Current challenges in development of differentially expressed and prognostic prostate cancer biomarkers

Construction of an immune gene expression meta signature to assess the prognostic risk of colorectal cancer patients

Despite recent advancements in colorectal cancer (CRC) treatment, particularly with the introduction of immunotherapy and checkpoint inhibitors, the efficacy of these therapies remains limited to a subset of patients. To address this challenge, our study aimed to develop a prognostic biomarker based on immune-related genes to predict better outcomes in CRC patients and aid in treatment decision-making. We comprehensively analysed immune gene expression signatures associated with CRC prognosis to construct an immune meta-signature with prognostic potential. Utilising data from The Cancer Genome Atlas (TCGA), we employed Cox regression to identify immune-related genes with prognostic significance from multiple studies. Subsequently, we compared the expression levels of immune genes, levels of immune cell infiltration, and various immune-related molecules between high-risk and low-risk patient groups. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses provided insights into the biological pathways associated with the identified prognostic genes. Finally, we validated our findings using a separate CRC cohort from the Gene Expression Omnibus (GEO). Integration of the prognostic genes revealed significant disparities in survival outcomes. Differential expression analysis identified a set of immune-associated genes, which were further refined using LASSO penalisation and Cox regression. Univariate Cox regression analyses confirmed the autonomy of the gene signature as a prognostic indicator for CRC patient survival. Our risk prediction model effectively stratified CRC patients based on their prognosis, with the high-risk group showing enrichment in pro-oncogenic terms and pathways. Immune infiltration analysis revealed an augmented presence of certain immunosuppressive subsets in the high-risk group. Finally, we validated the performance of our prognostic model by applying the risk score equation to a different CRC patient dataset, confirming its prognostic potential in this new cohort. Overall, our study presents a novel immune-related gene signature with promising implications for predicting cancer progression and prognosis, thereby enabling more personalised management strategies for CRC patients.

Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data

Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions. In this paper, we propose a new statistical method to identify differentially co-expressed genes from microarray gene expression data. The proposed method first estimates co-expression levels of paired genes using covariance regularization by thresholding, and then significance of difference in covariance estimation between two conditions is evaluated. We demonstrated that the proposed method is more powerful than the existing main-stream methods to detect co-expressed genes through extensive simulation studies. Also, we applied it to various microarray gene expression datasets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer.

Sequential analysis of myocardial gene expression with phenotypic change: Use of cross-platform concordance to strengthen biologic relevance

Objectives

To investigate the biologic relevance of cross-platform concordant changes in gene expression in intact human failing/hypertrophied ventricular myocardium undergoing reverse remodeling.

Background

Information is lacking on genes and networks involved in remodeled human LVs, and in the associated investigative best practices.

Methods

We measured mRNA expression in ventricular septal endomyocardial biopsies from 47 idiopathic dilated cardiomyopathy patients, at baseline and after 3–12 months of β-blocker treatment to effect left ventricular (LV) reverse remodeling as measured by ejection fraction (LVEF). Cross-platform gene expression change concordance was investigated in reverse remodeling Responders (R) and Nonresponders (NR) using 3 platforms (RT-qPCR, microarray, and RNA-Seq) and two cohorts (All 47 subjects (A-S) and a 12 patient “Super-Responder” (S-R) subset of A-S).

Results

For 50 prespecified candidate genes, in A-S mRNA expression 2 platform concordance (C_cpT), but not single platform change, was directly related to reverse remodeling, indicating C_cpT has biologic significance. Candidate genes yielded a C_cpT (PCR/microarray) of 62% for Responder vs. Nonresponder (R/NR) change from baseline analysis in A-S, and ranged from 38% to 100% in S-R for PCR/microarray/RNA-Seq 2 platform comparisons. Global gene C_cpT measured by microarray/RNA-Seq was less than for candidate genes, in S-R R/NR 17.5% vs. 38% (P = 0.036). For S-R global gene expression changes, both cross-cohort concordance (C_ccT) and C_cpT yielded markedly greater values for an R/NR vs. an R-only analysis (by 22 fold for C_ccT and 7 fold for C_cpT). Pathway analysis of concordant global changes for R/NR in S-R revealed signals for downregulation of multiple phosphoinositide canonical pathways, plus expected evidence of a β₁-adrenergic receptor gene network including enhanced Ca²⁺ signaling.

