Scholarlink

Abstract <p>Prostate cancer exhibits complex transcriptional heterogeneity that underlies disease progression and therapeutic resistance. We developed an integrative omics-to-therapy pipeline to identify actionable biomarkers and screen drug candidates using a combination of single-cell transcriptomics, cheminformatics, and machine learning. Single-cell RNA sequencing (scRNA-seq) of prostate cancer tissues enabled fine-grained clustering and differential gene expression analysis across malignant and non-malignant cell populations. ASPM (Abnormal Spindle Microtubule Assembly) emerged as a statistically significant and cell-type-enriched biomarker associated with proliferative tumor phenotypes. We curated a library of drug-like molecules and developed Prosta-Omics, a supervised machine learning tool trained to predict Topological Polar Surface Area (TPSA) from molecular SMILES using a Random Forest model. High-ranking compounds as predicted by Prosta-Omics were docked against a 3D model of ASPM revealing multiple candidates with strong binding affinities and key interaction motifs. The drugs with higher docking score were subjected into molecular dynamics (MD) simulation and ADMET analysis. This integrative strategy highlights ASPM as a viable therapeutic target and introduces Prosta-Omics as a robust predictive platform bridging single-cell analytics with AI-driven drug discovery for precision oncology in prostate cancer.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prosta-omics: machine learning-driven TPSA prediction and molecular modeling of ASPM-inhibitors for prostate cancer treatment

  • 期刊论文

  • 发表日期：

    2026年3月4日

查看全文
  • Kashif Iqbal Sahibzada,
  • Shumaila Shahid,
  • Saba Shahid,
  • Andleeb Batool,
  • Yuansen Hu,
  • Dong-Qing Wei

摘要

Abstract

Prostate cancer exhibits complex transcriptional heterogeneity that underlies disease progression and therapeutic resistance. We developed an integrative omics-to-therapy pipeline to identify actionable biomarkers and screen drug candidates using a combination of single-cell transcriptomics, cheminformatics, and machine learning. Single-cell RNA sequencing (scRNA-seq) of prostate cancer tissues enabled fine-grained clustering and differential gene expression analysis across malignant and non-malignant cell populations. ASPM (Abnormal Spindle Microtubule Assembly) emerged as a statistically significant and cell-type-enriched biomarker associated with proliferative tumor phenotypes. We curated a library of drug-like molecules and developed Prosta-Omics, a supervised machine learning tool trained to predict Topological Polar Surface Area (TPSA) from molecular SMILES using a Random Forest model. High-ranking compounds as predicted by Prosta-Omics were docked against a 3D model of ASPM revealing multiple candidates with strong binding affinities and key interaction motifs. The drugs with higher docking score were subjected into molecular dynamics (MD) simulation and ADMET analysis. This integrative strategy highlights ASPM as a viable therapeutic target and introduces Prosta-Omics as a robust predictive platform bridging single-cell analytics with AI-driven drug discovery for precision oncology in prostate cancer.

Graphical abstract