scholarly journals Rapid Point-Of-Care Breath Test Predicts Breast Cancer And Abnormal Mammograms in Symptomatic Women

2020 ◽  
Author(s):  
Michael Phillips ◽  
Therese B Bevers ◽  
Linda Hovanessian Larsen ◽  
Nadine Pappas ◽  
Sonali Pathak
Keyword(s):  
Breast Cancer ◽  
Point Of Care ◽  
Nipple Discharge ◽  
Monte Carlo Analysis ◽  
Invasive Cancer ◽  
Data Sets ◽  
Training Set ◽  
Data Set ◽  
Point Of Care Test ◽  
Validation Set

ABSTRACTBackgroundPrevious studies have reported volatile organic compounds (VOCs) in the breath as biomarkers of breast cancer. These biomarkers may be derived from cancer-associated fibroblasts, in which oxidative stress degrades polyunsaturated fatty acids to volatile alkanes and methylated alkane derivatives that are excreted in the breath. We evaluated a rapid point-of-care test for breath VOC biomarkers as predictors of breast cancer and abnormal mammograms.MethodsWe studied 593 women aged ≥ 18 yr referred to three sites for mammography for a symptomatic breast-related concern (e.g. breast mass, nipple discharge). A rapid point-of-care breath testing system collected and concentrated alveolar breath VOCs on a sorbent trap and analyzed them with gas chromatography and surface acoustic wave detection in < 6 min. Breath VOC chromatograms were randomly assigned to a training set or to a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer and abnormal mammograms in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set.ResultsPrediction of breast cancer: 50 women had biopsy-proven breast cancer (invasive cancer 41, ductal non-invasive cancer 9) Unsplit data set: Breath VOCs identified breast cancer with 83% accuracy (area under curve of receiver operating characteristic), 82% sensitivity and 77.1% specificity. Split data sets: Training set breath VOCs identified breast cancer with 80.3% accuracy, 84% sensitivity and 74.3% specificity. Corresponding values in the validation set were 68%% accuracy, 72.4% sensitivity and 61.5% specificity.Prediction of BIRADS 4 and 5 mammograms (versus BIRADS 1, 2 and 3): Unsplit data set: Breath VOCs identified abnormal mammograms with 76.2% accuracy. Split data sets: Breath VOCs identified abnormal mammograms with 74.2% accuracy, 73.3% sensitivity and 60% specificity. Corresponding values in the validation set were 60.5% accuracy, 64.2% sensitivity and 51% specificity.ConclusionsA rapid point-of-care test for breath VOC biomarkers accurately predicted risk of breast cancer and abnormal mammograms in women with breast-related symptoms.

Neural network-based sperm whale click classification

2007 ◽  
Vol 87 (1) ◽  
pp. 35-38 ◽  
Author(s):  
M. van der Schaar ◽  
E. Delory ◽  
A. Català ◽  
M. André
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Radial Basis Function Network ◽  
Sperm Whale ◽  
Data Sets ◽  
Sperm Whales ◽  
Training Set ◽  
Data Set ◽  
Validation Set ◽  
Function Network

Recordings of a group of foraging sperm whales usually result in a mixture of clicks from different animals. To analyse the click sequences of individual whales these clicks need to be separated, and for this an automatic classifier would be preferred. Here we study the use of a radial basis function network to perform the separation. The neural network's ability to discriminate between different whales was tested with six data sets of individually diving males. The data consisted of five shorter click trains and one complete dive which was especially important to evaluate the capacity of the network to generalize. The network was trained with characteristics extracted from the six click series with the help of a wavelet packet-based local discriminant basis. The selected features were separated in a training set containing 50 clicks of each data set and a validation set with the remaining clicks. After the network was trained it could correctly classify around 90% of the short click series, while for the entire dive this percentage was around 78%.

scholarly journals Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on CT Localization Images for Radiotherapy

