Novel biomarkers for the detection of prostate cancer

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Amperometric Miniaturised Portable Enzymatic Nanobiosensor for the Ultrasensitive Analysis of a Prostate Cancer Biomarker

Screen-printing technology is a game changer in many fields including electrochemical biosensing. Two-dimensional nanomaterial MXene Ti3C2Tx was integrated as a nanoplatform to immobilise enzyme sarcosine oxidase (SOx) onto the interface of screen-printed carbon electrodes (SPCEs). A miniaturised, portable, and cost-effective nanobiosensor was constructed using chitosan as a biocompatible glue for the ultrasensitive detection of prostate cancer biomarker sarcosine. The fabricated device was characterised with energy-dispersive X-ray spectroscopy (EDX), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Sarcosine was detected indirectly via the amperometric detection of H2O2 formed during enzymatic reaction. The nanobiosensor could detect sarcosine down to 7.0 nM with a maximal peak current output at 4.10 ± 0.35 × 10−5 A using only 100 µL of a sample per measurement. The assay run in 100 μL of an electrolyte showed the first linear calibration curve in a concentration window of up to 5 μM with a slope of 2.86 μA·μM−1, and the second linear calibration curve in the range of 5–50 μM with a slope of 0.32 ± 0.01 μA·μM−1 (R2 = 0.992). The device provided a high recovery index of 92.5% when measuring an analyte spiked into artificial urine, and could be used for detection of sarcosine in urine for at least a period of 5 weeks after the preparation.

The gaps between the new EU legislation on in vitro diagnostics and the on-the-ground reality

The background to this debate is now well-known: an EU policy decision to tighten controls on the devices and diagnostics sector led to the adoption of a regulation in 2017 with a schedule for implementation over coming years - a timetable extended still further by last-minute legislation in early 2022, to provide the sector and regulators with more time to adapt to the changes. Discussions among experts organised in April by the European Alliance for Personalized Medicine (EAPM) exposed continuing challenges that cannot be fully resolved by the recent deferral of implementation deadlines. One salient problem is that there is little awareness of the In Vitro Diagnostic Regulation (IVDR) across Europe, and only limited awareness of the different structures of national systems involved in implementing IVDR, with consequent risks for patient and consumer access to in vitro diagnostics (IVDs). The tentative conclusion from these consultations is that despite a will across the sector to seek workable solutions, the obstacles remain formidable, and the potential solutions so far proposed remain more a matter of aspirations than of clear pathways.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study

Simple Summary

Prostate cancer (PCa) is one of the most common cancers. Due to the limited and invasive approaches for PCa diagnosis, it is crucial to identify more accurate and non-invasive biomarkers for its detection. The aim of our study was to non-invasively uncover new protein targets for detecting PCa using a proteomics and proteogenomics approach. This work identified several dysregulated mutant protein isoforms in urine from PCa patients, some of them predicted to have a protective or an adverse role in these patients. These results are promising given urine’s non-invasive nature and offers an auspicious opportunity for research and development of PCa biomarkers.

Abstract

To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2*Q1062H and VASN*R161Q and an adverse role of AMBP*A286G and CD55*S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues

Identifying molecular alterations occurring during cancer progression is essential for a deeper understanding of the underlying biological processes. Here we have analyzed cancerous and healthy prostate biopsies using nanoLC-MS(MS) to detect proteins with altered expression and N-glycosylation. We have identified 75 proteins with significantly changing expression during disease progression. The biological processes involved were assigned based on protein-protein interaction networks. These include cellular component organization, metabolic and localization processes. Multiple glycoproteins were identified with aberrant glycosylation in prostate cancer, where differences in glycosite-specific sialylation, fucosylation, and galactosylation were the most substantial. Many of the glycoproteins with altered N-glycosylation were extracellular matrix constituents, and are heavily involved in the establishment of the tumor microenvironment.

Multiplexed Prostate Cancer Companion Diagnostic Devices

Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Since the early 1990s, Prostate-Specific Antigen (PSA) has been a commonly recognized PCa-associated protein biomarker. However, PSA testing has been shown to lack in specificity and sensitivity when needed to diagnose, monitor and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA, also known as multiplexing. If conventional methods such as the enzyme-linked immunosorbent assay (ELISA) are used, multiplexed detection of such protein biomarkers can result in an increase in the required sample volume, in the complexity of the analytical procedures, and in adding to the cost. Using companion diagnostic devices such as biosensors, which can be portable and cost-effective with multiplexing capacities, may address these limitations. This review explores recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms. Some of the novel and potential serum-based PCa protein biomarkers will be discussed in this review. In addition, this review discusses the importance of converting research protocols into multiplex point-of-care testing (xPOCT) devices to be used in near-patient settings, providing a more personalized approach to PCa patients’ diagnostic, surveillance and treatment management.

