The Proteome of Primary Prostate Cancer

Neuropeptide Y and Derivates Are Not Ready for Prime Time in Prostate Cancer Early Detection

Neuropeptide Y (NPY) and related peptides have been proposed as promising biomarkers for the diagnosis of prostate cancer by previous immunoassays and immunohistochemical studies. In this study, we evaluated the additional value of NPY and related peptides compared with prostate-specific antigen (PSA). We performed a comprehensive analysis of NPY, its precursors, and metabolite concentrations in both plasma and tissue samples from 181 patients using a highly specific liquid chromatography tandem mass spectrometry method. Compared with PSA, NPY and related peptides (NPYs) were less accurate at diagnosing significant prostate cancer. Combinations of NPYs in a stepwise approach did not improve a model that would be beneficial for patients. NPY may be beneficial for patients presenting with a PSA concentration in the gray area between 4 and 9 ng/ml, but the strength of this conclusion is limited. Thus, the use of NPYs as standalone or in combination with other variables, such as PSA, prostate volume, or age, to improve the diagnosis is not supported by our study.

Patient summary

This study evaluated neuropeptide Y (NPY) of the family of endogenous peptides as a new biomarker to diagnose prostate cancer. We found that NPY in a patient's blood was not more helpful at diagnosing prostate cancer than the standard prostate-specific antigen blood test. Further research is needed to explore the potential of NPY and related peptides in specific subgroups of patients.

Proteomic profiling of prostate cancer reveals molecular signatures under antiandrogen treatment

BACKGROUND

Tumorigenesis and progression of prostate cancer (PCa) are indispensably dependent on androgen receptor (AR). Antiandrogen treatment is the principal preference for patients with advanced PCa. However, the molecular characteristics of PCa with antiandrogen intervention have not yet been fully uncovered.

METHODS

We first performed proteome analysis with 32 PCa tumor samples and 10 adjacent tissues using data-independent acquisition (DIA)- parallel accumulation serial fragmentation (PASEF) proteomics. Then label-free quantification (LFQ) mass spectrometry was employed to analyze protein profiles in LNCaP and PC3 cells.

RESULTS

M-type creatine kinase CKM and cartilage oligomeric matrix protein COMP were demonstrated to have the potential to be diagnostic biomarkers for PCa at both mRNA and protein levels. Several E3 ubiquitin ligases and deubiquitinating enzymes (DUBs) were significantly altered in PCa and PCa cells under enzalutamide treatment, and these proteins might reprogram proteostasis at protein levels in PCa. Finally, we discovered 127 significantly varied proteins in PCa samples with antiandrogen therapy and further uncovered 4 proteins in LNCaP cells upon enzalutamide treatment.

CONCLUSIONS

Our research reveals new potential diagnostic biomarkers for prostate cancer and might help resensitize resistance to antiandrogen therapy.

Arsenic exposure at environmentally relevant levels induced metabolic toxicity in development mice: Mechanistic insights from integrated transcriptome and metabolome

Emerging evidence has linked arsenic exposure and metabolic homeostasis, but the mechanism is incompletely understood, especially at relatively low concentrations. In this study, we used a mouse model to evaluate the health impacts and metabolic toxicity of arsenic exposure in drinking water at environmentally relevant levels (0.25 and 1.0 ppm). Our results indicated that arsenic damaged intestinal barrier and induced arsenic accumulation, oxidative stress, and pathological changes in the liver and illum. Interestingly, arsenic increased the hepatic triglyceride (TG) and total cholesterol (TC), while reduced serum TG and TC levels. The liver transcriptome found that arsenic exposure caused transcriptome perturbation and promoted hepatic lipid accumulation by regulating the exogenous fatty acids degradation and apolipoproteins related genes. The serum metabolomics identified 74 and 88 differential metabolites in 0.25 and 1.0 ppm, respectively. The KEGG disease and subcellular location analysis indicated that arsenic induced liver and intestinal diseases, and the mitochondrion might be the target organelle for arsenic-induced toxicity. Co-enrichment of transcriptome and metabolome identified 24 metabolites and 9 genes as metabolic toxicity biomarkers. Moreover, 40 male (20 nonalcoholic fatty liver disease (NAFLD) cases and 20 healthy controls) was further selected to validate our findings. Importantly, the significantly changed L-palmitoylcarnitine, 3-hydroxybutyric acid, 2-hydroxycaproic acid and 6 genes of Hadha, Acadl, Aldh3a2, Cpt1a, Cpt2, and Acox1 were found in the NAFLD cases. The results from integrated multi-omics and chemical-protein network analysis indicated that L-palmitoylcarnitine played a critical role in metabolic toxicity by regulating mitochondrial fatty acids β-oxidation genes (Cpt1a, Cpt2). In conclusion, these findings provided new clues for the metabolic toxicity of arsenic exposure at environmentally relevant levels, which involved in the late-life NAFLD development. Our results also contribute to understanding the human responses and phenotypic changes to this hazardous material exposure in the environment.

