Insights into the Oncogenic, Prognostic, and Immunological Role of BRIP1 in Pan-Cancer: A Comprehensive Data-Mining-Based Study

Liu, Yongru; Wu, Xi; Feng, Yunlu; Jiang, Qingwei; Zhang, Sheng‐Yu; Wang, Qiang; Yang, Aiming

doi:10.1155/2023/4104639

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…For example, BRIP1 is a hub gene only in the differentiated subtype and is also involved in DNA damage repair, particularly in ovarian cancer predisposition [Lilyquist et al, 2017;Suszynska et al, 2019Suszynska et al, , 2020. BRIP1 binds to BRCA1 for homologous DNA double-strand repair and high BRIP1 expression is related to a higher risk of recurrence and shorter disease-free survival in ovarian cancer patients treated with platinum [Moes-Sosnowska et al, 2019], and with an increased cell proliferation [Tu et al, 2011], potentially contributing to earlier tumor recurrence [Liu et al, 2023]. In the immunoreactive subtype, three unique hub genes were identified: CDK12, RNF24, and RAD51.…”

Section: More Insights For Hgsoc-tcga Datamentioning

confidence: 99%

MORE interpretable multi-omic regulatory networks to characterize phenotypes

Aguerralde-Martin,

Clemente-Ciscar,

Conesa

et al. 2024

Preprint

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The emergence of omics has revolutionized how we study and understand biological systems, enabling breakthrough discoveries with profound implications for medicine, agriculture, biotechnology, and more. However, with the help of advanced computational tools and artificial intelligence, meaningful patterns and relationships can now be uncovered in omics data, offering a unique opportunity to gain a deeper understanding and contribute to new insights into the complex regulatory mechanisms of biological systems. In this context, we have developed MORE (Multi-Omics REgulation), a tool designed to identify relevant regulations of the gene expression for the biological system under study and subsequently construct the regulatory networks for the considered experimental conditions. The presented method not only allows the incorporation of prior biological information into network construction but also can infer relationships de novo in the absence of such information. Moreover, we effectively addressed multicollinearity issues inherent in such data types, ensuring precise and reliable inference of regulatory networks when performing GLM models. In our comparison to KiMONo, our tool exhibited superior evaluation metrics, including F1-score, R2, and computational efficiency. Finally, applying our tool to a real ovarian cancer dataset yielded intriguing and biologically meaningful results. Our developed methodology represents a versatile and powerful multi-omic regulatory network inference approach, demonstrating good performance and applicability to real-world biological datasets. It is freely available at https://github.com/ConesaLab/MORE.git.

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Section: More Insights For Hgsoc-tcga Datamentioning

confidence: 99%

MORE interpretable multi-omic regulatory networks to characterize phenotypes

Aguerralde-Martin,

Clemente-Ciscar,

Conesa

et al. 2024

Preprint

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Identification of Key Regulators in Pancreatic Ductal Adenocarcinoma using Network theoretical Approach

Bhattacharjee,

Ghosh

2024

Preprint

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Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes which is mainly because of delayed disease detection resistance to chemotherapy and lack of specific targeted therapies. The disease's development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif. Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

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Insights into the Oncogenic, Prognostic, and Immunological Role of BRIP1 in Pan-Cancer: A Comprehensive Data-Mining-Based Study

Cited by 2 publications

References 38 publications

MORE interpretable multi-omic regulatory networks to characterize phenotypes

MORE interpretable multi-omic regulatory networks to characterize phenotypes

Identification of Key Regulators in Pancreatic Ductal Adenocarcinoma using Network theoretical Approach

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