scholarly journals Network based multifactorial modelling of miRNA-target interactions

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11121
Author(s):  
Selcen Ari Yuka ◽  
Alper Yilmaz
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Mirna Target ◽  
R Package ◽  
Breast Cancer Patients ◽  
Competing Endogenous Rna ◽  
Seed Type ◽  
Complex Interactions ◽  
Non Coding Rna ◽  
Interaction Factors

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in humans is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this article, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values. Our approach calculates network-wide effects of perturbations in the expression level of one or more nodes in the presence or absence of miRNA interaction factors such as seed type, binding energy. We carried out the analysis of large-scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer-associated genes and miRNAs. Our network-based approach takes the sponge effect into account and helps to unveil the crosstalk between nodes in miRNA:target network. The model has potential to reveal unforeseen regulations that are only evident in the network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs, and available as R package ceRNAnetsim: https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.

scholarly journals Network based multifactorial modelling of miRNA-target interactions

2020 ◽  
Author(s):  
Selcen Ari Yuka ◽  
Alper Yilmaz
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Mirna Target ◽  
R Package ◽  
Breast Cancer Patients ◽  
Competing Endogenous Rna ◽  
Seed Type ◽  
Complex Interactions ◽  
Non Coding Rna ◽  
Interaction Factors

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in human is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this paper, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values and then calculates network-wide effects after perturbation in expression level of element(s) while utilizing miRNA interaction factors such as seed type, binding energy. We have carried out analysis of large scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer associated genes and miRNAs. Our network-based approach helps unveiling the crosstalk between node elements in miRNA:target network where abundance of targets leading to sponge effect is taken into account. The model has potential to reveal unforeseen and unpredicted regulations which are only evident when considered in network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs and available as R package ceRNAnet-sim https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals The Effect of Acupuncture in Breast Cancer–Related Lymphoedema (BCRL): A Systematic Review and Meta-Analysis

2019 ◽  
Vol 18 ◽  
pp. 153473541986691 ◽  
Author(s):  
Tsai-Ju Chien ◽  
Chia-Yu Liu ◽  
Ching-Ju Fang
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Randomized Controlled Trials ◽  
Large Scale ◽  
Meta Analysis ◽  
Lymphatic Drainage ◽  
Controlled Trials ◽  
Breast Cancer Patients ◽  
Eligibility Criteria ◽  
Randomized Controlled

Background: Breast cancer–related lymphedema (BCRL) is hard to control. Management may include lymphatic drainage, skin care, bandaging, or even surgery. Since acupuncture has been proven to affect the neurophysiology and neuroendocrine systems, it has the potential to control BCRL. Aim: To evaluate the effect of acupuncture in BCRL in randomized controlled trials. Design: A literature search was performed, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement and without language restrictions. Data Sources: Five databases were searched from inception tthrough September 2018. Only studies that fulfilled the eligibility criteria of evaluating the effect of acupuncture on lymphedema in breast cancer were included. The methodological quality of these trials was assessed using the Cochrane criteria, and meta-analysis software (RevMan 5.3) was used for analysis. Results: We examined 178 breast cancer patients from 6 trials. All included randomized controlled trials had medium to high quality, based on the modified Jadad scale. The systematic review showed that acupuncture is safe and has a trend to improve symptoms, but trials did not consistently measure outcomes. The meta-analysis showed that acupuncture produced no significant improvement in the extent of lymphedema as compared with the control intervention (−1.90; 95% confidence interval = −5.39 to 1.59, P = .29). None of the studies reported severe adverse events. Conclusions: Acupuncture is safe and has a trend to improve the lymphedema related to breast cancer, yet it did not significantly change arm circumference in BCRL. Future studies should include both subjective and objective measurements and large-scale studies are warranted.

scholarly journals Does HOTAIR expression level in the peripheral blood have veritably predictive/prognostic impact on breast cancer patients?