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.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

IMPLICATIONS

Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

Unlocking ferroptosis in prostate cancer - the road to novel therapies and imaging markers

Ferroptosis is a distinct form of regulated cell death that is predominantly driven by the build-up of intracellular iron and lipid peroxides. Ferroptosis suppression is widely accepted to contribute to the pathogenesis of several tumours including prostate cancer. Results from some studies reported that prostate cancer cells can be highly susceptible to ferroptosis inducers, providing potential for an interesting new avenue of therapeutic intervention for advanced prostate cancer. In this Perspective, we describe novel molecular underpinnings and metabolic drivers of ferroptosis, analyse the functions and mechanisms of ferroptosis in tumours, and highlight prostate cancer-specific susceptibilities to ferroptosis by connecting ferroptosis pathways to the distinctive metabolic reprogramming of prostate cancer cells. Leveraging these novel mechanistic insights could provide innovative therapeutic opportunities in which ferroptosis induction augments the efficacy of currently available prostate cancer treatment regimens, pending the elimination of major bottlenecks for the clinical translation of these treatment combinations, such as the development of clinical-grade inhibitors of the anti-ferroptotic enzymes as well as non-invasive biomarkers of ferroptosis. These biomarkers could be exploited for diagnostic imaging and treatment decision-making.

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.

A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes

Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.

Complementary analysis of proteome-wide proteomics reveals changes in RNA binding protein-profiles during prostate cancer progression

BACKGROUND

Accumulating evidence indicates importance of RNA regulation in cancer. This includes events such as splicing, translation, and regulation of noncoding RNAs, functions which are governed by RNA binding proteins (RBPs).

AIMS

To find which RBPs could be relevant for prostate cancer, we performed systematic screening of RBP expression in clinical prostate cancer.

METHODS AND RESULTS

We interrogated four proteome-wide proteomics datasets including tumor samples of primary, castration resistant, and metastatic prostate cancer. We found that, while the majority of RBPs are expressed but not significantly altered during prostate cancer development and progression, expression of several RBPs increases in advanced disease. Interestingly, most of the differentially expressed RBPs are not targets of differential posttranscriptional phosphorylation during disease progression. The RBPs undergoing expression changes have functions in, especially, poly(A)-RNA binding, nucleocytoplasmic transport, and cellular stress responses, suggesting that these may play a role in formation of castration resistance. Pathway analyzes indicate that increased ribosome production and chromatin-related functions of RBPs are also linked to castration resistant and metastatic prostate cancers. We selected a group of differentially expressed RBPs and studied their role in cultured prostate cancer cells. With siRNA screens, several of these were indicated in survival (DDX6, EIF4A3, PABPN1), growth (e.g., EIF5A, HNRNPH2, LRRC47, and NVL), and migration (e.g., NOL3 and SLTM) of prostate cancer cells. Our analyzes further show that RRP9, a U3 small nucleolar protein essential for ribosome formation, undergoes changes at protein level during metastasis in prostate cancer.

CONCLUSION

In this work, we recognized significant molecular alterations in RBP profiles during development and evolution of prostate cancer. Our study further indicates several functionally significant RBPs warranting further investigation for their functions and possible targetability in prostate cancer.

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.

Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study

Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression differences (p-value = 1.3×10-4). To conclude, we identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predicted their prognoses. These models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.

Neuropeptide Y and its receptors in prostate cancer: associations with cancer invasiveness and perineural spread

PURPOSE

Neuropeptide Y (NPY) is a pleiotropic peptide, which is involved in many biological mechanisms important in regulation of cell growth and survival. The aim of this study was a comprehensive analysis of the NPY system in prostate pathology.

