A microRNA DNA methylation signature for human cancer metastasis.
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ABSTRACT:
SUBMITTER: Lujambio A
PROVIDER: S-EPMC2528872 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Distant metastasis is the major cause of mortality in colorectal cancer (CRC). We performed a systemic, comprehensive discovery for expression patterns of metastasis-specific microRNAs (miRNAs) by directly comparing primary CRCs (pCRCs) and matched liver metastases (LMs) and evaluated the feasibility of their clinical application as metastasis-specific biomarkers. CRC metastasis-specific miRNA profiles were generated by analyzing nine pairs of pCRC and LM tissues, followed by quantitative validation in an independent cohort of 58 pairs of matched pCRC and LM tissues. We evaluated associations between miRNA expression and patient survival and ability to predict metastasis in another 84 patients with CRC. Subsequently, associations were quantitatively validated in 175 CRC tissues and 169 serum samples. Kaplan-Meier, Cox regression, and logistic regression analyses were used. All statistical tests were two-sided. Twenty-three miRNAs were identified that were differentially expressed between pCRC and LM (P < .001; FDR < .5). Four miRNAs downregulated in LM (let-7i, miR-10b, miR-221, and miR-320a) and one upregulated miR (miR-885-5p) were quantitatively validated in pCRC (P < .0001). Low let-7i expression in pCRC tissue predicted worsened prognosis (hazard ratio [HR] = 5.0, 95% confidence interval [CI] = 1.0 to 24.4, P = .0479) as well as distant metastasis (odds ratio [OR] = 5.5, 95% CI = 1.1 to 26.8, P = .0334). High miR-10b expression in pCRC tissue independently predicted distant metastasis (OR = 4.9, 95% CI = 1.2 to 19.7, P = .0248). High serum miR-885-5p expression independently predicted prognosis (HR = 2.9, 95% CI = 1.1 to 7.5, P = .0323), LN metastasis (OR = 3.0, 95% CI = 1.3 to 7.2, P = .0116), and distant metastasis (OR = 3.1, 95% CI = 1.0 to 10.0, P = .0456), whereas tissue miR-885-5p expression did not. Expression patterns of miRNAs were confirmed by in situ hybridization. We discovered a metastasis-specific miRNA signature in pCRCs and discovered novel tissue- and serum-based CRC metastasis-specific miRNA biomarkers through intensive validation. These unique miRNAs may be clinically applicable to predict prognosis and distant metastasis in CRC.
| S-EPMC4334826 | biostudies-literature
Project description:BackgroundLymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.MethodsFor marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.ResultsFifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.ConclusionsA novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.
| S-EPMC8811982 | biostudies-literature
Project description:MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate the expression of target mRNAs. MicroRNA genes themselves are regulated through epigenetic mechanisms, such as histone modifications and/or DNA methylation of CpG islands. Aberrant CpG island methylation patterns are frequently associated with cancer and thus researching DNA methylation of miRNA genes is a topic of increased research interest. Large quantities of available data from various studies are fragmented and incomplete; therefore, integration was performed. Data from 150 articles revealed 180 miRNA genes shown to be regulated via DNA methylation in 36 cancer types. From the total of 2588 known mature miRNA 6.9% (180/2588) miRNAs have been shown to be epigenetically regulated by DNA methylation. 45.5% (82/180) of miRNA genes were shown to be methylated in at least two cancer types among them hsa-miR-34b, hsa-miR-34c and hsa-miR-34a were found to be silenced in 24, 21 and 17 cancer types, respectively. The other 54.4% (98/180) miRNA genes regulated by DNA methylation were found to be specific for a certain type of cancer and therefore represent specific biomarker potential. Because specific miRNAs have diagnostic, prognostic and therapeutic potential, the systematically review of the field offers an overview of the field and facilitates experiment planning, generation of more targeted hypotheses and more efficient biomarker and target development.
