Dataset Information

Association of predicted pathogenic mutations in mitochondrial ND genes with distant metastasis in NSCLC and colon cancer.

ABSTRACT: Cancer cells have more mutations in their mitochondrial DNA (mtDNA) than do normal cells, and pathogenic mutations in the genes encoding mitochondrial NADH dehydrogenase (ND) subunits have been found to enhance the invasive and metastatic ability of various tumour cells in animal experiments. However, it is unknown whether single-nucleotide variants (SNVs) of the ND genes that decrease complex I activity are involved in distant metastasis in human clinical samples. Here, we demonstrated the enhancement of the distant metastasis of Lewis lung carcinoma cells by the ND6 13885insC mutation, which is accompanied by the overexpression of metastasis-related genes, metabolic reprogramming, the enhancement of tumour angiogenesis and the acquisition of resistance to stress-induced cell death. We then sequenced ND genes in primary tumour lesions with or without distant metastases as well as metastatic tumour lesions from 115 patients with non-small cell lung cancer (NSCLC) and colon cancer, and we subsequently selected 14 SNVs with the potential to decrease complex I activity. Intriguingly, a significant correlation was observed (P?

SUBMITTER: Koshikawa N

PROVIDER: S-EPMC5686070 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Association of predicted pathogenic mutations in mitochondrial ND genes with distant metastasis in NSCLC and colon cancer.

Koshikawa Nobuko N Akimoto Miho M Hayashi Jun-Ichi JI Nagase Hiroki H Takenaga Keizo K

Scientific reports 20171114 1

Cancer cells have more mutations in their mitochondrial DNA (mtDNA) than do normal cells, and pathogenic mutations in the genes encoding mitochondrial NADH dehydrogenase (ND) subunits have been found to enhance the invasive and metastatic ability of various tumour cells in animal experiments. However, it is unknown whether single-nucleotide variants (SNVs) of the ND genes that decrease complex I activity are involved in distant metastasis in human clinical samples. Here, we demonstrated the enha ...[more]

PMID: 29138417

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Project description:PurposeRadiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision medicine. Normal-appearing parenchymal tissues surrounding primary tumors can harbor microscopic disease that leads to increased risk of distant metastasis (DM). This study assesses whether computed-tomography (CT) imaging features of such peritumoral tissues can predict DM in locally advanced non-small cell lung cancer (NSCLC).Material and methods200 NSCLC patients of histological adenocarcinoma were included in this study. The investigated lung tissues were tumor rim, defined to be 3mm of tumor and parenchymal tissue on either side of the tumor border and the exterior region extended from 3 to 9mm outside of the tumor. Fifteen stable radiomic features were extracted and evaluated from each of these regions on pre-treatment CT images. For comparison, features from expert-delineated tumor contours were similarly prepared. The patient cohort was separated into training and validation datasets for prognostic power evaluation. Both univariable and multivariable analyses were performed for each region using concordance index (CI).ResultsUnivariable analysis reveals that six out of fifteen tumor rim features were significantly prognostic of DM (p-value < 0.05), as were ten features from the visible tumor, and only one of the exterior features was. Multivariablely, a rim radiomic signature achieved the highest prognostic performance in the independent validation sub-cohort (CI = 0.64, p-value = 2.4×10-5) significantly over a multivariable clinical model (CI = 0.53), a visible tumor radiomics model (CI = 0.59), or an exterior tissue model (CI = 0.55). Furthermore, patient stratification by the combined rim signature and clinical predictor led to a significant improvement on the clinical predictor alone and also outperformed stratification using the combined tumor signature and clinical predictor.ConclusionsWe identified peritumoral rim radiomic features significantly associated with DM. This study demonstrated that peritumoral imaging characteristics may provide additional valuable information over the visible tumor features for patient risk stratification due to cancer metastasis.

Project description:This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein-protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.

Dataset Information

Association of predicted pathogenic mutations in mitochondrial ND genes with distant metastasis in NSCLC and colon cancer.

Publications

Association of predicted pathogenic mutations in mitochondrial ND genes with distant metastasis in NSCLC and colon cancer.

Similar Datasets

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