scholarly journals CD24 is a Potential Biomarker for Prognosis in Human Breast Carcinoma

2018 ◽  
Vol 48 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Xuan Jing ◽  
Xiangrong Cui ◽  
Hongping Liang ◽  
Chonghua Hao ◽  
Zhining Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mrna Expression ◽  
Large Scale ◽  
Prognostic Indicator ◽  
Normal Breast ◽  
Prognostic Indicators ◽  
Cancer Tissue ◽  
Potential Biomarker ◽  
Increased Risk ◽  
Cd24 Mrna

Background/Aims: CD24 is a highly glycosylated mucin-like antigen on the cell surface, which has recently emerged as a novel oncogene and metastasis promoter. We performed bioinformatics analysis to investigate whether CD24 can serve as a prognostic indicator in breast cancer. Methods: CD24 expression was assessed using SAGE Genie tools and Oncomine analysis. The PrognoScan database, Kaplan-Meier Plotter, and bc-GenExMiner were used to identify the prognostic roles of CD24 in breast cancer. Results: We found that CD24 was more frequently overexpressed in breast cancer than in normal breast tissue and correlated with worse prognosis. Meanwhile, high CD24 expression was associated with increased risk of HER2, basal-like, triple-negative breast cancer, and higher Scarff-Bloom-Richardson grade. Data mining in multiple big databases confirmed a positive correlation between CD24 mRNA expression and SDC1 mRNA expression in breast cancer tissue. Conclusions: Our findings suggest that CD24 overexpression is more common in breast cancer than in corresponding normal tissue. In addition, CD24 and SDC1 can serve as prognostic indicators for breast cancer. However, large-scale and comprehensive research is needed to further confirm these results.

scholarly journals The association between XRCC3 rs1799794 polymorphism and cancer risk: a meta-analysis of 34 case–control studies

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Weiqing Liu ◽  
Shumin Ma ◽  
Lei Liang ◽  
Zhiyong Kou ◽  
Hongbin Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Thyroid Cancer ◽  
Cancer Risk ◽  
Large Scale ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Cancer Type ◽  
Increased Risk ◽  
Risk Of Cancer

Abstract Background Studies on the XRCC3 rs1799794 polymorphism show that this polymorphism is involved in a variety of cancers, but its specific relationships or effects are not consistent. The purpose of this meta-analysis was to investigate the association between rs1799794 polymorphism and susceptibility to cancer. Methods PubMed, Embase, the Cochrane Library, Web of Science, and Scopus were searched for eligible studies through June 11, 2019. All analyses were performed with Stata 14.0. Subgroup analyses were performed by cancer type, ethnicity, source of control, and detection method. A total of 37 studies with 23,537 cases and 30,649 controls were included in this meta-analysis. Results XRCC3 rs1799794 increased cancer risk in the dominant model and heterozygous model (GG + AG vs. AA: odds ratio [OR] = 1.04, 95% confidence interval [CI] = 1.00–1.08, P = 0.051; AG vs. AA: OR = 1.05, 95% CI = 1.00–1.01, P = 0.015). The existence of rs1799794 increased the risk of breast cancer and thyroid cancer, but reduced the risk of ovarian cancer. In addition, rs1799794 increased the risk of cancer in the Caucasian population. Conclusion This meta-analysis confirms that XRCC3 rs1799794 is related to cancer risk, especially increased risk for breast cancer and thyroid cancer and reduced risk for ovarian cancer. However, well-designed large-scale studies are required to further evaluate the results.

scholarly journals VWCE as a potential biomarker associated with immune infiltrates in breast cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qin Huo ◽  
Zhenwei Li ◽  
Siqi Chen ◽  
Juan Wang ◽  
Jiaying Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Her2 Status ◽  
Cancer Tissue ◽  
M2 Macrophage ◽  
Breast Cancer Patients ◽  
Immune Infiltration ◽  
Potential Biomarker ◽  
Significant Difference ◽  
Factor C

