فصلنامه بیماری های پستان ایران
سال چهاردهم شماره 1 (پیاپی 52، بهار 1400)

Breast cancer is the most common cancer and the second leading cause of death in women worldwide. The aim of this study was to analyze the trend and predict the incidence of breast cancer using time series analysis.

In this study, data on breast cancer incidence in Qazvin province between 2007 and 2016 were analyzed using time series analysis with autoregressive integrated moving average (ARIMA) modeling to forecast the future pattern. The Box-Jenkins time series model and its diagnosis and evaluation methods were used to show the trend and forecasting the next year new cancers. To describe and fit the appropriate models, R statistical software version 3.6.3 was used.

Between 2007 and 2016, a total number of 1229 new patients had been registered (monthly mean [SD]: 10.24 [1.03]). Although the overall trend in the raw number of new breast cancer cases has been increasing over time, the change in observations over time has been increasing and decreasing. According to Bartlett test results, the variances of the data were not constant. Also, according to the results of Kolmogorov-Smirnov test, breast cancer series data were not normal. Among the studied models, ARIMA (1, 1, 1) was selected due to lower AIC criteria than other models, and this model was selected as the final model for predicting breast cancer for the next year. The confidence interval of the predicted values ​​was relatively narrow, which indicates the appropriateness of the final model in the prediction.

Time series analysis is an efficient tool to model the past and future data on the raw number of new cancer cases, and the goodness-of-fit indicators of the model showed that the Box-Jenkins model is a reliable model for fitting similar data.

Breast cancer is one of the most common cancers among women worldwide. Patients with cancer may die due to disease progression or other types of events. These different event types are called competing risks. This study aimed to determine the factors affecting the survival of patients with breast cancer using three different approaches: cause-specific hazards regression, subdistribution hazards regression, and the random survival forest for competing risks.

A historical cohort study was conducted on 527 breast cancer patients diagnosed in Mahdieh Medical Center, Hamadan, between 2004 and 2015. To determine risk factors for death due to cancer progression or other competing risks, cause-specific hazards and substandard hazards models and a random survival forest for competing risk were fitted. Data analysis was performed with R 3.4.3.

Findings showed that for death from the progression of breast cancer, age and number of involved lymph nodes were significant in both models (P < 0.05), and in the random survival forest model for death due to cancer progression, tumor size, number of involved lymph nodes, progesterone, estrogen, and family history were the important identified variables.

In the presence of competing risks, when the underlying assumptions of cause-specific and subdistribution hazard regression models are not established, the use of random survival forest for competing events data to determine the risk factors affecting survival according to the coordination index and Brier score is more appropriate.

Breast cancer is the most prevalent cancer among women, and mastectomy, the surgical treatment for this cancer, affects different aspects of patients’ life. This study aimed to describe women’s lived experiences of femininity after mastectomy and treatment.

This qualitative study was conducted on 17 breast cancer survivors in Tehran in 2018-2019. The participants, who were selected through snowball sampling, had undergone mastectomy at least 6 months before and were younger than 50 years. Data were collected using in-depth semi-structured interviews and analyzed with the contractual content analysis method.

The analysis of patients’ experiences led to the identification of 7 main themes (and 18 subtheme): social life (social isolation, quitting the job or getting a part-time job, and dropping out of school), body image (being asymmetric, being unattractive, and requiring reconstructive surgery), others’ opinions (complaining about how others look at them and concerns of being pitied), marital relationships (fear of abandonment, fear of spouse’s pity, shame of being nude, and compromised sexual intimacy), reproductive health (premature menopause and possible infertility), perceived support (attending support group meetings and keeping in touch with cured ones), taking responsibilities of household works (inability to do housewife affairs and inability to do the housework).

Mastectomy and treatment for breast cancer greatly affect many areas of individual and social life and marital relationships of patients, necessitating the need to develop and establish free support resources by NGOs and highlighting the government’s role in reducing the psychological and family damage caused by the disease.

