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Identification of EGFR expression status association with metastatic lymph node density (ND) by expression microarray analysis of advanced gastric cancer.

ABSTRACT: Metastatic lymph node density (ND) has been reproducibly proven to be a prognostic factor in gastric cancer. The molecular mechanisms that underlie this aggressiveness are underexplored. Here, we aimed to identify molecules associated with this unique phenotype. Tumor specimens from patients with stage III gastric cancer with high or low ND (n = 4 for both) were compared at the mRNA level using Affymetrix microarray (harboring 54,675 genes). The expression data were prioritized, and genes that correlated with ND were selected. Ultimately, the EGFR was validated as such a candidate molecule in patients with primary advanced gastric cancer who underwent standard treatment (n = 167). Expression data of the microarray were prioritized based on gene expression ratio and frequency of gene expression. The first priority genes to be selected were genes that are known to be amplified in cancer, which included NKX2.1, CHST9, CTNND2, SLC25A27, FGFR2, EGFR, and PTGER1. Of these genes, the EGFR gene was of particular interest. EGFR expression in primary gastric cancer was examined using immunohistochemistry (IHC). The Student's t-test elucidated a significant difference in EGFR expression between IHC 2+/3+ and IHC 1+ according to ND (P = 0.0035). The Chi-square test also indicated a significant difference between high and low levels of EGFR immunohistochemical staining (IHC2+/3+ and IHC1+, respectively) and ND status (P = 0.0023). According to the least squares method, as ND increased, the risk that EGFR staining levels changed from IHC 1+ to IHC 2+ also increased. In this study, we determined that high EGFR expression may underlie the aggressive mechanism of advanced gastric cancer with high ND.

SUBMITTER: Ema A

PROVIDER: S-EPMC4312122 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Identification of EGFR expression status association with metastatic lymph node density (ND) by expression microarray analysis of advanced gastric cancer.

Ema Akira A Waraya Mina M Yamashita Keishi K Kokubo Kenichi K Kobayashi Hirosuke H Hoshi Keika K Shinkai Yoshiko Y Kawamata Hiroshi H Nakamura Kazunori K Nishimiya Hiroshi H Katada Natsuya N Watanabe Masahiko M

Cancer medicine 20140826 1

Metastatic lymph node density (ND) has been reproducibly proven to be a prognostic factor in gastric cancer. The molecular mechanisms that underlie this aggressiveness are underexplored. Here, we aimed to identify molecules associated with this unique phenotype. Tumor specimens from patients with stage III gastric cancer with high or low ND (n = 4 for both) were compared at the mRNA level using Affymetrix microarray (harboring 54,675 genes). The expression data were prioritized, and genes that c ...[more]

PMID: 25154973

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Project description:Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758-0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714-1.000) in internal validation cohort and 0.878 (95% CI, 0.696-1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

Dataset Information

Identification of EGFR expression status association with metastatic lymph node density (ND) by expression microarray analysis of advanced gastric cancer.

Publications

Identification of EGFR expression status association with metastatic lymph node density (ND) by expression microarray analysis of advanced gastric cancer.

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