A novel approach to identify subtype-specific network biomarkers of breast cancer survivability

Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis

Background

Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study's systematic review and meta-analysis.

Methods

Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms ("Breast Cancer", "Survival Prediction", and "Machine Learning") and their synonyms. Original articles applying ML algorithms for BC survival prediction using clinical data were included. The quality of studies was assessed via the Qiao Quality Assessment tool.

Results

Amongst 140 identified articles, 32 met the eligibility criteria. Analyzed ML methods achieved a mean validation accuracy of 89.73%. Hybrid models, combining traditional and modern ML techniques, were mostly considered to predict survival rates (40.62%). Supervised learning was the dominant ML paradigm (75%). Common ML methodologies included pre-processing, feature extraction, dimensionality reduction, and classification. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), emerged as the preferred modern algorithm within these methodologies. Notably, 81.25% of studies relied on internal validation, primarily using K-fold cross-validation and train/test split strategies.

Conclusion

The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. However, to ensure the robustness and generalizability of these predictive models, future research should emphasize the importance of rigorous external validation. Such endeavors will not only validate the efficacy of these models across diverse populations but also pave the way for their integration into clinical practice, ultimately contributing to personalized patient care and improved survival outcomes.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024513350.

Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset

Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an effective strategy for unravelling complex patterns in cancer oncology. This research delves into breast cancer staging, classification, and diagnosis by leveraging the comprehensive dataset provided by the The Cancer Genome Atlas (TCGA). By integrating advanced machine learning algorithms with bioinformatics analysis, it introduces a cutting-edge methodology for identifying complex molecular signatures associated with different subtypes and stages of breast cancer. This study utilizes TCGA gene expression data to detect and categorize breast cancer through the application of machine learning and systems biology techniques. Researchers identified differentially expressed genes in breast cancer and analyzed them using signaling pathways, protein-protein interactions, and regulatory networks to uncover potential therapeutic targets. The study also highlights the roles of specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs (such as hsa-let-7d-5p) that are the potential biomarkers in cancer progression founded on several analyses. In terms of diagnostic accuracy for cancer staging, the random forest method achieved 97.19%, while the XGBoost algorithm attained 95.23%. Bioinformatics and machine learning meet in this study to find potential biomarkers that influence the progression of breast cancer. The combination of sophisticated analytical methods and extensive genomic datasets presents a promising path for expanding our understanding and enhancing clinical outcomes in identifying and categorizing this intricate illness.

Association of Overweight and Inflammatory Indicators with Breast Cancer: A Cross-Sectional Study in Chinese Women

Objective

This cross-sectional study aimed to explore the association of overweight and inflammatory indicators with breast cancer risk in Chinese patients.

Methods

Weight, height, and peripheral blood inflammatory indicators, including white blood cell count (WBC), neutrophil count (NE), lymphocyte count (LY), platelet count (PLT) and the concentration of hypersensitivity C-reactive protein (hsCRP), were collected in 383 patients with benign breast lumps (non-cancer) and 358 patients with malignant breast tumors (cancer) at the First Affiliated Hospital of Soochow University, China, from March 2018 to July 2020. Body mass index (BMI), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) were determined according to the ratio equation. The correlations among overweight, inflammatory indicators, and the proportion of non-cancer or cancer cases were analyzed.

Results

BMI is associated with an increased breast cancer risk. Compared with non-cancer patients, the average WBC count, NE count, NLR, and level of hsCRP were significantly higher in cancer patients. The level of hsCRP was closely associated with the size of malignant breast tumors.

Conclusion

We conclude that overweight and high levels of hsCRP may serve as putative risk factors for malignant breast tumors in Chinese women.

Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues

Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs compared to tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens for various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of breast tissues towards identifying ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five subtypes (viz., Basal, Her2, Luminal A, Luminal B, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT, escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across all six tissues including subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, Luminal A: 5, Luminal B: 5, Normal-Like: 2, and ANT: 7). Additionally, differentially correlated switching gene pairs along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes, which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.

Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package "DESeq2" and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Radiomic analysis in contrast-enhanced mammography using a multivendor data set: accuracy of models according to segmentation techniques

OBJECTIVE

Radiomic analysis of contrast-enhanced mammographic (CEM) images is an emerging field. The aims of this study were to build classification models to distinguish benign and malignant lesions using a multivendor data set and compare segmentation techniques.

METHODS

CEM images were acquired using Hologic and GE equipment. Textural features were extracted using MaZda analysis software. Lesions were segmented with freehand region of interest (ROI) and ellipsoid_ROI. Benign/Malignant classification models were built using extracted textural features. Subset analysis according to ROI and mammographic view was performed.

RESULTS

269 enhancing mass lesions (238 patients) were included. Oversampling mitigated benign/malignant imbalance. Diagnostic accuracy of all models was high (>0.9). Segmentation with ellipsoid_ROI produced a more accurate model than with FH_ROI, accuracy:0.947 vs 0.914, AUC:0.974 vs 0.86, p < 0.05. Regarding mammographic view all models were highly accurate (0.947-0.955) with no difference in AUC (0.985-0.987). The CC-view model had the greatest specificity:0.962, the MLO-view and CC + MLO view models had higher sensitivity:0.954, p < 0.05.

CONCLUSIONS

Accurate radiomics models can be built using a real-life multivendor data set segmentation with ellipsoid-ROI produces the highest level of accuracy. The marginal increase in accuracy using both mammographic views, may not justify the increased workload.

ADVANCES IN KNOWLEDGE

Radiomic modelling can be successfully applied to a multivendor CEM data set, ellipsoid_ROI is an accurate segmentation technique and it may be unnecessary to segment both CEM views. These results will help further developments aimed at producing a widely accessible radiomics model for clinical use.

AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://github.com/NeuroSyd/breast-cancer-sub-types.

Notch Signaling in Breast Tumor Microenvironment as Mediator of Drug Resistance

Notch signaling dysregulation encourages breast cancer progression through different mechanisms such as stem cell maintenance, cell proliferation and migration/invasion. Furthermore, Notch is a crucial driver regulating juxtracrine and paracrine communications between tumor and stroma. The complex interplay between the abnormal Notch pathway orchestrating the activation of other signals and cellular heterogeneity contribute towards remodeling of the tumor microenvironment. These changes, together with tumor evolution and treatment pressure, drive breast cancer drug resistance. Preclinical studies have shown that targeting the Notch pathway can prevent or reverse resistance, reducing or eliminating breast cancer stem cells. In the present review, we will summarize the current scientific evidence that highlights the involvement of Notch activation within the breast tumor microenvironment, angiogenesis, extracellular matrix remodeling, and tumor/stroma/immune system interplay and its involvement in mechanisms of therapy resistance.

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Objective

We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.

Methods

Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.

Results

129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.

Conclusions

In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.

Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis

Advanced metastasis of colon cancer makes it more difficult to treat colon cancer. Finding the markers of colon cancer (Colon Cancer) can diagnose the stage of cancer in time and improve the prognosis with timely treatment. This paper uses gene expression profiling data from The Cancer Genome Atlas (TCGA) for the diagnosis of colon cancer and its staging. In this study, we first selected the gene modules with the greatest correlation with cancer by Weighted Gene Co-expression Network Analysis (WGCNA), extracted the characteristic genes for differential expression results using the least absolute shrinkage and selection operator algorithm (Lasso) and performed survival analysis, and then combined the genes in the modules with the Lasso-extracted feature genes were combined to diagnose colon cancer versus healthy controls using RF, SVM and decision trees, and colon cancer staging was diagnosed using differentially expressed genes for each stage. Finally, Protein-Protein Interaction Networks (PPI) networks were done for 289 genes to identify clusters of aggregated proteins for survival analysis. Finally, the RF model had the best results in the diagnosis of colon cancer versus control group fold cross-validation with an average accuracy of 99.81%, F1 value reaching 0.9968, accuracy of 99.88%, and recall of 99.5%, and an average accuracy of 91.5%, F1 value reaching 0.7679, accuracy of 86.94%, and recall in the diagnosis of colon cancer stages I, II, III and IV. The recall rate reached 73.04%, and eight genes associated with colon cancer prognosis were identified for GCNT2, GLDN, SULT1B1, UGT2B15, PTGDR2, GPR15, BMP5 and CPT2.