Conclusions

Two-platform concordant change in candidate gene expression is associated with LV biologic effects, and global expression concordant changes are best identified in an R/NR design that can yield novel information.

Effect of radical prostatectomy on levels of cancer related epitopes in circulating macrophages of patients with clinically localized prostate cancer

OBJECTIVE

Epitopes of the apoptosis related protein DNaseX (Apo10) and the pentose-phosphate-pathway associated protein transketolase-like 1 (TKTL1) have been shown to be increased in circulating macrophages of patients with different cancer types including prostate cancer (PC). So far, the effect of cancer-specific therapies on the levels of these markers in blood samples of patients with PC has not been evaluated yet. The aim of the present study was to prospectively assess the effect of surgical removal of the prostate on levels of Apo10 and TKTL1 in blood macrophages using Epitope Detection In Monocytes (EDIM).

METHODS

We prospectively enrolled 174 patients with clinically localized PC undergoing radical prostatectomy. Peripheral blood was collected preoperatively in all patients and postoperatively in a subgroup of 72 patients. We separately assessed the proportion of CD14/CD16-positive monocytes expressing Apo10 and TKTL1 using flow cytometry. The proportion of positive cells was multiplied by ten to generate a score for Apo10 and TKTL1, separately. Pre- and postoperative scores of Apo10 and TKTL1 were compared. Moreover, results were correlated with clinicopathologic parameters.

RESULTS

In the total cohort, Median preoperative Apo10 and TKTL1 scores were 136 (Range 101-254) and 139 (102-216). In patients who underwent blood collection and testing either pre- and postoperatively (n = 72), median pre- versus postoperative scores were 132 (101-215) versus 103 (70-156) for Apo10 (P < 0.0001) and 140 (102-212) versus 115 (84-187) (P < 0.0001) for TKTL1. Following radical prostatectomy, 56 (77.7%) and 59 (81.9%) patients in the cohort of patients with blood collection before and after prostatectomy showed a decrease of Apo10 and TKTL1 expressing monocytes. TKTL1 and Apo10 did not show any correlation with known histopathologic and clinical risk parameters.

CONCLUSIONS

The present study demonstrates that surgical removal of the primary tumor leads to a significant decrease of Apo10 and TKTL1 expressing macrophages. This observation further encourages studies assessing the optimal clinical utility of EDIM-based detection of Apo10 and TKTL1 in patients with PC.

Identification of Transcription Factor YY1 as a Regulator of a Prostate Cancer-Specific Pathway Using Proteomic Analysis

Prostate-specific antigen, a biomarker used to diagnose prostate cancer, exhibits poor sensitivity. Although previous studies have focused on identifying a new diagnostic biomarker, the molecules or networks identified in these studies are also present in other cancers, making it difficult to detect prostate cancer specifically. A unique characteristic of the prostate gland is the increased mitochondrial energy metabolism when normal prostate cells progress to cancer cells. Thus, we attempted to find a prostate cancer-specific signature present in this unique environment. Proteins that were differentially expressed between a prostate cell line and three prostate cancer cell lines were identified using proteomic analysis. Not surprisingly, the most prevalent proteins detected by network analysis of proteins that were up-regulated at least 1.2-fold in cancer cells, compared to that in normal prostate cells, were those involved in mitochondrial energy metabolism. In addition, we showed that Yin Yang 1 (YY1) was a major transcription factor involved in regulating energy metabolism. To determine whether YY1 regulates genes associated with mitochondrial energy metabolism in prostate cells, cells were subjected to quantitative polymerase chain reaction analysis in the presence or absence of the YY1 inhibitor NP-001. Notably, inhibition of YY1 resulted in reduced expression of genes related to the Krebs cycle and electron transport chain in prostate cancer cell lines. Based on this finding, we suggest that there is a tumor-specific signature that regulates mitochondrial energy metabolism in prostate cancer cells. This work provides a foundation for further work on identifying a means for the specific diagnosis of prostate cancer.