METHODS

The study was based on immunohistochemical analysis of NPY and its receptors, Y1R, Y2R and Y5R, in tissue samples from benign prostate (BP), primary prostate cancer (PCa) and PCa bone metastases. Tissue microarray (TMA) technique was employed, with analysis of multiple cores from each specimen. Intensity of the immunoreactivity and expression index (EI), as well as distribution of the immunostaining in neoplastic cells and stromal elements were evaluated. Perineural invasion (PNI) and extraprostatic extension (EPE) were areas of special interests. Moreover, a transwell migration assay on the LNCaP PCa cell line was used to assess the chemotactic properties of NPY.

RESULTS

Morphological analysis revealed homogeneous membrane and cytoplasmic pattern of NPY staining in cancer cells and its membrane localization with apical accentuation in BP glands. All elements of the NPY system were upregulated in pre-invasive prostate intraepithelial neoplasia, PCa and metastases. EI and staining intensity of NPY receptors were significantly higher in PCa then in BP with correlation between Y2R and Y5R. The strength of expression of the NPY system was further increased in the PNI and EPE areas. In bone metastases, Y1R and Y5R presented high expression scores.

CONCLUSION

The results of our study suggest that the NPY system is involved in PCa, starting from early stages of its development to disseminated states of the disease, and participates in the invasion of PCa into the auto and paracrine matter.

Proteotranscriptomic Discrimination of Tumor and Normal Tissues in Renal Cell Carcinoma

Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 (p < 10^-5 for each gene). Mass spectrometry further validated the differential protein abundance of these genes (IGFBP3, p = 7.53 × 10^-18; PLIN2, p = 3.9 × 10^-39; PLOD2, p = 6.51 × 10^-36; PFKP, p = 1.01 × 10^-47; VEGFA, p = 1.40 × 10^-22; CCND1, p = 1.04 × 10^-24). We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.

Potential Role of Seven Proteomics Tissue Biomarkers for Diagnosis and Prognosis of Prostate Cancer in Urine

As the currently available tests for the clinical management of prostate cancer (PCa) are still far from providing precise diagnosis and risk stratification, the identification of new molecular marker(s) remains a pertinent clinical need. Candidate PCa biomarkers from the published proteomic comparative studies of prostate tissue (2002-2020) were collected and systematically evaluated. AZGP1, MDH2, FABP5, ENO1, GSTP1, GSTM2, and EZR were chosen for further evaluation in the urine of 85 PCa patients and controls using ELISA. Statistically significant differences in protein levels between PCa and BPH showed FABP5 (p = 0.019) and ENO1 (p = 0.015). A biomarker panel based on the combination of FABP5, ENO1, and PSA provided the highest accuracy (AUC = 0.795) for PCa detection. The combination of FABP5, EZR, AZGP1, and MDH2 showed AUC = 0.889 in PCa prognosis, with 85.29% of the samples correctly classified into low and high Gleason score (GS) groups. The addition of PSA to the panel slightly increased the AUC to 0.914. AZGP1, FABP5, and EZR showed significant correlation with GS, stage, and percentage of positive biopsy cores. Although validation using larger patient cohorts will be necessary to establish the credibility of the proposed biomarker panels in a clinical context, this study opens a way for the further testing of more high-quality proteomics biomarkers, which could ultimately add value to the clinical management of PCa.

Proteomics reveals MRPL4 as a high-risk factor and a potential diagnostic biomarker for prostate cancer

Through digital rectal examinations (DRE) and routine prostate-specific antigen (PSA) screening, early prostate cancer (PC) treatment has become possible. However, PC is a complex and heterogeneous disease. In vivo, cancer cells can invade adjacent tissues and metastasize to other tissues resulting in hard cures. Therefore, the key to improving PC patients' survival time is preventing cancer cells' metastasis. We used mass spectrometry to profile primary PC in patients with versus without metastatic PC. We named these two groups of PC patients as high-risk primary PC (n = 11) and low-risk primary PC (n = 7), respectively. At the same time, patients with benign prostatic hyperplasia (BPH, n = 6) were used as controls to explore the possible factors driving PC metastasis. Based on comprehensive mass spectrometry analysis and biological validation, we found significant upregulation of MRPL4 expression in high-risk primary PC relative to low-risk primary PC and BPH. Further, through research of the extensive clinical cohort data in the database, we discovered that MRPL4 could be a high-risk factor for PC and serve as a potential diagnostic biomarker. The MRPL4 might be used as an auxiliary indicator for clinical status/stage of primary PC to predict patient survival time.

