Dataset Information

The impact of PRDX4 and the EGFR mutation status on cellular proliferation in lung adenocarcinoma.

ABSTRACT: Background: Oxidative stress plays key roles in the progression of lung adenocarcinoma. Recently, we reported that peroxiredoxin 4 (PRDX4), an antioxidant enzyme, can be a prognostic marker of lung adenocarcinoma (LUAD). In the present study, we aimed to further investigate the relationship among the PRDX4 expression, epidermal growth factor receptor (EGFR) mutations and cell proliferation in LUAD. Methods: The expression of PRDX4 was immunohistochemically analyzed and the EGFR mutation status was examined in 127 paraffin-embedded human surgical specimens from patients with stage I LUAD. The PRDX4 expression was considered to be high when >40% of the adenocarcinoma cells were positively stained. In vitro, using plasmid transfection methods, PRDX4 plasmid DNAs were transfected into human lung adenocarcinoma cell lines, A549 (EGFR-wild) or PC-9 (EGFR mutant). The viability of these cells was analyzed using a Cell Counting Kit-8 kit. Results: The number of cases with high PRDX4 expression levels among patients with LUAD with EGFR mutations was significantly larger than that in patients with EGFR wild-type. The combination of the PRDX4 expression level with the EGFR mutation status was closely associated with the prognosis of patients with stage I LUAD. Viability assays showed that the proliferation of A549 cells was significantly suppressed after PRDX4 plasmid transfection, while the overexpression of PRDX4 had no effect on the proliferation of EGFR-mutant PC-9 cells. Conclusions: The PRDX4 expression and EGFR mutation status were significantly associated with the prognosis of patients with stage I LUAD, and EGFR mutations affected the role of PRDX4 in the proliferation of LUAD cells.

SUBMITTER: Mizutani K

PROVIDER: S-EPMC6775271 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The impact of PRDX4 and the EGFR mutation status on cellular proliferation in lung adenocarcinoma.

Mizutani Kenichi K Guo Xin X Shioya Akihiro A Zhang Jing J Zheng Jianbo J Kurose Nozomu N Ishibashi Hiroaki H Motono Nozomu N Uramoto Hidetaka H Yamada Sohsuke S

International journal of medical sciences 20190814 9

<b>Background:</b> Oxidative stress plays key roles in the progression of lung adenocarcinoma. Recently, we reported that peroxiredoxin 4 (PRDX4), an antioxidant enzyme, can be a prognostic marker of lung adenocarcinoma (LUAD). In the present study, we aimed to further investigate the relationship among the PRDX4 expression, epidermal growth factor receptor (EGFR) mutations and cell proliferation in LUAD. <b>Methods:</b> The expression of PRDX4 was immunohistochemically analyzed and the EGFR mut ...[more]

PMID: 31588184

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Project description:IntroductionIn lung adenocarcinoma, the mutational spectrum is dominated by EGFR and KRAS mutations. Improved knowledge about genomic and transcriptional alterations in and between mutation-defined subgroups may identify genes involved in disease development or progression.MethodsGenomic profiles from 457 adenocarcinomas, including 113 EGFR-mutated, 134 KRAS-mutated and 210 EGFR and KRAS-wild type tumors (EGFRwt/KRASwt), and gene expression profiles from 914 adenocarcinomas, including 309 EGFR-mutated, 192 KRAS-mutated, and 413 EGFRwt/KRASwt tumors, were assembled from different repositories. Genomic and transcriptional differences between the three mutational groups were analyzed by both supervised and unsupervised methods.ResultsEGFR-mutated adenocarcinomas displayed a larger number of copy number alterations and recurrent amplifications, a higher fraction of total loss-of-heterozygosity, higher genomic complexity, and a more distinct expression pattern than EGFR-wild type adenocarcinomas. Several of these differences were also consistent when the three mutational groups were stratified by stage, gender and smoking status. Specific copy number alterations were associated with mutation status, predominantly including regions of gain with the highest frequency in EGFR-mutated tumors. Differential regions included both large and small regions of gain on 1p, 5q34-q35.3, 7p, 7q11.21, 12p12.1, 16p, and 21q, and losses on 6q16.3-q21, 8p, and 9p, with 20-40% frequency differences between the mutational groups. Supervised gene expression analyses identified 96 consistently differentially expressed genes between the mutational groups, and together with unsupervised analyses these analyses highlighted the difficulty in broadly resolving the three mutational groups into distinct transcriptional entities.ConclusionsWe provide a comprehensive overview of the genomic and transcriptional landscape in lung adenocarcinoma stratified by EGFR and KRAS mutations. Our analyses suggest that the overall genomic and transcriptional landscape of lung adenocarcinoma is affected, but only to a minor extent, by EGFR and KRAS mutation status.

Project description:To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.

Dataset Information

The impact of PRDX4 and the EGFR mutation status on cellular proliferation in lung adenocarcinoma.

Publications

The impact of PRDX4 and the EGFR mutation status on cellular proliferation in lung adenocarcinoma.

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