Influence of Smoking History on Breast Cancer Prognosis: Retrospective Study of 240 Operable Breast Cancer Patients Who Received Adjuvant Cyclophosphamide, Doxorubicin, and 5-Fluorouracil Chemotherapy Regimen

2007 ◽  
Vol 13 (4) ◽  
pp. 431-432 ◽  
Author(s):  
Sercan Aksoy ◽  
Hakan Harputluoglu ◽  
Nilufer Guler ◽  
Kadri Altundag ◽  
Mutlu Hayran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Retrospective Study ◽  
Cancer Patients ◽  
Chemotherapy Regimen ◽  
Operable Breast Cancer ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Smoking History ◽  
Breast Cancer Patients

scholarly journals Association between SNPs within MicroRNA Binding Sites and the Prognosis of Breast Cancer

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Lu Han ◽  
Yubei Huang ◽  
Ziwei Feng ◽  
Xin Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Binding Sites ◽  
Cox Regression ◽  
Breast Cancer Prognosis ◽  
Stage I ◽  
Cancer Prognosis ◽  
Stage Ii ◽  
Breast Cancer Patients ◽  
Survival Times

Abstract Background: Single nucleotide polymorphisms (SNPs) within microRNA binding sites can affect the binding of microRNA to mRNA and regulate gene expression, thereby contributing to the prognosis of cancer. We performed this study to explore the association between SNPs within microRNA binding sites and the prognosis of breast cancer.Methods: We carried out a two-stage study including 2647 breast cancer patients, with a median follow-up of 68 months (range 0-159). In stage I, we genotyped 192 SNPs within microRNA binding sites using the Illumina Goldengate platform. In stage II, we validated SNPs significantly associated with breast cancer prognosis in another dataset using the TaqMan platform. Survival times was calculated, and Kaplan-Meier curves and Cox regression model were used to analyze survival of breast cancer patients with different genotypes.Results: We identified 8 SNPs significantly associated with breast cancer prognosis in stage I (P<0.05), and only rs10878441 was statistically significant in stage II (AA vs CC: adjusted HR=2.21, 95% CI: 1.11-4.42, P=0.024). We combined the data from stage I and stage II, and found that, compared with rs10878441 AA genotype, CC genotype was significantly associated with poor survival of breast cancer (HR=1.69, 95% CI: 1.18-2.42, P=0.004; adjusted HR=2.19, 95% CI: 1.30-3.70, P=0.003). Stratified analyses demonstrated that rs10878441 was related to breast cancer prognosis in grade II patients and lymph node-negative patients (P<0.05).Conclusions: The LRKK2 rs10878441 CC genotype is associated with poor prognosis of breast cancer in a Chinese population, and it could be used as a potential prognostic biomarker for breast cancer. Further studies are warranted.

Exploratory Data Analysis on Breast Cancer Prognosis

2018 ◽  
pp. 1794-1805
Author(s):  
Mohammad Mehdi Owrang O. ◽  
Yasmine M. Kanaan ◽  
Robert L. Copeland Jr. ◽  
Melvin Gaskins ◽  
Robert L. DeWitty Jr.
Keyword(s):  
Breast Cancer ◽  
Marital Status ◽  
Cancer Patients ◽  
Proportional Hazards ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Breast Cancer Patients ◽  
Howard University ◽  
Kaplan Meier ◽  
Seer Data

Breast cancer prognosis is a vital element of providing effective treatment for breast cancer patients. Breast cancer prediction survivability has mainly been studied based on pathological factors such as tumor size, tumor grade, number of positive lymph nodes, and hormone receptors among others. This chapter looks at the significance of the non-clinical prognostic factors of age, ethnicity, and marital status in finding the prognosis for breast cancer patients. The National Cancer Institute's SEER data and the Howard University Cancer Center Tumor Registry data are analyzed. Prognostic tool NPI (Nottingham Prognostic Index) and survival analysis tools of Cox proportional hazards and Kaplan-Meier survival curve are used in analyzing the experiments. The results suggest that age, ethnicity, and marital status have some influence on the survivability rate of breast cancer patients.

scholarly journals The tumor microbiome is implicated in breast cancer prognosis

2020 ◽  
Author(s):  
Jia Zhu ◽  
Jie Wu ◽  
Changgan Mo ◽  
Siyuan Liang ◽  
Tao Lian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Alpha Diversity ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Rrna Gene ◽  
Breast Cancer Patients ◽  
Immune Microenvironment ◽  
Tumor Tissues ◽  
Tumor Immune Microenvironment