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.

Automated quantitative high-throughput multiplex immunofluorescence pipeline to evaluate OXPHOS defects in formalin-fixed human prostate tissue

Advances in multiplex immunofluorescence (mIF) and digital image analysis has enabled simultaneous assessment of protein defects in electron transport chain components. However, current manual methodology is time consuming and labour intensive. Therefore, we developed an automated high-throughput mIF workflow for quantitative single-cell level assessment of formalin fixed paraffin embedded tissue (FFPE), leveraging tyramide signal amplification on a Ventana Ultra platform coupled with automated multispectral imaging on a Vectra 3 platform. Utilising this protocol, we assessed the mitochondrial oxidative phosphorylation (OXPHOS) protein alterations in a cohort of benign and malignant prostate samples. Mitochondrial OXPHOS plays a critical role in cell metabolism, and OXPHOS perturbation is implicated in carcinogenesis. Marked inter-patient, intra-patient and spatial cellular heterogeneity in OXPHOS protein abundance was observed. We noted frequent Complex IV loss in benign prostate tissue and Complex I loss in age matched prostate cancer tissues. Malignant regions within prostate cancer samples more frequently contained cells with low Complex I & IV and high mitochondrial mass in comparison to benign-adjacent regions. This methodology can now be applied more widely to study the frequency and distribution of OXPHOS alterations in formalin-fixed tissues, and their impact on long-term clinical outcomes.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Identification of COPA as a potential prognostic biomarker and pharmacological intervention target of cervical cancer by quantitative proteomics and experimental verification

BACKGROUND

Cervical cancer is the most fatal gynecological carcinoma in the world. It is urgent to explore novel prognostic biomarkers and intervention targets for cervical cancer.

METHODS

Through integrated quantitative proteomic strategy, we investigated the protein expression profiles of cervical cancer; 28 fresh frozen tissue samples (11 adenocarcinoma (AC), 12 squamous cell carcinoma (SCC) and 5 normal cervixes (HC)) were included in discover cohort; 45 fresh frozen tissue samples (19 AC, 18 SCC and 8 HC) were included in verification cohort; 140 paraffin-embedded tissues samples of cervical cancer (85 AC and 55 SCC) were used for immunohistochemical evaluation (IHC) of coatomer protein subunit alpha (COPA) as a prognostic biomarker for cervical cancer; how deficiency of COPA affects cell viability and tumorigenic ability of cervical cancer cells (SiHa cells and HeLa cells) were evaluated by cell counting kit-8 and clone formation in vitro.

RESULTS

We identified COPA is a potential prognostic biomarker for cervical cancer in quantitative proteomics analysis. By retrospective IHC analysis, we additionally verified the proteomics results and demonstrated moderate or strong IHC staining for COPA is an unfavourable independent prognostic factor for cervical cancer. We also identified COPA is a potential pharmacological intervention target of cervical cancer by a series of in vitro experiments.

CONCLUSION

This study is the first to demonstrate that COPA may contribute to progression of cervical cancer. It can serve as a potential prognostic biomarker and promising intervention target for cervical cancer.

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

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.

2-Dodecyl-6-Methoxycyclohexa-2,5-Diene-1,4-Dione Ameliorates Diabetic Cognitive Impairment Through Inhibiting Hif3α and Apoptosis

