diagnostic mammogram, or breast biopsy between January 1, 2019 and September 30, 2020, at 7 academic and community breast imaging facilities in North Carolina. Changes in the number of mammography or breast biopsy examinations after March 3, 2020 (the first COVID-19 diagnosis in North Carolina) were evaluated and compared with the expected numbers based on trends between January 1, 2019 and March 2, 2020. Changes in the predicted mean monthly number of examinations were estimated using interrupted time series models. Differences in patient characteristics were tested using least squares means regression.ResultsFewer examinations than expected were received after the pandemic's onset. Maximum reductions occurred in March 2020 for screening mammography (-85.1%; 95% CI, -100.0%, -70.0%) and diagnostic mammography (-48.9%; 95% CI, -71.7%, -26.2%) and in May 2020 for biopsies (-40.9%; 95% CI, -57.6%, -24.3%). The deficit decreased gradually, with no significant difference between observed and expected numbers by July 2020 (diagnostic mammography) and August 2020 (screening mammography and biopsy). Several months after the pandemic's onset, women who were receiving care had higher predicted breast cancer risk (screening mammography, P < .001) and more commonly lacked insurance (diagnostic mammography, P < .001; biopsy, P < .001) compared with the prepandemic population.ConclusionsPandemic-associated deficits in the number of breast examinations decreased over time. Utilization differed by breast cancer risk and insurance status, but not by age or race/ethnicity. Long-term studies are needed to clarify the contribution of these trends to breast cancer disparities.

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Breast cancer gene expression datasets do not reflect the disease at the population level.

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