Exosomes and Exosomal MicroRNAs in Prostate Cancer Radiation Therapy

Despite current risk stratification systems using traditional clinicopathologic factors, many localized and locally advanced prostate cancers fail radical treatment (ie, radical prostatectomy, radiation therapy with or without androgen deprivation therapy). Therefore, a pressing need exists for enhanced methods of disease stratification through novel prognostic and predictive tools that can reliably be applied in clinical practice. Exosomes are 50- to 150-nm small vesicles released by cancer cells that reflect the genetic and nongenetic materials of parent cancer cells. Cancer cells can contain distinct sets of microRNA profiles, the expression of which can change owing to stress such as radiation therapy. These alterations or distinctions in contents allow exosomes to be used as prognostic and/or predictive biomarkers and to monitor the treatment response. Additionally, microRNAs have been shown to influence multiple processes in prostate tumorigenesis, including cell proliferation, induction of apoptosis, migration, oncogene inhibition, and radioresistance. Thus, comparative exosomal microRNA profiling at different levels could help portray tumor aggressiveness and response to radiation therapy. Although technical challenges persist in exosome isolation and characterization, recent improvements in microRNA profiling have evolved toward in-depth analyses of the exosomal cargo and its functions. We have reviewed the role of exosomes and exosomal microRNAs in biologic processes of prostate cancer progression and radiation therapy response, with a particular focus on the development of clinical assays for treatment personalization.

Screening for the optimal gene and functional gene sets related to breast cancer using differential co-expression and differential expression analysis

OBJECTIVE

To investigate novel gene sets related to breast cancer (BC) using differential co-expression and differential expression (DECODE).

METHODS

T statistics was used to quantify the degree of DE of each gene, and then Z was adopted to quantify the correlation difference between expression levels of two genes. Two optimal thresholds for defining substantial change in DE and DC were selected for each gene using chi-square maximization, and the corresponding gene was defined as the optimal gene. Based on the optimal thresholds, genes were categorized into four partitions with either high or low DC and DE characteristics. Finally, we evaluated the functional relevance of a gene partition with high DE and high DC, and the gene set with best association was considered as the optimal functional gene set.

RESULTS

The optimal thresholds for DC and DE were respective 2.254 and 1.616, and the optimal gene was UBE2Q2L. Based on the optimal thresholds, genes were divided into four partitions including HDE-HDC (875 genes), HED-LDC (8038 genes), LDE-HDC (678 genes), and LDE-LDC (10516 genes). The best associated gene set was ``fatty acid catabolic process'' with 34 HDC and HDE partitions. Among these partitions, UBE2Q2L attained the highest minimum FI gain of 18.973.

CONCLUSION

UBE2Q2L and fatty acid catabolic process might be potentially useful signatures in diagnostic purposes for BC.

Differentially Expressed Genes and Signature Pathways of Human Prostate Cancer

Genomic technologies including microarrays and next-generation sequencing have enabled the generation of molecular signatures of prostate cancer. Lists of differentially expressed genes between malignant and non-malignant states are thought to be fertile sources of putative prostate cancer biomarkers. However such lists of differentially expressed genes can be highly variable for multiple reasons. As such, looking at differential expression in the context of gene sets and pathways has been more robust. Using next-generation genome sequencing data from The Cancer Genome Atlas, differential gene expression between age- and stage- matched human prostate tumors and non-malignant samples was assessed and used to craft a pathway signature of prostate cancer. Up- and down-regulated genes were assigned to pathways composed of curated groups of related genes from multiple databases. The significance of these pathways was then evaluated according to the number of differentially expressed genes found in the pathway and their position within the pathway using Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis. The “transforming growth factor-beta signaling” and “Ran regulation of mitotic spindle formation” pathways were strongly associated with prostate cancer. Several other significant pathways confirm reported findings from microarray data that suggest actin cytoskeleton regulation, cell cycle, mitogen-activated protein kinase signaling, and calcium signaling are also altered in prostate cancer. Thus we have demonstrated feasibility of pathway analysis and identified an underexplored area (Ran) for investigation in prostate cancer pathogenesis.

