scholarly journals Study of Long Noncoding RNA FER1L4 and RB1, as Its Competing Endogenous RNA Network Target Gene, in Breast Cancer

2019 ◽  
Vol 12 (4) ◽  
pp. 21-30
Author(s):  
Zeinab Shaghaghi Torkdari ◽  
Mohammad Khalaj-Kondori ◽  
Mohammad Ali Hosseinpour Feizi
Keyword(s):  
Breast Cancer ◽  
Noncoding Rna ◽  
Target Gene ◽  
Ductal Carcinoma ◽  
Cancer Tissue ◽  
Breast Cancer Patients ◽  
Noncancerous Tissue ◽  
Tumor Tissues ◽  
Competing Endogenous Rna Network

Introduction: Breast cancer is the second most common cause of cancer-related death among females, which requires an exploration for markers to propose a more specific categorization of this cancer. Long noncoding RNAs (lncRNAs), the main subset of noncoding transcripts, are involved in tumorigenic processes. In this study, we investigated the expression of the fer-­1–­like family member 4 (FER1L4) lncRNA and its competitive endogenous RNA network target gene, RB transcriptional corepressor 1 (RB1), in ductal carcinoma (invasive and in situ) tissue and its adjacent noncancerous tissue (ANCT). Furthermore, associations of FER1L4 and RB1 with various clinical features of the patients were analyzed. Methods: Quantitative real-time PCR was used to measure the expression of the mentioned genes in 61 samples of ductal carcinoma and their ANCTs, and the data were analyzed using ANOVA and t tests. Results: FER1L expression was not significantly different in breast tumor samples compared with their ANCT samples, while RB1 showed significant downregulation in tumor tissues (P = 0.008). In addition, increased expression of FER1L4 and decreased RB1 expression were significantly correlated with lymph node metastasis in breast cancer patients (P < 0.05). Conclusion: FER1L4 is not upregulated in breast cancer tissue. However, RB1 expression is significantly downregulated.

scholarly journals Sharp Downregulation of Hub Genes Associated With the Pathogenesis of Breast Cancer From Ductal Carcinoma In Situ to Invasive Ductal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yao Wang ◽  
Faqing Liang ◽  
Yuting Zhou ◽  
Juanjuan Qiu ◽  
Qing Lv ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Protein Expression ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Cancer Tissue ◽  
Hub Genes ◽  
Expression Levels ◽  
Cancer Pathogenesis ◽  
Ductal Hyperplasia

IntroductionBreast atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are precursor stages of invasive ductal carcinoma (IDC). This study aimed to investigate the pathogenesis of breast cancer by dynamically analyzing expression changes of hub genes from normal mammary epithelium (NME) to simple ductal hyperplasia (SH), ADH, DCIS, and finally to IDC.MethodsLaser-capture microdissection (LCM) data for NME, SH, ADH, DCIS, and IDC cells were obtained. Weighted gene co-expression network analysis (WGCNA) was performed to dynamically analyze the gene modules and hub genes associated with the pathogenesis of breast cancer. Tissue microarray, immunohistochemical, and western blot analyses were performed to determine the protein expression trends of hub genes.ResultsTwo modules showed a trend of increasing expression during the development of breast disease from NME to DCIS, whereas a third module displayed a completely different trend. Interestingly, the three modules displayed inverse trends from DCIS to IDC compared with from NME to DCIS; that is, previously upregulated modules were subsequently downregulated and vice versa. We further analyzed the module that was most closely associated with DCIS (p=7e−07). Kyoto Gene and Genomic Gene Encyclopedia enrichment analysis revealed that the genes in this module were closely related to the cell cycle (p= 4.3e–12). WGCNA revealed eight hub genes in the module, namely, CDK1, NUSAP1, CEP55, TOP2A, MELK, PBK, RRM2, and MAD2L1. Subsequent analysis of these hub genes revealed that their expression levels were lower in IDC tissues than in DCIS tissues, consistent with the expression trend of the module. The protein expression levels of five of the hub genes gradually increased from NME to DCIS and then decreased in IDC. Survival analysis predicted poor survival among breast cancer patients if these hub genes were not downregulated from DCIS to IDC.ConclusionsFive hub genes, RRM2, TOP2A, PBK, MELK, and NUSAP1, which are associated with breast cancer pathogenesis, are gradually upregulated from NME to DCIS and then downregulated in IDC. If these hub genes are not downregulated from DCIS to IDC, patient survival is compromised. However, the underlying mechanisms warrant further elucidation in future studies.

scholarly journals A Nomogram Predicting Survival in Patients with Breast Ductal Carcinoma in Situ with Microinvasion

2021 ◽  
Author(s):  
Yi-Zi Zheng ◽  
Hong-Bin Qin ◽  
Zi-Zheng Li ◽  
He-Sheng Jiang ◽  
Greg Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Regression Model ◽  
Cancer Patients ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Multivariate Logistic Regression Model ◽  
Breast Cancer Patients

