A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications

Jin, Shilei; Zeng, Xiangxiang; Fang, Jiansong; Lin, Jiawei; Chan, Stephen Y.; Erzurum, Serpil C.; Cheng, Feixiong

doi:10.1038/s41540-019-0115-2

Cited by 27 publications

(23 citation statements)

References 71 publications

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“…Bhattacharyya et al used miRNA signatures for identifying subtypes of breast cancer [ 21 ]. Recently, various effective computational methods have been developed to understand the miRNA disease association [ 22 , 23 ]. Hence, miRNA signatures are proven to be important factors for predicting survival and subtypes in cancers.…”

Section: Introductionmentioning

confidence: 99%

Identification of the miRNA signature associated with survival in patients with ovarian cancer

Sathipati

2021

Aging

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Section: Introductionmentioning

confidence: 99%

Identification of the miRNA signature associated with survival in patients with ovarian cancer

Sathipati

2021

Aging

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“…Furthermore, miRNAs (micro-RNAs) have received substantial attention for their role in regulating pathway functionality [ 112 ]. For example, miR-15a/miR-16’s deletion or down-regulation contributes to dysregulated of cell cycle in chronic lymphocytic leukemia [ 113 ] and non-small cell lung cancer [ 114 ].…”

Section: Discussionmentioning

confidence: 99%

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

et al. 2020

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Background Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes. Methods In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer. Findings We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients. Interpretation We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens. Funding This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

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“…To identify the associations between miRNAs and DEGs, we examined the protein–protein interactions (PPIs) of the miRNA targets and the DEGs using our state‐of‐the‐art network medicine methodologies 18,19 . Detailed descriptions can be found in our recent publications 20–22 .…”

Section: Methodsmentioning

confidence: 99%

Identifying miRNAs in multiple sclerosis gray matter lesions that correlate with atrophy measures

Tripathi

Pandit

Perles

et al. 2021

Ann Clin Transl Neurol

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Objective: Multiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Though MS was initially considered to be a white matter demyelinating disease, myelin loss in cortical gray matter has been reported in all disease stages. We previously identified microRNAs (miRNAs) in white matter lesions (WMLs) that are detected in serum from MS patients. However, miRNA expression profiles in gray matter lesions (GMLs) from progressive MS brains are understudied. Methods: We used a combination of global miRNAs and gene expression profiling of GMLs and independent validation using real-time quantitative polymerase chain reaction (RT-qPCR), immuno-in situ hybridization, and immunohistochemistry. Results: Compared to matched myelinated gray matter (GM) regions, we identified 82 miRNAs in GMLs, of which 10 were significantly upregulated and 17 were significantly downregulated. Among these 82 miRNAs, 13 were also detected in serum and importantly were associated with brain atrophy in MS patients. The predicted target mRNAs of these miRNAs belonged to pathways associated with axonal guidance, TGF-b signaling, and FOXO signaling. Further, using state-of-the-art human protein-protein interactome network analysis, we mapped the four key GM atrophy-associated miRNAs (hsa-miR-149*, hsa-miR-20a, hsa-miR-29c, and hsa-miR-25) to their target mRNAs that were also changed in GMLs. Interpretation: Our study identifies miRNAs altered in GMLs in progressive MS brains that correlate with atrophy measures. As these miRNAs were also detected in sera of MS patients, these could act as markers of GML demyelination in MS.

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A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications

Cited by 27 publications

References 71 publications

Identification of the miRNA signature associated with survival in patients with ovarian cancer

Identification of the miRNA signature associated with survival in patients with ovarian cancer

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Identifying miRNAs in multiple sclerosis gray matter lesions that correlate with atrophy measures

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