2021 ◽  
Author(s):  
Xiaobo Wen ◽  
Biao Zhao ◽  
Meifang Yuan ◽  
Jinzhi Li ◽  
Mengzhen Sun ◽  
...  
Keyword(s):  
Thyroid Gland ◽  
Clinical Work ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Training Set ◽  
Data Set ◽  
Test Set ◽  
Noise Interference ◽  
Multi Scale ◽  
Validation Set

Abstract Objectives: To explore the performance of Multi-scale Fusion Attention U-net (MSFA-U-net) in thyroid gland segmentation on CT localization images for radiotherapy. Methods: CT localization images for radiotherapy of 80 patients with breast cancer or head and neck tumors were selected; label images were manually delineated by experienced radiologists. The data set was randomly divided into the training set (n=60), the validation set (n=10), and the test set (n=10). Data expansion was performed in the training set, and the performance of the MSFA-U-net model was evaluated using the evaluation indicators Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). Results: With the MSFA-U-net model, the DSC, JSC, PPV, SE, and HD indexes of the segmented thyroid gland in the test set were 0.8967±0.0935, 0.8219±0.1115, 0.9065±0.0940, 0.8979±0.1104, and 2.3922±0.5423, respectively. Compared with U-net, HR-net, and Attention U-net, MSFA-U-net showed that DSC increased by 0.052, 0.0376, and 0.0346 respectively; JSC increased by 0.0569, 0.0805, and 0.0433, respectively; SE increased by 0.0361, 0.1091, and 0.0831, respectively; and HD increased by −0.208, −0.1952, and −0.0548, respectively. The test set image results showed that the thyroid edges segmented by the MSFA-U-net model were closer to the standard thyroid delineated by the experts, in comparison with those segmented by the other three models. Moreover, the edges were smoother, over-anti-noise interference was stronger, and oversegmentation and undersegmentation were reduced. Conclusion: The MSFA-U-net model can meet basic clinical requirements and improve the efficiency of physicians' clinical work.

scholarly journals Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Dose-Response ◽  
2019 ◽  
Vol 17 (4) ◽  
pp. 155932581989417 ◽  
Author(s):  
Zhi Huang ◽  
Jie Liu ◽  
Liang Luo ◽  
Pan Sheng ◽  
Biao Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Signaling Pathway ◽  
Risk Score ◽  
Low Risk ◽  
Training Data ◽  
The Cancer Genome Atlas ◽  
Training Set ◽  
Data Set ◽  
Validation Set ◽  
Cox Analysis

Background: Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC). Methods: Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis. Results: After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction. Conclusions: Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

Estrogen-Regulated Genes Predict Survival in Hormone Receptor–Positive Breast Cancers

2006 ◽  
Vol 24 (11) ◽  
pp. 1656-1664 ◽  
Author(s):  
Daniel S. Oh ◽  
Melissa A. Troester ◽  
Jerry Usary ◽  
Zhiyuan Hu ◽  
Xiaping He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Published Data ◽  
Data Sets ◽  
Breast Cancers ◽  
Outcome Predictor ◽  
Training Set ◽  
Hormone Receptor Positive ◽  
Prognosis Group ◽  
Mcf 7

Purpose The prognosis of a patient with estrogen receptor (ER) and/or progesterone receptor (PR) –positive breast cancer can be highly variable. Therefore, we developed a gene expression–based outcome predictor for ER+ and/or PR+ (ie, luminal) breast cancer patients using biologic differences among these tumors. Materials and Methods The ER+ MCF-7 breast cancer cell line was treated with 17β-estradiol to identify estrogen-regulated genes. These genes were used to develop an outcome predictor on a training set of 65 luminal epithelial primary breast carcinomas. The outcome predictor was then validated on three independent published data sets. Results The estrogen-induced gene set identified in MCF-7 cells was used to hierarchically cluster a 65 tumor training set into two groups, which showed significant differences in survival (P = .0004). Supervised analyses identified 822 genes that optimally defined these two groups, with the poor-prognosis group IIE showing high expression of cell proliferation and antiapoptosis genes. The good prognosis group IE showed high expression of estrogen- and GATA3-regulated genes. Mean expression profiles (ie, centroids) created for each group were applied to ER+ and/or PR+ tumors from three published data sets. For all data sets, Kaplan-Meier survival analyses showed significant differences in relapse-free and overall survival between group IE and IIE tumors. Multivariate Cox analysis of the largest test data set showed that this predictor added significant prognostic information independent of standard clinical predictors and other gene expression–based predictors. Conclusion This study provides new biologic information concerning differences within hormone receptor–positive breast cancers and a means of predicting long-term outcomes in tamoxifen-treated patients.