The role of the p90 ribosomal S6 kinase family in prostate cancer progression and therapy resistance

Prostate cancer (PCa) is the second most commonly occurring cancer in men, with over a million new cases every year worldwide. Tumor growth and disease progression is mainly dependent on the Androgen Receptor (AR), a ligand dependent transcription factor. Standard PCa therapeutic treatments include androgen-deprivation therapy and AR signaling inhibitors. Despite being successful in controlling the disease in the majority of men, the high frequency of disease progression to aggressive and therapy resistant stages (termed castrate resistant prostate cancer) has led to the search for new therapeutic targets. The p90 ribosomal S6 kinase (RSK1-4) family is a group of highly conserved Ser/Thr kinases that holds promise as a novel target. RSKs are effector kinases that lay downstream of the Ras/Raf/MEK/ERK signaling pathway, and aberrant activation or expression of RSKs has been reported in several malignancies, including PCa. Despite their structural similarities, RSK isoforms have been shown to perform nonredundant functions and target a wide range of substrates involved in regulation of transcription and translation. In this article we review the roles of the RSKs in proliferation and motility, cell cycle control and therapy resistance in PCa, highlighting the possible interplay between RSKs and AR in mediating disease progression. In addition, we summarize the current advances in RSK inhibitor development and discuss their potential clinical benefits.

BUB1B Promotes Proliferation of Prostate Cancer via Transcriptional Regulation of MELK

BACKGROUND

Prostate cancer remains one of the most common and deadliest forms of cancer, generally respond well to radical prostatectomy and associated interventions, up to 30% of individuals will suffer disease relapse. Although BUB1B was found to be essential for cell growth and proliferation, even in several kinds of tumor cells, the specific importance and mechanistic role of BUB1B in prostate cancer remain unclear.

METHODS

Quantitative Real-Time PCR and Western-blot were used in the detection of mRNA and protein expression. Lentivirus infection was used to overexpression or knock down the target gene. Flow cytometry analysis was performed to test protein expression and apoptosis level. Immunohistochemistry was used to identify protein expression in tissue. Statistical differences between the two groups are evaluated by two-tailed t-tests. The comparison among multiple groups is performed by one-way Analysis of Variance (ANOVA) followed by Dunnett's posttest. The statistical significance of the Kaplan-Meier survival plot is determined by log-rank analysis.

RESULTS

In the present report, we found BUB1B expression to be highly increased in prostate cancer tissues relative to normal controls. We further found BUB1B to be essential for efficient tumor cell proliferation, and to correlate with poorer prostate cancer patient outcomes. From a mechanistic perspective, the ability of BUB1B to regulate MELK was found to be essential for its ability to promote prostate cancer cell proliferation.

CONCLUSION

Altogether, our data suggest that BUB1B is up-regulated in prostate cancer, suggesting that the growth of cancer cells may depend on BUB1B-dependent regulation of MELK transcription. BUB1B may serve as a clinical prognostic factor and a druggable target for prostate cancer.

Transmembrane and Tetratricopeptide Repeat Containing 4 Is a Novel Diagnostic Marker for Prostate Cancer with High Specificity and Sensitivity

The histopathologic diagnosis of prostate cancer (PCa) from biopsies is a current challenge if double or triple staining is needed. Therefore, there is an urgent need for development of a new reliable biomarker to diagnose PCa patients. We aimed to explore and compare the expression of TMTC4 in PCa cells and tissue specimens and evaluate its sensitivity and specificity. The expression of TMTC4 in PCa and normal prostate epithelial cells was determined by real-time PCR and Western blot analyses. Immunohistochemical (IHC) staining of TMTC4 was performed on tissues collected from PCa and benign prostatic hyperplasia (BPH). Our results show a high expression of TMTC4 on mRNA and protein levels in PCa versus BPH1 and normal cells (p < 0.05). IHC results show strong cytoplasmic expressions in PCa cases (p < 0.001) as compared to BPH cases. The overall accuracy as measured by the AUC was 1.0 (p < 0.001). The sensitivity and specificity of the protein were 100% and 96.6%, respectively. Taken together, we report a high TMTC4 expression in PCa cells and tissues and its ability to differentiate between PCa and BPH with high sensitivity and specificity. This finding can be carried over to clinical practice after its confirmation by further studies.

Clinical applications of artificial intelligence in urologic oncology

PURPOSE OF REVIEW

This review aims to shed light on recent applications of artificial intelligence in urologic oncology.

RECENT FINDINGS

Artificial intelligence algorithms harness the wealth of patient data to assist in diagnosing, staging, treating, and monitoring genitourinary malignancies. Successful applications of artificial intelligence in urologic oncology include interpreting diagnostic imaging, pathology, and genomic annotations. Many of these algorithms, however, lack external validity and can only provide predictions based on one type of dataset.

