Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification

Abstract

Abstract Background Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy. Materials and methods Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers. Results The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples. Conclusions These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.