The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment options available.To identify specific prognostic factors for ER-negative breast cancer,this study included 705,729 and 1034 br...The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment options available.To identify specific prognostic factors for ER-negative breast cancer,this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance,Epidemiology,and End Results(SEER)and The Cancer Genome Atlas(TCGA)databases,respectively.To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ERpositive breast cancer patients,we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network.Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer.Two promising kinase-substrate edge features,CSNK1A1-NFATC3 and SRC-OCLN,were identified for more accurate prognostic prediction in ERnegative breast cancer patients.展开更多
Background:Arsenic has a broad anti-cancer ability against hematologic malignancies and solid tumors.To systematically understand the biological functions of arsenic,we need to identify arsenic-binding proteins in hum...Background:Arsenic has a broad anti-cancer ability against hematologic malignancies and solid tumors.To systematically understand the biological functions of arsenic,we need to identify arsenic-binding proteins in human cells.However,due to lack of effective theoretical tools and experimental methods,only a few arsenic-binding proteins have been identified.Methods:Based on the crystal structure of ArsM,we generated a single mutation free energy profile for arsenic binding using free energy perturbation methods.Multiple validations provide an indication that our computational model has the ability to predict arsenic-binding proteins with desirable accuracy.We subsequently apply this computational model to scan the entire human genome to identify all the potential arsenic-binding proteins.Results:The computationally predicted arsenic-binding proteins show a wide range of biological functions,especially in the signaling transduction pathways.In the signaling transduction pathways,arsenic directly binds to the key factors(e.g.,Notch receptors,Notch ligands,Wnt family proteins,TGF-beta,and their interacting proteins)and results in significant inhibitions on their enzymatic activities,further having a crucial impact on the related signaling pathways.Conclusions:Arsenic has a significant impact on signaling transduction in cells.Arsenic binding to proteins can lead to dysfunctions of the target proteins,having crucial impacts on both signaling pathway and gene transcription.We hope that the computationally predicted arsenic-binding proteins and the functional analysis can provide a novel insight into the biological functions of arsenic,revealing a mechanism for the broad anti-cancer of arsenic.展开更多
基金supported by the National Key R&D Program of China(Grant No.2017YFA0505500)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA12010000)+2 种基金the National Program on Key Basic Research Project of China(Grant Nos.2014CBA02000 and 2014CB910500)the National Natural Science Foundation of China(Grant Nos.91029301,30700397,91529303,and 31771476)the support of the SANOFI-SIBS Distinguish Young Scientist Award Scholarship Program。
文摘The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment options available.To identify specific prognostic factors for ER-negative breast cancer,this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance,Epidemiology,and End Results(SEER)and The Cancer Genome Atlas(TCGA)databases,respectively.To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ERpositive breast cancer patients,we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network.Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer.Two promising kinase-substrate edge features,CSNK1A1-NFATC3 and SRC-OCLN,were identified for more accurate prognostic prediction in ERnegative breast cancer patients.
基金This work was supported by the National Key R&D Program of China(Nos.2016YFC0901704 and 2017YFA0505500)National High-Tech R&D Program(863 Program,No.2015AA020105)+2 种基金the National Natural Science Foundation of China(Nos.21377085 and 31770070)MOE New Century Excellent Talents in University(No.NCET-12-0354)SJTU Med-Eng Joint Program(No.YG2016MS33)for financial supports.
文摘Background:Arsenic has a broad anti-cancer ability against hematologic malignancies and solid tumors.To systematically understand the biological functions of arsenic,we need to identify arsenic-binding proteins in human cells.However,due to lack of effective theoretical tools and experimental methods,only a few arsenic-binding proteins have been identified.Methods:Based on the crystal structure of ArsM,we generated a single mutation free energy profile for arsenic binding using free energy perturbation methods.Multiple validations provide an indication that our computational model has the ability to predict arsenic-binding proteins with desirable accuracy.We subsequently apply this computational model to scan the entire human genome to identify all the potential arsenic-binding proteins.Results:The computationally predicted arsenic-binding proteins show a wide range of biological functions,especially in the signaling transduction pathways.In the signaling transduction pathways,arsenic directly binds to the key factors(e.g.,Notch receptors,Notch ligands,Wnt family proteins,TGF-beta,and their interacting proteins)and results in significant inhibitions on their enzymatic activities,further having a crucial impact on the related signaling pathways.Conclusions:Arsenic has a significant impact on signaling transduction in cells.Arsenic binding to proteins can lead to dysfunctions of the target proteins,having crucial impacts on both signaling pathway and gene transcription.We hope that the computationally predicted arsenic-binding proteins and the functional analysis can provide a novel insight into the biological functions of arsenic,revealing a mechanism for the broad anti-cancer of arsenic.