Treatment outcomes and its associated factors among breast cancer patients at Kitui Referral Hospital

Objectives: Despite breast cancer treatment outcomes being relatively poor or heterogeneous among breast cancer patients, there was a paucity of data in the African settings, especially in Kenya. Hence, this study aimed to determine treatment outcomes among breast cancer patients at Kitui Referral Hospital. Methods: A hospital-based retrospective cohort study design was conducted among adult patients with breast cancer. All eligible breast cancer patients undergoing treatment from January 2015 to June 2020 in the study setting were included. Hence, a total of 116 breast cancer patients’ medical records were involved in the study. Patients’ medical records were retrospectively reviewed using a predesigned data abstraction tool. The data were entered, cleaned, and analyzed using SPSS (Statistical Package for Social Sciences) version 26 software. Descriptive analysis—such as percentage, frequency, mean, and figures—was used to present the data. Kaplan–Meier survival analysis was used to estimate the mean survival estimate across different variables. A Cox regression analysis was employed to determine factors associated with mortality. Results: The study showed that the overall survival and mortality rate was 62.9% (73) and 37.1% (43), respectively. The regression analysis showed that patients who had an advanced stage of disease had a 3.82 times risk of dying (crude hazard ratio= 3.82, 95% confidence interval = 1.5–9.8) than an early stage of the disease. Besides, patients with distant metastasis had 4.4 times more hazards of dying than (crude hazard ratio = 4.4, 95% confidence interval = 2.1–9.4) their counterparts. Conclusion: The treatment outcome of breast cancer patients was poor, and its overall mortality among breast cancer patients was higher in the study setting. In the multivariate Cox regression analysis, the tumor size was the only statistically significant predictor of mortality among breast cancer patients. Stakeholders at each stage should, therefore, prepare a relevant strategy to improve treatment outcomes.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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Prognostic factors and survival according to tumour subtype in women presenting with breast cancer bone metastases at initial diagnosis: a SEER-based study

10.21203/rs.2.16521/v5 ◽

2020 ◽

Author(s):

Xiao Li ◽

Xiaoli Zhang ◽

Shen Yin Zhong ◽

Jie Liu

Keyword(s):

Breast Cancer ◽

Bone Metastases ◽

Cancer Patients ◽

Cox Regression ◽

Stage Iv ◽

Cox Proportional Hazards Model ◽

Significant Prognostic Factor ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Stage Iv Breast Cancer

Abstract Background: Tumour subtype has a significant effect on bone metastasis in breast cancer, but population-based estimates of the prognosis of patients with bone metastases at breast cancer diagnosis are lacking. The aim of this study was to analyse the influence of tumour subtype and other factors on the prognosis and survival of patients with bone metastases of breast cancer.Methods: Using the Surveillance, Epidemiology, and End Results (SEER) Program data from 2012 to 2016, a retrospective cohort study was conducted to investigate stage IV breast cancer patients with bone metastases. Stage IV patient characteristics according to subtype were compared using chi-square tests. Overall survival (OS) and prognostic factors were compared using the Kaplan-Meier method and the Cox proportional hazards model, respectively.Results: A total of 3384 stage IV patients were included in this study; 63.42% were HR+/HER2-, 19.86% were HR+/HER2+, 9.34% were HR-/HER2-, and 7.39% were HR-/HER2+. The median OS for the whole population was 38 months, and 33.9% of the patients were alive at five years. The median OS and five-year survival rate were significantly different among stage IV breast cancer patients with different molecular subtypes (p<0.05). Multivariate Cox regression analysis showed that age of 55-59 (HR=1.270), black race (HR=1.317), grade III or IV (HR=1.960), HR-/HER2- (HR=2.808), lung metastases (HR=1.378), liver metastases (HR=2.085), and brain metastases (HR=1.903) were independent risk factors for prognosis; married status (HR=0.819), HR+/HER2+ (HR=0.631), HR-/HER2+ (HR=0.716), insurance (HR=0.587) and surgery (HR=0.504) were independent protection factors of prognosis. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases, HR=0.694, 95% CI: 0.485-0.992), but the interaction between race and subtype did not reach significance for prognosis.Conclusions: There were substantial differences in OS according to tumour subtype. In addition to tumour subtype, other independent predictors of OS were age at diagnosis, race, marital status, insurance, grade, surgery and visceral metastases. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases) for prognosis. Tumour subtype, as a significant prognostic factor, warrants further investigation.

