scholarly journals A SVM Model for Candidate Y-chromosome Gene Discovery in Prostate Cancer

10.29007/3nzw ◽  
2019 ◽  
Author(s):  
Wageesha Rasanjana ◽  
Sandun Rajapaksa ◽  
Indika Perera ◽  
Dulani Meedeniya
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Y Chromosome ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Support Vector ◽  
Body Tissues ◽  
Average Accuracy ◽  
To Come ◽  
Significant Expression

Prostate cancer is widely known to be one of the most common cancers among men around the world. Due to its high heterogeneity, many of the studies carried out to identify the molecular level causes for cancer have only been partially successful. Among the techniques used in cancer studies, gene expression profiling is seen to be one of the most popular techniques due to its high usage. Gene expression profiles reveal information about the functionality of genes in different body tissues at different conditions. In order to identify cancer-decisive genes, differential gene expression analysis is carried out using statistical and machine learning methodologies. It helps to extract information about genes that have significant expression differences between healthy tissues and cancerous tissues. In this paper, we discuss a comprehensive supervised classification approach using Support Vector Machine (SVM) models to investigate differentially expressed Y-chromosome genes in prostate cancer. 8 SVM models, which are tuned to have 98.3% average accuracy have been used for the analysis. We were able to capture genes like CD99 (MIC2), ASMTL, DDX3Y and TXLNGY to come out as the best candidates. Some of our results support existing findings while introducing novel findings to be possible prostate cancer candidates.

scholarly journals 1100: Gene Expression Profiles of Doxazosin-Treated LNCaP Prostate Cancer Cells

2004 ◽  
Vol 171 (4S) ◽  
pp. 290-290
Author(s):  
José M. Arencibia ◽  
Mónica Del Río ◽  
Ana Bonnin ◽  
Mónica López-Barahona
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostate Cancer Cells

scholarly journals Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process

BMC Cancer ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Uma R Chandran ◽  
Changqing Ma ◽  
Rajiv Dhir ◽  
Michelle Bisceglia ◽  
Maureen Lyons-Weiler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Pathways ◽  
Metastatic Process

scholarly journals The Expression and Polymorphism of Entry Machinery for COVID-19 in Human: Juxtaposing Population Groups, Gender, and Different Tissues

2020 ◽  
Vol 17 (10) ◽  
pp. 3433 ◽  
Author(s):  
Behrooz Darbani
Keyword(s):  
Gene Expression ◽  
Viral Entry ◽  
Rare Variants ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Disease Outbreaks ◽  
Viral Disease ◽  
Population Groups ◽  
Public Health Challenges ◽  
Significant Expression

(1) Background: Combating viral disease outbreaks has doubtlessly been one of the major public health challenges for the 21st century. (2) Methods: The host entry machinery required for COVID-19 (SARS-CoV-2) infection was examined for the gene expression profiles and polymorphism. (3) Results: Lung, kidney, small intestine, and salivary glands were among the tissues which expressed the entry machinery coding genes Ace2, Tmprss2, CtsB, and CtsL. The genes had no significant expression changes between males and females. The four human population groups of Europeans, Africans, Asians, and Americans had specific and also a common pool of rare variants for the X-linked locus of ACE2 receptor. Several specific and common ACE2 variants including S19P, I21T/V, E23K, A25T, K26R, T27A, E35D/K, E37K, Y50F, N51D/S, M62V, N64K, K68E, F72V, E75G, M82I, T92I, Q102P, G220S, H239Q, G326E, E329G, G352V, D355N, H378R, Q388L, P389H, E467K, H505R, R514G/*, and Y515C were of the utmost importance to the viral entry and infection. The variants of S19P, I21T, K26R, T27A, E37K, N51D, N64K, K68E, F72V, M82I, G326E, H378R, Q388L, and P389H also had significant differences in frequencies among the population groups. Most interestingly, the analyses revealed that more than half of the variants can exist in males, i.e., as hemizygous. (4) Conclusions: The rare variants of human ACE2 seem to be one of the determinant factors associated with fitness in the battle against SARS viruses. The hemizygous viral-entry booster variants of ACE2 describe the higher SARS-CoV-2 mortality rate in males. This is also supported by the lack of gender bias for the gene expression profiles of entry machinery. A personalized medicine strategy is conceived for isolating high-risk individuals in epidemic circumstances.

scholarly journals cDNA macroarray for analysis of gene expression profiles in prostate cancer

2006 ◽  
Vol 119 (7) ◽  
pp. 570-573 ◽  
Author(s):  
Wei-de ZHONG ◽  
Hui-chan HE ◽  
Xue-cheng BI ◽  
Ru-biao OU ◽  
Shao-ai JIANG ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cdna Macroarray

Cancer classification of single-cell gene expression data by neural network

2019 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Kijin Yu ◽  
Peter C W Lee
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Supplementary Information ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Normal Tissues

Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. Availability and implementation Cancer classification by neural network. Supplementary information Supplementary data are available at Bioinformatics online.

Role of homeobox pathway in prostate carcinogenesis.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 126-126
Author(s):  
James Lin Chen ◽  
Kristen Otto ◽  
Donald Vander Griend
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Seminal Vesicle ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Network ◽  
Gene Profiling ◽  
Normal Prostate ◽  
Survival Difference

126 Background: Identifying aberrant activity of developmental pathways in prostate cancer provides therapeutic opportunities. To this end, despite a shared embryonic origin and similarities to prostate cancer in histology and androgen dependence, seminal vesicle cancer is exceptionally rare. Genomic pathway analyses of their critical developmental differences may reveal uncharacterized oncogenic pathways. Previous attempts to do so have used whole tissue preparations. We hypothesized that careful gene profiling of pure primary epithelial cultures from normal prostate and seminal vesicles would reduce confounding noise during analysis and provide more robust pathway prioritization. Methods: Paired normal prostate and seminal vesicle epithelium cultures were created from three de-identified patients. Derived gene expression profiles were grouped into cancer biomodules using a protein-protein network algorithm to analyze their relationship to known oncogenes. Each resultant biomodule was assayed for its prognostic ability in independent Kaplan-Meier analyses of prostate cancer patients for time to recurrence and overall survival. Protein products from prioritized biomodule genes were then evaluated in vitro. Results: Gene expression profiling and protein network prioritization resulted in three cancer biomodules. Survival analysis revealed that the embryonic developmental biomodule centered on homeobox genes Meis1, Meis2 and Pbx1 to have clinical import. This homeobox biomodule detected a survival difference in a set of active surveillance patients (n=172, p=0.05) and identified men who were more likely to recur biochemically post-prostatectomy (n=78, p=0.02). We analyzed in vitro protein expression of Meis1, Meis2, Pbx1 and confirmed decreased gene expression in independent datasets of prostate cancer versus normal tissue. Conclusions: The Meis1/Meis2/Pbx1 biomodule may explain key differences in seminal vesicle and normal prostate epithelium development. In contrast to other cancers, Meis1, Meis2, and Pbx1 may play a tumor suppressor role in prostate cancer. Thus deregulation of this biomodule may be critical in prostate cancer oncogenesis.

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