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Vahid Ezzatizadeh ◽  
Hossein Mozdarani
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Peripheral Blood ◽  
Expression Level ◽  
Prognostic Impact ◽  
Breast Cancer Patients ◽  
Blood Biomarker ◽  
Non Coding Rna ◽  
Before And After ◽  
Significant Expression

AbstractAs a peripheral blood biomarker, the crucial role of long non-coding RNA (lncRNA) HOTAIR has recently been suggested in many types of disorder. Among these reports, few investigations have indicated overexpression of HOTAIR transcript in the breast cancer patients’ peripheral blood. In this regard, we studied the potential impact of radiotherapy on the peripheral blood HOTAIR expression of different breast cancer patients. Curiously, no significant expression level of HOTAIR was determined in the breast cancer patients’ peripheral blood, before and after radiotherapy (10 Gy exposure). Deliberating these investigations raised some debates on the specificity of the utilized methods, the corresponding obtained findings and impact of HOTAIR expression on breast cancer predication, as a potential peripheral blood biomarker, which is discussed in this article.

scholarly journals Effect of Estrogen Receptor Expression Level and Hormonal Therapy on Prognosis of Early Breast Cancer

2021 ◽  
Author(s):  
Kyung-Hwak Yoon ◽  
Yeshong Park ◽  
Eunyoung Kang ◽  
Eun-Kyu Kim ◽  
Jee Hyun Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Hormonal Therapy ◽  
Large Scale ◽  
Receptor Expression ◽  
Recurrence Free Survival ◽  
Breast Cancer Patients ◽  
Ki 67 ◽  
Free Survival ◽  
Er Expression

Abstract PurposeEstrogen receptor (ER) expression in breast cancer plays an essential role in carcinogenesis and disease progression. Recently, tumors with low level (1-10%) of ER expression have been separately defined as ER Low Positive (ERlow). It is suggested that ERlow tumors might be morphologically and behaviorally different from tumors with high ER expression (ERhigh).MethodsRetrospective analysis of a prospective cohort database was performed. Patients who underwent curative surgery for early breast cancer and had available medical records were included for analysis. Difference in clinicopathological characteristics, endocrine responsiveness and five-year recurrence-free survival was evaluated between different ER subgroups (ERhigh, ERlow, and ER-negative (ER-)).ResultsA total of 2162 breast cancer patients were included in the analysis, Tis and T1 stage. Among them, 1654 (76.5%) were ERhigh, 54 (2.5%) were ERlow, and 454 (21.0%) were ER- patients. ERlow cases were associated with smaller size, higher histologic grade, positive human epidermal growth factor receptor 2 (HER2), negative progesterone receptor, and higher Ki-67 expression. Recurrence rate was highest in ER- tumors and was inversely proportional to ER expression. Recurrence-free survival was not affected by hormonal therapy in the ERlow group (P = 0.418).ConclusionERlow breast cancer showed distinct clinicopathological features. ERlow tumors seemed to have higher recurrence rates compared to ERhigh tumors, and they showed no significant benefit from hormonal therapy. Future large scale prospective studies are necessary to validate the treatment options for ERlow breast cancer.

Recruiting breast cancer patients for mHealth research: Obstacles to clinic-based recruitment for a mobile phone app intervention study

2020 ◽  
Vol 17 (6) ◽  
pp. 675-683
Author(s):  
Alisha Gupta ◽  
Gabrielle Ocker ◽  
Philip I Chow
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Pilot Study ◽  
Cancer Patients ◽  
Large Scale ◽  
Newly Diagnosed ◽  
Breast Cancer Patients ◽  
Pilot Studies ◽  
Staff Members ◽  
The Impact

Background Nearly half of newly diagnosed breast cancer patients will report clinically significant symptoms of depression and/or anxiety within the first year of diagnosis. Research on the trajectory of distress in cancer patients suggests that targeting patients early in the diagnostic pathway could be particularly impactful. Given the recent rise of smartphone adoption, apps are a convenient and accessible platform from which to deliver mental health support; however, little research has examined their potential impact among newly diagnosed cancer patients. One reason is likely due to the obstacles associated with in-clinic recruitment of newly diagnosed cancer patients for mHealth pilot studies. Methods This article draws from our experiences of a recently completed pilot study to test a suite of mental health apps in newly diagnosed breast cancer patients. Recruitment strategies included in-clinic pamphlets, flyers, and direct communication with clinicians. Surgical oncologists and research staff members approached eligible patients after a medical appointment. Research team members met with patients to provide informed consent and review the study schedule. Results Four domains of in-clinic recruitment challenges emerged: (a) coordination with clinic staff, (b) perceived burden among breast cancer patients, (c) limitations regarding the adoption and use of technology, and (d) availability of resources. Potential solutions are provided for each challenge. Conclusion Recruitment of newly diagnosed cancer patients is a major challenge to conducting mobile intervention studies for researchers on a pilot-study budget. To realize the impact of mobile interventions for the most vulnerable cancer patient populations, health researchers must address barriers to in-clinic recruitment to provide vital preliminary data in proposals of large-scale research projects.

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