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Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer.

Krishnan P, Ghosh S, Wang B, Li D, Narasimhan A, Berendt R, Graham K, Mackey JR, Kovalchuk O, Damaraju S - BMC Genomics (2015)

Bottom Line: Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders.In both the approaches, the risk scores were significant after adjusting for potential confounders.The study identified twelve non-redundant miRNAs associated with OS and/or RFS.

View Article: PubMed Central - PubMed

Affiliation: Department of Laboratory Medicine and Pathology, University of Alberta, 11560-University Avenue, Edmonton, AB, T6G 1Z2, Canada. preethi@ualberta.ca.

ABSTRACT

Background: Prognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC.

Results: In our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case-control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration.

Conclusions: The study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.

No MeSH data available.


Related in: MedlinePlus

Kaplan-Meier plots for Recurrence Free Survival (Discovery cohort). Kaplan-Meier plots were used to estimate RFS in Case–control approach (a) and Case-only approach (b). Log rank test was performed to assess differences in survival between the two risk groups. Patients belonging to the high-risk group had shorter RFS in both (a) and (b)
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Fig4: Kaplan-Meier plots for Recurrence Free Survival (Discovery cohort). Kaplan-Meier plots were used to estimate RFS in Case–control approach (a) and Case-only approach (b). Log rank test was performed to assess differences in survival between the two risk groups. Patients belonging to the high-risk group had shorter RFS in both (a) and (b)

Mentions: Case–control approach: Eighty DE miRNAs were treated as continuous variables and were subjected to univariate Cox analysis, followed by permutation test. Four miRNAs were associated with OS and two miRNAs were associated with RFS with permutation p ≤ 0.1. The four and two miRNAs identified for OS (Table 2A) and RFS (Table 3A), respectively were used for constructing the risk score. A risk score cut-off point of 1.07 for OS was used to dichotomize the cases into low- (≤1.07) and high-risk groups (>1.07). Similarly, samples were grouped into the two risk groups based on the cut-off point estimated for RFS (0.72). Risk score was then treated as a categorical variable and entered into the univariate Cox model. Tumor stage, grade, age at diagnosis and TNBC status were considered as other clinical covariates and were first tested for their significance in the univariate Cox model. Tumor stage, grade and age at diagnosis were considered as potential confounders, and, irrespective of their significance in the univariate analysis, they were entered into the multivariate model along with the risk score. The higher-risk group was found to have both shorter OS (Hazard ratio, HR = 2.71, p = 0.004; Table 4A, Fig. 3a) and RFS (HR = 2.27, p = 0.003; Table 5A, Fig. 4a), after adjusting for confounders (tumor stage and age at diagnosis for OS and tumor stage for RFS).Table 1


Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer.

Krishnan P, Ghosh S, Wang B, Li D, Narasimhan A, Berendt R, Graham K, Mackey JR, Kovalchuk O, Damaraju S - BMC Genomics (2015)

Kaplan-Meier plots for Recurrence Free Survival (Discovery cohort). Kaplan-Meier plots were used to estimate RFS in Case–control approach (a) and Case-only approach (b). Log rank test was performed to assess differences in survival between the two risk groups. Patients belonging to the high-risk group had shorter RFS in both (a) and (b)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4587870&req=5

Fig4: Kaplan-Meier plots for Recurrence Free Survival (Discovery cohort). Kaplan-Meier plots were used to estimate RFS in Case–control approach (a) and Case-only approach (b). Log rank test was performed to assess differences in survival between the two risk groups. Patients belonging to the high-risk group had shorter RFS in both (a) and (b)
Mentions: Case–control approach: Eighty DE miRNAs were treated as continuous variables and were subjected to univariate Cox analysis, followed by permutation test. Four miRNAs were associated with OS and two miRNAs were associated with RFS with permutation p ≤ 0.1. The four and two miRNAs identified for OS (Table 2A) and RFS (Table 3A), respectively were used for constructing the risk score. A risk score cut-off point of 1.07 for OS was used to dichotomize the cases into low- (≤1.07) and high-risk groups (>1.07). Similarly, samples were grouped into the two risk groups based on the cut-off point estimated for RFS (0.72). Risk score was then treated as a categorical variable and entered into the univariate Cox model. Tumor stage, grade, age at diagnosis and TNBC status were considered as other clinical covariates and were first tested for their significance in the univariate Cox model. Tumor stage, grade and age at diagnosis were considered as potential confounders, and, irrespective of their significance in the univariate analysis, they were entered into the multivariate model along with the risk score. The higher-risk group was found to have both shorter OS (Hazard ratio, HR = 2.71, p = 0.004; Table 4A, Fig. 3a) and RFS (HR = 2.27, p = 0.003; Table 5A, Fig. 4a), after adjusting for confounders (tumor stage and age at diagnosis for OS and tumor stage for RFS).Table 1

Bottom Line: Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders.In both the approaches, the risk scores were significant after adjusting for potential confounders.The study identified twelve non-redundant miRNAs associated with OS and/or RFS.

View Article: PubMed Central - PubMed

Affiliation: Department of Laboratory Medicine and Pathology, University of Alberta, 11560-University Avenue, Edmonton, AB, T6G 1Z2, Canada. preethi@ualberta.ca.

ABSTRACT

Background: Prognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC.

Results: In our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case-control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration.

Conclusions: The study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.

No MeSH data available.


Related in: MedlinePlus