| S-EPMC5932538 | biostudies-literature
Project description:DNA methylation plays a key role in the silencing of numerous cancer-related genes, thereby affecting a number of vital cellular processes, including the cell cycle checkpoint, apoptosis, signal transduction, cell adhesion and angiogenesis. Also widely altered in human malignancies is the expression of microRNAs (miRNAs), a class of small noncoding RNAs that act as posttranscriptional regulators of gene expression. Furthermore, emerging evidence now supports the idea that DNA methylation is crucially involved in the dysregulation of miRNAs in cancer. This is in part the result of technological advances that enable more comprehensive analysis of miRNA expression profiles and the epigenome in cancer cells, which has led to the identification of a number of epigenetically regulated miRNAs. As with protein-coding genes, it appears that miRNA genes involved in regulating cancer-related pathways are silenced in association with CpG island hypermethylation. In addition, methylation in CpG island shore regions and DNA hypomethylation also appear to contribute to miRNA dysregulation in cancer. Aberrant DNA methylation of miRNA genes is a potentially useful biomarker for detecting cancer and predicting its outcome. Moreover, re-expression of miRNAs and the replacement of tumor suppressive miRNAs using miRNA mimics or expression vectors could be effective approaches to cancer therapy.
| S-EPMC5528344 | biostudies-other
Project description:BACKGROUND:Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues. METHODS:We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of <?0.05 were considered statistically significant. RESULTS:Across 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n?=?6,795 tumor, n?=?922 nontumor, P?<?0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n?=?740 tumor, n?=?424 nontumor, P?<?0.05). CONCLUSIONS:The results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity.
| S-EPMC6642562 | biostudies-literature
Project description:Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation. Method: We developed multiple machine learning models to distinguish the metastasis by integrating miRNA, mRNA, and DNA methylation markers. We used the TCGA melanoma dataset to differentiate between metastatic melanoma samples by assessing a set of predictive models. For this purpose, machine learning models using a support vector machine with different kernels, artificial neural networks, random forests, AdaBoost, and Naïve Bayes are compared. An iterative combination of differentially expressed miRNA, mRNA, and methylation signatures is used as a candidate marker to reveal each new biomarker category's impact. In each iteration, the performances of the combined models are calculated. During all comparisons, the choice of the feature selection method and under and oversampling approaches are analyzed. Selected biomarkers of the highest performing models are further analyzed for the biological interpretation of functional enrichment. Results: In the initial model, miRNA biomarkers can identify metastatic melanoma with an 81% F-score. The addition of mRNA markers upon miRNA increased the F-score to 92%. In the final integrated model, the addition of the methylation data resulted in a similar F-score of 92% but produced a stable model with low variance across multiple trials. Conclusion: Our results support the role of miRNA regulation in metastatic melanoma as miRNA markers model metastasis outcomes with high accuracy. Moreover, the integrated evaluation of miRNA with mRNA and methylation biomarkers increases the model's power. It populates selected biomarkers on the metastasis-associated pathways of melanoma, such as the "osteoclast", "Rap1 signaling", and "chemokine signaling" pathways. Source Code: https://github.com/aysegul-kt/MelonomaMetastasisPrediction/.
| S-EPMC8495312 | biostudies-literature
Project description:The identification of patients with high-risk stage II colon cancer who may benefit from adjuvant therapy may allow the clinical approach to be tailored for these patients based on an understanding of tumour biology. MicroRNAs have been proposed as markers of the prognosis or treatment response in colorectal cancer. Recently, a 2-microRNA signature (let-7i and miR-10b) was proposed to identify colorectal cancer patients at risk of developing distant metastasis. We assessed the prognostic value of this signature and additional candidate microRNAs in an independent, clinically well-defined, prospectively collected cohort of primary colon cancer patients including stage I-II colon cancer without and stage III colon cancer with adjuvant treatment. The 2-microRNA signature specifically predicted hepatic recurrence in the stage I-II group, but not the overall ability to develop distant metastasis. The addition of miR-30b to the 2-microRNA signature allowed the prediction of both distant metastasis and hepatic recurrence in patients with stage I-II colon cancer who did not receive adjuvant chemotherapy. Available gene expression data allowed us to associate miR-30b expression with axon guidance and let-7i expression with cell adhesion, migration, and motility.