Comprehensive Analysis of the Expression and Prognostic Significance of the CENP Family in Breast Cancer

Background

Centromere proteins (CENPs) are a set of protein-coding genes involved in the transient assembly of the kinetochore which occurs during mitosis. This study intended to clarify the expression patterns, prognosis and potential mechanisms of CENPs in breast cancer (BC).

Methods

Coexpedia was used to screen GEO datasets and PubMed articles related to CENPs and BC. CENPs expressions, prognosis and alteration were analyzed by Oncomine, Ualcan and Kaplan Meier plotter and cBioPortal. The correlation and interaction of CENPs was performed by Breast Cancer Gene-Expression Miner, GeneMANIA and STRING portal. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to clarify the functional roles of CENPs. CENPF, E, U, A, N, I, K, W, M, L were selected for further analysis.

Results

All CENPs were highly expressed in BC compared to normal tissue. High expression of CENPF, E, U, A, N, I, W, M, L and CENPF, E, U, A, N, I, M correlated with worse relapse free survival (RFS) and worse overall survival (OS), respectively. All of 10 CENPs indicated positive correlations and complex interactions between each other at mRNA expression and protein level. CENPs were enriched GO terms mainly in centromere complex assembly and KEGG terms in progesterone-mediated oocyte maturation, cell cycle and oocyte meiosis.

Conclusion

The 10 CENPs could be diagnostic biomarkers and all of them except CENPK can be used as prognosis biomarkers in BC. CENPs play an oncogenic role and may be the potential therapy targets of treatment for BC patients.

Changing Patterns in Clinicopathological Characteristics of Breast Cancer and Prevalence of BRCA Mutations: Analysis in a Rural Area of Southern China

PURPOSE

Although the burden of breast cancer remains especially high in rural China, data on the clinicopathological characteristics and prevalence of the breast cancer susceptibility gene 1/2 (BRCA1/2) mutations in patients with breast cancer remain limited. We investigated the clinicopathological characteristics, changing patterns, and prevalence of BRCA1/2 mutations in patients with breast cancer.

PATIENTS AND METHODS

The clinicopathological characteristics of 3712 women with pathologically confirmed primary breast cancer treated at Meizhou People's Hospital between January 2005 and December 2018 were evaluated. The prevalence of BRCA1/2 mutations in 340 patients with breast cancer diagnosed between January 2017 and September 2018 was also evaluated.

RESULTS

The median age at diagnosis was 49±10.5 (range, 20-94) years. Positivity for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) was observed in 59.0%, 52.5%, and 24.9% of patients, respectively. Time trend analysis revealed that an increasing trend was observed for age at diagnosis (p = 0.001), proportion of patients without a reproductive history (p < 0.001), postmenopausal patients (p = 0.001), invasive pathological cancer type (p = 0.008), ER-positive rate (p < 0.001), PR-positive rate (p = 0.008), and HER2-positive rate (p < 0.001). Compared with patients without BRCA1/2 mutations, those with BRCA1/2 mutations were more likely to have a family history of breast or ovarian cancer (p < 0.001) and have triple-negative breast cancer (TNBC) (p < 0.001). Family history of breast or ovarian cancer (odds ratio [OR], 103.58; 95% confidence interval [CI], 20.58-521.45; p < 0.001) and TNBC subtype (OR, 5.97; 95% CI, 1.16-30.90; p = 0.033) were independent predictors for BRCA1/2 mutation.

CONCLUSION

The clinicopathological characteristics of patients with breast cancer in this rural area have changed during the past decade. BRCA1/2 testing should be performed in patients with breast cancer with a family history of breast or ovarian cancer and TNBC.

Inhibition of STAT3 enhances sensitivity to tamoxifen in tamoxifen-resistant breast cancer cells

BACKGROUND

The mechanisms of endocrine resistance are complex, and deregulation of several oncogenic signalling pathways has been proposed. We aimed to investigate the role of the EGFR and Src-mediated STAT3 signalling pathway in tamoxifen-resistant breast cancer cells.

