Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable. Context-specific essential genes represent valuable candidates for targeted anti-cancer therapies. Through the investigation of gene and protein annotations and the integration of published transcriptomic data, we identified two consensus lists to stratify PCa patients' risk and discriminate CRPC phenotypes based on androgen receptor activity. ROC and Kaplan-Meier survival analyses were used for gene set validation in independent datasets. We further evaluated these genes for their association with cancer dependency. The deregulated expression of the PCa-related genes was associated with overall and disease-specific survival, metastasis and/or high recurrence risk, while the CRPC-related genes clearly discriminated between adeno and neuroendocrine phenotypes. Some of the genes showed context-specific essentiality. We further identified candidate drugs through a computational repositioning approach for targeting these genes and treating lethal variants of PCa. This work provides a proof-of-concept for the use of an integrative approach to identify candidate biomarkers involved in PCa progression and CRPC pathogenesis within the goal of precision medicine.

Emerging role of miRNA in prostate cancer: A future era of diagnostic and therapeutics

Prostate cancer (PCa) is the most common cancer in men (20%) and is responsible for 6.8% (1/5) of all cancer-related deaths in men around the world. The development and spread of prostate cancer are driven by a wide variety of genomic changes and extensive epigenetic events. Because of this, the MicroRNA (miRNA) and associated molecular mechanisms involved in PCa genesis and aggressive were only partially identified until today. The miRNAs are a newly discovered category of regulatorsthat have recently been recognized to have a significant role in regulating numerous elements of cancer mechanisms, such as proliferation, differentiation, metabolism, and apoptosis. The miRNAs are a type of small (22-24 nucleotides), non-coding, endogenous, single-stranded RNA and work as potent gene regulators. Various types of cancer, including PCa, have found evidence that miRNA genes, which are often located in cancer-related genetic regions or fragile locations, have a role in the primary steps of tumorigenesis, either as oncogenes or tumorsuppressors. To explain the link between miRNAs and their function in the initiation and advancement of PCa, we conducted a preliminary assessment. The purpose of this research was to enhance our understanding of the connection between miRNA expression profiles and PCa by elucidating the fundamental processes of miRNA expression and the target genes.

A hnRNP K⁻AR-Related Signature Reflects Progression toward Castration-Resistant Prostate Cancer

The major challenge in castration-resistant prostate cancer (CRPC) remains the ability to predict the clinical responses to improve patient selection for appropriate treatments. The finding that androgen deprivation therapy (ADT) induces alterations in the androgen receptor (AR) transcriptional program by AR coregulators activity in a context-dependent manner, offers the opportunity for identifying signatures discriminating different clinical states of prostate cancer (PCa) progression. Gel electrophoretic analyses combined with western blot showed that, in androgen-dependent PCa and CRPC in vitro models, the subcellular distribution of spliced and serine-phosphorylated heterogeneous nuclear ribonucleoprotein K (hnRNP K) isoforms can be associated with different AR activities. Using mass spectrometry and bioinformatic analyses, we showed that the protein sets of androgen-dependent (LNCaP) and ADT-resistant cell lines (PDB and MDB) co-immunoprecipitated with hnRNP K varied depending on the cell type, unravelling a dynamic relationship between hnRNP K and AR during PCa progression to CRPC. By comparing the interactome of LNCaP, PDB, and MDB cell lines, we identified 51 proteins differentially interacting with hnRNP K, among which KLK3, SORD, SPON2, IMPDH2, ACTN4, ATP1B1, HSPB1, and KHDRBS1 were associated with AR and differentially expressed in normal and tumor human prostate tissues. This hnRNP K–AR-related signature, associated with androgen sensitivity and PCa progression, may help clinicians to better manage patients with CRPC.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

Inflammation: an important parameter in the search of prostate cancer biomarkers

BACKGROUND

A more specific and early diagnostics for prostate cancer (PCa) is highly desirable. In this study, being inflammation the focus of our effort, serum protein profiles were analyzed in order to investigate if this parameter could interfere with the search of discriminating proteins between PCa and benign prostatic hyperplasia (BPH).