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

A critical challenge in prostate cancer (PCa) clinical management is posed by the inadequacy of currently used biomarkers for disease screening, diagnosis, prognosis and treatment. In recent years, microRNAs (miRNAs) have emerged as promising alternate biomarkers for prostate cancer diagnosis and prognosis. However, the development of miRNAs as effective biomarkers for prostate cancer heavily relies on their accurate detection in clinical tissues. miRNA analyses in prostate cancer clinical specimens is often challenging owing to tumor heterogeneity, sampling errors, stromal contamination etc. The goal of this article is to describe a simplified workflow for miRNA analyses in archived FFPE or fresh frozen prostate cancer clinical specimens using a combination of quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH). Within this workflow, we optimize the existing methodologies for miRNA extraction from FFPE and frozen prostate tissues and expression analyses by Taqman-probe based miRNA RT-PCR. In addition, we describe an optimized method for ISH analyses formiRNA detection in prostate tissues using locked nucleic acid (LNA)- based probes. Our optimized miRNA ISH protocol can be applied to prostate cancer tissue slides or prostate cancer tissue microarrays (TMA).

EN2 in Prostate Cancer

Despite extensive efforts to identify a clinically useful diagnostic biomarker in prostate cancer, no new test has been approved by regulatory authorities. As a result, this unmet need has shifted to biomarkers that additionally indicate presence or absence of "significant" disease. EN2 is a homeodomain-containing transcription factor secreted by prostate cancer into the urine and can be detected by enzyme-linked immunoassay. EN2 may be an ideal biomarker because normal prostate tissue and benign prostatic hypertrophic cells do not secrete EN2. This review discusses the enormous potential of EN2 to address this unmet need and provide the urologist with a simple, inexpensive, and reliable prostate cancer biomarker.

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data

BACKGROUND

Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored.

RESULTS

In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism.

CONCLUSIONS

By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html .

Peer group normalization and urine to blood context in steroid metabolomics: the case of CAH and obesity

Traditional interpretation of GC-MS output involved the semi-quantitative estimation of outstanding low or high specific metabolites and the ratio between metabolites. Here, we utilize a systems biology approach to steroid metabolomics of a complex steroid-related disorder, using an all-inclusive analysis of the steroidal pathway in the form of a subject steroidal fingerprint and disease signature, providing novel methods of normalization and visualization. The study compares 324 normal children to pure enzymatic deficiency in 27 untreated 21-hydroxylase CAH patients and to complex disease in 70 children with obesity. Steroid profiles were created by quantitative data generated by GC-MS analyses. A novel peer-group normalization method defined each individual subject's control group in a multi-dimensional space of metadata parameters. Classical steroid pathway visualization was enhanced by adding urinary end-product sub-nodes and by color coding of semi-quantitative metabolic concentrations and enzymatic activities. Unbiased automated data analysis confirmed the common knowledge for CAH - the inferred 17-hydroxyprogesterone was up-regulated and the inferred 21-hydroxylase enzyme activity was down-regulated. In childhood obesity, we observe a general decrease of both glucocorticoid and mineralocorticoid metabolites, increased androgens, up-regulation of 17,20-lyase, 17-OHase and 11β-HSD1 activity and down-regulation of 21-OHase enzymatic activity. Our study proved novel normalization and visualization techniques are to be useful in identifying subject fingerprint and disease signature in enzymatic deficiency and insufficiency, while demonstrating hypothesis generation in a complex disease such as childhood obesity.

Gene signatures ESC, MYC and ERG-fusion are early markers of a potentially dangerous subtype of prostate cancer

Background

Good prognostic tools for predicting disease progression in early stage prostate cancer (PCa) are still missing. Detection of molecular subtypes, for instance by using microarray gene technology, can give new prognostic information which can assist personalized treatment planning. The detection of new subtypes with validation across additional and larger patient cohorts is important for bringing a potential prognostic tool into the clinic.

Methods

We used fresh frozen prostatectomy tissue of high molecular quality to further explore four molecular subtype signatures of PCa based on Gene Set Enrichment Analysis (GSEA) of 15 selected gene sets published in a previous study. For this analysis we used a statistical test of dependent correlations to compare reference signatures to signatures in new normal and PCa samples, and also explore signatures within and between sample subgroups in the new samples.