scholarly journals A Novel Systematic Oxidative Stress Score Predicts the Prognosis of Patients with Operable Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Kaiming Zhang ◽  
Liqin Ping ◽  
Tian Du ◽  
Yan Wang ◽  
Ya Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Patients ◽  
Operable Breast Cancer ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Oxidative Stress Biomarkers ◽  
Stress Biomarkers ◽  
Validation Set ◽  
The Relationship

Background. Breast cancer was associated with imbalance between oxidation and antioxidation. Local oxidative stress in tumors is closely related to the occurrence and development of breast cancer. However, the relationship between systematic oxidative stress and breast cancer remains unclear. This study is aimed at exploring the prognostic value of systematic oxidative stress in patients with operable breast cancer. Methods. A total of 1583 operable female breast cancer patients were randomly assigned into the training set and validation set. The relationship between systematic oxidative stress biomarkers and prognosis were analyzed in the training and validation sets. Results. The systematic oxidative stress score (SOS) was established based on five systematic oxidative stress biomarkers including serum creatinine (CRE), serum albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). SOS was an independent prognostic factor for operable breast cancer patients. A nomogram based on SOS and clinical characteristics could accurately predict the prognosis of operable breast cancer patients, and the area under the curve (AUC) of the nomogram was 0.823 in the training set and 0.872 in the validation set, which was much higher than the traditional prognostic indicators. Conclusions. SOS is an independent prognostic indicator for operable breast cancer patients. A prediction model based on SOS could accurately predict the outcome of operable breast cancer patients.

scholarly journals Risk Stratification Model for Predicting the Overall Survival of Elderly Triple-Negative Breast Cancer Patients: A Population-Based Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaozhu Liu ◽  
Song Yue ◽  
Haodong Huang ◽  
Minjie Duan ◽  
Binyi Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Risk Stratification ◽  
Elderly Patients ◽  
Triple Negative ◽  
Training Set ◽  
Medium Risk ◽  
Validation Set ◽  
Stratification System ◽  
Risk Stratification System

Background: The objective of this study was to evaluate the prognostic value of clinical characteristics in elderly patients with triple-negative breast cancer (TNBC).Methods: The cohort was selected from the Surveillance, Epidemiology, and End Results (SEER) program dating from 2010 to 2015. Univariate and multivariate analyses were performed using a Cox proportional risk regression model, and a nomogram was constructed to predict the 1-, 3-, and 5-year prognoses of elderly patients with TNBC. A concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to verify the nomogram.Results: The results of the study identified a total of 5,677 patients who were randomly divided 6:4 into a training set (n = 3,422) and a validation set (n = 2,255). The multivariate analysis showed that age, race, grade, TN stage, chemotherapy status, radiotherapy status, and tumor size at diagnosis were independent factors affecting the prognosis of elderly patients with TNBC. Together, the 1 -, 3 -, and 5-year nomograms were made up of 8 variables. For the verification of these results, the C-index of the training set and validation set were 0.757 (95% CI 0.743–0.772) and 0.750 (95% CI 0.742–0.768), respectively. The calibration curve also showed that the actual observation of overall survival (OS) was in good agreement with the prediction of the nomograms. Additionally, the DCA showed that the nomogram had good clinical application value. According to the score of each patient, the risk stratification system of elderly patients with TNBC was further established by perfectly dividing these patients into three groups, namely, low risk, medium risk, and high risk, in all queues. In addition, the results showed that radiotherapy could improve prognosis in the low-risk group (P = 0.00056), but had no significant effect in the medium-risk (P &lt; 0.4) and high-risk groups (P &lt; 0.71). An online web app was built based on the proposed nomogram for convenient clinical use.Conclusion: This study was the first to construct a nomogram and risk stratification system for elderly patients with TNBC. The well-established nomogram and the important findings from our study could guide follow-up management strategies for elderly patients with TNBC and help clinicians improve individual treatment.