Abstract Purpose Von Willebrand Factor C and EGF Domains (VWCE) is an important gene that regulates cell adhesion, migration, and interaction. However, the correlation between VWCE expression and immune infiltrating in breast cancer remain unclear. In this study, we investigated the correlation between VWCE expression and immune infiltration levels in breast cancer. Methods The expression of VWCE was analyzed by the tumor immune estimation resource (TIMER) and DriverDB databases. Furthermore, genes co-expressed with VWCE and gene ontology (GO) enrichment analysis were investigated by the STRING and Enrichr web servers. Also, we performed the single nucleotide variation (SNV), copy number variation (CNV), and pathway activity analysis through GSCALite. Subsequently, the relationship between VWCE expression and tumor immunity was analyzed by TIMER and TISIDB databases, and further verified the results using Quantitative Real-Time PCR (RT-PCR), Western blotting, and immunohistochemistry. Results The results showed that the expression of VWCE mRNA in breast cancer tissue was significantly lower than that in normal tissues. We found that the expression level of VWCE was associated with subtypes, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status of breast cancer patients, but there was no significant difference in the expression of VWCE was found in age and nodal status. Further analyses indicated that VWCE was correlated with the activation or inhibition of multiple oncogenic pathways. Additionally, VWCE expression was negatively correlated with the expression of STAT1 (Th1 marker, r = − 0.12, p = 6e−05), but positively correlated with the expression of MS4A4A (r = 0.28, p = 0). These results suggested that the expression of VWCE was correlated with immune infiltration levels of Th1 and M2 macrophage in breast cancer. Conclusions In our study, VWCE expression was associated with a better prognosis and was immune infiltration in breast cancer. These findings demonstrate that VWCE is a potential prognostic biomarker and correlated with tumor immune cell infiltration, and maybe a promising therapeutic target in breast cancer.

scholarly journals Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
C. Loukas ◽  
S. Kostopoulos ◽  
A. Tanoglidi ◽  
D. Glotsos ◽  
C. Sfikas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Rapid Assessment ◽  
Breast Cancer Diagnosis ◽  
Critical Issue ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Textural Features ◽  
Tissue Sections ◽  
Leave One Out

Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.

scholarly journals Biological Aging Marker p16INK4a in T Cells and Breast Cancer Risk

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3122
Author(s):  
Jie Shen ◽  
Renduo Song ◽  
Bernard F. Fuemmeler ◽  
Kandace P. McGuire ◽  
Wong-Ho Chow ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mrna Expression ◽  
Cellular Senescence ◽  
Human Cancer ◽  
Biological Aging ◽  
Increased Risk ◽  
Discovery Stage

Prior research has demonstrated that altered telomere length, a well-known marker for biological aging, is associated with various types of human cancer. However, whether such association extends to additional hallmarks of biological aging, including cellular senescence, has not been determined yet. In this two-stage study, we assessed the association between p16INK4a mRNA expression in T cells, a marker of cellular senescence, and breast cancer risk. The discovery stage included 352 breast cancer patients and 324 healthy controls. p16INK4a mRNA expression was significantly higher in individuals who were older, Black, and had family history of cancer than their counterparts in both cases and controls. p16INK4a mRNA expression also differed by marital status, annual income, and smoking status in cases. In the discovery stage, we found that increased p16INK4a mRNA expression was associated with 1.40-fold increased risk of breast cancer (OR = 1.40; 95%CI: 1.21, 1.68; p < 0.001). A marginally significant association was further observed in the validation stage with 47 cases and 48 controls using pre-diagnostic samples (OR = 1.28; 95%CI: 0.98, 2.97; p = 0.053). In addition, we found that p16INK4a mRNA expression was higher in tumors with selected aggressive characteristics (e.g., poorly differentiated and large tumors) than their counterparts. In summary, our results demonstrate that higher p16INK4a mRNA expression in T cells is a risk factor for breast cancer and further support the role of biological aging in the etiology of breast cancer development. Novelty and Impact Statements: The results from this study provide evidence that cellular senescence, a process of biological aging, plays a role in breast cancer etiology. In addition, our results also support that social demographics may modify cellular senescence and biological aging.