Diabetes mellitus (DM) is an independent risk factor for cognitive impairment. Although the etiology of diabetic cognitive impairment is complex and multifactorial, the hippocampus neuronal apoptosis is recognized as a main cause of diabetes-induced cognitive impairment. 2-Dodecyl-6-methoxycyclohexa-2,5-diene-1,4-dione (DMDD) was purified from the roots of Averrhoa carambola L. Previous research demonstrated that DMDD was safe and effective in delaying some diabetic complications. However, the efficacy of DMDD to ameliorate diabetic cognitive impairment in type 2 diabetes mice has not been reported. In the present study, the behavioral evaluation was performed by Y maze and novel object recognition in db/db mice. Gene expression profiles were detected using mouse lncRNA microarray analysis in the hippocampi of db/db mice. Changes in the neurodegeneration-associated proteins and the apoptosis-related proteins were determined in both db/db mice and high glucose-treated HT22 cells by Western blotting. We observed that DMDD treatment significantly ameliorated the spatial working memory and object recognition memory impairment in db/db mice. Further study showed that neurodegeneration-associated protein tau was decreased after DMDD treatment in the hippocampi of db/db mice. Eleven lncRNAs and four mRNAs including pro-apoptotic gene Hif3a were significantly differently expressed after DMDD treatment in the hippocampi of db/db mice. The expression of Hif3a, cleaved parp, and caspase 3 proteins was significantly increased in the hippocampi of diabetic db/db mice compared with db/m control mice and then decreased after DMDD treatment. Similar beneficial effects of DMDD were observed in HG-treated HT22 cells. These data indicate that DMDD can alleviate cognitive impairment by inhibiting neuronal apoptosis through decreasing the expression of pro-apoptotic protein Hif3a. In conclusion, our study suggests that DMDD has great potential to be a new preventive and therapeutic compound for diabetic cognitive impairment.

Proteomic Analysis Identifies NDUFS1 and ATP5O as Novel Markers for Survival Outcome in Prostate Cancer

Simple Summary

Due to the heterogeneity of prostate cancer (PCa), it is still difficult to provide risk stratification. Metabolic changes in PCa tissue have been described during tumor progression at genetic and transcriptomic level, but these have not yet clearly contributed to improved diagnosis and therapy. The aim of our study was to identify novel markers for aggressive prostate cancer in a proteomics-derived dataset by immunohistochemical analysis and correlation with transcriptomic data. Here, we provide potential new markers—NDUFS1 and ATP5O—for risk stratification in PCa. Additionally, we reveal for the first time a concordant increase of NDUFS1/ATP5O of mRNA expression in transcriptomic datasets and at protein level.

Abstract

We aimed to identify novel markers for aggressive prostate cancer in a STAT3-low proteomics-derived dataset of mitochondrial proteins by immunohistochemical analysis and correlation with transcriptomic data and biochemical recurrence in a STAT3 independent PCa cohort. Formalin-fixed paraffin-embedded tissue (FFPE) sample selection for proteomic analysis and tissue-microarray (TMA) generation was conducted from a cohort of PCa patients. Retrospective data analysis was performed with the same cohort. 153 proteins differentially expressed between STAT3-low and STAT3-high samples were identified. Out of these, 46 proteins were associated with mitochondrial processes including oxidative phosphorylation (OXPHOS), and 45 proteins were upregulated, including NDUFS1/ATP5O. In a STAT3 independent PCa cohort, high expression of NDUFS1/ATP5O was confirmed by immunocytochemistry (IHC) and was significantly associated with earlier biochemical recurrence (BCR). mRNA expression levels for these two genes were significantly higher in intra-epithelial neoplasia and in PCa compared to benign prostate glands. NDUFS1/ATP5O levels are increased both at the mRNA and protein level in aggressive PCa. Our results provide evidence that NDUFS1/ATP5O could be used to identify high-risk PCa patients.

Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer

Prostate cancer (PCa) is a carcinoma in which fatty acids are abundant. Fatty acid metabolism is rewired during PCa development. Although PCa can be treated with hormone therapy, after prolonged treatment, castration-resistant prostate cancer can develop and can lead to increased mortality. Changes to fatty acid metabolism occur systemically and locally in prostate cancer patients, and understanding these changes may lead to individualized treatments, especially in advanced, castration-resistant prostate cancers. The fatty acid metabolic changes are not merely reflective of oncogenic activity, but in many cases, these represent a critical factor in cancer initiation and development. In this review, we analyzed the literature regarding systemic changes to fatty acid metabolism in PCa patients and how these changes relate to obesity, diet, circulating metabolites, and peri-prostatic adipose tissue. We also analyzed cellular fatty acid metabolism in prostate cancer, including fatty acid uptake, de novo lipogenesis, fatty acid elongation, and oxidation. This review broadens our view of fatty acid switches in PCa and presents potential candidates for PCa treatment and diagnosis.

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.

