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Transcript expression in endometrial cancers from Black and White patients.

Publication ,  Journal Article
Maxwell, GL; Allard, J; Gadisetti, CVR; Litzi, T; Casablanca, Y; Chandran, U; Darcy, KM; Levine, DA; Berchuck, A; Hamilton, CA; Conrads, TP ...
Published in: Gynecol Oncol
July 2013

OBJECTIVE: Previous studies suggest that differences in molecular features of endometrial cancers between racial groups may contribute to the poorer survival in Blacks. The objective of this investigation was to determine whether gene expression among endometrial cancers is different between Blacks and Whites. METHODS: Fresh frozen tumors from 25 Black patients were matched by stage, grade, and histology to endometrial cancer specimens from 25 White patients. Each case was macrodissected to produce specimens possessing a minimum of 75% cancer cellularity. A subset of 10 matched pairs was also prepared using laser microdissection (LMD) to produce specimens possessing a minimum of 95% cancer cells. Total RNA isolated from each sample was analyzed using the Affymetrix Human Genome U133 Plus 2.0 arrays. Data were analyzed using principal component analysis and binary class comparison analyses. RESULTS: Unsupervised analysis of the 50 endometrial cancers failed to identify global gene expression profiles unique to Black or White patients. In a subset analysis of 10 matched pairs from Blacks and Whites prepared using LMD and macrodissection, unsupervised analysis did not reveal a unique gene expression profile associated with race in either set, but associations were identified that relate to sample preparation technique, histology and stage. CONCLUSIONS: Our microarray data revealed no global gene expression differences and identified few individual gene differences between endometrial cancers from Blacks and Whites. More comprehensive methods of transcriptome analysis could uncover RNAs that may underpin the disparity of outcome or prevalence of endometrial cancers in Blacks and Whites.

Duke Scholars

Published In

Gynecol Oncol

DOI

EISSN

1095-6859

Publication Date

July 2013

Volume

130

Issue

1

Start / End Page

169 / 173

Location

United States

Related Subject Headings

  • White People
  • RNA, Neoplasm
  • RNA, Messenger
  • Principal Component Analysis
  • Oncology & Carcinogenesis
  • Oligonucleotide Array Sequence Analysis
  • Neoplasm Staging
  • Neoplasm Grading
  • Humans
  • Gene Expression Profiling
 

Citation

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ICMJE
MLA
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Maxwell, G. L., Allard, J., Gadisetti, C. V. R., Litzi, T., Casablanca, Y., Chandran, U., … Risinger, J. I. (2013). Transcript expression in endometrial cancers from Black and White patients. Gynecol Oncol, 130(1), 169–173. https://doi.org/10.1016/j.ygyno.2013.04.017
Maxwell, G Larry, Jay Allard, Chandramouli V. R. Gadisetti, Tracy Litzi, Yovanni Casablanca, Uma Chandran, Kathleen M. Darcy, et al. “Transcript expression in endometrial cancers from Black and White patients.Gynecol Oncol 130, no. 1 (July 2013): 169–73. https://doi.org/10.1016/j.ygyno.2013.04.017.
Maxwell GL, Allard J, Gadisetti CVR, Litzi T, Casablanca Y, Chandran U, et al. Transcript expression in endometrial cancers from Black and White patients. Gynecol Oncol. 2013 Jul;130(1):169–73.
Maxwell, G. Larry, et al. “Transcript expression in endometrial cancers from Black and White patients.Gynecol Oncol, vol. 130, no. 1, July 2013, pp. 169–73. Pubmed, doi:10.1016/j.ygyno.2013.04.017.
Maxwell GL, Allard J, Gadisetti CVR, Litzi T, Casablanca Y, Chandran U, Darcy KM, Levine DA, Berchuck A, Hamilton CA, Conrads TP, Risinger JI. Transcript expression in endometrial cancers from Black and White patients. Gynecol Oncol. 2013 Jul;130(1):169–173.
Journal cover image

Published In

Gynecol Oncol

DOI

EISSN

1095-6859

Publication Date

July 2013

Volume

130

Issue

1

Start / End Page

169 / 173

Location

United States

Related Subject Headings

  • White People
  • RNA, Neoplasm
  • RNA, Messenger
  • Principal Component Analysis
  • Oncology & Carcinogenesis
  • Oligonucleotide Array Sequence Analysis
  • Neoplasm Staging
  • Neoplasm Grading
  • Humans
  • Gene Expression Profiling