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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Châtelier’s principle. Consider the dissolution of silver iodide:
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
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Twiner: correlation-based regularization for identifying common cancer gene signatures.

Marta B Lopes1,2, Sandra Casimiro3, Susana Vinga4,5

  • 1Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal. marta.lopes@tecnico.ulisboa.pt.

BMC Bioinformatics
|June 27, 2019
PubMed
Summary

Researchers identified shared gene signatures between breast and prostate cancers by analyzing transcriptomic data. This discovery could lead to new targeted therapies for common hormone-dependent cancer features and bone relapse.

Keywords:
Breast invasive carcinomaGene networkProstate adenocarcinomaSparse logistic regressionTriple-negative breast cancer

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Breast and prostate cancers are hormone-dependent and share signaling pathways, often metastasizing to bone.
  • Identifying common gene signatures could reveal shared therapeutic targets, particularly for bone relapse.
  • Targeting shared pathways may improve cancer management and reduce healthcare costs.

Purpose of the Study:

  • To extract common gene signatures from transcriptomic data of breast invasive carcinoma (BRCA) and prostate adenocarcinoma (PRAD).
  • To identify potential therapeutic targets for shared hormone-dependent cancer features and bone relapse.
  • To explore similarities between BRCA and PRAD for developing novel therapeutic strategies.

Main Methods:

  • Utilized sparse logistic regression to analyze transcriptomic data from BRCA and PRAD samples.
  • Incorporated gene network information using a novel twin networks recovery (twiner) penalty.
  • Investigated gene correlation patterns between BRCA and PRAD to identify similarly correlated features.

Main Results:

  • Identified genes with similar correlation patterns in BRCA and PRAD transcriptomic data.
  • Selected key genes crucial for classifying tumor and normal tissues in both cancer types.
  • Found associations between identified genes and survival time distributions.

Conclusions:

  • The study successfully identified shared gene signatures between breast and prostate cancers.
  • These findings are expected to reveal common biomarkers and disease similarities.
  • The results pave the way for developing more effective, targeted therapies for both cancers.