Investigation of Neural Microenvironment in Prostate Cancer in Context of Neural Density, Perineural Invasion, and Neuroendocrine Profile of Tumors

Background

Cancer stroma contains the neural compartment with specific components and action. Neural microenvironment processing includes among others axonogenesis, perineural invasion (PNI), neurosignaling, and tumor cell neural/neuroendocrine differentiation. Growing data suggest that tumor-neural crosstalk plays an important function in prostate cancer (PCa) biology. However, the mechanisms involved in PNI and axonogenesis, as well as their patho-clinical correlations in this tumor are unclear.

Methods

The present study was carried out on FFPE samples of 73 PCa and 15 benign prostate (BP) cases. Immunohistochemistry with neural markers PGP9.5, TH, and NFP was performed on constructed TMAs and selected tissue sections. The analyzed parameters of tumor innervation included small nerve density (ND) measured on pan-neural marker (PGP9.5) and TH s4tained slides, as well assessment of PNI presence and morphology. The qualitative and topographic aspects were studied. In addition, the expression of neuroendocrine marker chromogranin and NPY was assessed with dedicated indexes. The correlations of the above parameters with basic patho-clinical data such as patients’ age, tumor stage, grade, angioinvasion, and ERG status were examined.

Results

The study showed that innervation parameters differed between cancer and BP. The neural network in PCa revealed heterogeneity, and ND PGP9.5 in tumor was significantly lower than in its periphery. The density of sympathetic TH-positive fibers and its proportion to all fibers was lower in cancer than in the periphery and BP samples. Perineural invasion was confirmed in 76% of cases, usually multifocally, occurring more commonly in tumors with a higher grade. NPY expression in PCa cells was common with its intensity often rising towards PNI. ERG+ tumors showed higher ND, more frequent PNI, and a higher stage. Moreover, chromogranin-positive cells were more pronounced in PCa with higher NPY expression.

Conclusions

The analysis showed an irregular axonal network in prostate cancer with higher neural density (panneural and adrenergic) in the surroundings and the invasive front. ND and PNI interrelated with NPY expression, neuroendocrine differentiation, and ERG status. The above findings support new evidence for the presence of autocrine and paracrine interactions in prostate cancer neural microenvironment.

Quantitative Proteomics Analysis of FFPE Tumor Samples Reveals the Influences of NET-1 siRNA Nanoparticles and Sonodynamic Therapy on Tetraspanin Protein Involved in HCC

Hepatocellular carcinoma (HCC) poses a severe threat to human health. The NET-1 protein has been proved to be strongly associated with HCC proliferation and metastasis in our previous study. Here, we established and validated the NET-1 siRNA nanoparticles system to conduct targeted gene therapy of HCC xenograft in vivo with the aid of sonodynamic therapy. Then, we conducted a label-free proteome mass spectrometry workflow to analyze formalin-fixed and paraffin-embedded HCC xenograft samples collected in this study. The result showed that 78 proteins were differentially expressed after NET-1 protein inhibited. Among them, the expression of 17 proteins upregulated and the expression of 61 proteins were significantly downregulated. Of the protein abundance, the vast majority of Gene Ontology enrichment terms belong to the biological process. The KEGG pathway enrichment analysis showed that the 78 differentially expressed proteins significantly enriched in 45 pathways. We concluded that the function of the NET-1 gene is not only to regulate HCC but also to participate in a variety of biochemical metabolic pathways in the human body. Furthermore, the protein–protein interaction analysis indicated that the interactions of differentially expressed proteins are incredibly sophisticated. All the protein–protein interactions happened after the NET-1 gene has been silenced. Finally, our study also provides a useful proposal for targeted therapy based on tetraspanin proteins to treat HCC, and further mechanism investigations are needed to reveal a more detailed mechanism of action for NET-1 protein regulation of HCC.

