Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Gene Expression Markers of Prognostic Importance for Prostate Cancer Risk in Patients with Benign Prostate Hyperplasia

Comparative analyses utilizing publicly available big data have the potential to generate novel hypotheses and knowledge. However, this approach is underutilized in the realm of cancer research, particularly for prostate cancer. While the general progression of prostate cancer is now well understood, how individual cell types transition from healthy, to pre-cancerous, to cancerous cell types, remains to be further elucidated. To address this, we re-analyzed two publicly available single-cell RNA-seq datasets of prostate cancer and benign prostate hyperplasia cell types. The differential expression analysis of 15,505 epithelial cell profiles across 18,638 genes revealed 791 genes that were up regulated in prostate cancer epithelial cells. Here we report six markers that show significant upregulation in prostate cancer cells relative to BPH epithelial cells: HPN (5.62X), RAC3 (3.51X), CD24 (2.18X), HOXC6 (1.77X), AGR2 (1.71X), and IGFBP2 (1.28X). In particular, the significant differential expression of AGR2 further supports its clinical relevance in supplementing prostate-specific antigen screening for detecting prostate cancer. These findings have the potential to further advance our knowledge of genes governing the development of cancer in prostate epithelial cells. Clinical Relevance- Our results establish the importance of 6 prostate cancer markers (HPN, RAC3, CD24, HOXC6, AGR2, and IGFBP3) in distinguishing between prostate cancer epithelial cells and benign prostate hyperplasia epithelial cells.

Identification of novel prostate cancer genes in patients stratified by Gleason classification: role of antitumoral genes

Prostate cancer (PCa) is a tumor with a great heterogeneity, both at a molecular and clinical level. Despite its global good prognosis, cases can vary from indolent to lethal metastatic and scientific efforts are aimed to discern those with worse outcomes. Current prognostic markers, as Gleason score, fall short when it comes to distinguishing these cases. Identification of new early biomarkers to enable a better PCa distinction and classification remains a challenge. In order to identify new genes implicated in PCa progression we conducted several differential gene expression analyses over paired samples comparing primary PCa tissue against healthy prostatic tissue of PCa patients. The results obtained show that this approach is a serious alternative to overcome patient heterogeneity. We were able to identify 250 genes whose expression varies along with tissue differentiation—healthy to tumor tissue, 161 of these genes are described here for the first time to be related to PCa. The further manual curation of these genes allowed to annotate 39 genes with antitumoral activity, 22 of them described for the first time to be related to PCa proliferation and metastasis. These findings could be replicated in different cohorts for most genes. Results obtained considering paired differential expression, functional annotation and replication results point to: CGREF1, UNC5A, C16orf74, LGR6, IGSF1, QPRT and CA14 as possible new early markers in PCa. These genes may prevent the progression of the disease and their expression should be studied in patients with different outcomes.

Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.

Attitudes Toward Genomic Testing and Prostate Cancer Research Among Black Men

INTRODUCTION

Black men are diagnosed with prostate cancer at nearly twice the rate of white men and are underrepresented in prostate cancer research, including validation studies of new clinical tools (e.g., genomic testing). Because healthcare system mistrust has contributed to these disparities for centuries, black men may be less inclined to pursue novel testing, and identification of facilitators to their participation in prostate cancer research studies remains warranted.

METHODS

A community-engaged approach involving a partnership with a community organization was used to conduct seven focus groups in Minnesota, Alabama, and California to explore black men's attitudes toward prostate cancer research participation and genomic testing for prostate cancer. Data were collected and analyzed from April 2015 to April 2017.

RESULTS

Identified genomic testing barriers included a lack of terminology understanding, healthcare system mistrust, reluctance to seek medical care, and unfavorable attitudes toward research. Facilitators included family history, value of prevention, and the desire for health education. Lack of prostate cancer knowledge, prostate-specific antigen testing confusion, healthcare system distrust, and misuse of personal health information were barriers to research study participation. Some black men were motivated to participate in research if it was seen as constructive and transparent.

CONCLUSIONS

Disparities for black men can both motivate and disincentivize participation depending upon a positive or negative view of research. Confusion over prostate cancer clinical care has fueled some mistrust among black men affecting both clinical care and research participation. With increased education, health literacy, and assurances of research integrity and transparency, black men may be more willing to participate in prostate cancer testing and research.

SUPPLEMENT INFORMATION

This article is part of a supplement entitled African American Men's Health: Research, Practice, and Policy Implications, which is sponsored by the National Institutes of Health.