Survival Time Of Breast Cancer Patients Research Articles

Survivability Prediction of Breast Cancer Patients Using Three Data Mining Methods: A Comparative Study

Background and aims: Breast cancer (BC) is the leading cause of mortality among women. Early diagnosis is crucial for effective treatment. This study applied suitable data mining methods that provide rules and present influential prognostic factors on the survival time of BC patients. Methods: The dataset consisted of 1574 women diagnosed between January 2002 and December 2012 at the Cancer Registry Center of Nemazi hospital in Fars Province, Iran. Patients were classified based on prognostic factors using three popular data mining methods, including decision tree (J48), Naïve Bayes (NB), and nominal logistic regression (NLR). The Weka software was considered to compare these methods using sensitivity, specificity, and accuracy metrics. The outcome of the study was the median survival time, which was categorized into three classes. Results: In total, 212 women (13.5%) died of BC, whose mean age was 49.74 years old. Overall survival rates at 2, 3, 5, and 10 years were 0.98, 0.94, 0.87, and 0.76, respectively. The mean and median survival times were 4.81 and 4.27 years. Sensitivity, specificity, and accuracy for J48 and NB were 0.480, 0.570, and 0.572, as well as 0.483, 0.610, and 0.584, respectively. In addition, the corresponding values were 0.488, 0.584, and 0.579 for NLR, respectively. Further, J48 showed that the Nottingham Prognostic Index (NPI) was the most influential prognostic factor. Conclusion: This paper sought to improve the accuracy of BC classification using data mining methods. Comparing multiple prediction models gave us an insight into the relative prediction abilities of different data mining methods. The results suggested NB as the best classifier due to its higher accuracy and specificity. Finally, J48 identified the NPI as the most effective prognostic factor.

The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.

Analysis of clinicopathological features and prognostic factors of breast cancer brain metastasis.

Breast cancer (BC) has become the most common malignancy in women. The incidence and detection rates of BC brain metastasis (BCBM) have increased with the progress of imaging, multidisciplinary treatment techniques and the extension of survival time of BC patients. BM seriously affects the quality of life and sur-vival prognosis of BC patients. Therefore, clinical research on the clinicopathological features and prognostic factors of BCBM is valuable. By analyzing the clinicopathological parameters of BCBM patients, and assessing the risk factors and prognostic indicators, we can perform hierarchical diagnosis and treatment on the high-risk population of BCBM, and achieve clinical benefits of early diagnosis and treatment. To explore the clinicopathological features and prognostic factors of BCBM, and provide references for diagnosis, treatment and management of BCBM. The clinicopathological data of 68 BCBM patients admitted to the Air Force Medical Center, Chinese People's Liberation Army (formerly Air Force General Hospital) from 2000 to 2022 were collected. Another 136 BC patients without BM were matched at a ratio of 1:2 based on the age and site of onset for retrospective analysis. Categorical data were subjected to χ2 test or Fisher's exact probability test, and the variables with P < 0.05 in the univariate Cox proportional hazards model were incorporated into the multivariate model to identify high-risk factors and independent prognostic factors of BCBM, with a hazard ratio (HR) > 1 suggesting poor prognostic factors. The survival time of patients was estimated by the Kaplan-Meier method, and overall survival was compared between groups by log-rank test. Multivariate Cox regression analysis showed that patients with stage III/IV tumor at initial diagnosis [HR: 5.58, 95% confidence interval (CI): 1.99-15.68], lung metastasis (HR: 24.18, 95%CI: 6.40-91.43), human epidermal growth factor receptor 2 (HER2)-overexpressing BC and triple-negative BC were more prone to BM. As can be seen from the prognostic data, 52 of the 68 BCBM patients had died by the end of follow-up, and the median time from diagnosis of BC to the occurrence of BM and from the occurrence of BM to death or last follow-up was 33.5 and 14 mo, respectively. It was confirmed by multivariate Cox regression analysis that patients with neurological symptoms (HR: 1.923, 95%CI: 1.005-3.680), with bone metastasis (HR: 2.011, 95%CI: 1.056-3.831), and BM of HER2-overexpressing and triple-negative BC had shorter survival time. HER2-overexpressing, triple-negative BC, late tumor stage and lung metastasis are risk factors of BM. The presence of neurological symptoms, bone metastasis, and molecular type are influencing prognosis factors of BCBM.

Identification and evaluation of a risk model predicting the prognosis of breast cancer based on characteristic signatures.