Abstract Background: Some breast cancer patients are prone to recurrence and metastasis. Increasing evidence suggests that the breast tissue contains a diverse population of bacteria, which may be modulating the risk of breast cancer development or progression. However, the extent of microbial contribution to the tumor immune microenvironment in breast cancer remains unknown. Here, we explored the potential influence of the tumor microbiota on the local immune microenvironment and breast cancer prognosis.Methods: Using 16S rRNA gene sequencing, we analyzed the tumor microbiome composition and identified bacteria that were differentially abundant between breast cancer patients with recurrence or metastasis (R/M) and those without recurrence or metastasis (NRM). We performed total RNA sequencing in tumor tissues from patients in both groups to determine differentially expressed genes (DEGs). The landscape of tumor-infiltrating immune cells (TIICs) subtypes in the tumor immune microenvironment was analyzed using CIBERSORT, based on the gene expression profiling of tumor tissues. Differences in the tumor microbiomes were then correlated with DEGs and differences in TIICs, in order to determine how microbial abundance may contribute to cancer progression.Results: Microbial alpha-diversity was higher in NRM patients than in R/M patients. The composition and functions of the tumor microbiome communities differed between the two groups. We found higher alpha-diversity, higher abundance of Ruminococcus, Butyrivibrio, and Deinococcus, and lower abundance of Microbacterium could serve as a predictor of better prognosis in breast cancer patients. We also found that 16 genes, including CD36, showed differential expression in NRM compared to R/M, and differences in the composition of TIICs were observed between the two groups. In addition, we observed that the different tumor microbiome profiles were associated with DEGs and differences in TIICs between the two groups.Conclusions: The tumor microbiome may affect the prognosis of breast cancer patients by influencing the tumor immune microenvironment. Thus, the tumor microbiome may be a useful prognostic indicator.

Exploratory Data Analysis on Breast Cancer Prognosis

2019 ◽  
pp. 367-380
Author(s):  
Mohammad Mehdi Owrang O. ◽  
Yasmine M. Kanaan ◽  
Robert L. Copeland Jr. ◽  
Melvin Gaskins ◽  
Robert L. DeWitty Jr.
Keyword(s):  
Breast Cancer ◽  
Marital Status ◽  
Cancer Patients ◽  
Proportional Hazards ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Breast Cancer Patients ◽  
Howard University ◽  
Kaplan Meier ◽  
Seer Data

Breast cancer prognosis is a vital element of providing effective treatment for breast cancer patients. Breast cancer prediction survivability has mainly been studied based on pathological factors such as tumor size, tumor grade, number of positive lymph nodes, and hormone receptors among others. This chapter looks at the significance of the non-clinical prognostic factors of age, ethnicity, and marital status in finding the prognosis for breast cancer patients. The National Cancer Institute's SEER data and the Howard University Cancer Center Tumor Registry data are analyzed. Prognostic tool NPI (Nottingham Prognostic Index) and survival analysis tools of Cox proportional hazards and Kaplan-Meier survival curve are used in analyzing the experiments. The results suggest that age, ethnicity, and marital status have some influence on the survivability rate of breast cancer patients.

scholarly journals Association Study between SNPs within MicroRNA Binding Sites and the Prognosis of Breast Cancer

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Lu Han ◽  
Yubei Huang ◽  
Ziwei Feng ◽  
Xin Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Binding Sites ◽  
Cox Regression ◽  
Breast Cancer Prognosis ◽  
Stage I ◽  
Cancer Prognosis ◽  
Stage Ii ◽  
Breast Cancer Patients ◽  
Survival Times

Abstract Background: Single nucleotide polymorphisms (SNPs) within microRNA binding sites can affect the binding of microRNA to mRNA and regulate gene expression, thereby contributing to the prognosis of cancer. We performed this study to explore the association between SNPs within microRNA binding sites and the prognosis of breast cancer.Methods: We carried out a two-stage study including 2647 breast cancer patients. In stage I, we genotyped 192 SNPs within microRNA binding sites using the Illumina Goldengate platform. In stage II, we validated SNPs significantly associated with breast cancer prognosis in another dataset using the TaqMan platform. Survival times was calculated, and Kaplan-Meier curves and Cox regression model were used to analyze survival of breast cancer patients with different genotypes.Results: We identified 8 SNPs significantly associated with breast cancer prognosis in stage I (P<0.05), and only rs10878441 was statistically significant in stage II (AA vs CC: adjusted HR=2.21, 95% CI: 1.11-4.42, P=0.024). We combined the data from stage I and stage II, and found that, compared with rs10878441 AA genotype, CC genotype was significantly associated with poor survival of breast cancer (HR=1.69, 95% CI: 1.18-2.42, P=0.004; adjusted HR=2.19, 95% CI: 1.30-3.70, P=0.003). Stratified analyses demonstrated that rs10878441 was related to breast cancer prognosis in grade II patients and lymph node-negative patients (P<0.05).Conclusions: The LRKK2 rs10878441 CC genotype is associated with poor prognosis of breast cancer in a Chinese population, and it could be used as a potential prognostic biomarker for breast cancer. Further studies are warranted.