scholarly journals A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Li ◽  
Jiajia Du ◽  
Yanhong Wang ◽  
Hongyan Jia
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Prognostic Model ◽  
Ductal Carcinoma ◽  
Risk Group ◽  
Metastatic Breast ◽  
Related Gene ◽  
Training Set ◽  
Gene Set ◽  
Validation Set

Background: Invasive ductal carcinoma (IDC) is the most common type of metastatic breast cancer. Due to the lack of valuable molecular biomarkers, the diagnosis and prognosis of IDC remain a challenge. A large number of studies have confirmed that coagulation is positively correlated with angiogenesis-related factors in metastatic breast cancer. Therefore, the purpose of this study was to construct a COAGULATION-related genes signature for IDC using the bioinformatics approaches.Methods: The 50 hallmark gene sets were obtained from the molecular signature database (MsigDB) to conduct Gene Set Variation Analysis (GSVA). Gene Set Enrichment Analysis (GSEA) was applied to analyze the enrichment of HALLMARK_COAGULATION. The COAGULATION-related genes were extracted from the gene set. Then, Limma Package was used to identify the differentially expressed COAGULATION-related genes (DECGs) between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) samples in GSE26340 data set. A total of 740 IDC samples from The Cancer Genome Atlas (TCGA) database were divided into a training set and a validation set (7:3). The univariate and multivariate Cox regression analyses were performed to construct a risk signature, which divided the IDC samples into the high- and low-risk groups. The overall survival (OS) curve and receiver operating characteristic (ROC) curve were drawn in both training set and validation set. Finally, a nomogram was constructed to predict the 1-, 2-, 3-, 4-, and 5-year survival rates of IDC patients. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic genes.Results: The “HALLMARK_COAGULATION” was significantly activated in IDC. There was a significant difference in the clinicopathological parameters between the DCIS and IDC patients. Twenty-four DECGs were identified, of which five genes (SERPINA1, CAPN2, HMGCS2, MMP7, and PLAT) were screened to construct the prognostic model. The high-risk group showed a significantly lower survival rate than the low-risk group both in the training set and validation set (p=3.5943e-06 and p=0.014243). The risk score was demonstrated to be an independent predictor of IDC prognosis. A nomogram including risk score, pathological_stage, and pathological_N provided a quantitative method to predict the survival probability of 1-, 2-, 3-, 4-, and 5-year in IDC patients. The results of decision curve analysis (DCA) further demonstrated that the nomogram had a high potential for clinical utility.Conclusion: This study established a COAGULATION-related gene signature and showed its prognostic value in IDC through a comprehensive bioinformatics analysis, which may provide a potential new prognostic mean for patients with IDC.

scholarly journals Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244693
Author(s):  
Lingchen Wang ◽  
Wenhua Wang ◽  
Shaopeng Zeng ◽  
Huilie Zheng ◽  
Quqin Lu
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cancer Metastasis ◽  
External Validation ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Validation Set ◽  
Robust Likelihood ◽  
Six Genes

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What’s more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.

scholarly journals T203. RELEVANT CLOZAPINE BLOOD LEVELS USING A POINT OF CARE TEST (POCT)

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S309-S309
Author(s):  
Deanna Kelly ◽  
Ann Marie Kearns ◽  
Matthew Glassman ◽  
Matthew Atkins ◽  
Philip McQuire
Keyword(s):  
Whole Blood ◽  
Predictive Value ◽  
Patient Adherence ◽  
Point Of Care ◽  
Target Range ◽  
Blood Levels ◽  
Clinical Implementation ◽  
Training Set ◽  
Average Bias ◽  
Validation Set

