scholarly journals A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 219 ◽  
Author(s):  
Osama Hamzeh ◽  
Abedalrhman Alkhateeb ◽  
Julia Zhuoran Zheng ◽  
Srinath Kandalam ◽  
Crystal Leung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Gleason Score ◽  
Class Imbalance ◽  
Gene Expression Omnibus ◽  
Gleason Grade ◽  
Next Generation Sequencing Technology ◽  
Potential Biomarker

(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analysed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node was a set of genes that could differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes were used to classify a second dataset of 499 prostate cancer patients collected from cBioportal. (3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%; the method was further validated to have 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4 + 3 = 7, and UBE2V2 for Gleason score 6. (4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.

scholarly journals A Hierarchical Machine Learning Model to Discover Gleason Grade Group-specific Biomarkers in Prostate Cancer

2019 ◽  
Author(s):  
Osama Hamzeh ◽  
Abedalrhman Alkhateeb ◽  
Julia Zheng ◽  
Srinath Kandalam ◽  
Crystal Lueng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Gleason Score ◽  
Class Imbalance ◽  
Gleason Grade ◽  
Grade Group ◽  
Next Generation Sequencing Technology ◽  
Potential Biomarker

1) Background: One of the most common cancer that affects men worldwide and North American men is prostate cancer. Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. The advancement in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason score more accurately and effectively. 2) Methods: In this study, we used a novel machine learning method to analyze gene expression of prostate tumors with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) repository, and categorized patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analyzed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node is a set of genes that can differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes are used to classify a second dataset of 499 prostate cancer patients collected from cBioportal [1]. 3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%, and further validated to 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4+3=7, and UBE2V2 for Gleason score 6. 4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.

scholarly journals A Hierarchical Machine Learning Model to Discover Gleason Grade Group-specific Biomarkers in Prostate Cancer

2019 ◽  
Author(s):  
Osama Hamzeh ◽  
Abedalrhman Alkhateeb ◽  
Julia Zheng ◽  
Srinath Kandalam ◽  
Crystal Lueng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Gleason Score ◽  
Cancer Progression ◽  
Class Imbalance ◽  
Gleason Grade ◽  
Gene Expressions ◽  
Grade Group ◽  
Potential Biomarkers

1) Background: One of the deadliest cancers that affect men worldwide and North American men is prostate cancer. This disease motivates parts of the cells in the prostate to lose control of their growth and division. 2) Methods: We are proposing a machine learning method used to analyze gene expressions of prostate tumors with different Gleason scores, and to identify potential genetic biomarkers for each group. A publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients have been retrieved from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository. We categorize patients by their Gleason scores into different groups to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups (hereinafter called nodes) to identify and predict nodes based on their mRNA or gene expressions. At each node, patient samples are analyzed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome of each node is a set of genes that can separate the Gleason group from the remaining groups. To validate the proposed method, the set of identified genes are used to classify a second dataset of 499 prostate cancer patients that have been collected from cBioportal.. 3) Results: Two genes have been found to be potential biomarkers of specific Gleason groups; PIAS3 has been identifed for Gleason score 4+3=7, while UBE2 could be a poteintial biomarker for Gleason score 6. Other proposed genes that were not found in the literature might be potential biomarkers. 4) Conclusion: The latest literature supports that the genes predicted by the proposed method are strongly correlated with prostate cancer progression and tumour development processes. Furthermore, pathway analysis shows that both PIAS3 and UBE2 share the same protein interaction pathway, the JAK/STAT signaling process.

scholarly journals The role of FOXD2-AS1 in cancer: a comprehensive study based on data mining and published articles

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Yongping Zhang ◽  
Chaojie Liang ◽  
Yu Zhang ◽  
Zhinmin Wang ◽  
Ruihuan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Tumor Size ◽  
Meta Analysis ◽  
Disease Free Survival ◽  
Gene Expression Omnibus ◽  
Tnm Stage ◽  
Potential Biomarker ◽  
Tcga Dataset

Abstract Background and aims: Long non-coding RNA (lncRNA) FOXD2 adjacent opposite strand RNA 1 (FOXD2-AS1) is aberrantly expressed in various cancers and associated with cancer progression. A comprehensive meta-analysis was performed based on published literature and data in the Gene Expression Omnibus database, and then the Cancer Genome Atlas (TCGA) dataset was used to assess the clinicopathological and prognostic value of FOXD2-AS1 in cancer patients. Methods: Gene Expression Omnibus databases of microarray data and published articles were used for meta-analysis, and TCGA dataset was also explored using the GEPIA analysis program. Hazard ratios (HRs) and pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the role of FOXD2-AS1 in cancers. Results: This meta-analysis included 21 studies with 2391 patients and 25 GEO datasets with 3311 patients. The pooled HRs suggested that highly expressed FOXD2-AS1 expression was correlated with poor overall survival (OS) and disease-free survival (DFS). Similar results were obtained by analysis of TCGA data for 9502 patients. The pooled results also indicated that FOXD2-AS1 expression was associated with bigger tumor size and advanced TNM stage, but was not related to age, gender, differentiation and lymph node metastasis. Conclusion: The present study demonstrated that FOXD2-AS1 is closely related to tumor size and TNM stage. Additionally, increased FOXD2-AS1 was a risk factor of OS and DFS in cancer patients, suggesting FOXD2-AS1 may be a potential biomarker in human cancers.

scholarly journals The Utilization of Gleason Grade as the Primary Criterion for Ordering Nuclear Bone Scan in Newly Diagnosed Prostate Cancer Patients