scholarly journals Abnormal Long Non-Coding RNAs Expression Patterns Have the Potential Ability for Predicting Survival and Treatment Response in Breast Cancer

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 996
Author(s):  
Ana Carolina Pavanelli ◽  
Flavia Rotea Mangone ◽  
Luciana R. C. Barros ◽  
Juliana Machado-Rugolo ◽  
Vera L. Capelozzi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Predictive Value ◽  
Expression Patterns ◽  
Survival Rates ◽  
In Silico Analysis ◽  
Cancer Prognosis ◽  
Response To Treatment ◽  
Breast Cancer Patients ◽  
Non Coding Rnas

Abnormal long non-coding RNAs (lncRNAs) expression has been documented to have oncogene or tumor suppressor functions in the development and progression of cancer, emerging as promising independent biomarkers for molecular cancer stratification and patients’ prognosis. Examining the relationship between lncRNAs and the survival rates in malignancies creates new scenarios for precision medicine and targeted therapy. Breast cancer (BRCA) is a heterogeneous malignancy. Despite advances in its molecular classification, there are still gaps to explain in its multifaceted presentations and a substantial lack of biomarkers that can better predict patients’ prognosis in response to different therapeutic strategies. Here, we performed a re-analysis of gene expression data generated using cDNA microarrays in a previous study of our group, aiming to identify differentially expressed lncRNAs (DELncRNAs) with a potential predictive value for response to treatment with taxanes in breast cancer patients. Results revealed 157 DELncRNAs (90 up- and 67 down-regulated). We validated these new biomarkers as having prognostic and predictive value for breast cancer using in silico analysis in public databases. Data from TCGA showed that compared to normal tissue, MIAT was up-regulated, while KCNQ1OT1, LOC100270804, and FLJ10038 were down-regulated in breast tumor tissues. KCNQ1OT1, LOC100270804, and FLJ10038 median levels were found to be significantly higher in the luminal subtype. The ROC plotter platform results showed that reduced expression of these three DElncRNAs was associated with breast cancer patients who did not respond to taxane treatment. Kaplan–Meier survival analysis revealed that a lower expression of the selected lncRNAs was significantly associated with worse relapse-free survival (RFS) in breast cancer patients. Further validation of the expression of these DELncRNAs might be helpful to better tailor breast cancer prognosis and treatment.

scholarly journals Relationship Between Obesity and Pathologic Response to Neoadjuvant Chemotherapy Among Women With Operable Breast Cancer

2008 ◽  
Vol 26 (25) ◽  
pp. 4072-4077 ◽  
Author(s):  
Jennifer K. Litton ◽  
Ana M. Gonzalez-Angulo ◽  
Carla L. Warneke ◽  
Aman U. Buzdar ◽  
Shu-Wan Kau ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Pathologic Complete Response ◽  
Operable Breast Cancer ◽  
Obese Patients ◽  
Breast Cancer Patients ◽  
Significant Difference ◽  
Response To Neoadjuvant Chemotherapy

Purpose To understand the mechanism through which obesity in breast cancer patients is associated with poorer outcome, we evaluated body mass index (BMI) and response to neoadjuvant chemotherapy (NC) in women with operable breast cancer. Patients and Methods From May 1990 to July 2004, 1,169 patients were diagnosed with invasive breast cancer at M. D. Anderson Cancer Center and received NC before surgery. Patients were categorized as obese (BMI ≥ 30 kg/m2), overweight (BMI of 25 to < 30 kg/m2), or normal/underweight (BMI < 25 kg/m2). Logistic regression was used to examine associations between BMI and pathologic complete response (pCR). Breast cancer–specific, progression-free, and overall survival times were examined using the Kaplan-Meier method and Cox proportional hazards regression analysis. All statistical tests were two-sided. Results Median age was 50 years; 30% of patients were obese, 32% were overweight, and 38% were normal or underweight. In multivariate analysis, there was no significant difference in pCR for obese compared with normal weight patients (odds ratio [OR] = 0.78; 95% CI, 0.49 to 1.26). Overweight and the combination of overweight and obese patients were significantly less likely to have a pCR (OR = 0.59; 95% CI, 0.37 to 0.95; and OR = 0.67; 95% CI, 0.45 to 0.99, respectively). Obese patients were more likely to have hormone-negative tumors (P < .01), stage III tumors (P < .01), and worse overall survival (P = .006) at a median follow-up time of 4.1 years. Conclusion Higher BMI was associated with worse pCR to NC. In addition, its association with worse overall survival suggests that greater attention should be focused on this risk factor to optimize the care of breast cancer patients.

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