| S-EPMC5869672 | biostudies-literature
Project description:Approximately 21% of patients with renal cell cancer (RCC) present with synchronous metastatic disease at the time of diagnosis, and metachronous metastatic disease occurs in 20-50% of cases within 5 years. Recent advances in adjuvant treatment of aggressive RCC following surgery suggest that biomarker-based prediction of risk for distant metastasis could improve patient selection. Biometrical analysis of TCGA-KIRC data identified candidate loci in the NK6 homeobox 2 gene (NKX6-2) that are hypermethylated in primary metastatic RCC. Analyses of NKX6-2 DNA methylation in three gene regions including a total of 16 CpG sites in 154 tumor-adjacent normal tissue, 189 RCC, and 194 metastatic tissue samples from 95 metastasized RCC patients revealed highly significant tumor-specific, primary metastatic-specific, and metastatic tissue-specific hypermethylation of NKX6-2. Combined CpG site methylation data for NKX6-2 and metastasis-associated genes (INA, NHLH2, and THBS4) demonstrated similarity between metastatic tissues and metastatic primary RCC tissues. The random forest method and evaluation of an unknown test cohort of tissues using receiver operator characteristic curve analysis revealed that metastatic tissues can be differentiated by a median area under the curve of 0.86 (p = 1.7 × 10-8-7.5 × 10-3) in 1000 random runs. Analysis of variable importance demonstrated an above median contribution for decision-making of at least one CpG site in each of the genes, suggesting superior informativity for sites annotated to NHLH2 and NKX6-2. Thus, DNA methylation of NKX6-2 is associated with the metastatic state of RCC tissues and contributes to a four-gene-based statistical predictor of tumoral and metastatic renal tissues.
| S-EPMC9569431 | biostudies-literature
Project description:BackgroundPrenatal alcohol exposure is the leading preventable cause of behavioral and cognitive deficits, which may affect between 2 and 5 % of children in North America. While the underlying mechanisms of alcohol's effects on development remain relatively unknown, emerging evidence implicates epigenetic mechanisms in mediating the range of symptoms observed in children with fetal alcohol spectrum disorder (FASD). Thus, we investigated the effects of prenatal alcohol exposure on genome-wide DNA methylation in the NeuroDevNet FASD cohort, the largest cohort of human FASD samples to date.MethodsGenome-wide DNA methylation patterns of buccal epithelial cells (BECs) were analyzed using the Illumina HumanMethylation450 array in a Canadian cohort of 206 children (110 FASD and 96 controls). Genotyping was performed in parallel using the Infinium HumanOmni2.5-Quad v1.0 BeadChip.ResultsAfter correcting for the effects of genetic background, we found 658 significantly differentially methylated sites between FASD cases and controls, with 41 displaying differences in percent methylation change >5 %. Furthermore, 101 differentially methylated regions containing two or more CpGs were also identified, overlapping with 95 different genes. The majority of differentially methylated genes were highly expressed at the level of mRNA in brain samples from the Allen Brain Atlas, and independent DNA methylation data from cortical brain samples showed high correlations with BEC DNA methylation patterns. Finally, overrepresentation analysis of genes with up-methylated CpGs revealed a significant enrichment for neurodevelopmental processes and diseases, such as anxiety, epilepsy, and autism spectrum disorders.ConclusionsThese findings suggested that prenatal alcohol exposure is associated with distinct DNA methylation patterns in children and adolescents, raising the possibility of an epigenetic biomarker of FASD.
| S-EPMC4926300 | biostudies-literature
Project description:In order to obtain a comprehensive DNA methylation signature of HER2-positive breast cancer (HER2+ breast cancer), we performed a genome-wide methylation analysis on 17 HER2+ breast cancer and compared with ten normal breast tissue samples using the Illumina Infinium HumanMethylation450 BeadChip (450K). In HER2+ breast cancer, we found altered DNA methylation in genes involved in multicellular development, differentiation and transcription. Within these genes, we observed an overrepresentation of homeobox family genes, including several genes that have not been previously reported in relation to cancer (DBX1, NKX2-6, SIX6). Other affected genes included several belonging to the PI3K and Wnt signaling pathways. Notably, HER2, AKT3, HK1, and PFKP, genes for which altered methylation has not been previously reported, were also identified in this analysis. In total, we report 69 candidate biomarker genes with maximum differential methylation in HER2+ breast cancer. External validation of gene expression in a selected group of these genes (n = 13) revealed lowered mean gene expression in HER2+ breast cancer. We analyzed DNA methylation in six top candidate genes (AKR1B1, INA, FOXC2, NEUROD1, CDKL2, IRF4) using EpiTect Methyl II Custom PCR Array and confirmed the 450K array findings. Future clinical studies focusing on these genes, as well as on homeobox-containing genes and HER2, AKT3, HK1, and PFKP, are warranted which could provide further insights into the biology of HER2+ breast cancer.
| S-EPMC4164500 | biostudies-literature