Analysis of Breast Cancer Based on the Dysregulated Network

Huo, Yanhao; Li, Xianbin; Xu, Peng; Bao, Zhenshen; Liu, Wenbin

doi:10.3389/fgene.2022.856075

Cited by 7 publications

(7 citation statements)

References 45 publications

(44 reference statements)

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“…STAT3 is involved in breast cancer progression [63]. E2F1 and EP300 have been reported to be involved in breast cancer development and metastasis [64–66]. These results demonstrate the importance of these TFs and such mutations or alterations can affect gene regulation and thereby contribute to breast cancer development.…”

Section: Resultsmentioning

confidence: 92%

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2023

Preprint

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Understanding breast cancer drug response mechanism can play a crucial role in improving the treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient version of support vector machines (esvm) working as follows. First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to the treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in the dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, results demonstrate superiority and efficiency of esvm achieving high performance results and having more expressed genes in (1) well-established breast cancer cell lines including MD-MB231, MCF7, and HS578T; and (2) breast tissues. Moreover, esvm is able to identify (1) various drugs including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanism, progression, and metastasizing. Our method is available publicly in the maGENEgerZ web server at https://aibio.shinyapps.io/maGENEgerZ/.

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Section: Resultsmentioning

confidence: 92%

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2023

Preprint

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“…STAT3 is involved in breast cancer progression [71]. E2F1 and EP300 have been reported to be involved in breast cancer development and metastasis [72][73][74]. These results demonstrate the importance of these TFs, and such mutations or alterations can affect gene regulation and thereby contribute to breast cancer development.…”

Section: Dataset2mentioning

confidence: 83%

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2024

Mathematics

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Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

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“…Huo et al 26 . proposed the method to assess the interaction strength between the genes using their relative expression values and calculated the dysregulation score for each gene by considering all the dysregulated strength of gene i to all its downstream gene.…”

Section: Methodsmentioning

confidence: 99%

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

Mahajan,

Sarkar,

Mondal

2024

Cancer Medicine

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IntroductionCross‐talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross‐talk in colorectal cancer (CRC) development at both the pathway and gene levels.MethodsIn this study, we integrated the gene expression data with domain knowledge to construct state‐dependent pathway cross‐talk networks. The significance of the genes involved in pathway cross‐talk was assessed by analyzing their association with cancer hallmarks, disease‐gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.ResultsCross‐talk was observed between immune‐related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial‐mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high‐risk from low‐risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.ConclusionsThis study highlights the crucial role of pathway cross‐talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

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Analysis of Breast Cancer Based on the Dysregulated Network

Cited by 7 publications

References 45 publications

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

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