METHODS

The ER-positive luminal breast cancer cell lines, MCF-7 and T47D, were used. We have established an MCF-7-derived tamoxifen-resistant cell line (TamR) by long-term culture of MCF-7 cells with 4-hydroxytamoxifen. Cell viability was determined using an MTT assay, and protein expression levels were determined using western blot. Cell cycle and annexin V staining were analysed using flow cytometry.

RESULTS

TamR cells showed decreased expression of estrogen receptor and increased expression of EGFR. TamR cells showed an acceleration of the G1 to S phase transition. The protein expression levels of phosphorylated Src, EGFR (Y845), and STAT3 was increased in TamR cells, while phosphorylated Akt was decreased. The expression of p-STAT3 was enhanced according to exposure time of tamoxifen in T47D cells, suggesting that activation of STAT3 can cause tamoxifen resistance in ER-positive breast cancer cells. Both dasatinib (Src inhibitor) and stattic (STAT3 inhibitor) inhibited cell proliferation and induced apoptosis in TamR cells. However, stattic showed a much stronger effect than dasatinib. Knockdown of STAT3 expression by siRNA had no effect on sensitivity to tamoxifen in MCF-7 cells, while that enhanced sensitivity to tamoxifen in TamR cells. There was not a significant synergistic effect of dasatinib and stattic on cell survival. TamR cells have low nuclear p21(Cip1) expression compared to MCF-7 cells and inhibition of STAT3 increased the expression of nuclear p21(Cip1) in TamR cells.

CONCLUSIONS

The EGFR and Src-mediated STAT3 signalling pathway is activated in TamR cells, and inhibition of STAT3 may be a potential target in tamoxifen-resistant breast cancer. An increase in nuclear p21(Cip1) may be a key step in STAT3 inhibitor-induced cell death in TamR cells.

Comprehensive Analysis of the Expression, Prognosis, and Biological Significance of OVOLs in Breast Cancer

Background

The study aimed to investigate the expression of OVOLs in breast cancer (BRCA) tissues and their value in prognosis.

Methods

ONCOMINE was used to analyze the expressions of OVOL1, OVOL2, and OVOL3 mRNA between BRCA tissues and normal breast tissues. The Wilcoxon rank sum test and t-test were used to assess the expression of OVOLs between BRCA tissues and unpaired/paired normal breast tissues. GEPIA and ROC curves were used to analyze the relationship between OVOLs expression and clinical pathological stage. Kaplan–Meier plotter was used to analyze prognosis. cBioPortal was used to analyze the mutation of OVOLs. GEPIA was used to analyze the co-expression of OVOLs. GO and KEGG analyses were performed by the DAVID software to predict the function of OVOLs co-expression genes.

Results

The expression of OVOL1/2 was significantly higher in BRCA tissues than in normal breast tissues. The OVOL3 expression correlated with tumor stage. The AUC of OVOLs was 0.757, 0.754, and 0.537, respectively. OVOL1 high expression was associated with shorter overall survival (HR: 1.48; 95% CI: 1.07–2.04; P=0.018). The OVOLs were associated with pathways including axon guidance, thyroid hormone signaling pathway, and ubiquinone and other terpenoid-quinone biosynthesis.

Conclusion

OVOL1 is a new potential marker of prognosis in BRCA, and OVOL1/2 are potential therapeutic targets in BRCA.

Elevated circRNAs circ_0000745, circ_0001531 and circ_0001640 in human whole blood: Potential novel diagnostic biomarkers for breast cancer

BACKGROUND AND OBJECTIVES

Increasing studies have shown that circular RNAs (circRNAs) have great diagnostic potential in cancer. Here, we examined whether the blood circRNAs could be promising candidates as diagnostic biomarkers in breast cancer.

METHODS

Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to detect levels of five circRNAs (circ_0000501, circ_0000745, circ_0001531, circ_0001640 and circ_0001978) in 129 patients with breast cancer, 19 patients with benign breast tumor and 13 healthy controls. The diagnostic accuracy of circRNAs was assessed using the receiver operating characteristic (ROC) curve. A circRNA-miRNA-mRNA network was constructed based on bioinformatic analysis.