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Breast Cancer in Mosul: A Survival Analysis

Al Mustansiriyah Journal of Pharmaceutical Sciences ◽

10.32947/ajps.20.02.0447 ◽

2020 ◽

pp. 31-36

Keyword(s):

Breast Cancer ◽

Survival Analysis ◽

Cancer Patients ◽

Cox Regression ◽

Hazard Ratio ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Radical Removal ◽

One Year ◽

The Right

Breast cancer is the most common cancer in women, and the major cause of death. This study was conducted in order to make a descriptive study and survival analysis for breast cancer patients in Mosul. Two hundred forty-six early diagnosed women with breast cancer out of 290 patients were included during the period from March 2007 to February 2012. The average follows up was 36. months (range: 11-67 months). The patients were undergone modified radical removal of the breast, chemotherapy and deep radiation. Patients with estrogen positive were given tamoxifen for five years. Patients with Her2/neu positive were given trastuzumab with docytaxil for one year. Only 25 patients (10.2%) died during the study. The highest incidence of breast cancer (35.8%) was between the ages 51 ≥ 60 years. The presentation of cancer was high (90.1%) in the lumber. Tumor in the right side (66.35%) was significantly higher than the left side. Metastasis was high (25%) and most of them in the liver (19.1%). The percentage of patients with positive estrogen, progesterone, and Her2/neu receptors were not different from negative receptors. Cox regression analysis showed that metastasis had significant effect on death (hazard ratio=2.917). Age 31 ≥ 40 years was the least affected age (hazard ratio=0.034). In conclusion, survival rate of breast cancer patients in Mosul is high due to good management. The early detection of cancer is the best way for survival of the patients, by developing the educational programs.

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Relationship between red cell distribution width and prognosis in patients with breast cancer after operation: a retrospective cohort study

Bioscience Reports ◽

10.1042/bsr20190740 ◽

2019 ◽

Vol 39 (7) ◽

Cited By ~ 4

Author(s):

Deshun Yao ◽

Zhiwu Wang ◽

Haifeng Cai ◽

Ying Li ◽

Baosheng Li

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Tumor Size ◽

Cox Regression ◽

Disease Free Survival ◽

Cell Distribution ◽

Breast Cancer Patients ◽

Large Tumor ◽

Distribution Width ◽

Cox Regression Analysis

Abstract We retrospectively enrolled 825 breast cancer patients, who was primarily diagnosed in our hospital between January 2009 and December 2014 and explored the relationship between red blood cell distribution width (RDW) and long-term prognosis in patients with breast cancer. There were 412 patients with high RDW (RDW > 13.82) and 413 patients with low RDW (RDW ≤ 13.82). Compared with low RDW group, the high w group has large tumor size (the rate of tumor size >2 cm: 60.7 vs 44.8%, P=0.013). The rate of lymph node metastases was higher in the high RDW group thaten that in the low RDW group (62.1 vs 45.8%, P=0.000). RDW was positively associated with tumor stage. The high RDW tended to be advanced stage (P=0.000). Compared with low RDW group, the high RDW group tended to be higher lymphocyte count (P=0.004), elevated fibrinogen (P=0.043), and elevated high-sensitivity C-reactive protein (P=0.000). The Kaplan–Meier analysis indicated elevated RDW was positively associated with disease-free survival (DFS) (P=0.004) and overall survival (OS) (P=0.011). The multivariate Cox regression analysis indicated that the high RDW group had poorer OS (Hazard risk [HR] = 2.43; 95% CI: 1.62–3.21; P=0.024) and DFS (HR = 1.89; 95% CI: 1.28–3.62; P=0.000) compared with low RDW group. The present study found that high pretreatment RDW levels in breast cancer patients were associated with poor OS and DFS. RDW could be a potential predictive factor in differential diagnosis of poor prognosis from all patients.