SUMMARY

Future applications of artificial intelligence will need to incorporate several forms of data in order to truly make headway in urologic oncology. Researchers must actively ensure future artificial intelligence developments encompass the entire prospective patient population.

A Systematic Review of Artificial Intelligence in Prostate Cancer

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways

Prostate cancer (PCa) is one of the most common cancer types among men. The quantification of prostate-specific antigen used for PCa detection has revealed limited applicability. Thus, it is crucial to identify new minimally invasive biomarkers for PCa. It is believed that the integration of proteomics data from different studies is vital for identifying new biomarkers for PCa, but studies carried out in this regard have few converging results. Using a different approach, this study aimed to unveil molecular features consistently dysregulated in PCa and potential urinary biomarkers for PCa. The novelty of this analysis relies on the comparison of urinary and tissue proteomes from PCa patients and consequent exclusion of kidney and bladder cancer interference. The conducted bioinformatic analysis revealed molecular processes dysregulated in urine from PCa patients that mirror the alterations in prostate tumor tissue. To identify putative urinary biomarkers, proteins previously detected in kidney and bladder tissues were eliminated from the final list of potential urinary biomarkers for PCa. After a detailed analysis, MSMB, KLK3, ITIH4, ITIH2, HPX, GP2, APOA2 and AZU1 proteins stood out as candidate urinary biomarkers for PCa.

The Role of Immunohistochemical Analysis as a Tool for the Diagnosis, Prognostic Evaluation and Treatment of Prostate Cancer: A Systematic Review of the Literature

Background: Prostate cancer (PCa) is a heterogeneous disease that lends itself toward numerous therapeutic options depending on its risk stratification. One of the greatest challenges in PCa urologic practice is to select patients who should be referred for biopsy and, for those patients who are diagnosed with cancer, to differentiate between patients with indolent disease from those with an unfavorable prognosis and, to determine ideal patient management and avoid unnecessary interventions. Accordingly, there is a growing body of literature reporting immunohistochemical studies with the objective of determining a prostate cancer prognosis. Among the most frequent biomarkers studied are Ki-67, p53, PTEN, MYC, and ERG. Based on these findings, we systematically reviewed articles that assessed the role of these main prognostic markers in prostate cancer.

Methods: Consistent with PRISMA guidelines, we performed a systematic literature search throughout the Web of Science and PubMed Medline databases. We considered all types of studies evaluating the role of Ki-67, p53, PTEN, MYC, and ERG immunohistochemical analysis in prostate cancer until July 2017.

Results: We identified 361 articles, 44 of which were summarized in this review. Diagnostically, no single immunohistochemical marker was able to define a tumor as benign or malignant. Prognostically, Ki-67, p53, and MYC were related to the tumor grade given by Gleason score and to the tumor stage (higher levels related to higher tumor grade). Furthermore, Ki-67 was also related to higher PSA levels, shorter disease-free intervals and shorter tumor-specific survival; the latter was also related to p53. The loss of PTEN protein expression showed a higher association with biochemical recurrence and with a worse prognosis, beyond that predicted by the Gleason score and tumor stage. ERG staining also showed a strong association with biochemical recurrence.

Conclusion: There are several studies relating immunohistochemical markers with clinical-laboratorial outcomes in prostate cancer, the most frequent being Ki-67, p53, ERG, PTEN, and MYC. However, none of these markers have been validated by literary consensus to be routinely applied in medical practice.

Are localized prostate cancer biomarkers useful in the clinical practice?

Prostate cancer presents itself in a heterogeneous way with both aggressive and indolent forms. Despite the controversy surrounding its use, prostate-specific antigen screening ultimately leads to a greater number of diagnosed patients. One of the biggest challenges in clinical practice is to select the right patients for biopsy and, among diagnosed patients, to differentiate tumors with an indolent course from those with an unfavorable prognosis, in order to determine the best therapeutic decision for each case, avoiding unnecessary interventions. Currently, several types of biomarkers are available for clinical use in patients with prostate cancer, which include blood-based (prostate-specific antigen, Prostate Health Index®, 4K score®); urine sample-based (PCA3, SelectMDx®, ExoDx Prostate IntelliScore®); and biopsy, transurethral resection, or radical prostatectomy tissue-based (ConfirmMDx®, Oncotype®, Prolaris®, Decipher®). The aim of this review is to provide an overview of the current state of evidence and to highlight recent advances in the evaluation and diagnosis of prostate cancer, with emphasis on biomarkers related to diagnosis and to prognostic evaluation of localized prostate cancer.