RESULTS

QRT-PCR validated that circ_0000745, circ_0001531 and circ_0001640 were upregulated in breast cancer, compared with benign tumor and healthy control. ROC curve analysis revealed that circ_0000745, circ_0001531 and circ_0001640 had good diagnostic potential. Notably, a signature comprising the three circRNAs showed better diagnostic potential, with the area under curve (AUC) of 0.9130 (P < 0.0001). And a circRNA-miRNA-mRNA network revealed that the circRNAs could participate in complex regulated network and thus involve in cancer development and progression.

CONCLUSIONS

Taken together, our findings support the potential of circ_0000745, circ_0001531, circ_0001640 and the three-circRNA signature as biomarkers for breast cancer diagnosis.

A two-stage modeling approach for breast cancer survivability prediction

BACKGROUND

Despite the increasing number of studies in breast cancer survival prediction, there is little attention put toward deceased patients and their survival lengths. Moreover, developing a model that is both accurate and interpretable remains a challenge.

OBJECTIVE

This paper proposes a two-stage data analytic framework, where Stage I classifies the survival and deceased statuses and Stage II predicts the number of survival months for deceased females with cancer. Since medical data are not entirely clean nor prepared for model development, we aim to show that data preparation can strengthen a simple Generalized Linear Model (GLM)¹ to predict as accurate as the complex models like Extreme Gradient Boosting (XGB)² and Multilayer Perceptron based on Artificial Neural Networks (MLP-ANNs)³ in both stages.

METHODS

In Stage I, we use recent Surveillance, Epidemiology, and End Results (SEER)⁴ data from 2004 to 2016 to predict short term survival statuses from 6-months to 3-years with 6 month increments. Synthetic Minority Over-sampling Technique (SMOTE),⁵ Relocating Safe-Level SMOTE (RSLS)⁶, Adaptive Synthetic (ADASYN)⁷ re-sampling techniques, Least Absolute Shrinkage and Selection Operator (LASSO)⁸ and Random Forest (RF)⁹ feature selection methods along with integer and one-hot encoding are combined with the three popular data mining methods: GLM, XGB, and MLP. In Stage II, we predict the number of survival months for patients who are correctly predicted as deceased within 3-years. Again, we employ GLM, XGB, and MLP for regression along with LASSO and RF for feature selection and one-hot encoding to encode the categorical features.

RESULTS

We obtain Area Under the Receiver Operating Characteristic Curve (AUC)¹⁰ values of 0.900, 0.898, 0.877, 0.852, 0.852, and 0.858 for 6-month, 1-, 1.5-, 2-, 2.5, and 3-year survival time-points, respectively, using OneHotEncoding-GLM-LASSO-ADASYN. We use the change in the Odds Ratio values in GLM to manifest the impact of individual categorical levels and numerical features on the odds of death. In Stage II, we obtain Mean Absolute Error (MAE)¹¹ of 7.960 months using OneHotEncoding-GLM-LASSO when predicting the number of survival months for deceased patients. We present the top contributing features and their coefficient values to illustrate how the presence of each feature alters the predicted number of survival months.

CONCLUSION

To the best of our knowledge, this is the first study that implements both breast cancer survival classification and regression in a two-stage approach. All data-driven findings are presented in order to assist clinicians make better care decisions using GLM, an interpretable and computationally efficient method that predicts survival status and survival lengths for deceased patients, to help foster human and machine interactions.

Synthesis and Biological Evaluation of 1-(Diarylmethyl)-1H-1,2,4-triazoles and 1-(Diarylmethyl)-1H-imidazoles as a Novel Class of Anti-Mitotic Agent for Activity in Breast Cancer