Results

An important finding was the consistent signatures observed for samples from the same patient independent of Gleason score. This proves that the signatures are robust and can surpass a normally high tumor heterogeneity within each patient. Our data did not distinguish between four different subtypes of PCa as previously published, but rather highlighted two groups of samples which could be related to good and poor prognosis based on survival data from the previous study.The poor prognosis group highlighted a set of samples characterized by enrichment of ESC, ERG-fusion and MYC + rich signatures in patients diagnosed with low Gleason score,. The other group consisted of PCa samples showing good prognosis as well as normal samples. Accounting for sample composition (the amount of benign structures such as stroma and epithelial cells in addition to the cancer component) was important to improve subtype assignments and should also be considered in future studies.

Conclusion

Our study validates a previous molecular subtyping of PCa in a new patient cohort, and identifies a subgroup of PCa samples highly interesting for detecting high risk PCa at an early stage. The importance of taking sample tissue composition into account when assigning subtype is emphasized.

EN2: a novel prostate cancer biomarker

Extensive efforts to identify a clinically useful biomarker for the diagnosis of prostate cancer have resulted in important insights into the biology of the disease, but no new test has been approved by regulatory authorities. The unmet need has also shifted to identifying biomarkers that not only diagnose prostate cancer but also indicate whether the patient has 'significant' disease. EN2 is a homeobox-containing transcription factor secreted specifically by prostate cancers into urine, where it can be detected by a simple ELISA assay. A number of studies have demonstrated the enormous potential of EN2 to address this unmet need and provide the urologist with a simple, cheap and efficient prostate cancer biomarker.

DNA methylation status is more reliable than gene expression at detecting cancer in prostate biopsy

Background:

We analysed critically the potential usefulness of RNA- and DNA-based biomarkers in supporting conventional histological diagnostic tests for prostate carcinoma (PCa) detection.

Methods:

Microarray profiling of gene expression and DNA methylation was performed on 16 benign prostatic hyperplasia (BPH) and 32 cancerous and non-cancerous prostate samples extracted by radical prostatectomy. The predictive value of the selected biomarkers was validated by qPCR-based methods using tissue samples extracted from the 58 prostates and, separately, using 227 prostate core biopsies.

Results:

HOXC6, AMACR and PCA3 expression showed the best discrimination between PCa and BPH. All three genes were previously reported as the most promising mRNA-based markers for distinguishing cancerous lesions from benign prostate lesions; however, none were sufficiently sensitive and specific to meet the criteria for a PCa diagnostic biomarker. By contrast, DNA methylation levels of the APC, TACC2, RARB, DGKZ and HES5 promoter regions achieved high discriminating sensitivity and specificity, with area under the curve (AUCs) reaching 0.95−1.0. Only a small overlap was detected between the DNA methylation levels of PCa-positive and PCa-negative needle biopsies, with AUCs ranging between 0.854 and 0.899.

Conclusions:

DNA methylation-based biomarkers reflect the prostate malignancy and might be useful in supporting clinical decisions for suspected PCa following an initial negative prostate biopsy.

Application of the simple and efficient Mpeak modeling in binding peak identification in ChIP-chip studies

Chromatin immunoprecipitation and hybridization of high-density promoter microarray (ChIP-chip) is a powerful strategy to identify target genes for specific transcription factors and other DNA-binding nuclear proteins in a genome-wide manner. Services of core facilities have greatly enhanced the accessibility of these technologies to new investigators to the field. The Mpeak modeling is a simple and efficient computer program, capable of identifying chromatin-binding peaks in ChIP-chip datasets. It utilizes advanced statistical computation, but yet offers a simple procedure with user inputs on parameters in its operation. The Mpeak-fitted signals are tabulation in convenient formats and can be visualized in various genome-display graphic programs, including SignalMap and Genome Browser, and analyzed together with other datasets, such as microarray expression patterns. Several research groups have used the Mpeak program in their respective ChIP-chip studies. The various features of Mpeak will be illustrated with ChIP-chip datasets from a study designed to identify the target genes for the sex-determining factor, SRY, in mouse embryonic gonads at the time of sex determination.