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Tumor microenvironment subtypes and immune-related signatures for the prognosis of breast cancer

10.21203/rs.3.rs-53701/v1 ◽

2020 ◽

Author(s):

Yiqun Han ◽

Jiayu Wang ◽

Binghe Xu

Keyword(s):

Breast Cancer ◽

Tumor Microenvironment ◽

T Lymphocytes ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Cell Types ◽

Well Performance ◽

Breast Cancer Patients ◽

Cox Regression Analysis

Abstract To better understand the heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice, the leukocyte infiltrations of 22 cell types of interest from 2620 breast cancer patients were quantitatively estimated using deconvolution algorithms, and three TME subtypes with distinct molecular and clinical features were identified by unsupervised clustering approach. Then, we carried out systematic analyses to illustrate the contributing mechanisms for differential phenotypes, which suggested that the divergences were distinguished by cell cycle dysfunction, variation of cytotoxic T lymphocytes activity. Next, through dimensionally reduction and selection based on random-forest analysis, least absolute shrinkage and selection operator (LASSO) analysis, and uni- and multivariate COX regression analysis, a total of 15 significant genes were proposed to construct the prognostic immune-related score (pIRS) system and, in combinations with clinicopathological characteristics, a predictive model was ultimately built with well performance for survival of breast cancer patients. Comparative analyses demonstrated that proactivity of CD8 T lymphocytes and hyper-angiogenesis could be attributed to distinct prognostic outcomes. In conclusion, we retrieved three TME phenotypes and the curated prognostic model based on pIRS system for breast cancer. This model is justified for validation and optimized in the coming future.

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NKX6.1 Expression is Associated With The Prognosis in Breast Cancer.

10.21203/rs.3.rs-47888/v1 ◽

2020 ◽

Author(s):

Jie Zhang ◽

Sujie Zhang ◽

Xiaoyan Li ◽

Fan Zhang ◽

Lei Zhao

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Cancer Patients ◽

Cox Regression ◽

Expression Level ◽

Rank Test ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Log Rank Test ◽

Cancer Tissues

Abstract Background: Breast cancer is the most common cancer among women in the world. NKX6.1 is proved to be involved in several human cancers, but fewer researches have reported the functional roles of NKX6.1 in breast cancer. In this study, we investigated the clinical significance of NKX6.1 expression in breast cancer prognosis.Methods: The expression level of NKX6.1 in breast cancer tissues and paired non-cancerous tissues were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Chi-square test was applied to evaluate the relationship between NKX6.1 expression and clinicopathologic parameters. The overall survival of breast cancer patients were analyzed by Kaplan-Meier method with log rank test. Additionally, cox regression analysis was used for prognosis analysis.Results: NKX6.1 expression level is increased in breast cancer tissues (P<0.001). Moreover, the elevated levels were significantly correlated with tumor size (P=0.002), TNM stage (P=0.018) and lymph node metastasis (P=0.007). In addition, breast cancer patients with high NKX6.1 level had a poorer overall survival than those with low level (log rank test, P=0.001). NKX6.1 was an independent prognostic factor for breast cancer (HR=2.961, 95%CI=1.368-6.411, P=0.006).Conclusions: NKX6.1 is up-regulated in breast cancer, which may be a potential prognostic biomarker for the cancer.

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Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

Biomarkers in Medicine ◽

10.2217/bmm-2020-0268 ◽

2021 ◽

Vol 15 (3) ◽

pp. 167-180

Author(s):

Na Li ◽

Zubin Li ◽

Xin Li ◽

Bingjie Chen ◽

Huibo Sun ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Cox Regression ◽

Expression Profiles ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Breast Cancer Patients ◽

Cox Regression Analysis

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

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Identification of an independent immune-genes prognostic index for breast cancer

10.21203/rs.3.rs-41670/v1 ◽

2020 ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Chen Chen ◽

Junyi Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

R Package ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model ◽

Survival Risk

Abstract Objective Increasing evidence has indicated an association between immune micro-environment in breast cancer and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of breast cancer patients. Methods 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with breast cancer from the TCGA database through the R package, as well as relevant clinical information. Survival R package was applied in survival analyses for hub-genes. Cox regression analysis was used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P < 0.05 was considered to be statistically significant. Results The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and breast cancer tissues (p < 0. 05). Univariate cox regression analysis revealed 66 immune genes statistically correlated with breast cancer related survival risk, of which 30 were associated with overall survival (P < 0.05). In addition, a 15-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p < 0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC = 0.752). Our model showed satisfying AUC and survival correlation in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of breast cancer. A nomogram was established to comprehensively predict the survival of breast cancer patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion Collectively, tumor immune genes played an essential role in the prognosis of breast cancer. Furthermore, immune risk score was an independent predictive factor of breast cancer, indicating a poor survival.

