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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Precise diagnosis of three top cancers using dbGaP data.

Xu-Qing Liu¹, Xin-Sheng Liu², Jian-Ying Rong³

¹Huaiyin Institute of Technology, Huaian, 223003, China. liuxuqing688@163.com.

Scientific Reports

|January 13, 2021

Summary

Precise diagnosis of complex diseases like cancer is now possible using specific single nucleotide polymorphism (SNP) genotypes. This study identifies a limited set of SNPs for accurate disease prediction, bypassing traditional methods.

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) linked to complex diseases.
Despite progress, precise diagnostic models for complex diseases using SNP data remain elusive.
Existing models often lack the accuracy required for reliable clinical application.

Purpose of the Study:

To investigate the potential of utilizing a specific subset of SNP genotypes for accurate disease prediction.
To develop precise diagnostic models for common complex diseases, including lung, breast, and prostate cancers.
To explore the sufficiency of SNP genotype information for disease diagnosis, independent of phenotypic features.

Main Methods:

Analysis of five dbGaP studies involving large datasets of SNP genotypes.
Application of leave-one-out and 10-fold cross-validation techniques for model assessment.
Development of predictive models using a targeted panel of 240-370 SNPs.

Main Results:

Accurate prediction of lung, breast, and prostate cancers with up to 99% accuracy using 240-370 SNPs.
Validation of Dr. Mitchell H. Gail's hypothesis regarding the number of SNPs needed for breast cancer risk forecasting.
Demonstration that SNP genotypes alone contain substantial information for disease diagnosis.

Conclusions:

SNP genotypes hold significant potential for the precise diagnosis of complex diseases.
A targeted set of SNPs can effectively predict major cancers, offering a novel diagnostic approach.
Future research can leverage SNP data for early and accurate disease detection, potentially reducing reliance on phenotypic data.