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Background <p>Metastatic breast cancer (MBC) is the most significant clinical challenge in patient care; hence, we need new personalized therapeutic targets and innovative inhibitors. Genomic, proteomic, and structural analysis can be a pivotal tool for faster and more cost-efficient lead discovery.</p> Methods <p>The study employed gene expression profiling (Microarray, PCR, Bulk RNA-Seq) and computer-aided drug design, including structural modeling, virtual screening, docking, pharmacophore modeling, ADMET analysis, and NMA dynamics. A large compound library was screened to identify stable, high-affinity protein-ligand interactions targeting key proteins in metastatic breast cancer.</p> Results <p>Across primary breast tumors and metastatic organs (lung, liver, bone, and brain), we identified significantly altered genes after filtering a large set of redundant DEG entries. Scored Network analysis showed 8 gene modules linked to metastasis, validated through pathway databases. Key genes including AR, AKT1, UBC, CDH1, SMAD3, ROR1, and ROR2 were associated with chemotherapy resistance and poor prognosis. Structural and drug interaction studies identified therapeutically targetable genes, with candidate compounds showing promising pharmacokinetics and safety. These findings offer insights into metastatic breast cancer and potential paths for improved diagnosis and treatment.</p> Conclusion <p>The computational results reveal that kinases like AKT1, ROR1, and ROR2, and non-kinase targets like UBC, RPS6, CDH1, AR, and SMAD3, are the most promising candidates. All screened compounds showed varying strong interacting profiles, with Ellagic Acid and Erioflorin standing out as potent candidates against critical targets in metastatic breast cancer.</p>

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Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月10日

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  • Zarlish Attique,
  • Hafiz Muhammad Faraz Azhar,
  • Sajid Khan

摘要

Background

Metastatic breast cancer (MBC) is the most significant clinical challenge in patient care; hence, we need new personalized therapeutic targets and innovative inhibitors. Genomic, proteomic, and structural analysis can be a pivotal tool for faster and more cost-efficient lead discovery.

Methods

The study employed gene expression profiling (Microarray, PCR, Bulk RNA-Seq) and computer-aided drug design, including structural modeling, virtual screening, docking, pharmacophore modeling, ADMET analysis, and NMA dynamics. A large compound library was screened to identify stable, high-affinity protein-ligand interactions targeting key proteins in metastatic breast cancer.

Results

Across primary breast tumors and metastatic organs (lung, liver, bone, and brain), we identified significantly altered genes after filtering a large set of redundant DEG entries. Scored Network analysis showed 8 gene modules linked to metastasis, validated through pathway databases. Key genes including AR, AKT1, UBC, CDH1, SMAD3, ROR1, and ROR2 were associated with chemotherapy resistance and poor prognosis. Structural and drug interaction studies identified therapeutically targetable genes, with candidate compounds showing promising pharmacokinetics and safety. These findings offer insights into metastatic breast cancer and potential paths for improved diagnosis and treatment.

Conclusion

The computational results reveal that kinases like AKT1, ROR1, and ROR2, and non-kinase targets like UBC, RPS6, CDH1, AR, and SMAD3, are the most promising candidates. All screened compounds showed varying strong interacting profiles, with Ellagic Acid and Erioflorin standing out as potent candidates against critical targets in metastatic breast cancer.