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Association of Perioperative Cell-free DNA (cfDNA) Concentrations with Risk of Recurrence in Patients with Breast Cancer

10.21203/rs.3.rs-1017958/v1 ◽

2021 ◽

Author(s):

Fara Hassan ◽

Jiang Huai Wang ◽

Carolyn Cullinane ◽

Michael Ita ◽

Mark Corrigan ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Cox Regression ◽

Perioperative Period ◽

Breast Cancer Patients ◽

Cell Free Dna ◽

Cox Regression Analysis ◽

Free Dna ◽

Post Operative Period ◽

Potential Biomarker

Abstract Background: Circulating cell-free DNA (cfDNA) is a potential biomarker of disease status in cancer patients and provides valuable diagnostic and prognostic information in breast cancer. In this study, we sought to quantify the cfDNA concentrations in the perioperative period and to investigate its prognostic relevance in breast cancer patients.Methods: Sixty-three (n=63) breast cancer patients undergoing curative surgery were screened for inclusion. Blood samples were collected: pre-operatively (Pre-op), post-operatively (POD) within weeks 1-2, weeks 3-4 and weeks 5-12 following surgery. cfDNA was extracted and quantified using nanodrop spectrophotometer. All patients were followed up for 5 years.Results: The median age was 52(26-84) years. During the perioperative period, patients with high cfDNA concentrations(cutoff:480ng/ml) had inferior recurrence free survival (RFS) than those with lower cfDNA concentrations (pre-operative period: median RFS: 30(3-60) months versus 60(6-60) months (p<0.0001), post-operative period: median RFS: 24(3-60) months versus 60(6-60) months (p<0.0001). Multivariate Cox regression analysis showed that post op cfDNA concentration (p=0.017), subtypes (p=0.011) and tumour size (p=0.006) were negative prognostic factor for RFS in the pre-operative period and post-operative period.Conclusion: Our study demonstrated the prognostic ability of perioperative cfDNA concentrations in breast cancer patients. Further, prospective studies are warranted to validate its clinical utility in breast cancer.

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Prognostic factors and survival according to tumour subtype in women presenting with breast cancer bone metastases at initial diagnosis: a SEER-based study

BMC Cancer ◽

10.1186/s12885-020-07593-8 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Xiao Li ◽

Xiaoli Zhang ◽

Jie Liu ◽

Yinzhong Shen

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Bone Metastases ◽

Cancer Patients ◽

Cox Regression ◽

Stage Iv ◽

Significant Prognostic Factor ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Stage Iv Breast Cancer

Abstract Background Tumour subtype has a significant effect on bone metastasis in breast cancer, but population-based estimates of the prognosis of patients with bone metastases at breast cancer diagnosis are lacking. The aim of this study was to analyse the influence of tumour subtype and other factors on the prognosis and survival of patients with bone metastases of breast cancer. Methods Using the Surveillance, Epidemiology, and End Results (SEER) Program data from 2012 to 2016, a retrospective cohort study was conducted to investigate stage IV breast cancer patients with bone metastases. Stage IV patient characteristics according to subtype were compared using chi-square tests. Overall survival (OS) and prognostic factors were compared using the Kaplan-Meier method and the Cox proportional hazards model, respectively. Results A total of 3384 stage IV patients were included in this study; 63.42% were HR+/HER2-, 19.86% were HR+/HER2+, 9.34% were HR−/HER2-, and 7.39% were HR−/HER2+. The median OS for the whole population was 38 months, and 33.9% of the patients were alive at 5 years. The median OS and five-year survival rate were significantly different among stage IV breast cancer patients with different molecular subtypes (p < 0.05). Multivariate Cox regression analysis showed that age of 55–59 (HR = 1.270), black race (HR = 1.317), grade III or IV (HR = 1.960), HR−/HER2- (HR = 2.808), lung metastases (HR = 1.378), liver metastases (HR = 2.085), and brain metastases (HR = 1.903) were independent risk factors for prognosis; married status (HR = 0.819), HR+/HER2+ (HR = 0.631), HR−/HER2+ (HR = 0.716), insurance (HR = 0.587) and surgery (HR = 0.504) were independent protection factors of prognosis. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases, HR = 0.694, 95% CI: 0.485–0.992), but the interaction between race and subtype did not reach significance for prognosis. Conclusions There were substantial differences in OS according to tumour subtype. In addition to tumour subtype, other independent predictors of OS were age at diagnosis, race, marital status, insurance, grade, surgery and visceral metastases. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases) for prognosis. Tumour subtype, as a significant prognostic factor, warrants further investigation.

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