METHODS

Patients with clinical suspect of PCa and candidates for trans-rectal ultrasound guided prostate biopsy (TRUS) were enrolled. Histological specimens were examined in order to grade and classify the tumor, identify BPH and detect inflammation. Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry (SELDI-ToF-MS) and two-dimensional gel electrophoresis (2-DE) coupled with Liquid Chromatography-MS/MS (LC-MS/MS) were used to analyze immuno-depleted serum samples from patients with PCa and BPH.

RESULTS

The comparison between PCa (with and without inflammation) and BPH (with and without inflammation) serum samples by SELDI-ToF-MS analysis did not show differences in protein expression, while changes were only observed when the concomitant presence of inflammation was taken into consideration. In fact, when samples with histological sign of inflammation were excluded, 20 significantly different protein peaks were detected. Subsequent comparisons (PCa with inflammation vs PCa without inflammation, and BPH with inflammation vs BPH without inflammation) showed that 16 proteins appeared to be modified in the presence of inflammation, while 4 protein peaks were not modified. With 2-DE analysis, comparing PCa without inflammation vs PCa with inflammation, and BPH without inflammation vs the same condition in the presence of inflammation, were identified 29 and 25 differentially expressed protein spots, respectively. Excluding samples with inflammation the comparison between PCa vs BPH showed 9 unique PCa proteins, 4 of which overlapped with those previously identified in the presence of inflammation, while other 2 were new proteins, not identified in our previous comparisons.

CONCLUSIONS

The present study indicates that inflammation might be a confounding parameter during the proteomic research of candidate biomarkers of PCa. These results indicate that some possible biomarker-candidate proteins are strongly influenced by the presence of inflammation, hence only a well-selected protein pattern should be considered for potential marker of PCa.

Proteomic and other analyses to determine the functional consequences of deregulated kallikrein-related peptidase (KLK) expression in prostate and ovarian cancer

Rapidly developing proteomic tools are improving detection of deregulated kallikrein-related peptidase (KLK) expression, at the protein level, in prostate and ovarian cancer, as well as facilitating the determination of functional consequences downstream. MS-driven proteomics uniquely allows for the detection, identification, and quantification of thousands of proteins in a complex protein pool, and this has served to identify certain KLKs as biomarkers for these diseases. In this review, we describe applications of this technology in KLK biomarker discovery and elucidate MS-based techniques that have been used for unbiased, global screening of KLK substrates within complex protein pools. Although MS-based KLK degradomic studies are limited to date, they helped to discover an array of novel KLK substrates. Substrates identified by MS-based degradomics are reported with improved confidence over those determined by incubating a purified or recombinant substrate and protease of interest, in vitro. We propose that these novel proteomic approaches represent the way forward for KLK research, in order to correlate proteolysis of biological substrates with tissue-related consequences, toward clinical targeting of KLK expression and function for cancer diagnosis, prognosis, and therapies.

Advances in the proteomic discovery of novel therapeutic targets in cancer

Proteomic approaches are continuing to make headways in cancer research by helping to elucidate complex signaling networks that underlie tumorigenesis and disease progression. This review describes recent advances made in the proteomic discovery of drug targets for therapeutic development. A variety of technical and methodological advances are overviewed with a critical assessment of challenges and potentials. A number of potential drug targets, such as baculoviral inhibitor of apoptosis protein repeat-containing protein 6, macrophage inhibitory cytokine 1, phosphoglycerate mutase 1, prohibitin 1, fascin, and pyruvate kinase isozyme 2 were identified in the proteomic analysis of drug-resistant cancer cells, drug action, and differential disease state tissues. Future directions for proteomics-based target identification and validation to be more translation efficient are also discussed.

