Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approach...Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.展开更多
基金funded by the National Key Research and Development Program of China(2022YFC3502000)the National Natural Science Foundation of China(82141203,82274172,82430119)+4 种基金Shanghai Municipal Science and Technology Major Project(ZD2021CY001)Key project at central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(2060302)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTDD-202004)the support of Wild Goose Array Project,Zhengzhou Center of PLAJLSF,the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23CGA45,Saisai Tian)Tianjin Health Research Project(TJWJ2024QN100).
文摘Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.
