Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Association of clinicopathologic and molecular factors with the occurrence of positive margins in breast cancer

PURPOSE

To explore the association of clinicopathologic and molecular factors with the occurrence of positive margins after first surgery in breast cancer.

METHODS

The clinical and RNA-Seq data for 951 (75 positive and 876 negative margins) primary breast cancer patients from The Cancer Genome Atlas (TCGA) were used. The role of each clinicopathologic factor for margin prediction and also their impact on survival were evaluated using logistic regression, Fisher's exact test, and Cox proportional hazards regression models. In addition, differential expression analysis on a matched dataset (71 positive and 71 negative margins) was performed using Deseq2 and LASSO regression.

RESULTS

Association studies showed that higher stage, larger tumor size (T), positive lymph nodes (N), and presence of distant metastasis (M) significantly contributed (p ≤ 0.05) to positive surgical margins. In case of surgery, lumpectomy was significantly associated with positive margin compared to mastectomy. Moreover, PAM50 Luminal A subtype had higher chance of positive margin resection compared to Basal-like subtype. Survival models demonstrated that positive margin status along with higher stage, higher TNM, and negative hormone receptor status was significant for disease progression. We also found that margin status might be a surrogate of tumor stage. In addition, 29 genes that could be potential positive margin predictors and 8 pathways were identified from molecular data analysis.

CONCLUSION

The occurrence of positive margins after surgery was associated with various clinical factors, similar to the findings reported in earlier studies. In addition, we found that the PAM50 intrinsic subtype Luminal A has more chance of obtaining positive margins compared to Basal type. As the first effort to pursue molecular understanding of the margin status, a gene panel of 29 genes including 17 protein-coding genes was also identified for potential prediction of the margin status which needs to be validated using a larger sample set.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Deciphering Urogenital Cancers through Proteomic Biomarkers: A Systematic Review and Meta-Analysis

Urogenital cancers, which include prostate, bladder, and kidney malignancies, exert a substantial impact on global cancer-related morbidity and mortality. Proteomic biomarkers, emerging as valuable tools, aim to enhance early detection, prognostic accuracy, and the development of personalized therapeutic strategies. This study undertook a comprehensive systematic review and meta-analysis of the existing literature investigating the role and potential of proteomic biomarkers in plasma, tissue, and urine samples in urogenital cancers. Our extensive search across several databases identified 1879 differentially expressed proteins from 37 studies, signifying their potential as unique biomarkers for these cancers. A meta-analysis of the significantly differentially expressed proteins was executed, accentuating the findings through visually intuitive volcano plots. A functional enrichment analysis unveiled their significant involvement in diverse biological processes, including signal transduction, immune response, cell communication, and cell growth. A pathway analysis highlighted the participation of key pathways such as the nectin adhesion pathway, TRAIL signaling pathway, and integrin signaling pathways. These findings not only pave the way for future investigations into early detection and targeted therapeutic approaches but also underscore the fundamental role of proteomics in advancing our understanding of the molecular mechanisms underpinning urogenital cancer pathogenesis. Ultimately, these findings hold remarkable potential to significantly enhance patient care and improve clinical outcomes.

siRNA-based approaches for castration-resistant prostate cancer therapy targeting the androgen receptor signaling pathway

Androgen deprivation therapy is a common treatment method for metastatic prostate cancer through lowering androgen levels; however, this therapy frequently leads to the development of castration-resistant prostate cancer (CRPC). This is attributed to the activation of the androgen receptor (AR) signaling pathway. Current treatments targeting AR are often ineffective mostly due to AR gene overexpression and mutations, as well as the presence of splice variants that accelerate CRPC progression. Thus there is a critical need for more specific medication to treat CRPC. Small interfering RNAs have shown great potential as a targeted therapy. This review discusses prostate cancer progression and the role of AR signaling in CRPC, and proposes siRNA-based targeted therapy as a promising strategy for CRPC.

Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Proteomic Analysis of Prostate Cancer FFPE Samples Reveals Markers of Disease Progression and Aggressiveness

Simple Summary

Prostate cancer (PCa) is the second most frequently diagnosed type of cancer in men. The lack of tools for accurate risk assessment is causing over-treatment of men with indolent PCa but also delayed detection of metastatic disease and thus high mortality. The aim of our study was to identify proteins related to the progression and aggressiveness of PCa that could serve as potential biomarkers for better risk stratification. To this end, we performed proteomic analysis of Formalin Fixed Paraffin Embedded (FFPE) prostate tissue specimens (n = 86) and compared them based on grade groups and biochemical recurrence status. Based on the valuable data generated by these comparisons, we have selected seven proteins (NMP1, UQCRH, HSPA9, MRPL3, VCAN, SERBP1, HSPE1) as common denominators of PCa aggressiveness and persistence that could potentially be used for the development of risk assessment tools. Notably, our observations are largely validated by transcriptomics data and literature.

Abstract

Prostate cancer (PCa) is the second most common cancer in men. Diagnosis and risk assessment are widely based on serum Prostate Specific Antigen (PSA) and biopsy, which might not represent the exact degree of PCa risk. Towards the discovery of biomarkers for better patient stratification, we performed proteomic analysis of Formalin Fixed Paraffin Embedded (FFPE) prostate tissue specimens using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Comparative analysis of 86 PCa samples among grade groups 1–5 identified 301 significantly altered proteins. Additional analysis based on biochemical recurrence (BCR; BCR+ n = 14, BCR- n = 51) revealed 197 significantly altered proteins that indicate disease persistence. Filtering the overlapping proteins of these analyses, seven proteins (NPM1, UQCRH, HSPA9, MRPL3, VCAN, SERBP1, HSPE1) had increased expression in advanced grades and in BCR+/BCR- and may play a critical role in PCa aggressiveness. Notably, all seven proteins were significantly associated with progression in Prostate Cancer Transcriptome Atles (PCTA) and NPM1NPM1, UQCRH, and VCAN were further validated in The Cancer Genome Atlas (TCGA), where they were upregulated in BCR+/BCR-. UQCRH levels were also associated with poorer 5-year survival. Our study provides valuable insights into the key regulators of PCa progression and aggressiveness. The seven selected proteins could be used for the development of risk assessment tools.

Emerging Proteins in CRPC: Functional Roles and Clinical Implications

Prostate cancer (PCa) is the most common cancer in men in the western world, but the lack of specific and sensitive markers often leads to overtreatment of prostate cancer which eventually develops into castration-resistant prostate cancer (CRPC). Novel protein markers for diagnosis and management of CRPC will be promising. In this review, we systematically summarize and discuss the expression pattern of emerging proteins in tissue, cell lines, and serum when castration-sensitive prostate cancer (CSPC) progresses to CRPC; focus on the proteins involved in CRPC growth, invasion, metastasis, metabolism, and immune microenvironment; summarize the current understanding of the regulatory mechanisms of emerging proteins in CSPC progressed to CRPC at the molecular level; and finally summarize the clinical applications of emerging proteins as diagnostic marker, prognostic marker, predictive marker, and therapeutic marker.

Construction and validation of N6-methyladenosine long non-coding RNAs signature of prognostic value for early biochemical recurrence of prostate cancer

PURPOSE

Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer.

METHODS

Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study.

RESULTS

Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study.

CONCLUSIONS

The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.

Citric Acid as a Potential Prostate Cancer Biomarker Determined in Various Biological Samples

Despite numerous studies, the molecular mechanism of prostate cancer development is still unknown. Recent investigations indicated that citric acid and lipids—with a special emphasis on fatty acids, steroids and hormones (ex. prolactin)—play a significant role in prostate cancer development and progression. However, citric acid is assumed to be a potential biomarker of prostate cancer, due to which, the diagnosis at an early stage of the disease could be possible. For this reason, the main goal of this study is to determine the citric acid concentration in three different matrices. To the best of our knowledge, this is the first time for citric acid to be determined in three different matrices (tissue, urine and blood). Samples were collected from patients diagnosed with prostate cancer and from a selected control group (individuals with benign prostatic hyperplasia). The analyses were performed using the rapid fluorometric test. The obtained results were correlated with both the histopathological data (the Gleason scale as well as the Classification of Malignant Tumors (pTNM) staging scale) and the biochemical data (the values of the following factors: prostate specific antigen, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, total cholesterol, creatinine and prolactin) using chemometric methods. For tissue samples, the results indicated a decreased level of citric acid in the case of prostate cancer. The analyte average concentrations in serum and urine appeared to be corresponding and superior in the positive cohort. This trend was statistically significant in the case of urinary citric acid. Moreover, a significant negative correlation was demonstrated between the concentration of citric acid and the tumor stage. A negative correlation between the total cholesterol and high-density lipoprotein and prolactin was particularly prominent in cancer cases. Conversely, a negative association between low-density lipoprotein and prolactin levels was observed solely in the control group. On the basis of the results, one may assume the influence of hormones, particularly prolactin, on the development of prostate cancer. The present research allowed us to verify the possibility of using citric acid as a potential biomarker for prostate cancer.

Screening to Identify an Immune Landscape-Based Prognostic Predictor and Therapeutic Target for Prostate Cancer

Objectives

Existing prognostic risk assessment strategies for prostate cancer (PCa) remain unsatisfactory. Similar treatments for patients at the same disease stage can lead to different survival outcomes. Thus, we aimed to explore a novel immune landscape-based prognostic predictor and therapeutic target for PCa patients.

Methods

A total of 490 PCa patients from The Cancer Genome Atlas Project (TCGA) cohort were analyzed to obtain immune landscape-based prognostic features. Then, analyses at different levels were performed to explore the relevant survival mechanisms, prognostic predictors, and therapeutic targets. Finally, experimental verification was performed using a tissue microarray (TMA) from 310 PCa patients. Furthermore, a nomogram was constructed to provide a quantitative approach for predicting the prognosis of patients with PCa.

Results

The immune landscape-based risk score (ILBRS) was obtained. Then, VAV1, which presented a significant positive correlation with Treg infiltration and ILBRS, was screened and identified to be significantly related to the prognosis of PCa. Finally, experimental verification confirmed the prognostic value of VAV1 for PCa prognosis at the protein level.

Conclusions

VAV1 has the potential to be developed as an immune landscape-based PCa prognostic predictor and therapeutic target and will help improve prognosis by enabling the selection of individualized, targeted therapy.

Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database

Prostate cancer (PC) is a polygenic disease with multiple gene interactions. Therefore, a detailed analysis of its epidemiology and evaluation of risk factors can help to identify more accurate predictors of aggressive disease. We used the transcriptome data from a cohort of 243 patients from the Cancer Genome Atlas (TCGA) database. Key regulatory genes involved in proliferation activity, in the regulation of stress, and in the regulation of inflammation processes of the tumor microenvironment were selected to test a priori multi-dimensional scaling (MDS) models and create a combined score to better predict the patients' survival and disease-free intervals. Survival was positively correlated with cortisol expression and negatively with Mini-Chromosome Maintenance 7 (MCM7) and Breast-Related Cancer Antigen2 (BRCA2) expression. The disease-free interval was negatively related to the expression of enhancer of zeste homolog 2 (EZH2), MCM7, BRCA2, and programmed cell death 1 ligand 1 (PD-L1). MDS suggested two separate pathways of activation in PC. Within these two dimensions three separate clusters emerged: (1) cortisol and brain-derived neurotrophic factor BDNF, (2) PD-L1 and cytotoxic-T-lymphocyte-associated protein 4 (CTL4); (3) and finally EZH2, MCM7, BRCA2, and c-Myc. We entered the three clusters of association shown in the MDS in several Kaplan-Meier analyses. It was found that only Cluster 3 was significantly related to the interval-disease free, indicating that patients with an overall higher activity of regulatory genes of proliferation and DNA repair had a lower probability to have a longer disease-free time. In conclusion, our data study provided initial evidence that selecting patients with a high grade of proliferation and DNA repair activity could lead to an early identification of an aggressive PC with a potentials for metastatic development.

Identification of crucial genes and pathways associated with prostate cancer in multiple databases

OBJECTIVE

Prostate cancer (PCa) is a malignant neoplasm of the urinary system. This study aimed to use bioinformatics to screen for core genes and biological pathways related to PCa.

METHODS

The GSE5957 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were constructed by R language. Furthermore, protein-protein interaction (PPI) networks were generated to predict core genes. The expression levels of core genes were examined in the Tumor Immune Estimation Resource (TIMER) and Oncomine databases. The cBioPortal tool was used to study the co-expression and prognostic factors of the core genes. Finally, the core genes of signaling pathways were determined using gene set enrichment analysis (GSEA).

RESULTS

Overall, 874 DEGs were identified. Hierarchical clustering analysis revealed that these 24 core genes have significant association with carcinogenesis and development. LONRF1, CDK1, RPS18, GNB2L1 (RACK1), RPL30, and SEC61A1 directly related to the recurrence and prognosis of PCa.

CONCLUSIONS

This study identified the core genes and pathways in PCa and provides candidate targets for diagnosis, prognosis, and treatment.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

Background and objective

Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.

Methods

Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature’s prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs.

Results

Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.

Conclusion

Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

A Novel Set of Immune-associated Gene Signature predicts Biochemical Recurrence in Localized Prostate Cancer Patients after Radical Prostatectomy

Background: Decision-making regarding biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) mainly relies on clinicopathological parameters with a low predictive accuracy. Currently, accumulating evidence suggests that immune-associated genes (IAGs) play irreplaceable roles in tumorigenesis, progression and metastasis. Considering the critical role of immune in PCa, we therefore attempted to identify the novel IAGs signature and validate its prognostic value that can better forecast the risk for BCR and guide clinical treatment. Methods: RNA-sequencing and corresponding clinicopathological data were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was utilized to screen out the candidate module closely related to BCR, and univariate and LASSO Cox regression analyses were performed to build the gene signature. Kaplan-Meier (KM) survival analysis, time-dependent receiver operating curve (ROC), independent prognostic analysis and nomogram were also applied to evaluate the prognostic value of the signature. Besides, Gene ontology analysis (GO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore potential biological pathways. Results: A total of six IAGs (SSTR1, NFATC3, NRP1, TUBB3, IL1R1, GDF15) were eventually identified and used to establish a novel IAGs signature. The Kaplan-Meier analysis revealed that patients with low-risk scores had longer recurrence-free survival (RFS) than those with high-risk scores in both GSE70769 and TCGA cohorts. Further, our signature was also proven to be a valuable independent prognostic factor for BCR. We also constructed a nomogram based on the gene signature and related clinicopathologic features, which excellently predict 1-year, 3-year and 5-year prognosis of localized PCa patients after RP. Moreover, functional enrichment analysis demonstrated the vital biological processes, and stratified GSEA revealed that a crucial immune-related pathway (T cell receptor signaling pathway) was notably enriched in the high-risk group. Conclusions: We successfully developed a novel robust IAGs signature that is powerful in BCR prediction in localized PCa patients after RP, and created a prognostic nomogram. In addition, the signature might help clinicians in selecting high-risk subpopulation, predicting survival status of patients and promoting more individualized therapies than traditional clinical factors.

Malignant Evaluation and Clinical Prognostic Values of M6A RNA Methylation Regulators in Prostate Cancer

Objective: M6A RNA modification is closely associated with tumor genesis and progression of several malignancies; however, its role in prostate cancer (PCa) remains poorly understood.

Materials and methods: Expression data and corresponding clinicopathologic information were available freely from the Cancer Genome Atlas (TCGA) dataset. We compared the expression level of m6A RNA methylation regulators in PCa with different clinicopathologic characteristics and identified subgroups based on their expressions with consensus clustering. To build the signature and assess its prognostic value, several methods were used for the analysis, including univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, time-dependent receiver operating curve (ROC), and Kaplan-Meier (KM) survival analysis.

Results: Most of the m6A RNA methylation regulators were differentially expressed not only between normal and tumor tissue but also among PCa stratified by different clinicopathologic characteristics. There were obvious differences between two clusters, cluster 1 and 2, regarding clinicopathologic features, and the recurrence-free survival (RFS) in cluster 2 was significantly worse than cluster 1. We developed an eleven-gene signature which exhibited a high prognostic value and was able to independently predict RFS. Moreover, a nomogram which integrated clinical information and the gene signature was capable of distinguishing high-risk recurrent patients.

Conclusion: These methylation regulators are correlated to clinicopathologic characteristics in PCa and a prognostic model using m6A methylation-related genes is constructed and of high predictive value for recurrence after RP.

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

BACKGROUND

The aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer.

METHODS

Four microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer.

RESULTS

243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient' disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway.

CONCLUSIONS

UBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

A Fast and Memory-Efficient Spectral Library Search Algorithm Using Locality-Sensitive Hashing

With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.

Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

Background

Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR.

Methods

The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR.

Results

By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, P < 0.001). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, P = 0.005). Time-dependent ROC at 1 year suggested that the CSC gene signature (AUC = 0.800) possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature.

Conclusions

We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

Identification of Novel Prognosis and Prediction Markers in Advanced Prostate Cancer Tissues Based on Quantitative Proteomics

BACKGROUND/AIM

Prostate cancer (PCa) is the most frequent cancer found in males worldwide, and its mortality rate is increasing every year. However, there are no known molecular markers for advanced or aggressive PCa, and there is an urgent clinical need for biomarkers that can be used for prognosis and prediction of PCa.

MATERIALS AND METHODS

Mass spectrometry-based proteomics was used to identify new biomarkers in tissues obtained from patients with PCa who were diagnosed with T2, T3, or metastatic PCa in regional lymph nodes.

RESULTS

Among 1,904 proteins identified in the prostate tissues, 344 differentially expressed proteins were defined, of which 124 were up-regulated and 216 were down-regulated. Subsequently, based on the results of partial least squares discriminant analysis and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, we proposed that spermidine synthase (SRM), nucleolar and coiled-body phosphoprotein 1 (NOLC1), and prostacyclin synthase (PTGIS) represent new protein biomarkers for diagnosis of advanced PCa. These proteomics results were verified by immunoblot assays in metastatic PCa cell lines and by indirect enzyme-linked immunosorbent assay in prostate specimens.

CONCLUSION

SRM was significantly increased depending on the cancer stage, confirming the possibility of using SRM as a biomarker for prognosis and prediction of advanced PCa.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Characterization of N-linked intact glycopeptide signatures of plasma IgGs from patients with prostate carcinoma and benign prostatic hyperplasia for diagnosis pre-stratification

The discovery of novel non-invasive biomarkers for discriminating between prostate carcinoma (PCa) patients and benign prostatic hyperplasia (BPH) patients is necessary to reduce the burden of biopsies, avoid overdiagnosis and improve quality of life. Previous studies suggest that abnormal glycosylation of immunoglobulin gamma molecules (IgGs) is strongly associated with immunological diseases and prostate diseases. Hence, characterizing N-linked intact glycopeptides of IgGs that correspond to the N-glycan structure with specific site information might enable a better understanding of the molecular pathogenesis and discovery of novel signatures in preoperative discrimination of BPH from PCa. In this study, we profiled N-linked intact glycopeptides of purified IgGs from 51 PCa patients and 45 BPH patients by our developed N-glycoproteomic method using hydrophilic interaction liquid chromatography enrichment coupled with high resolution LC-MS/MS. The quantitative analysis of the N-linked intact glycopeptides using pGlyco 2.0 and MaxQuant software provided quantitative information on plasma IgG subclass-specific and site-specific N-glycosylation. As a result, we found four aberrantly expressed N-linked intact glycopeptides across different IgG subclasses. In particular, the N-glycopeptide IgG2-GP09 (EEQFNSTFR (H5N5S1)) was dramatically elevated in plasma from PCa patients, compared with that in BPH patients (PCa/BPH ratio = 5.74, p = 0.001). Additionally, the variations in these N-linked intact glycopeptide abundances were not caused by the changes in the IgG concentrations. Furthermore, IgG2-GP09 displayed a more powerful prediction capability (auROC = 0.702) for distinguishing PCa from BPH than the clinical index t-PSA (auROC = 0.681) when used alone or in combination with other indicators (auROC = 0.853). In conclusion, these abnormally expressed N-linked intact glycopeptides have potential for non-invasive monitoring and pre-stratification of prostate diseases.

A novel gene signature to predict immune infiltration and outcome in patients with prostate cancer

Prostate cancer (PCa) is one of the most common malignancies in male. We aim to establish a novel gene signature for immune infiltration and outcome (biochemical recurrence (BCR) and overall survival (OS)) of patients with prostate cancer (PCa) to augment Gleason patterns for evaluating prognosis and managing patients undergoing radical prostatectomy (RP). Combined with our microarray data and the Cancer Genome Atlas Project (TCGA) database (discovery set), we identified a six-gene signature. The Gene Expression Omnibus (GEO) database served as the test set. The databases of Fudan University Shanghai Cancer Center (FUSCC) and Third Affiliated Hospital of Nantong University (TAHNU) served as an external validation set. Immunohistochemistry was used to investigate the relationship between risk groups and the immune infiltrate. We identified a six-gene signature to predict immune cell infiltration and outcome of PCa patients. The AUC values used to predict early BCR in the discovery, test, FUSCC, and TAHNU sets were 0.73, 0.76, 0.72, and 0.81, respectively. Low-risk score patients in each dataset experienced significantly longer OS (P = .01, 0.04, 0.02, respectively). The signature also predicted high regulatory T cells (Tregs) and M2-polarized macrophages infiltration in high-risk score patients with PCa. Additionally, high mutation load, related signal pathways, and sensitivity to anticancer drugs that correlated with high-risk score of cancer progression and death were also identified. The six-gene signature may improve prognostic information, serve as a prognostic tool to manage patients after RP, and advance basic studies of PCa.

Tissue Proteome Signatures Associated with Five Grades of Prostate Cancer and Benign Prostatic Hyperplasia

The histology-based Gleason score (GS) of prostate cancer (PCa) tissue biopsy is the most accurate predictor of disease aggressiveness and an important measure to guide treatment strategies and patient management. The variability associated with PCa tumor sampling and the subjective determination of the GS are challenges that limit accurate diagnostication and prognostication. Thus, novel molecular signatures are needed to distinguish between indolent and aggressive forms of PCa for better patient management and outcomes. Herein, label-free LC-MS/MS proteomics is used to profile the proteome of 50 PCa tissues spanning five grade groups (n = 10 per group) relative to tissues from individuals with benign prostatic hyperplasia (BPH). Over 2000 proteins are identified albeit at different levels between and within the patient groups, revealing biological processes associated with specific grades. A panel of 11 prostate-derived proteins including IGKV3D-20, RNASET2, TACC2, ANXA7, LMOD1, PRCP, GYG1, NDUFV1, H1FX, APOBEC3C, and CTSZ display the potential to stratify patients from low and high PCa grade groups. Parallel reaction monitoring of the same sample cohort validate the differential expression of LMOD1, GYG1, IGKV3D-20, and RNASET2. The four proteins associated with low and high PCa grades reported here warrant further exploration as candidate biomarkers for PCa aggressiveness.

Development and validation of lncRNAs-based nomogram for prediction of biochemical recurrence in prostate cancer by bioinformatics analysis

Background: Early biochemical recurrence (BCR) was considered as a sign for clinical recurrence and metastasis of prostate cancer (PCa). The purpose of the present study was to identify a lncRNA-based nomogram that can predict BCR of PCa accurately.

Materials and methods: Bioinformatics analysis, such as propensity score matching (PSM) and differentially expressed genes (DEGs) analyses were used to identify candidate lncRNAs for further bioinformatics analysis. LASSO Cox regression model was used to select the most significant prognostic lncRNAs and construct the lncRNAs signature for predicting BCR in discovery set. Additionally, a nomogram based on our lncRNAs signature was also formulated. Both lncRNAs signature and nomogram were validated in test set. GSEA was carried out to identify various gene sets which share a common biological function, chromosomal location, or regulation.

Results: A total of 457 patients with sufficient BCR information were included in our analysis. Finally, a five lncRNAs signature significantly associated with BCR was identified in discovery set (HR=0.44, 95%CI: 0.27-0.72, C-index = 0.63) and validated in test set (HR=0.22, 95%CI: 0.09-0.56, C-index = 0.65). Additionally, the lncRNAs-based nomogram showed significant performance for predicting BCR in both discovery set (C-index = 0.74) and test set (C-index = 0.78).

