Skip to main content

Main menu

  • Home
  • Content
    • Current
    • Ahead of print
    • Past Issues
    • JNM Supplement
    • SNMMI Annual Meeting Abstracts
    • Continuing Education
    • JNM Podcasts
  • Subscriptions
    • Subscribers
    • Institutional and Non-member
    • Rates
    • Journal Claims
    • Corporate & Special Sales
  • Authors
    • Submit to JNM
    • Information for Authors
    • Assignment of Copyright
    • AQARA requirements
  • Info
    • Reviewers
    • Permissions
    • Advertisers
  • About
    • About Us
    • Editorial Board
    • Contact Information
  • More
    • Alerts
    • Feedback
    • Help
    • SNMMI Journals
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI

User menu

  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Journal of Nuclear Medicine
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Journal of Nuclear Medicine

Advanced Search

  • Home
  • Content
    • Current
    • Ahead of print
    • Past Issues
    • JNM Supplement
    • SNMMI Annual Meeting Abstracts
    • Continuing Education
    • JNM Podcasts
  • Subscriptions
    • Subscribers
    • Institutional and Non-member
    • Rates
    • Journal Claims
    • Corporate & Special Sales
  • Authors
    • Submit to JNM
    • Information for Authors
    • Assignment of Copyright
    • AQARA requirements
  • Info
    • Reviewers
    • Permissions
    • Advertisers
  • About
    • About Us
    • Editorial Board
    • Contact Information
  • More
    • Alerts
    • Feedback
    • Help
    • SNMMI Journals
  • View or Listen to JNM Podcast
  • Visit JNM on Facebook
  • Join JNM on LinkedIn
  • Follow JNM on Twitter
  • Subscribe to our RSS feeds
Meeting ReportOncology: Clinical Diagnosis

A regression model to predict tumor cellularity in patients with prostate cancer and low FDG uptake

Xiaofei Wang, Peng Huang, Shirley Yang and Richard Wahl
Journal of Nuclear Medicine May 2015, 56 (supplement 3) 1450;
Xiaofei Wang
1Radiology, Johns Hopkins University, Baltimore, MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peng Huang
1Radiology, Johns Hopkins University, Baltimore, MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shirley Yang
1Radiology, Johns Hopkins University, Baltimore, MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Richard Wahl
1Radiology, Johns Hopkins University, Baltimore, MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
Loading

Abstract

1450

Objectives To explore underlying biological processes of low FDG uptake in prostate cancer using gene expression, measured by array chips (over 20,000 transcripts and variants).

Methods We analyzed a U133A array dataset published by YP Wang et al. (GSE8218) that includes 136 prostate samples with documenting percentages of tumor, stroma, hypertrophy, and atrophy. Since glucose transporters (SLC2A), hexokinase (HK), and glucose-6-phosphatase (G6PC) are directly involved in FDG retention in cells, we used tumor percentage as dependent variable.

Results Five of 17 SLC2A transcripts were significantly associated with tumor percentage. The regression model is yT=Tumor percentage= -96.01 + 0.174*(SLC2A1) + 0.170*(SLC2A8) +0.035*(SLC2A10) -0.016*(SLC2A3) - 0.068*(SLC2A5) with p-value < 0.05 for all 5 genes in the model. The regression model had adjusted R square=0.553. Two of 6 HK transcripts were significantly associated with tumor percentage. In regression model yT= 25.038 -0.011*(HK1) + 0.144*(HK2), both HK1 and HK2 had p-value<0.002. HK1 and HK2 abundances in this dataset were 2462.01 ± 613.5 and 149.50 ± 58.34. One of 4 G6PC transcripts was found significantly associated with tumor percentage (p<0.001). The regression model yT= -35.466 + 0.053*. Since the current FDG oncology protocol is approximately 60 min incubation post injection of FDG, we combined three steps related to FDG uptake and retention. In the new model, 4 transcripts were significantly associated with tumor percentage (all p-values < 0.0001). The final regression model yT= -114.706 + 0.224*(SLC2A1) + 0.026*(SLC2A10) - 0.013*(HK1) + 0.046*(G6PC3) yielded adjusted R square=0.478.

Conclusions 55.3% of the increase in tumor percentage in 136 samples was explained by SLC2A family. SLC2A1 (Glut1) weighs more than other variable. For the combination, 47.8% of the variation is explained by SLC2A1 and SLC2A10 increases, HK1 decrease, and G6PC3 increase, suggesting that FDG increases in prostate cancer cell without enough phosphorylated FDG in prostate cancer cells for the imaging 1 hour post FDG injection.

Research Support T32EB006351

Previous
Back to top

In this issue

Journal of Nuclear Medicine
Vol. 56, Issue supplement 3
May 1, 2015
  • Table of Contents
  • Index by author
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Journal of Nuclear Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
A regression model to predict tumor cellularity in patients with prostate cancer and low FDG uptake
(Your Name) has sent you a message from Journal of Nuclear Medicine
(Your Name) thought you would like to see the Journal of Nuclear Medicine web site.
Citation Tools
A regression model to predict tumor cellularity in patients with prostate cancer and low FDG uptake
Xiaofei Wang, Peng Huang, Shirley Yang, Richard Wahl
Journal of Nuclear Medicine May 2015, 56 (supplement 3) 1450;

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
A regression model to predict tumor cellularity in patients with prostate cancer and low FDG uptake
Xiaofei Wang, Peng Huang, Shirley Yang, Richard Wahl
Journal of Nuclear Medicine May 2015, 56 (supplement 3) 1450;
Twitter logo Facebook logo LinkedIn logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
  • Info & Metrics

Related Articles

  • No related articles found.
  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

Oncology: Clinical Diagnosis

  • Role of F-18 FDG PET-CT in detection of recurrence in renal cell carcinoma
  • Associations of iodine content obtained from dual-energy contrast-enhanced CT and texture features or volumetric parameters on FDG PET in pancreatic cancer
  • The mutuality and robustness of radiomics features in [18F]FDG PET/CT lung cancer studies
Show more Oncology: Clinical Diagnosis

MTA I: Prostate/GU Posters

  • Radiographic and laboratory assessment of bone metastases in castration-resistant prostate cancer patients undergoing Radium-223 dichloride therapy
  • Histopathologic Features that Influence the Detection of Occult Lymph Node Metastasis Using PET/CT Imaging with Anti-PSMA 89Zr-Df-IAB2M in Newly Diagnosed High Risk Prostate Cancer Patients
  • Preoperative detection of lymph node metastases using SUVmax on 18F-FDG PET/CT in patients with high risk non-invasive and muscle invasive bladder cancer
Show more MTA I: Prostate/GU Posters

Similar Articles

SNMMI

© 2025 SNMMI

Powered by HighWire