Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Verb specificity effects on semantic processing in Parkinson's disease.

Cortex; a journal devoted to the study of the nervous system and behavior·2026
Same author

What most captures the physician's interest when evaluating a multiparameter monitor in a Neonatal ICU? - A simulation study.

Jornal de pediatria·2026
Same author

Cortical Brain Activation During Robot-Assisted Gait in Humans With Acute and Chronic Spinal Cord Injury: A Functional Near-Infrared Spectroscopy Study.

The European journal of neuroscience·2026
Same author

Integrative RNA-Seq and TCGA-BRCA Analyses Highlight the Role of LINC01133 in Triple-Negative Breast Cancer.

Biomedicines·2026
Same author

Magnetic Bead-Guided Assembly of 3D Primary Human Islet Cells in Decellularized Pancreatic Scaffolds.

Cells·2026
Same author

Spectral densities approximations of incidence-based locally treelike hypergraph matrices via the cavity method.

Physical review. E·2026

Related Experiment Video

Updated: Jun 27, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues
11:29

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

Published on: September 8, 2015

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates.

André Fujita1, Luciana Rodrigues Gomes, João Ricardo Sato

  • 1Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan. afujita@ims.u-tokyo.ac.jp

BMC Systems Biology
|December 6, 2008
PubMed
Summary

Changes in gene functional connectivity, not just expression, are key to prostate cancer development. This study identified seven informative genes, including known biomarkers like KLK3 and KLK2, highlighting network alterations in tumoral prostate tissue.

More Related Videos

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Related Experiment Videos

Last Updated: Jun 27, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues
11:29

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

Published on: September 8, 2015

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Oncology

Background:

  • Prostate cancer is a significant cause of mortality in men.
  • Understanding genes and molecular networks in prostate tumoral processes is crucial.
  • Analysis of 57 cDNA microarrays with ~25,000 genes was performed to identify potential biomarkers.

Purpose of the Study:

  • To identify genes with discriminative information between normal and tumoral prostate tissues.
  • To understand the biological processes and molecular networks underlying prostate cancer genesis.
  • To explore the role of functional connectivity changes in malignant transformation.

Main Methods:

  • Applied Principal Component Analysis (PCA) and Maximum-entropy Linear Discriminant Analysis (MLDA).
  • Analyzed gene expression differences from univariate and multivariate perspectives.
  • Utilized a dependence network approach to assess functional connectivity.

Main Results:

  • Malignant transformation is more associated with functional connectivity changes than differential gene expression.
  • Seven genes (MYLK, KLK2, KLK3, HAN11, LTF, CSRP1, TGM4) showed significant functional connectivity changes.
  • These seven genes were identified as the most informative for prostate cancer genesis via discriminant analysis, including known biomarkers (KLK3, KLK2).

Conclusions:

  • Changes in functional connectivity are integral to the biological processes driving gene informativeness in distinguishing normal from tumoral prostate conditions.
  • The MLDA method effectively captured dependence network alterations related to cell transformation.
  • Functional connectivity analysis offers a novel approach to identifying prostate cancer biomarkers.