HER2-enriched subtype of breast cancer has a worse prognosis than luminal subtypes. Recently, the discovery of targeted therapies in other groups of breast cancer has increased patient survival. The aim of this study was to identify genes that affect the overall survival of this group of patients based on a systems biology approach.

Gene expression data and clinical information on 58 patients with HER2-enriched cancer were downloaded from The Cancer Genome Atlas (TCGA). Co-expression modules were identified using the weighted gene co-expression network analysis (WGCNA). The Cox regression was used to determine the modules that had a significant relationship with the overall survival (OS) endpoint. Single-gene survival analysis was performed within the selected module. Finally, functional annotation to explore the significance of genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID).

Of the six identified co-expression modules, two had significantly poor prognoses. Single-gene survival analysis showed that 39% of genes in the selected modules were identified as significant. The genes were mainly related to the biological pathways such as Ubiquitin-mediated proteolysis and RNA degradation. CHAMP1, PPP1R26, PRRC2B, KANSL3, and ANAPC2 were identified as the 5 most important genes associated with reduced OS, in order of significance.

The systems biology approach can provide appropriate results relate to patient survival analysis. In this study, some genes were identified to be used as prognostic biomarkers in experimental studies related to the OS in the HER2-enriched subgroup. These genes can be considered potential candidates for therapeutic targets in this group of patients.

The use of chemotherapy drugs has side effects and the use of herbal compounds in the treatment of cancer faces challenges. Nanoparticles, especially liposomes, with appropriate properties in drug delivery, such as slow drug release, low toxicity at the target cell site, can solve some of these problems. Therefore, the aim of the present study was to construct a liposomal system containing Artemisia absintium extract and to investigate its toxicity on MCF-7 cell line.

Three liposomal systems containing extracts with different molar percentages of SPC80 and SPC60 along with cholesterol were prepared by thin film method.The most suitable formulation was selected according to the loading percentage and release rate. The release pattern of the extract from the selected formulation, particle size and zeta potential were investigated. Finally, the toxicity of the selected system containing the extract, the free extract and the non-extracted system on the MCF-7 cell line was measured.

Extract load, particle size, dispersion index, and zeta potential for the selected formulation are 49.2 ± 0.4%, 121.9 nm, 0.129 and -10.6 mV, respectively. Selected system at similar temperatures to healthy and cancer cells was slow to release. Also, liposome extract was more toxic to MCF-7 cell line than free extract and extract-free liposomes have little toxicity on MCF-7 cell line.

The selected liposomal system containing Artemisia absintium extract has a higher toxicity than the free extract on breast cancer cells, which can be due to the appropriate physicochemical properties of the system, including targeted release.

Breast cancer is the most important cause of cancer mortality among women, therefore the study of its causative or aggravating factors seems necessary. In this study, the mRNA levels of the PHB2 gene were evaluated in tumor and adjacent non-tumor tissues of 50 women diagnosed with invasive ductal carcinoma of the breast.

RNX-Plus solution was used to isolate total RNA from tumor and adjacent non-tumor tissues of breast cancer patients. Thereafter, total RNA was converted to cDNA using PrimeScriptTM RT reagent kit. The mRNA levels of PHB2 were quantified by qRT-PCR and data were analyzed with a paired-samples t test. Furthermore, ROC curve analysis was performed to evaluate the diagnostic capacity of PHB2 expression in breast cancer tumor tissues.

A significantly lower level of PHB2 mRNA was observed in tumor tissues of breast cancer patients compared with adjacent non-tumor tissues (P<0.0001). PHB2 mRNA levels showed an approximately 2.5-fold reduction in tumor tissues compared with normal tissues (P = 0.001). Roc cure analysis showed that PHB2 mRNA level can discriminate tumors from non-tumor tissues with 91.3% specificity and 64.3% sensitivity (AUC=0.712, P<0.001).

Downregulation of PHB2 in breast cancer patients shows that its mRNA levels can possibly discriminate tumors from non-tumor tissues.