scholarly journals Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

Biomolecules ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Suma L. Sivan ◽  
Vinod Chandra S. Sukumara Pillai
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
Drug Targets ◽  
Minimum Spanning Tree ◽  
Biological Significance ◽  
Molecular Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Cancer Data ◽  
Nature Inspired Algorithm

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

Screening Breast MRI and Gadolinium Deposition: Cause for Concern?

2021 ◽  
Author(s):  
Colleen H Neal
Keyword(s):  
Breast Cancer ◽  
Renal Function ◽  
Contrast Agents ◽  
Biological Significance ◽  
Breast Mri ◽  
Reliable Data ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Contrast Enhanced ◽  
Gadolinium Retention

Abstract Gadolinium-based contrast agents (GBCAs) have been used worldwide for over 30 years and have enabled lifesaving diagnoses. Contrast-enhanced breast MRI is frequently used as supplemental screening for women with an elevated lifetime risk of breast cancer. Data have emerged that indicate a fractional amount of administered gadolinium is retained in the bone, skin, solid organs, and brain tissues of patients with normal renal function, although there are currently no reliable data regarding the clinical or biological significance of this retention. Linear GBCAs are associated with a higher risk of gadolinium retention than macrocyclic agents. Over the course of their lives, screened women may receive high cumulative doses of GBCA. Therefore, as breast MRI screening utilization increases, thoughtful use of GBCA is indicated in this patient population.

scholarly journals Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri

2018 ◽  
Author(s):  
◽  
Jiang Du
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
Surveillance System ◽  
The United States ◽  
Cancer Surveillance ◽  
Mortality Data ◽  
Bayesian Hierarchical ◽  
Cancer Data ◽  
Related Data ◽  
Incidence And Mortality

Data on cancer in the United States is collected through cancer registries. The Missouri Cancer Registry and Research Center (MCR-ARC) maintains a statewide cancer surveillance system and participate in research in support of the prevention of cancer and the reduction of the cancer burden among Missouri residents. We applied Bayesian hierarchical models to colorectal cancer (CRC) and breast cancer related data collected by the MCR-ARC. In the first project, CRC incidence and mortality rates in Missouri were studied with emphasis on different groups of people categorized by age, gender and county at diagnosis. The incidence and mortality data were aggregated into different spatial regions due to data confidential requirements, which was identified as a misaligned-region problem in multivariate disease mapping literature. The Marginally and Conditionally CAR models were built to address the problem. Later on, colorectal cancer screening (CRCS) prevalences were analyzed due to its importance to the early detection of CRC. We applied small area estimation techniques to produce county-level CRCS prevalences from the state-level Behavioral Risk Factor Surveillance System (BRFSS) data. The last two projects focused on breast cancer related data. One is about breast cancer survival analysis in Missouri with emphasis on detecting the spatial variation of survival time among counties in Missouri, after accounting for the differences in demographic and cancer stages. The other one is studying the disparities of breast cancer treatment delay with respect to patient's race, age, stage of cancer, county at diagnosis and year of diagnosis.

scholarly journals Breast cancer brain metastasis: molecular mechanisms and directions for treatment

2018 ◽  
Vol 20 (11) ◽  
pp. 1439-1449 ◽  
Author(s):  
Rute M S M Pedrosa ◽  
Dana A Mustafa ◽  
Riccardo Soffietti ◽  
Johan M Kros
Keyword(s):  
Breast Cancer ◽  
Brain Metastasis ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Treatment Options ◽  
Current Data ◽  
Molecular Pathways ◽  
Primary Tumors ◽  
Cancer Data ◽  
Breast Cancer Brain Metastasis

AbstractThe development of brain metastasis (BM) of breast cancer is usually a late event with deleterious effect on the prognosis. Treatment options for intracerebral seeding of breast cancer are limited and, so far, nonspecific. Molecular detailing of subsequent events of penetration, seeding, and outgrowth in brain is highly relevant for developing therapeutic strategies to treat, or prevent, BM.We scrutinize recent literature for molecules and pathways that are operative in the formation of breast cancer BM. We also summarize current data on therapeutic efforts to specifically address BM of breast cancer. Data on molecular pathways underlying the formation of BM of breast cancer are sketchy and to some extent inconsistent. The molecular makeup of BM differs from that of the primary tumors, as well as from metastases at other sites. Current efforts to treat breast cancer BM are limited, and drugs used have proven effects on the primary tumors but lack specificity for the intracerebral tumors.More basic research is necessary to better characterize BM of breast cancer. Apart from the identification of drug targets defined by the intracerebral tumors, also targets in the molecular pathways involved in passing the blood–brain barrier and intracerebral tumor cell growth should be revealed.

Colorectal cancer knowledge and screening rates are lower than for breast cancer even in individuals at high risk of cancer

2001 ◽  
Vol 120 (5) ◽  
pp. A741-A741
Author(s):  
P ANG ◽  
D SCHRAG ◽  
K SCHNEIDER ◽  
K SHANNON ◽  
J JOHNSON ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
High Risk ◽  
Cancer Knowledge ◽  
Risk Of Cancer

Bayesian Frailty Model for Time to Event Breast Cancer Data

2011 ◽  
Vol 4 (2) ◽  
pp. 8-12
Author(s):  
Leo Alexander T Leo Alexander T ◽  
◽  
Pari Dayal L Pari Dayal L ◽  
Valarmathi S Valarmathi S ◽  
Ponnuraja C Ponnuraja C ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Frailty Model ◽  
Breast Cancer Data ◽  
Time To Event ◽  
Cancer Data

Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

AGE FEATURES OF MOLECULAR-BIOLOGICAL SUBTYPES OF BREAST CANCER IN THE REPUBLIC OF UZBEKISTAN

2018 ◽  
Vol 64 (2) ◽  
pp. 196-199
Author(s):  
Gulya Miryusupova ◽  
G. Khakimov ◽  
N. Shayusupov
Keyword(s):  
Breast Cancer ◽  
Cancer Care ◽  
Indigenous Women ◽  
Morbidity And Mortality ◽  
Breast Cancer Data ◽  
Female Population ◽  
Cancer Data ◽  
Age Related ◽  
The Republic ◽  
Age Features

According to the results of breast cancer data in the Republic of Uzbekistan in addition to the increase in morbidity and mortality from breast cancer among women the presence of age specific features among indigenous women in the direction of “rejuvenating” of the disease with all molecular-biological (phenotypic) subtypes of breast cancer were marked. Within the framework of age-related features the prevalence of the least favorable phenotypes of breast cancer was found among indigenous women: Her2/neu hyperexpressive and three times negative subtype of breast cancer. The data obtained made it possible to build a so-called population “portrait” of breast cancer on the territory of the Republic, which in turn would contribute to further improvement of cancer care for the female population of the country.

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