Conclusion: In conclusion, our lncRNAs-based nomogram is a reliable prognostic tool for BCR in PCa patients. In addition, the present study put forward the direction for the further investigation on the mechanism of PCa progression.

Mapping human N-linked glycoproteins and glycosylation sites using mass spectrometry

N-linked glycoprotein is a highly interesting class of proteins for clinical and biological research. Over the last decade, large-scale profiling of N-linked glycoproteins and glycosylation sites from biological and clinical samples has been achieved through mass spectrometry-based glycoproteomic approaches. In this paper, we reviewed the human glycoproteomic profiles that have been reported in more than 80 individual studies, and mainly focused on the N-glycoproteins and glycosylation sites identified through their deglycosylated forms of glycosite-containing peptides. According to our analyses, more than 30,000 glycosite-containing peptides and 7,000 human glycoproteins have been identified from five different body fluids, twelve human tissues (or related cell lines), and four special cell types. As the glycoproteomic data is still missing for many organs and tissues, a systematical glycoproteomic analysis of various human tissues and body fluids using a uniform platform is still needed for an integrated map of human N-glycoproteomes.

A Rich Array of Prostate Cancer Molecular Biomarkers: Opportunities and Challenges

Prostate cancer is the most prevalent non-skin cancer in men and is the leading cause of cancer-related death. Early detection of prostate cancer is largely determined by a widely used prostate specific antigen (PSA) blood test and biopsy is performed for definitive diagnosis. Prostate cancer is asymptomatic in the early stage of the disease, comprises of diverse clinico-pathologic and progression features, and is characterized by a large subset of the indolent cancer type. Therefore, it is critical to develop an individualized approach for early detection, disease stratification (indolent vs. aggressive), and prediction of treatment response for prostate cancer. There has been remarkable progress in prostate cancer biomarker discovery, largely through advancements in genomic technologies. A rich array of prostate cancer diagnostic and prognostic tests has emerged for serum (4K, phi), urine (Progensa, T2-ERG, ExoDx, SelectMDx), and tumor tissue (ConfirmMDx, Prolaris, Oncoytype DX, Decipher). The development of these assays has created new opportunities for improving prostate cancer diagnosis, prognosis, and treatment decisions. While opening exciting opportunities, these developments also pose unique challenges in terms of selecting and incorporating these assays into the continuum of prostate cancer patient care.

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.

Ceramide and Exosomes: A Novel Target in Cancer Biology and Therapy

Exosomes are secreted extracellular vesicles (EVs) that carry micro RNAs and other factors to reprogram cancer cells and tissues affected by cancer. Exosomes are exchanged between cancer cells and other tissues, often to prepare a premetastatic niche, escape immune surveillance, or spread multidrug resistance. Only a few studies investigated the function of lipids in exosomes although their lipid composition is different from that of the secreting cells. Ceramide is one of the lipids critical for exosome formation, and it is also enriched in these EVs. New research suggests that lipids in the exosomal membrane may organize and transmit "mobile rafts" that turn exosomes into extracellular signalosomes spreading activation of cell signaling pathways in oncogenesis and metastasis. Ceramide may modulate the function of mobile rafts and their effect on these cell signaling pathways. The critical role of lipids and, in particular, ceramide for formation, secretion, and function of exosomes may lead to a radically new understanding of cancer biology and therapy.

The Peripheral and Intratumoral Immune Cell Landscape in Cancer Patients: A Proxy for Tumor Biology and a Tool for Outcome Prediction

Functional systemic and local immunity is required for effective anti-tumor responses. In addition to an active engagement with cancer cells and tumor stroma, immune cells can be affected and are often found to be dysregulated in cancer patients. The impact of tumors on local and systemic immunity can be assessed using a variety of approaches ranging from low-dimensional analyses that are performed on large patient cohorts to multi-dimensional assays that are technically and logistically challenging and are therefore confined to a limited sample size. Many of these strategies have been established in recent years leading to exciting findings. Not only were analyses of immune cells in tumor patients able to predict the clinical course of the disease and patients' survival, numerous studies also detected changes in the immune landscape that correlated with responses to novel immunotherapies. This review will provide an overview of established and novel tools for assessing immune cells in tumor patients and will discuss exemplary studies that utilized these techniques to predict patient outcomes.