Breast cancer (BC) is one of the most common fatal cancers in women. Identifying new biomarkers is thus of great significance for the diagnosis and prognosis of BC. In this study, 1,030 BC cases from The Cancer Genome Atlas (TCGA) were obtained for differential expression analysis and Short Time-series Expression Miner (STEM) analysis to identify characteristic BC development genes, which were further divided into upregulated and downregulated genes. Two predictive prognosis models were both defined by Least Absolute Shrinkage and Selection Operator (LASSO). Survival analysis and receiver operating characteristic (ROC) curve analysis were used to determine the diagnostic and prognostic capabilities of the two gene set model scores, respectively. Our findings from this study suggested that both the unfavorable (BC1) and favorable (BC2) gene sets are reliable biomarkers for the diagnosis and prognosis of BC, although the BC1 model presents better diagnostic and prognostic value. Associations between the models and M2 macrophages and sensitivity to Bortezomib were also found, indicating that unfavorable BC genes are significantly involved in the tumor immune microenvironment. We successfully established one predictive prognosis model (BC1) based on characteristic gene sets of BC to diagnose and predict the survival time of BC patients using a cluster of 12 differentially expressed genes (DEGs).

Identifying tumour microenvironment-related signature that correlates with prognosis and immunotherapy response in breast cancer

Tumor microenvironment (TME) plays important roles in prognosis and immune evasion. However, the relationship between TME-related genes and clinical prognosis, immune cell infiltration, and immunotherapy response in breast cancer (BRCA) remains unclear. This study described the TME pattern to construct a TME-related prognosis signature, including risk factors PXDNL, LINC02038 and protective factors SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108, as an independent prognostic factor for BRCA. We found that the prognosis signature was negatively correlated with the survival time of BRCA patients, infiltration of immune cells and the expression of immune checkpoints, while positively correlated with tumor mutation burden and adverse treatment effects of immunotherapy. Upregulation of PXDNL and LINC02038 and downregulation of SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108 in high-risk score group synergistically contribute to immunosuppressive microenvironment which characterized by immunosuppressive neutrophils, impaired cytotoxic T lymphocytes migration and natural killer cell cytotoxicity. In summary, we identified a TME-related prognostic signature in BRCA, which was connected with immune cell infiltration, immune checkpoints, immunotherapy response and could be developed for immunotherapy targets.

FRMD3 inhibits the growth and metastasis of breast cancer through the ubiquitination-mediated degradation of vimentin and subsequent impairment of focal adhesion

Recurrence and metastasis are the main causes of breast cancer (BRCA)-related death and remain a challenge for treatment. In-depth research on the molecular mechanisms underlying BRCA progression has been an important basis for developing precise biomarkers and therapy targets for early prediction and treatment of progressed BRCA. Herein, we identified FERM domain-containing protein 3 (FRMD3) as a novel potent BRCA tumor suppressor which is significantly downregulated in BRCA clinical tissue and cell lines, and low FRMD3 expression has been closely associated with progressive BRCA and shortened survival time in BRCA patients. Overexpression and knockdown experiments have revealed that FRMD3 significantly inhibits BRCA cell proliferation, migration, and invasion in vitro and suppresses BRCA xenograft growth and metastasis in vivo as well. Mechanistically, FRMD3 can interact with vimentin and ubiquitin protein ligase E3A(UBE3A) to induce the polyubiquitin-mediated proteasomal degradation of vimentin, which subsequently downregulates focal adhesion complex proteins and pro-cancerous signaling activation, thereby resulting in cytoskeletal rearrangement and defects in cell morphology and focal adhesion. Further evidence has confirmed that FRMD3-mediated vimentin degradation accounts for the anti-proliferation and anti-metastasis effects of FRMD3 on BRCA. Moreover, the N-terminal ubiquitin-like domain of FRMD3 has been identified as responsible for FRMD3-vimentin interaction through binding the head domain of vimentin and the truncated FRMD3 with the deletion of ubiquitin-like domain almost completely loses the anti-BRCA effects. Taken together, our study indicates significant potential for the use of FRMD3 as a novel prognosis biomarker and a therapeutic target of BRCA and provides an additional mechanism underlying the degradation of vimentin and BRCA progression.

A circadian rhythm-related gene signature for prognosis, invasion and immune microenvironment of breast cancer.

Background: Circadian dysregulation is linked to the onset and progression of cancer, but current knowledge of the role of circadian rhythm-related genes (CRRGs) in breast cancer (BC) is limited and incomplete. The purpose of this study was to investigate the potential role and immune-related prognostic significance of CRRGs in BC. Methods: The Cancer Genome Atlas breast cancer (TCGA-BRCA) genetic data were combined with 1369 CRRGs to create a model of BC prognosis-related CRRGs. To validate the model's predictive power in TCGA and other external datasets, the Kaplan-Meier survival curve and receptor operation characteristic curve were plotted. The relationship between CRRGs model and gene enrichment pathways, immune cell infiltration, and differences in patient response to immune checkpoint inhibitors (ICIs) therapy was then discussed. Results: A CRRG-based eighteen-gene model was developed that accurately predicted the survival time of BC patients. Based on this model, BC patients can be classified as high or low risk. The high-risk group has negative immune cell infiltration (such as macrophages M0 and M2) and a poor therapeutic response to ICIs due to lower immune checkpoint gene expression. Furthermore, TCF7 and IFNG were found to be strongly associated with immune checkpoints in CRRGs model. Conclusion: The 18 CRRGs may be useful in assessing the prognosis of BC patients, studying immune infiltration, and developing more effective immunotherapy strategies.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

A comprehensive analysis of the diagnostic and prognostic value associated with the SLC7A family members in breast cancer.

The solute carrier (SLC) 7 family genes play central roles in cancer cell metabolism as glucose and glutamate transporters. However, their expression and prognostic value in breast cancer (BC) remains to be elucidated. Clinical data from BC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Kaplan-Meier (KM) plotter database. The mechanisms underlying the association between SLC7A expression and overall survival (OS) were explored using Cox regression and log-rank tests. ESTIMATE gives a measure of the immune-cell infiltrates. Single-sample (ss) Gene Set Enrichment Analysis (GSEA) was conducted to quantify immune cell infiltration. High SLC7A5 expression was associated with a poorer survival time in BC patients according to the TCGA and KM plotter data. SLC7A4 was associated with good progression-free interval (PFI) and disease-specific survival (DSS) according to the TCGA data. Furthermore, SLC7A4 was correlated with good prognosis of OS, distant metastasis-free survival (DMFS), relapse-free survival (RFS), and post-progression survival (PPS) according to the KM plotter data. SLC7A3 expression was positively associated with OS, but was not strongly associated with PFI nor DSS in the TCGA data. However, SLC7A3 was positively correlated with DMFS and RFS in the KM database analysis. SLC7A had excellent diagnostic value in BC patients and was strongly correlated with tumor infiltration. T helper 2 (Th2) cells, CD56 bright natural killer (NK) cells, and NK cells were the most strongly correlated with the SLC7A family genes, suggesting that these genes play a crucial role in BC partly by modulating immune infiltration. SLC7A4 and SLC7A5 expression levels may be sensitive biomarkers for predicting BC outcomes. SLC7A3 may be a potential diagnostic and prognostic biomarker in BC, but further studies are warranted to verify these results.

A Comparison between Accelerated Failure Time Models in Analyzing the Survival of Breast Cancer Patients

Background: Female breast cancer (BC) has surpassed lung cancer as the most prevalent reason for cancer-related diagnosis in the world. BC has geographical disparities in the intensity of effect of its associated risk factors on patients’ survival. Several models can be employed to determine the effect of risk factors on patients’ survival. The present study aims at evaluating these models.&#x0D; Methods: The secondary data of 558 BC patients diagnosed at Korle Bu teaching hospital during 2010-2015 and followed-up (right censored) to the end of 2015 were analysed. The survival status, demographic and tumour characteristics of these patients were determined by event history analysis. To compare various models of survival, Akaike Information Criterion (AIC) , Bayesian Information Criteria (BIC) and Receiver Operation Characteristic (ROC) curve were used. R software was used for data analyses. The data consisted of BC patients in the age range of 13 to 97 years. The dataset was partitioned into training (holding 70%) and validation set (30%).&#x0D; Results: Based on AIC, BIC and ROC curve values the Gompertz (AIC=2322, BIC=2391) was the best model fit for the survival data. Generalised Gamma (AIC=2378, BIC=2451) and Weibull (AIC=2382, BIC=2452) models were respectively the next alternatives among the nine (9) accelerated failure time (AFT) models considered in our study. Results from the three best fitted AFT models showed that covariates such as Age at diagnosis, Progesterone receptor, Molecular Subtype, Grade, Stage, Metastasis, number of Lymph nodes involved and genetic status were the significant factors that have an effect on the survival time of BC patients in Ghana (P&lt;0.05). The Area under the ROC curve (AUC=0.945) shows an outstanding performance of the Gompertz AFT model to discriminate the true disease status of patients.&#x0D; Conclusion: Although the Cox proportional hazard model has seen wide usage and remains a robust approach in survival analyses for the past four decades; its proportional hazards assumption is most often violated by some covariates in medical research. Under such violations, AFT models are a strong alternative.

