Breast cancer (BC) is the most common malignant tumor in women and the leading cause of cancer deaths worldwide. This work was conducted to estimate the roles of oxidative stress, vitamin B12, homocysteine (HCY), and DNA methylation in BC disease progression. Sixty BC patients (age range 33–80 years) and 30 healthy controls were recruited for this study. Patients with BC were split to group 1 consisted of stage II BC women (low level), and group 2 consisted of patients in stages III and IV (high level). Malondialdehyde (MDA), glutathione peroxidase 3 (GPX3), HCY, and vitamin B12 levels in the study groups were measured. Also, the 5-methylcytosine (5mC) global DNA methylation levels were evaluated. The results showed a significant

Colorectal cancer (CRC), the second most fatal cancer and the 3rd most common cancer is expected to cause 0.9 million deaths globally in 2025. Carcinoembryonic antigen (CEA) is currently used in the follow-up of patients with colorectal cancer, and in this study, we are trying to find a better marker than CEA in following up on patients' health and knowing the effectiveness of the treatment used and as a diagnostic marker for colorectal cancer. To determine the significance of Cancer antigen 72-4 (CA72-4) as a prognosis predictor in patients with colorectal cancer, compare its prognostic validity to the CEA biomarker. this case-control study includes (150) participants, 100 patients (59 males and 41 females), and 50 healthy controls

Abstract Background: Kaposi’s sarcoma (KS) is an angioproliferative neoplastic disorder that occurs in different epidemiological forms. Human Herpesvirus type 8 (HHV-8) is established as a causative agent of KS that has been mentioned in textbooks and literature. In the last two decades, KS cases were up searched through many Iraqi medical researches which have been published, but unfortunately, none of which had confirmed this association. Objectives: To assess the association of latent nuclear antigen-1(LANA-1) of HHV-8 among KS patients with clinicopathological parameters and to evaluate if this procedure is valuable for diagnosing this disease through the first immunohistochemical study in Iraq. Methods: This is a clinico-immunohis

Abstract: E2F6 is a member of the E2F family of transcription factors involved in regulation of a wide variety of genes through both activation and repression. E2F6 has been reported as overexpressed in breast cancers but whether or not this is important for tumor development is unclear. We first checked E2F6 expression in tumor cDNAs and the protein level in a range of breast cancer cell lines. RNA interference-mediated depletion was then used to assess the importance of E2F6 expression in cell lines with regard to cell cycle profile using fluorescence-activated cell sorting and a cell survival assay using (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The overexpression of E2F6 was confirmed in breast tumor cDNA samp

Background: Breast cancer remains a substantial cause of morbidity and mortality, there is a need for continued efforts to understand the etiology of the disease, maintain screening effort, implement prevention strategies, and develop better treatments.Objective: To analyze the risk factors, improve early detection and prevention of breast cancer in Al-Russafa district- Baghdad, aiming to increase survival rate and improve the quality of life.Methods: A cross sectional audit of 258 breast cancer cases seen at Al-Elwiya maternity teaching hospital from January2009 to December 2011,data collected from patients files were: age, gender , residency, marital status, parity, age at menarche and menopause age at first live birth, hormonal therap

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Objective: Assessment the psychological problems in patients with colorectal cancer, and to find out the
relationship between socio-demographic characteristics such as (age, sex, marital status, educational level,
and occupation) and psychological problems for those patients.
Methodology: A descriptive design is employed through the present study from 1
st July 2011 to 25
th December
2011 in order to study the quality of life in colorectal cancer patients with psychological problems.
A purposive (non probability) sample is selected for the study which includes (60) patients diagnosed with
colorectal cancer were treated in Mosul Oncology and Nuclear Medicine hospital or the patients who visited
the outpatient cl

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti