ArticleData in brief2025
SNP-array profiling data from breast cancer patients and healthy women's blood DNA samples.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is commonly acknowledged as the primary type of cancer on a global scale, exerting a substantial influence on death rates, particularly in developing countries. The aforementioned discovery provides evidence in favor of the concept that genetic factors may contribute to the onset of breast cancer. This paper presents the unprocessed idat data containing single nucleotide polymorphisms (SNPs) acquired from breast cancer patients and a control group comprising of healthy women. The DNA was obtained from stored blood samples that were collected from a total of 48 female patients diagnosed with breast cancer at Cipto Mangunkusumo National Hospital Jakarta and Dharmais National Cancer Center Hospital Jakarta. Additionally, 24 healthy women were included as control subjects. Subsequently, the DNA samples were subjected to hybridization onto Infinium Asian Screening Array (ASA)'s beadchips. The chip was then subjected to fluorescence intensity measurements using an iScan machine manufactured by Illumina. The data output is produced in the form of a .idat file for each sample. Subsequently, further quality control measures and population stratification analysis were conducted using PLINK (v1.9). After the conclusion of the quality control procedure, 72 individuals and a dataset consisting of 424,285 genetic variants were selected for further analysis. The idat raw data files have been added to the Gene Expression Omnibus (GEO) with accession number: GSE245794 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE245794)
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