Integrative Bioinformatics Analysis Reveals That miR-524-5p/MEF2C Regulates Bone Metastasis in Prostate Cancer and Breast Cancer

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Abstract

Bone metastases are highly prevalent in patients with advanced prostate cancer and breast cancer and have a serious impact on the survival time and quality of life of these patients. It has been reported that microRNAs (miRNAs) are expressed abnormally in different types of cancer and metastases. However, it remains unknown whether the underlying miRNAs are associated with prostate and breast cancer bone metastasis. Differentially expressed miRNAs (DE-miRNAs) and their potential targets in the metastatic process were identified by bioinformatics analysis. Additionally, qPCR confirmed that the miR-524-5p expression was downregulated in prostate and breast cancer cells. The overexpression of miR-524-5p restrained cell proliferation, invasion, and metastasis in prostate and breast cancer cells. Meanwhile, miR-524-5p could target and inhibit the expression of MEF2C, which was verified by a luciferase assay. In conclusion, our data strongly suggest that downregulation of miR-524-5p appears to be a precocious event in prostate and breast cancer, and the miR-524-5p/MEF2C axis plays a novel role in bone metastases from prostate and breast cancers.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Paul Shannon, Andrew Markiel, Owen Ozier … (2003)

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

R. A. Irizarry (2003)

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.

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Author and article information

Contributors

ChunGen Wu:

ORCID: https://orcid.org/0000-0002-4215-4812

Journal

Journal ID (nlm-ta): Comput Math Methods Med

Journal ID (iso-abbrev): Comput Math Methods Med

Journal ID (publisher-id): cmmm

Title: Computational and Mathematical Methods in Medicine

Publisher: Hindawi

ISSN (Print): 1748-670X

ISSN (Electronic): 1748-6718

Publication date Collection: 2022

Publication date (Electronic): 10 September 2022

Volume: 2022

Electronic Location Identifier: 5211329

Affiliations

Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China

Author notes

Academic Editor: Lei Chen

Author information

ChunGen Wu https://orcid.org/0000-0002-4215-4812

Article

DOI: 10.1155/2022/5211329

PMC ID: 9482681

PubMed ID: 36128051

SO-VID: 328c7353-c6d8-4dc3-80f7-be7c5c8080a7

License:

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 6 June 2022

Date accepted : 28 July 2022

Funding

Funded by: Shanghai Municipal Health Commission

Award ID: 202040340

Funded by: Natural Fund from Shanghai Science And Technology Commission

Award ID: 19411971800

Award ID: 18ZR1429400

Funded by: National Natural Scientific Fund of China

Award ID: 81703751

Award ID: 81701798

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Integrative Bioinformatics Analysis Reveals That miR-524-5p/MEF2C Regulates Bone Metastasis in Prostate Cancer and Breast Cancer

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Abstract

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Online interactive cancer simulations and demos

Most cited references 46

limma powers differential expression analyses for RNA-sequencing and microarray studies

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

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