Abstract Background: Ductal carcinoma in situ with microinvasion (DCISM) can be challenging to balance the risks of overtreatment versus undertreatment. We aim to identify prognostic factors in patients with DCISM and construct a nomogram to predict breast cancer-specific survival (BCSS).Methods: Women diagnosed with DCISM were selected from the Surveillance, Epidemiology and End Results database (1998-2015). Clinical variables and tumor characteristics were evaluated and Cox proportional-hazards regression model was performed. A nomogram was con­structed from the multivariate logistic regression model to combine all the prognostic factors to predict the prognosis of DCISM patients at 5 years, 10 years, and 15 years. Results: We identified 5,438 total eligible breast cancer patients with a median and max survival time of 78 and 227 months, respectively. Here, patients with poorer survival outcomes were those diagnosed between 1988-2001, African-American race, under 40 years of age, higher tumor N stage, progesterone receptor-negative tumor, and received no surgery (all P < 0.05). The nomogram was constructed by the seven variables and passed the calibration and validation steps. The area under the receiver operating characteristic (ROC) curve (AUC) of both the training set and the validating set (5-year AUC: 0.77 and 0.88, 10-year AUC: 0.75 and 0.73, 15-year AUC: 0.72 and 0.65) demonstrated excellent reliability and robust performance.Conclusion: Our current study is the first to construct nomograms of patients with DCISM which could help physicians identify breast cancer patients that more likely to benefit from more intensive treatment and follow-up.

scholarly journals Use of social service counseling by cancer patients: an analysis of quality assurance data of 6339 breast cancer patients from 13 certified centers in Germany treated between 2015 and 2017

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Clara Breidenbach ◽  
Simone Wesselmann ◽  
Nora Tabea Sibert ◽  
Olaf Ortmann ◽  
Katrin Blankenburg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Assurance ◽  
Cancer Patients ◽  
Social Service ◽  
Ductal Carcinoma ◽  
Patient Characteristics ◽  
Social Risk ◽  
Breast Cancer Patients ◽  
Cancer Centers

Abstract Background Integrated social care may help to mitigate social risk factors in order to achieve more equitable health outcomes. In cancer centers certified according to the criteria set out by the German Cancer Society, every patient must be given low-threshold access to qualified social workers at the center for in-house social service counseling (SSC). Previous analyses have demonstrated large variation in the utilization of these services across individual centers. Therefore, this research aims at investigating whether SSC utilization varies regarding breast cancer patient characteristics and center characteristics presenting a unique approach of using routine data. Methods Multilevel modeling was performed using quality assurance data based on 6339 patients treated in 13 certified breast cancer centers in Germany in order to investigate whether SSC utilization varies with patient sex, age, and disease characteristics as well as over time and across centers. Results In the sample, 80.3% of the patients used SSC. SSC use varies substantially between centers for the unadjusted model (ICC = 0.24). Use was statistically significantly (P < .001) more likely in women, patients with invasive (in comparison to tumor in situ/ductal carcinoma in situ) diseases (P < .001), patients with both breasts affected (P = .03), patients who received a surgery (P < .001), patients who were diagnosed in 2015 or 2017 compared to 2016 (P < .001) and patients older than 84 years as compared to patients between 55 and 64 years old (P = .002). Conclusion The analysis approach allows a unique insight into the reality of cancer care. Sociodemographic and disease-related patient characteristics were identified to explain SSC use to some extent.

scholarly journals Severe depression more common in patients with ductal carcinoma in situ than early-stage invasive breast cancer patients

2017 ◽  
Vol 167 (1) ◽  
pp. 205-213 ◽  
Author(s):  
M. L. Gregorowitsch ◽  
H. J. G. D. van den Bongard ◽  
D. A. Young-Afat ◽  
J. P. Pignol ◽  
C. H. van Gils ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Ductal Carcinoma In Situ ◽  
Invasive Breast Cancer ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Early Stage ◽  
Severe Depression ◽  
Breast Cancer Patients

scholarly journals Detection of RASSF1A aberrant promoter hypermethylation in sputum from chronic smokers and ductal carcinoma in situ from breast cancer patients

Oncogene ◽  
2003 ◽  
Vol 22 (1) ◽  
pp. 147-150 ◽  
Author(s):  
Sofia Honorio ◽  
Angelo Agathanggelou ◽  
Marcus Schuermann ◽  
Wulf Pankow ◽  
Paolo Viacava ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Promoter Hypermethylation ◽  
Breast Cancer Patients

Overtreatment in surgery – does it concern also the patients with ductal breast carcinoma in situ

2018 ◽  
Vol 90 (1) ◽  
pp. 47-51
Author(s):  
Tomasz Nowikiewicz ◽  
Wojciech Zegarski ◽  
Iwona Głowacka-Mrotek
Keyword(s):  
Breast Cancer ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Breast Cancer Patients ◽  
Screening Programs ◽  
Patients With Cancer ◽  
Clinical Problems ◽  
Current Standards

