Using Bayesian networks to built a diagnosis and prognosis model for breast cancer

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

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Computerized Breast Cancer Diagnosis and Prognosis From Fine-Needle Aspirates

Archives of Surgery ◽

10.1001/archsurg.1995.01430050061010 ◽

1995 ◽

Vol 130 (5) ◽

pp. 511 ◽

Cited By ~ 36

Author(s):

William H. Wolberg

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Fine Needle ◽

Fine Needle Aspirates ◽

Diagnosis And Prognosis ◽

Cancer Diagnosis And Prognosis

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Protein biomarkers in breast cancer-derived extracellular vesicles for use in liquid biopsies

AJP Cell Physiology ◽

10.1152/ajpcell.00048.2021 ◽

2021 ◽

Author(s):

Dan Li ◽

Wenjia Lai ◽

Di Fan ◽

Qiaojun Fang

Keyword(s):

Breast Cancer ◽

Early Diagnosis ◽

Extracellular Vesicles ◽

Liquid Biopsy ◽

Breast Cancer Diagnosis ◽

Extracellular Vesicle ◽

Detection Methods ◽

Protein Markers ◽

Liquid Biopsies ◽

Diagnosis And Prognosis

Breast cancer is the most common malignant disease in women worldwide. Early diagnosis and treatment can greatly improve the management of breast cancer. Liquid biopsies are becoming convenient detection methods for diagnosing and monitoring breast cancer due to their non-invasiveness and ability to provide real-time feedback. A range of liquid biopsy markers, including circulating tumor proteins, circulating tumor cells, and circulating tumor nucleic acids, have been implemented for breast cancer diagnosis and prognosis, with each having its own advantages and limitations. Circulating extracellular vesicles are messengers of intercellular communication that are packed with information from mother cells and are found in a wide variety of bodily fluids; thus, they are emerging as ideal candidates for liquid biopsy biomarkers. In this review, we summarize extracellular vesicle protein markers that can be potentially used for the early diagnosis and prognosis of breast cancer or determining its specific subtypes.

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Clinical Presentation, Diagnosis and Prognosis of Pregnancy-Associated Breast Cancer

Advances in Experimental Medicine and Biology - Diseases of the Breast during Pregnancy and Lactation ◽

10.1007/978-3-030-41596-9_11 ◽

2020 ◽

pp. 87-93

Author(s):

James Sun ◽

Marie Catherine Lee

Keyword(s):

Breast Cancer ◽

Clinical Presentation ◽

Diagnosis And Prognosis

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Prognostic Value of A Risk Score Model Constructed By m6A Methylation Regulators In Breast Cancer

10.21203/rs.3.rs-832380/v1 ◽

2021 ◽

Author(s):

Xiaowei Qiu ◽

Qiaoli Zhang ◽

Jingnan Xu ◽

Xin Jiang ◽

Xuewei Qi ◽

...

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Prognostic Value ◽

Validation Cohort ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Rna Methylation ◽

Model Based ◽

Prognosis Model ◽

M6a Rna Methylation

Abstract Background: N6-methyladenosine (m6A) methylation modification can affect the tumorigenesis, progression, and metastasis of breast cancer (BC). Up to now, a prognostic model based on m6A methylation regulators for BC is still lacking. This study aimed to construct an accurate prediction prognosis model by m6A methylation regulators for BC patients.Methods: After processing of The Cancer Genome Atlas (TCGA) datasets, the differential expression and correlation analysis of m6A RNA methylation regulators were applied. Next, tumor samples were clustered into different groups and clinicopathologic features in different clusters were explored. By univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analysis, m6A regulators with prognostic value were identified to develop a prediction model. Furthermore, we constructed and validated a predictive nomogram to predict the prognosis of BC patients.Results: 19 m6A related genes were extracted and 908 BC patients enrolled from TCGA dataset. After univariate Cox and LASSO analysis, 3 m6A RNA methylation regulators (YTHDF3, ZC3H13 and HNRNPC) were selected to establish the prognosis model based on median risk score (RS) in training and validation cohort. With the increasing of RS, the expression levels of YTHDF3 and ZC3H13 were individually elevated, while the HNRNPC expressed decreasingly. By survival analysis and Receiver Operating Characteristic (ROC) curve, we found that the overall survival (OS) of high-risk group was significantly shorter than that of the low-risk group based on Kaplan-Meier (KM) analysis in each cohort. Univariate and multivariate analysis identified the RS, age, and pathological stage are independent prognostic factors. A nomogram was constructed to predict 1- and 3-year OS and the calibration plots validate the performance. The C-index of nomogram reached 0.757 (95% CI:0.7-0.814) in training cohort and 0.749 (95% CI:0.648-0.85) in validation cohort, respectively.Conclusions: We successfully constructed a predictive prognosis model by m6A RNA methylation regulators. These results indicated that the m6A RNA methylation regulators are potential therapeutic targets of BC patients.

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VGH-TAYLOR: Comprehensive precision medicine study protocol on the heterogeneity of Taiwanese breast cancer patients

Future Oncology ◽

10.2217/fon-2021-0131 ◽

2021 ◽

Author(s):

Chun-Yu Liu ◽

Chi-Cheng Huang ◽

Yi-Fang Tsai ◽

Ta-Chung Chao ◽

Pei-Ju Lien ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Recurrence ◽

Cell Receptor ◽

Breast Cancer Patients ◽

Genetic Profiling ◽

Treatment Regimens ◽

Diagnosis And Prognosis ◽

Receptor Repertoire ◽

Cell Receptor Repertoire ◽

Selection Of

Heterogeneity in breast cancer leads to diverse morphological features and different clinical outcomes. There are inherent differences in breast cancer between the populations in Asia and in western countries. The use of immune-based treatment in breast cancer is currently in the developmental stage. The VGH-TAYLOR study is designed to understand the genetic profiling of different subtypes of breast cancer in Taiwan and define the molecular risk factors for breast cancer recurrence. The T-cell receptor repertoire and the potential effects of immunotherapy in breast cancer subjects is evaluated. The favorable biomarkers for early detection of tumor recurrence, diagnosis and prognosis may provide clues for the selection of individualized treatment regimens and improvement in breast cancer therapy.

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