NR2F1-AS1 Acts as an Oncogene in Breast Cancer by Competitively Binding with miR-641.

Background Long noncoding RNA (lncRNA) NR2F1-AS1 has been previously reported to be dysregulated in human cancers and implicated in the tumorigenesis and development of tumors. In this research, we detected the expression level and biological function of NR2F1-AS1 in breast cancer (BC). Methods The expression of NR2F1-AS1 in BC tissues and cell lines was determined by qRT-PCR analysis. The associations of NR2F1-AS1 expression with clinical characteristics and survival rate of BC patients were also analyzed. Cell proliferation, migration, and invasion were measured by the CCK-8 and Transwell assay. Results The results revealed that the total survival time of BC patients with high NR2F1-AS1 expression was lower than that of BC patients with low NR2F1-AS1 expression. Moreover, functional experiments demonstrated that knockdown of NR2F1-AS1 inhibited BC cell viability, migration, and invasion abilities, whereas overexpression of NR2F1-AS1 had the opposite effect. Mechanistic investigation revealed that NR2F1-AS1 can competitively bind with microRNA-641 (miR-641) in BC. These results revealed that NR2F1-AS1 functioned as an oncogene by sponging miR-641 expression in BC cell progression. Moreover, miR-641 was negatively correlated with NR2F1-AS1 in BC tissues. Conclusion Hence, NR2F1-AS1 was found to act as an oncogene in breast cancer by suppressing miR-641. We suggested that NR2F1-AS1 could be a potential biomarker for BC diagnosis and therapy.

Application of additive hazards models for analyzing survival of breast cancer patients.

Background:Survival rates for breast cancer (BC) are often based on the outcomes of this disease. The aim of this study was to compare the performance of three survival models, namely Cox regression, Aalen's, and Lin and Ying's additive hazards (AH) models for identifying the prognostic factors regarding the survival time of BC patients.Materials and Methods:This study was a historical cohort study which used 1025 females' medical records that underwent modified radical mastectomy or breast saving. These patients were admitted to Besat and Chamran Hospitals, Tehran, Iran, during 2010–2015 and followed until 2017. The Aalen's and Lin and Ying's AH models and also traditional Cox model were applied for analysis of time to death of BC patients using R 3.5.1 software.Results:In Aalen's and also Lin and Ying's AH models, age at diagnosis, history of disease, number of lymph nodes, metastasis, hormonal therapy, and evacuation lymph nodes were prognostic factors for the survival of BC patients (P < 0.05). In addition, in the Lin and Ying's AH model tumor size (P = 0.048) was also identified as a significant factor. According to Aalen's plot, metastasis, age at diagnosis, and number of lymph nodes had a time-varying effect on survival time. These variables had a different slope as the times go on.Conclusion:AH model may yield new insights in prognostic studies of survival time of patients with BC over time. Because of the positive slope of estimated cumulative regression function in Aalen's plot, metastasis, higher age at diagnosis, and high number of lymph nodes are important factors in reducing the survival BC, and then based on these factors, the therapists should consider a special therapeutic protocol for BC patients.

Secondary hematological malignancies following breast cancer treatment

Breast cancer (BC) incidence has increased among women in most Western countries. Concurrently, the survival time of BC patients has increased with 5-year survival rates reaching 80–90%. Secondary hematological malignancies (SHM) following BC treatment are an issue of concern to clinicians and also to patients and their families. However, therapy-induced leukemia after BC is an underemphasized clinical problem. In this review,we will focus on the incidences and patterns of occurrence ofSHM in patients with BC. We will address risk factors for the development of SHM and we will explore how secondary hematological malignancies impact the survival of BC patients.

Secondary hematological malignancies following breast cancer treatment

Breast cancer (BC) incidence has increased among women in most Western countries. Concurrently, the survival time of BC patients has increased with 5-year survival rates reaching 80–90%. Secondary hematological malignancies (SHM) following BC treatment are an issue of concern to clinicians and also to patients and their families. However, therapy-induced leukemia after BC is an underemphasized clinical problem. In this review, we will focus on the incidences and patterns of occurrence of SHM in patients with BC. We will address risk factors for the development of SHM and we will explore how secondary hematological malignancies impact the survival of BC patients.