2009 ◽  
Vol 9 ◽  
pp. 1040-1045 ◽  
Author(s):  
Chad W. M. Ritenour ◽  
John T. Abbott ◽  
Michael Goodman ◽  
Naomi Alazraki ◽  
Fray F. Marshall ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Gleason Score ◽  
Bone Scan ◽  
Stage Migration ◽  
Gleason Grade ◽  
Newly Diagnosed ◽  
High Gleason Score ◽  
Bone Scans ◽  
The Relationship

Utilization of nuclear bone scans for staging newly diagnosed prostate cancer has decreased dramatically due to PSA-driven stage migration. The current criteria for performing bone scans are based on limited historical data. This study evaluates serum PSA and Gleason grade in predicting positive scans in a contemporary large series of newly diagnosed prostate cancer patients. Eight hundred consecutive cases of newly diagnosed prostate cancer over a 64-month period underwent a staging nuclear scan. All subjects had histologically confirmed cancer. The relationship between PSA, Gleason grade, and bone scan was examined by calculating series of crude, stratified, and adjusted odds ratios with corresponding 95% confidence intervals. Four percent (32/800) of all bone scans were positive. This proportion was significantly lower in patients with Gleason score ≤7 (1.9%) vs. Gleason score ≥8 (18.8%,p< 0.001). Among patients with Gleason score ≤7, the rate of positive bones scans was 70-fold higher when the PSA was >30 ng/ml compared to ≤30 ng/ml (p< 0.001). For Gleason score ≥8, the rate was significantly higher (27.9 vs. 0%) when PSA was >10 ng/ml compared to ≤10 ng/ml (p= 0.002). The combination of Gleason score and PSA enhances predictability of bone scans in newly diagnosed prostate cancer patients. The PSA threshold for ordering bone scans should be adjusted according to Gleason score. For patients with Gleason scores ≤7, we recommend a bone scan if the PSA is >30 ng/ml. However, for patients with a high Gleason score (8–10), we recommend a bone scan if the PSA is >10 ng/ml.

scholarly journals A Gene-Based Machine Learning Classifier Associated to the Colorectal Adenoma—Carcinoma Sequence

Biomedicines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1937
Author(s):  
Antonio Lacalamita ◽  
Emanuele Piccinno ◽  
Viviana Scalavino ◽  
Roberto Bellotti ◽  
Gianluigi Giannelli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Selection Procedure ◽  
Normal Mucosa ◽  
Gene Expression Omnibus ◽  
Machine Learning Algorithms ◽  
Microarray Gene Expression ◽  
Potential Biomarker

Colorectal cancer (CRC) carcinogenesis is generally the result of the sequential mutation and deletion of various genes; this is known as the normal mucosa–adenoma–carcinoma sequence. The aim of this study was to develop a predictor-classifier during the “adenoma-carcinoma” sequence using microarray gene expression profiles of primary CRC, adenoma, and normal colon epithelial tissues. Four gene expression profiles from the Gene Expression Omnibus database, containing 465 samples (105 normal, 155 adenoma, and 205 CRC), were preprocessed to identify differentially expressed genes (DEGs) between adenoma tissue and primary CRC. The feature selection procedure, using the sequential Boruta algorithm and Stepwise Regression, determined 56 highly important genes. K-Means methods showed that, using the selected 56 DEGs, the three groups were clearly separate. The classification was performed with machine learning algorithms such as Linear Model (LM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN). The best classification method in terms of accuracy (88.06 ± 0.70) and AUC (92.04 ± 0.47) was k-NN. To confirm the relevance of the predictive models, we applied the four models on a validation cohort: the k-NN model remained the best model in terms of performance, with 91.11% accuracy. Among the 56 DEGs, we identified 17 genes with an ascending or descending trend through the normal mucosa–adenoma–carcinoma sequence. Moreover, using the survival information of the TCGA database, we selected six DEGs related to patient prognosis (SCARA5, PKIB, CWH43, TEX11, METTL7A, and VEGFA). The six-gene-based classifier described in the current study could be used as a potential biomarker for the early diagnosis of CRC.

Ectonucleotide pyrophosphatase/phosphodiesterase (E-NPP) and adenosine deaminase (ADA) activities in prostate cancer patients: Influence of Gleason score, treatment and bone metastasis

2013 ◽  
Vol 67 (3) ◽  
pp. 203-208 ◽  
Author(s):  
Vanessa Battisti ◽  
Liési D.K. Maders ◽  
Margarete D. Bagatini ◽  
Iara E. Battisti ◽  
Luziane P. Bellé ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Bone Metastasis ◽  
Adenosine Deaminase ◽  
Cancer Patients ◽  
Gleason Score

scholarly journals Dysregulated gene expression predicts tumor aggressiveness in African-American prostate cancer patients

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamdy E. A. Ali ◽  
Pei-Yau Lung ◽  
Andrew B. Sholl ◽  
Shaimaa A. Gad ◽  
Juan J. Bustamante ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
African American ◽  
Cancer Patients ◽  
Tumor Aggressiveness

Neutrophil, Platelets, and Eosinophil to Lymphocyte Ratios Predict Gleason Score Upgrading in Low-Risk Prostate Cancer Patients

2018 ◽  
Vol 102 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Matteo Ferro ◽  
Gennaro Musi ◽  
Alessandro Serino ◽  
Gabriele Cozzi ◽  
Francesco Alessandro Mistretta ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Gleason Score ◽  
Low Risk ◽  
Risk Prostate Cancer ◽  
Low Risk Prostate Cancer
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