scholarly journals A Review of the Hereditary Component of Triple Negative Breast Cancer: High- and Moderate-Penetrance Breast Cancer Genes, Low-Penetrance Loci, and the Role of Nontraditional Genetic Elements

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Darrell L. Ellsworth ◽  
Clesson E. Turner ◽  
Rachel E. Ellsworth
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Large Scale ◽  
Triple Negative ◽  
Association Studies ◽  
African Ancestry ◽  
Genome Wide Association Studies ◽  
Genetic Elements ◽  
Genome Wide ◽  
Increased Risk

Triple negative breast cancer (TNBC), representing 10-15% of breast tumors diagnosed each year, is a clinically defined subtype of breast cancer associated with poor prognosis. The higher incidence of TNBC in certain populations such as young women and/or women of African ancestry and a unique pathological phenotype shared between TNBC and BRCA1-deficient tumors suggest that TNBC may be inherited through germline mutations. In this article, we describe genes and genetic elements, beyond BRCA1 and BRCA2, which have been associated with increased risk of TNBC. Multigene panel testing has identified high- and moderate-penetrance cancer predisposition genes associated with increased risk for TNBC. Development of large-scale genome-wide SNP assays coupled with genome-wide association studies (GWAS) has led to the discovery of low-penetrance TNBC-associated loci. Next-generation sequencing has identified variants in noncoding RNAs, viral integration sites, and genes in underexplored regions of the human genome that may contribute to the genetic underpinnings of TNBC. Advances in our understanding of the genetics of TNBC are driving improvements in risk assessment and patient management.

scholarly journals Scale-Specific Multifractal Medical Image Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Boris Braverman ◽  
Mauro Tambasco
Keyword(s):  
Breast Cancer ◽  
Fractal Analysis ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Cancer Tissue ◽  
Clinical Value ◽  
Box Counting ◽  
Dependent Parameter ◽  
Single Number

Fractal geometry has been applied widely in the analysis of medical images to characterize the irregular complex tissue structures that do not lend themselves to straightforward analysis with traditional Euclidean geometry. In this study, we treat the nonfractal behaviour of medical images over large-scale ranges by considering their box-counting fractal dimension as a scale-dependent parameter rather than a single number. We describe this approach in the context of the more generalized Rényi entropy, in which we can also compute the information and correlation dimensions of images. In addition, we describe and validate a computational improvement to box-counting fractal analysis. This improvement is based on integral images, which allows the speedup of any box-counting or similar fractal analysis algorithm, including estimation of scale-dependent dimensions. Finally, we applied our technique to images of invasive breast cancer tissue from 157 patients to show a relationship between the fractal analysis of these images over certain scale ranges and pathologic tumour grade (a standard prognosticator for breast cancer). Our approach is general and can be applied to any medical imaging application in which the complexity of pathological image structures may have clinical value.

Tissue Plasminogen Activator Levels and Risk of Breast Cancer in a Case–Cohort Study on Italian Women: Results from the Moli-sani Study

2020 ◽  
Author(s):  
Simona Costanzo ◽  
Roberta Parisi ◽  
Amalia De Curtis ◽  
Sara Gamba ◽  
Laura Russo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Plasminogen Activator ◽  
Tissue Plasminogen Activator ◽  
Cox Regression ◽  
Enzyme Linked Immunosorbent Assay ◽  
Potential Biomarker ◽  
Increased Risk ◽  
Tissue Plasminogen ◽  
Incident Cases

Abstract Background Elevated levels of key enzymes of the fibrinolytic system, such as tissue plasminogen activator (tPA), are reported as predictors of poor outcome in cancer patients. Limited information is available about their potential predictive value for breast cancer (BC) risk in the general population. Aim We examined the association of tPA levels with BC risk in a case–cohort study including women from the prospective Moli-sani cohort. Methods A sample of 710 women (mean age: 54.6 ± 12.1 years) was selected as a subcohort and compared with 84 BC cases, in a median follow-up of 4.2 years. Incident cases of BC were validated through medical records. tPA plasma levels were measured using an enzyme-linked immunosorbent assay kit. Hazard ratio (HR) and 95% confidence interval (CI), adjusted for relevant covariates, were estimated by a Cox regression model using the Prentice method. Results Compared with the lowest quartile (<4.9 ng/mL), women in the highest quartile of tPA (>11.2 ng/mL) had increased risk of BC (HRIVvsI: 2.20, 95% CI: 1.13–4.28) after adjusted for age, smoking, education, menopause, and residence. Further adjustment for biochemical markers did not modify this association. The risk of BC increased by 34% for each increase in 1 standard deviation of log-transformed tPA levels (p = 0.046). Elevated levels of tPA were associated mainly with estrogen-receptor-positive BC (2.08, 95% CI: 1.18–3.66). Conclusion Higher levels of tPA, reported to predict cardiovascular risk, are a potential biomarker for BC risk, supporting the hypothesis of a “common soil” linking the pathogenic mechanisms of hormone-dependent tumors and cardiovascular disease.

Evaluation of mRNA markers for micrometastases detection by RT-PCR in lymph nodes (LN), peripheral blood (PB) and bone marrow (BM) of breast cancer patients (pts)

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 20068-20068
Author(s):  
A. M. Morelle ◽  
A. Frasson ◽  
F. Zerwes ◽  
S. Alves ◽  
G. Devenz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Normal Breast ◽  
Variable Number ◽  
Cancer Tissue ◽  
Breast Cancer Patients ◽  
Rt Pcr ◽  
Normal Gland ◽  
Cea Mrna ◽  
Mrna Markers

20068 Background: Previous RT-PCR study showed that hMAM and CEA mRNA could be detected in 95% and 78% breast carcinomas, respectively. Analyzed in normal LN, these two markers were the only ones not expressed, compared to other mRNA markers which could be detected in a variable number of cases (Marchetti 2001). We evaluate the expression of CEA and hMAM mRNA in breast cancer pts in order to investigate if the identification of these transcripts could be correlated to in the LN, PB and BM. Methods: Tumor, normal gland, LN and PB were obtained from 49 breast cancer pts who have been referred to surgery during the period of Jun/2000 to Jan/2004.BM could be obtained from 21 of these cases. Before the manipulation of the tumor, 5 mL of PB was collected and 5 mL of BM was aspirated from the iliac crest. First level axillary LN were sectioned in two. One of the halves was immediately frozen in liquid nitrogen. CEA and hMAM transcripts have been analysed by RT-PCR. Results: Tumors were positive for CEA in 38 (77.6%) cases and LN were positive in 15 cases (36.7%). 6/49 pts expressed CEA in normal breast (12.2%). Five of these, tumor also expressed CEA. Two pts (4.1%) showed CEA expression in PB and in BM. One patient expressed CEA in BM but not in PB. All pts that showed expression in PB, BM and LN, also showed transcript of CEA in tumor. hMAM transcripts were found in 39 (79.6%) normal gland tissue and in 41 of tumor samples (85.7%). LN were positive to hMAM in 12 cases (24.5%). 5 of these (10.2%) were also positive in PB and BM samples. The pts showing hMAM expression in PB, BM and LN also showed its expression in neoplasic tissue. Analyzing both markers, two pts showed the expression of CEA and hMAM in the PB and BM samples. In 21 of the 49 cases the comparison of hMAM and CEA was possible to be performed in all tissues. CEA and hMAM expression associated to larger number of LN affected. Conclusions: CEA and hMAM transcripts can be detected in majority of breast cancer tissue. Specificity of hMAM seems to be higher than CEA, which can be detected in more than 10% of non-tumor breast tissue. Synchronicity of CEA or hMAM expression in the tumor and in extra-mammary sites was high, suggesting that these markers may provide an interesting strategy for follow-up micrometastatic disease. No significant financial relationships to disclose.

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