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

Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction

Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets and one additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer

BACKGROUND

Primary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential.

METHODS

This study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis.

RESULTS

In this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p < 0.0053). On the other hand, we validated the diagnostic efficiency of the four ultrasound image features in clinical data from 112 patients with PPCWOM and 150 patients with PPCWM, obtaining a combined diagnostic AUC of 0.904. In summary, using ultrasound imaging features for predicting whether prostate cancer is metastatic has many applications.

CONCLUSION

The above study reveals noninvasive ultrasound image biomarkers and their underlying biological significance, which provide a basis for early diagnosis, treatment, and prognosis of primary prostate cancer with metastasis.

Distinct Expression of Surface and Genetic Biomarkers in Prostate Cancer Cell Lines

BACKGROUND/AIM

Surface biomarkers, such as CD44 and CD133, have been demonstrated to be expressed in prostate cancer cells, and our previous study has shown that prostate cancer cell lines could be divided into three groups according to the single and combined expression pattern of CD44 and 133. In order to refine prognostication in prostate cancer cells, we further investigated genetic biomarkers, prostate cancer antigen 3 (PCA3), kallikrein 4 (KLK4), and KLK9 in different prostate cancer cell lines.

MATERIALS AND METHODS

CWR22Rv1, PC3, and DU145 cell lines were cultured until 95% confluence. The single expression of CD44 or CD133 and their combined expression were analyzed by flow cytometry, and gene expression of b-actin, PCA3, KLK4, and KLK9 was analyzed by real-time polymerase chain reaction.

RESULTS

The single expression of CD133 was less than 4% in all cell lines examined. PC3 and DU145 cells displayed a high expression of CD44 (>91%), whereas CWR22Rv1 was the only cell line that demonstrated a high co-expression of both CD44 and CD133 (>91%). In addition, PC3 and DU145 displayed low expression of PCA3, KLK4, and KLK9 when compared with their own b-actin expression. In contrast, CWR22Rva showed high expression of PCA3 and KLK4 although KLK9 expression was also low.

CONCLUSION

Both surface and genetic biomarkers should be validated for a more accurate prognosis in prostate cancer.

Development of a polyamine gene expression score for predicting prognosis and treatment response in clear cell renal cell carcinoma

Backgrounds

Polyamine metabolism (PM) is closely related to the tumor microenvironment (TME) and is involved in antitumor immunity. Clear cell renal cell carcinoma (ccRCC) not only has high immunogenicity but also has significant metabolic changes. However, the role of PM in the immune microenvironment of ccRCC remains unclear. This study aimed to reveal the prognostic value of PM-related genes (PMRGs) expression in ccRCC and their correlation with the TME.

Methods

The expression levels PMRGs in different cells were characterized with single-cell sequencing analysis. The PMRG expression pattern of 777 ccRCC patients was evaluated based on PMRGs. Unsupervised clustering analysis was used in identifying PMRG expression subtypes, and Lasso regression analysis was used in developing polyamine gene expression score (PGES), which was validated in external and internal data sets. The predictive value of PGES for immunotherapy was validated in the IMvigor210 cohort. Multiple algorithms were used in analyzing the correlation between PGES and immune cells. The sensitivity of PGES to chemotherapeutic drugs was analyzed with the "pRRophetic" package. We validated the genes that develop PGES in tissue samples. Finally, weighted gene co-expression network analysis was used in identifying the key PMRGs closely related to ccRCC, and cell function experiments were carried out.

Results

PMRGs were abundantly expressed on tumor cells, and PMRG expression was active in CD8⁺ T cells and fibroblasts. We identified three PMRG expression subtypes. Cancer and immune related pathways were active in PMRG expression cluster A, which had better prognosis. PGES exhibited excellent predictive value. The high-PGES group was characterized by high immune cell infiltration, high expression of T cell depletion markers, high tumor mutation burden and tumor immune dysfunction and exclusion, was insensitive to immunotherapy but sensitive to sunitinib, temsirolimus, and rapamycin, and had poor prognosis. Spermidine synthetase (SRM) has been identified as a key gene and is highly expressed in ccRCC at RNA and protein levels. SRM knockdown can inhibit ccRCC cell proliferation, migration, and invasion.

Conclusions

We revealed the biological characteristics of PMRG expression subtypes and developed PGES to accurately predict the prognosis of patients and response to immunotherapy.

Prostanoid Signaling in Cancers: Expression and Regulation Patterns of Enzymes and Receptors

Cancer-associated disturbance of prostanoid signaling provides an aberrant accumulation of prostanoids. This signaling consists of 19 target genes, encoding metabolic enzymes and G-protein-coupled receptors, and prostanoids (prostacyclin, thromboxane, and prostaglandins E2, F2α, D2, H2). The study addresses the systems biology analysis of target genes in 24 solid tumors using a data mining pipeline. We analyzed differential expression patterns of genes and proteins, promoter methylation status as well as tissue-specific master regulators and microRNAs. Tumor types were clustered into several groups according to gene expression patterns. Target genes were characterized as low mutated in tumors, with the exception of melanoma. We found at least six ubiquitin ligases and eight protein kinases that post-translationally modified the most connected proteins PTGES3 and PTGIS. Models of regulation of PTGIS and PTGIR gene expression in lung and uterine cancers were suggested. For the first time, we found associations between the patient’s overall survival rates with nine multigene transcriptomics signatures in eight tumors. Expression patterns of each of the six target genes have predictive value with respect to cytostatic therapy response. One of the consequences of the study is an assumption of prostanoid-dependent (or independent) tumor phenotypes. Thus, pharmacologic targeting the prostanoid signaling could be a probable additional anticancer strategy.

Improving the detection of aggressive prostate cancer using immunohistochemical staining of protein marker panels

Prostate cancer (PCa) is a heterogeneous group of tumors, including non-aggressive (NAG) and aggressive (AG) cancer, with variable clinical outcomes. Clinically, in order to assess the aggressiveness of a PCa, a core needle biopsy of a tumor is usually obtained to evaluate the Gleason pattern and score of the tumor. However, it may be difficult to assign on a small biopsy sample using histology. Therefore, additional tool is needed to aid in the assessment. We studied the diagnostic utility of 12 protein markers to identify AG tumors using immunohistochemistry (IHC) and tumor tissue microarray (TMA), including 215 cores of PCa and 111 cores of tumor-matched normal adjacent tissue (NAT). Protein markers were evaluated for their potential utility as single or combined panels for identification of AG. Of 12 proteins, PSMA, phospho-EGFR, AR and P16 were over-expressed in AG. Galectin-3, DPP4 and MAN1B1 revealed stronger staining patterns in NAG. The sensitivity and specificity of individual marker varied widely. Based on AUC values of individual marker, we constructed two- and three-marker panels. In two-marker panels, especially in the panel of DPP4 and PSMA, the AUC value reached 0.83 (ranging from 0.76 to 0.83). In three-marker panels, containing both DPP4 and PSMA with either Galectin-3 or phospho-EGFR, the AUC value reached 0.86 (ranging from 0.83 to 0.86). The specificities at 95% sensitivity of three-marker panels were also significantly improved. In addition to Gleason score, our IHC panels provide a practical tool to assess the aggressiveness of PCa.

Nucleolar and Coiled-Body Phosphoprotein 1 Is Associated With Stemness and Represents a Potential Therapeutic Target in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and lacks approved specific targeted therapies. One of the major reasons why TNBC is difficult to treat is the high proportion of cancer stem cells within the tumor tissue. Nucleolus is the location of ribosome biogenesis which is frequently overactivated in cancer cells and overactivation of ribosome biogenesis frequently drives the malignant transformation of cancer. Nucleolar and coiled-body phosphoprotein 1 (NOLC1) is a nucleolar protein responsible for nucleolus organization and rRNA synthesis and plays an important role in ribosome biogenesis. However, the correlation of NOLC1 expression with patient prognosis and its value as a therapeutic target have not been evaluated in TNBC. In the current study, based on bioinformatics analysis of the online databases, we found that the expression of NOLC1 was higher in breast cancer tissues than normal tissues, and NOLC1 was expressed at a higher level in TNBC than other subtypes of breast cancer. GSEA analysis revealed that stemness-related pathways were significantly enriched in breast cancer with high NOLC1 gene expression. Further analyses using gene expression profiling interactive analysis 2 (GEPIA2), tumor immune estimation resource (TIMER) and search tool for retrieval of interacting genes/proteins (STRING) demonstrated that NOLC1 was significantly associated with stemness in both all breast cancer and basal-like breast cancer/TNBC patients at both gene and protein levels. Knockdown of NOLC1 by siRNA decreased the protein level of the key stemness regulators MYC and ALDH and inhibited the sphere-forming capacity in TNBC cell line MDA-MB-231. Univariate and multivariate Cox regression analyses demonstrated that NOLC1 was an independent risk factor for overall survival in breast cancer. PrognoScan and Kaplan-Meier plotter analyses revealed that high expression of NOLC1 was associated with poor prognosis in both all breast cancer and TNBC patients. Further immunohistochemical analysis of breast cancer patient samples revealed that TNBC cells had a lower level of NOLC1 in the nucleus compared with non-TNBC cells. These findings suggest that NOLC1 is closely associated with the stemness properties of TNBC and represents a potential therapeutic target for TNBC.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Molecular Histology Analysis of Cryopreserved Tissue Using Peptide/Protein MALDI-TOF Imaging Mass Spectrometry (MALDI-IMS)

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) has emerged as a powerful tool for analyzing the spatial distribution of peptides, small proteins, and other molecules within biological tissues. The obtained signals can be correlated with underlying tissue architecture, without any geometrical distortion, enabling the so-called molecular histology. Here, we analyzed cryopreserved tissue samples employing the MALDI-IMS for proteins and peptides. We used a nonstandard OCT-free cryo-slicing protocol, followed by Carnoy delipidation. Automated matrix spray was utilized to circumvent some of MALDI-IMS technology drawbacks in protein and peptide analysis.

Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes

Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

Proteomic characterization of primary and metastatic prostate cancer reveals reduced proteinase activity in aggressive tumors

Prostate cancer (PCa) is a heterogeneous group of tumors with variable clinical courses. In order to improve patient outcomes, it is critical to clinically separate aggressive PCa (AG) from non-aggressive PCa (NAG). Although recent genomic studies have identified a spectrum of molecular abnormalities associated with aggressive PCa, it is still challenging to separate AG from NAG. To better understand the functional consequences of PCa progression and the unique features of the AG subtype, we studied the proteomic signatures of primary AG, NAG and metastatic PCa. 39 PCa and 10 benign prostate controls in a discovery cohort and 57 PCa in a validation cohort were analyzed using a data-independent acquisition (DIA) SWATH-MS platform. Proteins with the highest variances (top 500 proteins) were annotated for the pathway enrichment analysis. Functional analysis of differentially expressed proteins in NAG and AG was performed. Data was further validated using a validation cohort; and was also compared with a TCGA mRNA expression dataset and confirmed by immunohistochemistry (IHC) using PCa tissue microarray (TMA). 4,415 proteins were identified in the tumor and benign control tissues, including 158 up-regulated and 116 down-regulated proteins in AG tumors. A functional analysis of tumor-associated proteins revealed reduced expressions of several proteinases, including dipeptidyl peptidase 4 (DPP4), carboxypeptidase E (CPE) and prostate specific antigen (KLK3) in AG and metastatic PCa. A targeted analysis further identified that the reduced expression of DPP4 was associated with the accumulation of DPP4 substrates and the reduced ratio of DPP4 cleaved peptide to intact substrate peptide. Findings were further validated using an independently-collected tumor cohort, correlated with a TCGA mRNA dataset, and confirmed by immunohistochemical stains of PCa tumor microarray (TMA). Our study is the first large-scale proteomics analysis of PCa tissue using a DIA SWATH-MS platform. It provides not only an interrogative proteomic signature of PCa subtypes, but also indicates the critical roles played by certain proteinases during tumor progression. The spectrum map and protein profile generated in the study can be used to investigate potential biological mechanisms involved in PCa and for the development of a clinical assay to distinguish aggressive from indolent PCa.

Characterization of Novel Progression Factors in Castration-Resistant Prostate Cancer Based on Global Comparative Proteome Analysis

Simple Summary

Here, we investigated prostate cancer (PCa) tissues at each stage of progression, from benign prostatic hyperplasia to castration-resistant prostate cancer (CRPC), based on quantitative proteomic technology, including tissues after androgen deprivation therapy (ADT). In total, we identified 4768 proteins, and 4069 of them were quantified. We performed a systematic bioinformatics analysis of 865 differentially expressed proteins (DEPs) in the combined PCa tissues. We found 15 DEPs, including FOXA1 and HMGN1–3, as novel factors were significantly involved in the progression to CRPC after ADT in T3G3. All targets were verified to have increased levels of FOXA1 and HMGN1–3 in CRPC by immunoblotting and indirect enzyme-linked immunosorbent assay. The FOXA1 and HMGN1–3 proteins could be used as CRPC-related factors in clinical therapeutic agents.