Serum proteomics in biomedical research: a systematic review

Proteins that are important indicators of physiological or pathological states may contribute to the early diagnosis of disease, which may provide a basis for identifying the underlying mechanism of disease development. Serum, contains an abundance of proteins, offers an easy and inexpensive approach for disease detection and possesses a high potential to revolutionize the diagnostics. These differentially expressed proteins in serum have become an important role to monitoring the state for disease. Availability of emerging proteomic techniques gives optimism that serum can eventually be placed as a biomedium for clinical diagnostics. Advancements have benefited biomarker research to the point where serum is now recognized as an excellent diagnostic medium for the detection of disease. Comprehensive proteome of human serum fluid with high accuracy and availability has the potential to open new doors for disease biomarker discovery and for disease diagnostics, providing insights useful for future study. Thus, this review presents an overview of the value of serum as a credible diagnostic tool, and we aim to summarize the proteomic technologies currently used for global analysis of serum proteins and to elaborate on the application of serum proteomics to the discovery of disease biomarkers, and discuss some of the critical challenges and perspectives for this emerging field.

Colon cancer stem cells--from basic to clinical application

Based on cancer stem cell (CSC) concept of carcinogenesis, tumors represent complex heterogeneous organ-like systems with a hierarchical cellular organization, and only minority phenotypic subpopulations with stem-like properties possess a dual ability to self-renew indefinitely and produce all the heterogeneous cell phenotypes comprising the bulk tumor cells. Large experimental and clinical data indicate that conventional anti-cancer therapies cannot eradicate CSCs, and moreover, they usually increase their number leading to cancer recurrence and further drug resistance. In this review, several current controversies in the CSC field and recent studies, which help to shed light on their origin, are discussed. The emerging necessity for the development of complex, multimodal CSC-targeted treatment strategies, which combine conventional therapeutics with promising pathway-specific modulators, and natural compounds, which can improve the efficacy of conventional anti-cancer therapeutics and decrease their undesirable side effects is presented. Also, novel requirements and criteria necessary for evaluation of the CSC-targeted drug efficacy and relevant experimental models are discussed.

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Engineering of targeted nanoparticles for cancer therapy using internalizing aptamers isolated by cell-uptake selection

One of the major challenges in the development of targeted nanoparticles (NPs) for cancer therapy is to discover targeting ligands that allow for differential binding and uptake by the target cancer cells. Using prostate cancer (PCa) as a model disease, we developed a cell-uptake selection strategy to isolate PCa-specific internalizing 2'-O-methyl RNA aptamers (Apts) for NP incorporation. Twelve cycles of selection and counter-selection were done to obtain a panel of internalizing Apts, which can distinguish PCa cells from nonprostate and normal prostate cells. After Apt characterization, size minimization, and conjugation of the Apts with fluorescently labeled polymeric NPs, the NP-Apt conjugates exhibit PCa specificity and enhancement in cellular uptake when compared to nontargeted NPs lacking the internalizing Apts. Furthermore, when docetaxel, a chemotherapeutic agent used for the treatment of PCa, was encapsulated within the NP-Apt, a significant improvement in cytotoxicity was achieved in targeted PCa cells. Rather than isolating high-affinity Apts as reported in previous selection processes, our selection strategy was designed to enrich cancer cell-specific internalizing Apts. A similar cell-uptake selection strategy may be used to develop specific internalizing ligands for a myriad of other diseases and can potentially facilitate delivering various molecules, including drugs and siRNAs, into target cells.

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

Proteomics and biomarkers for ovarian cancer diagnosis

Ovarian cancer remains a leading cause of death from gynecological malignancy. Early diagnosis is the most important determinant of survival. Current diagnostic tools have had very limited success in early detection. In recent years, the advancing techniques for proteomics have accelerated the discovery of ovarian cancer biomarkers. Numerous proteomics-based molecular biomarkers/panels have been identified and hold great potential for diagnostic applications, but they need further development and validation. This article reviews recently published data on the diagnosis of ovarian cancer with proteomics, including the major proteomics technologies and promising strategies for biomarker discovery and development.