{"title":"探索乳腺肿瘤和邻近正常组织蛋白相互作用网络中的脆弱构建块","authors":"Swapnil Kumar, Avantika Agrawal, Vaibhav Vindal","doi":"10.1016/j.compbiolchem.2025.108647","DOIUrl":null,"url":null,"abstract":"<div><div>Tumor-adjacent normal tissues (TANTs) histologically and morphologically look normal and are commonly used as a control in patient-based cancer studies. Previous studies have revealed that TANTs present a unique transitional state between healthy normal and tumor tissues. However, little or no knowledge exists about the landscape of protein-protein interactions (PPIs) in TANTs and how they differ from the tumor tissues. Herein, we integrated the PPI data mapped onto the differentially expressed genes in TANTs and tumor tissues compared to healthy normal tissues. This led to the reconstruction of six tissue-specific PPI networks, including TANTs and breast tumor tissues (viz., Luminal A, Luminal B, Her2, Basal, and Normal-Like). First, these PPI networks were analyzed using network influence and vulnerability analyses from the NetVA R package. Consequently, it revealed 134 vulnerable proteins (VPs), 21 vulnerable protein pairs (VPPs), and 94 influential proteins (IPs) that were present across all six tissue networks. Further, we identified a set of 34 proteins as common hubs and another set of seven proteins as common bottlenecks across all six tissue networks. Next, all VPs, IPs, hubs, and bottlenecks were investigated for their associations with various diseases, including cancers, and found sharing a significant number of well-known cancer-associated proteins, viz., AR, BRCA1, ERBB2, FN1, FOXA1, JUN, MKI67, and NRAS. Thus, by applying network vulnerability, influence, and gene-disease association-based analyses, we suggest lists of known and candidate proteins along with their associated protein complexes potentially involved in breast cancer tumorigenesis and present across TANTs and different breast cancer subtypes.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108647"},"PeriodicalIF":3.1000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring vulnerable building blocks in protein-protein interaction networks of breast tumor and adjacent normal tissues\",\"authors\":\"Swapnil Kumar, Avantika Agrawal, Vaibhav Vindal\",\"doi\":\"10.1016/j.compbiolchem.2025.108647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Tumor-adjacent normal tissues (TANTs) histologically and morphologically look normal and are commonly used as a control in patient-based cancer studies. Previous studies have revealed that TANTs present a unique transitional state between healthy normal and tumor tissues. However, little or no knowledge exists about the landscape of protein-protein interactions (PPIs) in TANTs and how they differ from the tumor tissues. Herein, we integrated the PPI data mapped onto the differentially expressed genes in TANTs and tumor tissues compared to healthy normal tissues. This led to the reconstruction of six tissue-specific PPI networks, including TANTs and breast tumor tissues (viz., Luminal A, Luminal B, Her2, Basal, and Normal-Like). First, these PPI networks were analyzed using network influence and vulnerability analyses from the NetVA R package. Consequently, it revealed 134 vulnerable proteins (VPs), 21 vulnerable protein pairs (VPPs), and 94 influential proteins (IPs) that were present across all six tissue networks. Further, we identified a set of 34 proteins as common hubs and another set of seven proteins as common bottlenecks across all six tissue networks. Next, all VPs, IPs, hubs, and bottlenecks were investigated for their associations with various diseases, including cancers, and found sharing a significant number of well-known cancer-associated proteins, viz., AR, BRCA1, ERBB2, FN1, FOXA1, JUN, MKI67, and NRAS. Thus, by applying network vulnerability, influence, and gene-disease association-based analyses, we suggest lists of known and candidate proteins along with their associated protein complexes potentially involved in breast cancer tumorigenesis and present across TANTs and different breast cancer subtypes.</div></div>\",\"PeriodicalId\":10616,\"journal\":{\"name\":\"Computational Biology and Chemistry\",\"volume\":\"120 \",\"pages\":\"Article 108647\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Biology and Chemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1476927125003081\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927125003081","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
肿瘤邻近正常组织(ants)在组织学和形态学上看起来正常,通常用作基于患者的癌症研究的对照。先前的研究表明,在健康正常组织和肿瘤组织之间,蚂蚁呈现出一种独特的过渡状态。然而,关于蛋白质-蛋白质相互作用(PPIs)的情况以及它们与肿瘤组织的区别知之甚少。在此,我们将PPI数据整合到与健康正常组织相比,在肿瘤组织和肿瘤组织中差异表达的基因上。这导致了六个组织特异性PPI网络的重建,包括ants和乳腺肿瘤组织(即,Luminal A, Luminal B, Her2, Basal和Normal-Like)。首先,使用NetVA R软件包的网络影响和漏洞分析对这些PPI网络进行了分析。因此,它揭示了134个易损蛋白(VPs), 21个易损蛋白对(vpp)和94个影响蛋白(IPs)存在于所有六个组织网络中。此外,我们确定了一组34个蛋白质作为共同枢纽,另一组7个蛋白质作为所有6个组织网络的共同瓶颈。接下来,研究了所有的副总裁、IPs、枢纽和瓶颈与各种疾病(包括癌症)的关联,发现它们共享大量已知的癌症相关蛋白,即AR、BRCA1、ERBB2、FN1、FOXA1、JUN、MKI67和NRAS。因此,通过应用网络脆弱性、影响和基于基因疾病关联的分析,我们提出了已知和候选蛋白及其相关蛋白复合物的列表,这些蛋白及其相关蛋白复合物可能参与乳腺癌肿瘤的发生,并存在于ant和不同的乳腺癌亚型中。
Exploring vulnerable building blocks in protein-protein interaction networks of breast tumor and adjacent normal tissues
Tumor-adjacent normal tissues (TANTs) histologically and morphologically look normal and are commonly used as a control in patient-based cancer studies. Previous studies have revealed that TANTs present a unique transitional state between healthy normal and tumor tissues. However, little or no knowledge exists about the landscape of protein-protein interactions (PPIs) in TANTs and how they differ from the tumor tissues. Herein, we integrated the PPI data mapped onto the differentially expressed genes in TANTs and tumor tissues compared to healthy normal tissues. This led to the reconstruction of six tissue-specific PPI networks, including TANTs and breast tumor tissues (viz., Luminal A, Luminal B, Her2, Basal, and Normal-Like). First, these PPI networks were analyzed using network influence and vulnerability analyses from the NetVA R package. Consequently, it revealed 134 vulnerable proteins (VPs), 21 vulnerable protein pairs (VPPs), and 94 influential proteins (IPs) that were present across all six tissue networks. Further, we identified a set of 34 proteins as common hubs and another set of seven proteins as common bottlenecks across all six tissue networks. Next, all VPs, IPs, hubs, and bottlenecks were investigated for their associations with various diseases, including cancers, and found sharing a significant number of well-known cancer-associated proteins, viz., AR, BRCA1, ERBB2, FN1, FOXA1, JUN, MKI67, and NRAS. Thus, by applying network vulnerability, influence, and gene-disease association-based analyses, we suggest lists of known and candidate proteins along with their associated protein complexes potentially involved in breast cancer tumorigenesis and present across TANTs and different breast cancer subtypes.
期刊介绍:
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.