Dataset Information

Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis.

ABSTRACT: Purpose: Breast cancer is among the leading causes of cancer-related death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated miRNA-associated genes and breast cancer survival in a well-characterized population-based study.Experimental Design: A recently developed algorithm, ActMiR, was used to identify key miRNA "activities" corresponding to target gene degradation, which were predictive of breast cancer mortality in published databases. We profiled miRNA-associated genes in tumors from our well-characterized population-based cohort of 606 women with first primary breast cancer. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI), after 15+ years of follow-up with 119 breast cancer-specific deaths.Results: miR-500a activity was identified as a key miRNA for estrogen receptor-positive breast cancer mortality using public databases. From a panel of 161 miR-500a-associated genes profiled, 73 were significantly associated with breast cancer-specific mortality (FDR < 0.05) in our population, among which two clusters were observed to have opposing directions of association. For example, high level of SUSD3 was associated with reduced breast cancer-specific mortality (HR = 0.3; 95% CI, 0.2-0.4), whereas the opposite was observed for TPX2 (HR = 2.7; 95% CI, 1.8-3.9). Most importantly, we identified set of genes for which associations with breast cancer-specific mortality were independent of known prognostic factors, including hormone receptor status and PAM50-derived risk-of-recurrence scores. These results are validated in independent datasets.Conclusions: We identified novel markers that may improve prognostic efficiency while shedding light on molecular mechanisms of breast cancer progression. Clin Cancer Res; 24(3); 581-91. ©2017 AACR.

SUBMITTER: Aushev VN

PROVIDER: S-EPMC6103440 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis.

Aushev Vasily N VN Lee Eunjee E Zhu Jun J Gopalakrishnan Kalpana K Li Qian Q Teitelbaum Susan L SL Wetmur James J Degli Esposti Davide D Hernandez-Vargas Hector H Herceg Zdenko Z Parada Humberto H Santella Regina M RM Gammon Marilie D MD Chen Jia J

Clinical cancer research : an official journal of the American Association for Cancer Research 20171114 3

Purpose: Breast cancer is among the leading causes of cancer-related death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated miRNA-associated genes and breast cancer survival in a well-characterized population-based study.Experimental Design: A recently developed algorithm, ActMiR, was used to identify key miRNA "activities" corresponding to target gene degradation, which were predictive of breas ...[more]

PMID: 29138345

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Project description:pancreatic cancer (PC) has increasing incidence and mortality in developing countries, and drug resistance is a significant hindrance to the efficacy of successful treatment. The objective of this systematic review and meta-analysis was to evaluate the association between miRNAs and response to chemotherapy in pancreatic cancer patients. the systematic review and meta-analysis was based on articles collected from a thorough search of PubMed and Science Direct databases for publications spanning from January 2008 to December 2018. The articles were screened via a set of inclusion and exclusion criteria based on the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. Data was extracted, collated and tabulated in MS Excel for further synthesis. Hazard ratio (HR) was selected as the effect size metric to be pooled across studies for the meta-analysis, with the random effects model being applied. Subgroup analysis was also conducted, and the presence of publication bias in the selected studies was assessed. Publication bias of the included studies was quantified. of the 169 articles screened, 43 studies were included in our systematic review and 13 articles were included in the meta-analysis. Gemcitabine was observed to be the principal drug used in a majority of the studies. A total of 48 miRNAs have been studied, and 18 were observed to have possible contributions to chemoresistance, while 15 were observed to have possible contributions to chemosensitivity. 41 drug-related genetic pathways have been identified, through which the highlighted miRNA may be affecting chemosensitivity/resistance. The pooled HR value for overall survival was 1.603; (95% Confidence Interval (CI) 1.2-2.143; p-value: 0.01), with the subgroup analysis for miR-21 showing HR for resistance of 2.061; 95% CI 1.195-3.556; p-value: 0.09. our results highlight multiple miRNAs that have possible associations with modulation of chemotherapy response in pancreatic cancer patients. Further studies are needed to discover the molecular mechanisms underlying these associations before they can be suggested for use as biomarkers of response to chemotherapeutic interventions in pancreatic cancer.

Project description:BACKGROUND:Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. METHODS:We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan-Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. RESULTS:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p?=?1.215e?-?06 in the training set; p?=?0.0069 in the validation set; p?=?1.233e?-?07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR?=?1.432; 95% CI 1.204-1.702, p?<?0.001), validation set (HR?=?1.162; 95% CI 1.004-1.345, p?=?0.044), and whole set (HR?=?1.240; 95% CI 1.128-1.362, p?<?0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. CONCLUSIONS:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Project description:BackgroundMicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level.ResultsWe adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm.ConclusionsThe obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.

Dataset Information

Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis.

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Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis.

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