Overtreatment means treatment that goes beyond current standards, and patients with any disease can be overtreated. Overtreatment is also given to patients with cancer, including those who need surgery. Overtreatment is closely related to the problem of overdiagnosis. In patients with cancer, unnecessary surgery may cause complications and generates unnecessary costs. The size of the problem of unnecessary surgery in patients with cancer can best be shown among patients with the most common cancers, which dedicated screening programs. Breast cancer patients, particularly those with pre-invasive types of the tumor, who typically have ductal carcinoma in situ (80%), are likely to undergo unnecessary surgery. We describe the most common clinical problems caused by overtreating patients with ductal carcinoma in situ.

scholarly journals The long noncoding RNA TINCR promotes breast cancer cell proliferation and migration by regulating OAS1

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Die Lu ◽  
Shihao Di ◽  
Shuaishuai Zhuo ◽  
Linyan Zhou ◽  
Rumeng Bai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Proliferation ◽  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Cancer Tissue ◽  
Breast Cancer Patients ◽  
Cell Proliferation And Migration ◽  
And Migration ◽  
High Level ◽  
Proliferation And Migration

AbstractBreast cancer is the leading cause of cancer-related death in women around the world. It is urgently needed to identify genes associated with tumorigenesis and prognosis, as well as to elucidate the molecular mechanisms underlying the oncogenic process. Long noncoding RNAs (lncRNAs) are widely involved in the pathological and physiological processes of organisms and play an important role as oncogenes or tumor suppressor genes, affecting the development and progression of tumors. In this study, we focused on terminal differentiation-induced non-coding RNA (TINCR) (GeneID:257000) and explore its role in the pathogenesis of breast cancer. The results showed that TINCR was increased in breast cancer tissue, and high expression level of TINCR was associated with older age, larger tumor size, and advanced TNM stage. High level of TINCR can promote proliferation and metastasis of breast cancer cells, while downregulation of TINCR induces G1-G0 arrest and apoptosis. Mechanismly, TINCR can bind to staufen1 (STAU1) and then guide STAU1 (GeneID:6780) to bind to OAS1 mRNA (NM_016816.4) to mediate its stability. Thus low level of OAS1(GeneID:4938) can lead to cell proliferation and migration. This result elucidates a new mechanism for TINCR in breast cancer development and provides a survival indicator and potential therapeutic target for breast cancer patients.

scholarly journals Histological profile of patients served in a reference center in the city of São Paulo

Mastology ◽  
2020 ◽  
Vol 30 (Suppl 1) ◽  
Author(s):  
Juliana da Costa Souza ◽  
Jamila Vieira de Sousa ◽  
Felipe Andreotta Cavagna ◽  
Jorge Yoshinori Shida ◽  
Luiz Henrique Gebrim
Keyword(s):  
Breast Cancer ◽  
Ductal Carcinoma ◽  
Lobular Carcinoma ◽  
Sao Paulo ◽  
São Paulo ◽  
Breast Cancer Patients ◽  
Cross Sectional ◽  
Morphological Criteria ◽  
Malignant Lesions

Introduction: Breast cancer is the most common cancer in women worldwide. In Brazil, according to INCA, it had an incidence of 57,900 new cases in 2018, 16,340 cases in the state of São Paulo. The WHO classifies tumors according to morphological criteria, including non-special breast cancer (CMI-SOE), which is seen in 70% of cases, with more than 20 other special histological types, among which invasive lobular carcinoma (ILC) has a higher prevalence (10‒15%). Objective: To describe the histological profile of breast cancer patients at the Pérola Byington Hospital between the years 2009 to 2019. Method: Cross-sectional, descriptive study, obtained through a database review. It included patients with suspected invasive neoplasms seen at the service. Results: During the period, 10,539 patients with malignant lesions and ductal carcinoma in situ (DCIS) were treated, among those who had cancer, 91% were CMI-SOE, 5% lobular neoplasia, and 3.95% of the other special subtypes. The least frequent type was metaplastic, with only 20 cases in the period. In addition, 7.14% of the patients seen had DCIS. Conclusions: It is possible to observe the predominance of CMI-SOE in this series, as well as described in analyses carried out throughout the country and in the world, followed by lobular carcinoma and other special subtypes.

scholarly journals Identification and transfer of spatial transcriptomics signatures for cancer diagnosis

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Niyaz Yoosuf ◽  
José Fernández Navarro ◽  
Fredrik Salmén ◽  
Patrik L. Ståhl ◽  
Carsten O. Daub
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Ductal Carcinoma ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Cancer Tissue ◽  
Diagnostic Challenge ◽  
Expression Signature ◽  
Tissue Sections

Abstract Background Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. Methods We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. Results We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. Conclusions This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

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