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Original Article
- Gene variant profiles and tumor metabolic activity as measured by FOXM1 expression and glucose uptake in lung adenocarcinoma
- Ashley Goodman, Waqas Mahmud, Lela Buckingham
- J Pathol Transl Med. 2020;54(3):237-245. Published online March 4, 2020
- DOI: https://doi.org/10.4132/jptm.2020.02.08
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- 2 Web of Science
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Abstract
PDF - Background
Cancer cells displaying aberrant metabolism switch energy production from oxidative phosphorylation to glycolysis. Measure of glucose standardized uptake value (SUV) by positron emission tomography (PET), used for staging of adenocarcinoma in high-risk patients, can reflect cellular use of the glycolysis pathway. The transcription factor, FOXM1 plays a role in regulation of glycolytic genes. Cancer cell transformation is driven by mutations in tumor suppressor genes such as TP53 and STK11 and oncogenes such as KRAS and EGFR. In this study, SUV and FOXM1 gene expression were compared in the background of selected cancer gene mutations.
Methods
Archival tumor tissue from cases of lung adenocarcinoma were analyzed. SUV was collected from patient records. FOXM1 gene expression was assessed by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Gene mutations were detected by allele-specific PCR and gene sequencing.
Results
SUV and FOXM1 gene expression patterns differed in the presence of single and coexisting gene mutations. Gene mutations affected SUV and FOXM1 differently. EGFR mutations were found in tumors with lower FOXM1 expression but did not affect SUV. Tumors with TP53 mutations had increased SUV (p = .029). FOXM1 expression was significantly higher in tumors with STK11 mutations alone (p < .001) and in combination with KRAS or TP53 mutations (p < .001 and p = .002, respectively).
Conclusions
Cancer gene mutations may affect tumor metabolic activity. These observations support consideration of tumor cell metabolic state in the presence of gene mutations for optimal prognosis and treatment strategy. -
Citations
Citations to this article as recorded by
- Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study
Kun-Han Lue, Yu-Hung Chen, Sung-Chao Chu, Chih-Bin Lin, Tso-Fu Wang, Shu-Hsin Liu
Annals of Nuclear Medicine.2024; 38(8): 647. CrossRef
- Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study
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