Abstract Background Clozapine is one of the most underused medications in psychiatry for many reasons including mandatory blood testing, fear of serious side-effects, lack of patient adherence. A critical barrier to adoption could be addressed with the ability to measure clozapine at point-of-care (POC) from a fingerstick. Current practice of clozapine measurements, however, was developed based on serum levels. Therefore, meaningful POC results must be reported as the serum equivalent. We evaluated a new immunoassay method to measure clozapine in whole blood to establish standardization to serum, and to assess the ability of the POCT to detect differences in patients’ clozapine levels compared to an existing laboratory method. Methods A whole blood POCT (MyCare® Insite Clozapine Test on the MyCare Insite)* immunoassay was compared to liquid chromatography tandem mass-spectrometry (LC-MS/MS) in serum with 95 matched patient samples. Passing-Bablok regression was used to compare results and establish calibrator values to standardize the POCT to report whole blood results as equivalent to serum. The standardization was validated by a method comparison to LC-MS/MS with 304 samples collected from patients with schizophrenia who were being treated with clozapine. Serial blood levels were taken for 13 patients to compare deviation from baseline for POCT and LC-MS/MS results. To detect a discordant difference in clozapine levels, the difference to the preceding value was calculated for 73 sequential samples of the 86 total results. Because of high intra-patient variability changes of &gt; ±50% were considered significant. Results There was good correlation (R = 0.9) between the POCT and LC-MS/MS in the training set (N=95). Passing-Bablok statistics were: slope = 1.02, intercept = -2.3, R = 0.9, average bias -17.7 (-3.8%). The average values (± SD) were 479.7 (± 181.5) ng/mL for LC-MS/MS and 462.0 (± 199.1) ng/mL for POCT. The Passing-Bablok regression of the validation set (N=304), using the reassigned calibrator values as the training set, gave a slope = 0.971, intercept = -21.2, R = 0.9, mean values (± SD) of 445.6 (± 242.4) for LC-MS/MS and, 412.6 (± 245.7) for the POCT, average bias was -33.0 (-7.7%). Bias between POCT and LC-MS/MS for 12 individuals ranged from -22% to 22%. One patient with five sequential measurements had a total bias of -34% with 4 of 5 results, agreeing with assignment in or out of the presumptive target range of 350 – 600 ng/mL. The frequency of &gt;±50% change in clozapine levels was &lt;5%. Ninety percent (66 of 73) of results agreed, selectivity = 50%, specificity = 94%, positive predictive value (PPV) = 42.9%, negative predictive value (NPV) = 95.5%. Seven samples had a 50% change by one method and not the other. There was only one discrepant sample that was 66% lower with POCT. Discussion Differences in measurement methods are expected. The good correlation and similarity of results between the calibrator assignment training set and the validation set demonstrates the accuracy of the calibrator value assignment. The POCT was highly selective in detecting important changes in clozapine levels of more than 50% which would occur secondary to non-adherence, change in life-style habits or drug-drug interactions. The collection conditions gave consistent levels for most patients, with few large shifts in concentration, thus underestimating the PPV. These data suggest that clozapine levels can be accurately measured from a small volume of capillary blood collected via a fingerstick sample. This method makes blood sampling easier for both patients and clinical staff, and provides a result in a few minutes, at point of care. Its clinical implementation may facilitate the safe and effective use of clozapine in schizophrenia. *CE mark/US RUO

scholarly journals Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Chaowang Lan ◽  
Hui Peng ◽  
Gyorgy Hutvagner ◽  
Jinyan Li
Keyword(s):  
Breast Cancer ◽  
Mutual Information ◽  
Noncoding Rna ◽  
Regulation Mechanism ◽  
Data Sets ◽  
Rna Seq ◽  
Competing Endogenous Rna ◽  
Data Set ◽  
Cerna Network ◽  
Pointwise Mutual Information

Abstract Background A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. Results We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. Conclusion Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

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