Serum procalcitonin levels in prostate cancer: A new biomarker?

Background:

To examine the role of serum procalcitonin as a biomarker for the detection of prostate cancer in patients with a serum prostate-specific antigen less than 20.0 ng/mL.

Methods:

The prospective study included patients with a prostate-specific antigen level of 2–20 ng/mL, who underwent prostate biopsy. Clinical and pathological data such as age, prostate volume, prostate-specific antigen, procalcitonin, and Gleason score were reviewed. All patients were divided into three groups with total prostate-specific antigen level between 2 and 4 ng/mL, 4.1 and 10 ng/mL, and 10.1 and 20 ng/mL.

Results:

Of 227 patients who underwent biopsy, prostate cancer was diagnosed in 74 (32.6%) patients and the remaining 153 patients had a benign condition. The difference in mean serum procalcitonin values was significantly higher in the prostate cancer compared with the benign group (0.06 ± 0.03 vs 0.04 ± 0.03 ng/mL; p = 0.0001). Using a threshold of 0.045 ng/mL, procalcitonin was 54.1% sensitive and 80.3% specific (area under curve = 0.683). Serum procalcitonin levels were not able to differentiate between prostate cancer patients with prostate-specific antigen level of 2–4, 4.1–10, and 10.1–20 ng/mL.

Conclusion:

Based on this prospective study, procalcitonin can be a novel supplementary biomarker to increase the accuracy of prostate cancer screening.

The molecular biology of prostate cancer: current understanding and clinical implications

BACKGROUND

With continuous progress over the past few decades in understanding diagnosis, treatment, and genetics, much has been learned about the prostate cancer-diagnosed genome.

METHODS

A comprehensive MEDLINE® and Google scholar literature search was conducted using keyword variations relating to the genetics of prostate cancer such as chromosomal alterations, androgen receptor, castration-resistant, inheritance, polymorphisms, oncogenes, metastasis, biomarkers, and immunotherapy.

RESULTS

Traditionally, androgen receptors (AR) have been the focus of research. Recently, identification of recurrent chromosomal alterations that lead to either multiplication of regions (gain-of-function) or deletion of regions (loss-of-function) has opened the door to greater genetic accessibility. These chromosomal aberrations lead to variation in copy number and gene expression. Some of these chromosomal alterations are inherited, while others undergo somatic mutations during disease progression. Inherited gene mutations that make one susceptible to prostate cancer have been identified with familial-linked studies. Somatic genes that progress tumorigenesis have also been identified. Research on the molecular biology of prostate cancer has characterized these genes into tumor suppressor genes or oncogenes. Additionally, genome-wide assay studies have identified many high-risk single-nucleotide polymorphisms recurrent throughout the prostate cancer-diagnosed genome. Castration-resistant prostate cancer is the most aggressive form of prostate cancer, and its research has elucidated many types of mutations associated with AR itself, including enhanced expression and amplification, point mutations, and alternative splicing. Understanding the molecular biology of prostate cancer has permitted more accurate identification using advanced biomarkers and therapy for aggressive forms using immunotherapy.

CONCLUSIONS

An age-related disease, prostate cancer commands profound attention. With increasing life expectancy and the continuous pursuit of it, prostate cancer is a powerful obstacle best defeated using targeted therapies specifically designed for the unique molecular profile of the malignancy.

High levels of DEPDC1B predict shorter biochemical recurrence-free survival of patients with prostate cancer

DEP domain-containing protein 1B (DEPDC1B) has been reported to serve important functions in breast cancer and non-small cell lung cancer. However, its involvement in the development of prostate cancer (PCa) remains unclear. Therefore, the present study aimed to investigate the expression and clinical significance of DEPDC1B in tumor tissues from patients diagnosed with PCa. A total of 80 prostate tissue samples were collected following prostatectomy to generate a tissue microarray for immunohistochemical analysis of DEPDC1B protein expression. High throughput sequencing of mRNAs from 179 prostate tissue samples, either from patients with PCa or from healthy controls, was included in the Taylor dataset. The expression levels of DEPDC1B in tumor tissues from patients with PCa were revealed to be significantly increased compared with those in normal prostate tissues (P=0.039). Increased expression of DEPDC1B was significantly associated with advanced clinical stage (P=0.006), advanced T stage (P=0.012) and lymph node metastasis (P=0.004). Kaplan-Meier analysis demonstrated that patients with high levels of DEPDC1B mRNA had significantly shorter biochemical recurrence (BCR)-free survival times. Multivariate analysis using Cox proportional hazards model revealed that levels of DEPDC1B mRNA were significant independent predictors of BCR-free survival time of patients with PCa. Therefore, the expression of DEPDC1B may be used as an independent predictor of biochemical recurrence-free survival time of patients with PCa.