Abstract

Identifying the biological change from hormone-naïve prostate cancer to castration-resistant prostate cancer (CRPC) is a major clinical challenge for developing therapeutic agents. Although the pathways that lead to CRPC are not fully completely understood, recent evidence demonstrates that androgen signaling is often maintained through varied mechanisms. Androgen deprivation therapy (ADT) is used as a primary treatment for preventing the progression of prostate cancer (PCa). Here we investigated PCa tissues at each stage of progression, from benign prostatic hyperplasia (BPH) to CRPC, based on quantitative proteomic technology, including tissues after ADT. In total, 4768 proteins were identified in this study, of which 4069 were quantified in the combined PCa tissues. Among the quantified proteins, 865 were differentially expressed proteins (21.2%). Based on the quantitative protein results, we performed systematic bioinformatics analysis and found that the levels of 15 proteins, including FOXA1 and HMGN1–3, increased among T3G3, T3GX, and CRPC, despite the ADT. Among all targets, we verified the increased levels of FOXA1 and HMGN1–3 in CRPC by immunoblotting and indirect enzyme-linked immunosorbent assay. In summary, we discuss the changes in intracellular factors involved in the progression of CRPC PCa despite ADT. Moreover, we suggest that FOXA1 and HMGN1–3 proteins could be used as potential CRPC-related factors in clinical therapeutic agents.

Proteomic profiling reveals a signature for optimizing prognostic prediction in Colon Cancer

Previous studies developed prognostic signatures largely depended on transcriptome profiles. The purpose of our present study was to develop a proteomic signature to optimize the evaluation of prognosis of colon cancer patients. The proteomic data of colon cancer patient cohorts were downloaded from The Cancer Proteome Atlas (TCPA). Patients were randomized 3:2 to train set and internal validation set. Univariate Cox regression and lasso Cox regression analysis were performed to identify the prognostic proteins. A four-protein signature was developed to divide patients into a high-risk group and low-risk group with significantly different survival outcomes in both train set and internal validation set. Time-dependent receiver-operating characteristic at 1 year demonstrated that the proteomic signature presented more prognostic accuracy [area under curve (AUC = 0.704)] than the American Joint Commission on Cancer tumor-node-metastasis (AJCC-TNM) staging system (AUC = 0.681) in entire set. In conclusion, we developed a proteomic signature which can improve prognostic accuracy of patients with colon cancer and optimize the therapeutic and follow-up strategies.

Circular RNA CircNOLC1, Upregulated by NF-KappaB, Promotes the Progression of Prostate Cancer via miR-647/PAQR4 Axis

Background

CircRNAs recently have shown critical roles in tumor biology. However, their roles in prostate cancer (PCa) remains largely unclear.

Methods

CircRNA microarrays were performed in immortal prostate cell line RWPE1 and PCa cell lines as DU145, PC3, LNCaP, C4-2, and 22RV1. Combined with upregulated circRNAs in PCa tissues, circNOLC1 expression was validated in PCa cells and tissues via qRT-PCR and FISH. Sanger sequencing, actinomycin D, gDNA, and cDNA, RNase R assays were used to assess the circular characteristics of circNOLC1. CCK-8, colony formation, transwell migration assays, and mice xenograft models were conducted to evaluate the functions of PCa cells after circNOLC1 knockdown and overexpression. RNA pulldown, luciferase reporter assay, FISH (fluorescence in situ hybridization), and CHIP were utilized to illustrate the further mechanisms of circNOLC1.

Results

Our research indicated that circNOLC1 was overexpressed in PCa cells and tissues, and circNOLC1 was more stable than linear NOLC1 mRNA. CircNOLC1 promoted PCa cells proliferation and migration in vitro and vivo. Additionally, we found that circNOLC1 could upregulate PAQR4 expression by sponging miR-647, leading to the activation of PI3K/Akt pathway. Moreover, NF-kappaB was identified to bind to the NOLC1 promoter sites and upregulated both NOLC1 and circNOLC1 expression.

Conclusion

CircNOLC1, elevated by transcription factor NF-kappaB, promotes PCa progression via a miR-647/PAQR4 axis, and circNOLC1 is a potential biomarker and target for PCa treatment.

A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development

Traditional techniques for cancer diagnosis, such as nuclear magnetic resonance, ultrasound and tissue analysis, require sophisticated devices and highly trained personnel, which are characterized by elevated operation costs. The use of biomarkers has emerged as an alternative for cancer diagnosis, prognosis and prediction because their measurement in tissues or fluids, such as blood, urine or saliva, is characterized by shorter processing times. However, the biomarkers used currently, and the techniques used for their measurement, including ELISA, western-blot, polymerase chain reaction (PCR) or immunohistochemistry, possess low sensitivity and specificity. Therefore, the search for new proteomic, genomic or immunological biomarkers and the development of new noninvasive, easier and cheaper techniques that meet the sensitivity and specificity criteria for the diagnosis, prognosis and prediction of this disease has become a relevant topic. The purpose of this review is to provide an overview about the search for new cancer biomarkers, including the strategies that must be followed to identify them, as well as presenting the latest advances in the development of biosensors that possess a high potential for cancer diagnosis, prognosis and prediction, mainly focusing on their relevance in lung, prostate and breast cancers.