We report the synthesis and biochemical evaluation of compounds that are designed as hybrids of the microtubule targeting benzophenone phenstatin and the aromatase inhibitor letrozole. A preliminary screening in estrogen receptor (ER)-positive MCF-7 breast cancer cells identified 5-((2H-1,2,3-triazol-1-yl)(3,4,5-trimethoxyphenyl)methyl)-2-methoxyphenol 24 as a potent antiproliferative compound with an IC50 value of 52 nM in MCF-7 breast cancer cells (ER+/PR+) and 74 nM in triple-negative MDA-MB-231 breast cancer cells. The compounds demonstrated significant G2/M phase cell cycle arrest and induction of apoptosis in the MCF-7 cell line, inhibited tubulin polymerisation, and were selective for cancer cells when evaluated in non-tumorigenic MCF-10A breast cells. The immunofluorescence staining of MCF-7 cells confirmed that the compounds targeted tubulin and induced multinucleation, which is a recognised sign of mitotic catastrophe. Computational docking studies of compounds 19e, 21l, and 24 in the colchicine binding site of tubulin indicated potential binding conformations for the compounds. Compounds 19e and 21l were also shown to selectively inhibit aromatase. These compounds are promising candidates for development as antiproliferative, aromatase inhibitory, and microtubule-disrupting agents for breast cancer.

Identification of Metastasis-Associated Genes in Triple-Negative Breast Cancer Using Weighted Gene Co-expression Network Analysis

Triple-negative breast cancer (TNBC) is the most aggressive and fatal sub-type of breast cancer. This study aimed to identify metastasis-associated genes that could serve as biomarkers for TNBC diagnosis and prognosis. RNA-seq data and clinical information on TNBC from the Cancer Genome Atlas were used to conduct analyses. Expression data were used to establish co-expression modules using average linkage hierarchical clustering. We used weighted gene co-expression network analysis to explore the associations between gene sets and clinical features and to identify metastasis-associated candidate biomarkers. The K-M plotter website was used to explore the association between the expression of candidate biomarkers and patient survival. In addition, receiver operating characteristic curve analysis was used to illustrate the diagnostic performance of candidate genes. The pale turquoise module was significantly associated with the occurrence of metastasis. In this module, 64 genes were identified, and its functional enrichment analysis revealed that they were mainly associated with transcriptional misregulation in cancer, microRNAs in cancer, and negative regulation of angiogenesis. Further, 4 genes, IGSF10, RUNX1T1, XIST, and TSHZ2, which were negatively associated with relapse-free survival and have seldom been reported before in TNBC, were selected. In addition, the mRNA expression levels of the 4 candidate genes were significantly lower in TNBC tumor tissues compared with healthy tissues. Based on the K-M plotter, these 4 genes were correlated with poor prognosis of TNBC. The area under the curve of IGSF10, RUNX1T1, TSHZ2, and XIST was 0.918, 0.957, 0.977, and 0.749. These findings provide new insight into TNBC metastasis. IGSF10, RUNX1T1, TSHZ2, and XIST could be used as candidate biomarkers for the diagnosis and prognosis of TNBC metastasis.

Divergent Patterns and Trends in Breast Cancer Incidence, Mortality and Survival Among Older Women in Germany and the United States

Background: Breast cancer treatment has changed tremendously over the last decades. In addition, the use of mammography screening for early detection has increased strongly. To evaluate the impact of these developments, long-term trends in incidence, mortality, stage distribution and survival were investigated for Germany and the United States (US). Methods: Using population-based cancer registry data, long-term incidence and mortality trends (1975–2015), shifts in stage distributions (1998–2015), and trends in five-year relative survival (1979–2015) were estimated. Additionally, trends in five-year relative survival after standardization for stage were explored (2004–2015). Results: Age-standardized breast cancer incidence rates were much higher in the US than in Germany in all periods, whereas age-standardized mortality began to lower in the US from the 1990s on. The largest and increasing differences were observed for patients aged 70+ years with a 19% lower incidence but 45% higher mortality in Germany in 2015. For this age group, large differences in stage distributions were observed, with 29% (Germany) compared to 15% (US) stage III and IV patients. Age-standardized five-year relative survival increased strongly between 1979–1983 and 2013–2015 in Germany (+17% units) and the US (+19% units) but was 9% units lower in German patients aged 70+ years in 2013–2015. This difference was entirely explained by differences in stage distributions. Conclusions: Overall, our results are in line with a later uptake and less extensive utilization of mammography screening in Germany. Further studies and efforts are highly needed to further explore and overcome the increased breast cancer mortality among elderly women in Germany.