An integrated landscape of protein expression in human cancer

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Identification and Characterization of Alternatively Spliced Transcript Isoforms of IRX4 in Prostate Cancer

Alternative splicing (AS) is tightly regulated to maintain genomic stability in humans. However, tumor growth, metastasis and therapy resistance benefit from aberrant RNA splicing. Iroquois-class homeodomain protein 4 (IRX4) is a TALE homeobox transcription factor which has been implicated in prostate cancer (PCa) as a tumor suppressor through genome-wide association studies (GWAS) and functional follow-up studies. In the current study, we characterized 12 IRX4 transcripts in PCa cell lines, including seven novel transcripts by RT-PCR and sequencing. They demonstrate unique expression profiles between androgen-responsive and nonresponsive cell lines. These transcripts were significantly overexpressed in PCa cell lines and the cancer genome atlas program (TCGA) PCa clinical specimens, suggesting their probable involvement in PCa progression. Moreover, a PCa risk-associated SNP rs12653946 genotype GG was corelated with lower IRX4 transcript levels. Using mass spectrometry analysis, we identified two IRX4 protein isoforms (54.4 kDa, 57 kDa) comprising all the functional domains and two novel isoforms (40 kDa, 8.7 kDa) lacking functional domains. These IRX4 isoforms might induce distinct functional programming that could contribute to PCa hallmarks, thus providing novel insights into diagnostic, prognostic and therapeutic significance in PCa management.

Dysregulated Free Fatty Acid Receptor 2 Exacerbates Colonic Adenoma Formation in Apc Min/+ Mice: Relation to Metabolism and Gut Microbiota Composition

Free fatty acid receptor 2 (FFAR2) has been reported as a tumor suppressor in colon cancer development. The current study investigated the effects of FFAR2 signaling on energy metabolism and gut microbiota profiling in a colorectal cancer mouse model (Apc Min/+ ). Ffar2 deficiency promoted colonic polyp development and enhanced fatty acid oxidation and bile acid metabolism. Gut microbiome sequencing analysis showed distinct clustering among wild-type, Apc Min/+ , and Apc Min/+ -Ffar2 -/- mice. The relative abundance of Flavobacteriaceae and Verrucomicrobiaceae was significantly increased in the Apc Min/+ -Ffar2 -/- mice compared to the Apc Min/+ mice. In addition, knocking-down FFAR2 in the human colon cancer cell lines (SW480 and HT29) resulted in increased expression of several key enzymes in fatty acid oxidation, such as carnitine palmitoyltransferase 2, acyl-CoA dehydrogenase, long-chain acyl-CoA dehydrogenase, C-2 to C-3 short chain, and hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase, alpha subunit. Collectively, these results demonstrated that Ffar2 deficiency significantly altered profiles of fatty acid metabolites and gut microbiome, which might promote colorectal cancer development.

Benchmarking mass spectrometry based proteomics algorithms using a simulated database

Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithmand the software might encounter in real-world. To this end, we present a database of simulated spectra that can be used to benchmark any spectra to peptide search engine. We demonstrate the usability of this database by bench marking two popular peptide sequencing engines. We show wide variation in the accuracy of peptide deductions and a complete quality profile of a given algorithm can be useful for practitioners and algorithm developers. All benchmarking data is available at https://users.cs.fiu.edu/~fsaeed/Benchmark.html.

TAGLN Is Downregulated by TRAF6-Mediated Proteasomal Degradation in Prostate Cancer Cells

Transgelin (TAGLN, also named SM22) is an actin-associated protein and affects dynamics of actin filaments. Deregulation of TAGLN contributes to the development of different cancers, and it is commonly considered to be a tumor suppressor. TAGLN is usually downregulated in prostate cancer; however, the detailed functions of TAGLN in prostate cancer and how TAGLN is regulated remains unclear. In this study, we confirmed that TAGLN is downregulated in prostate cancer tissues and demonstrated that the downregulation of TAGLN occurs through proteasomal degradation. Next, we found that the expression level of TAGLN is inversely correlated with TRAF6. We screened more than 20 E2-E3 pairs by in vitro ubiquitination assay and found that the E2A-TRAF6 pair catalyzed mono ubiquitination of TAGLN. We then identified the ubiquitination sites of TAGLN to be on K89 or K108 residues and demonstrated that ubiquitination of TAGLN on K89/K108 are important for TRAF6-mediated proteasomal degradation. Furthermore, we investigated the function of TAGLN in prostate cancer cells. We found that ablation of TAGLN promoted prostate cancer cell proliferation and suppressed their migration via activation of NF-κB and Myc signaling pathways. Overall, our study provided new insights into the mechanisms underlying TAGLN expression and activity in prostate cancer. IMPLICATIONS: E3 ligase TRAF6 mediate mono-ubiquitination and degradation of TAGLN, which leads to activation of NF-κB and Myc signaling pathways in prostate cancer cells.

Proteomic analyses identify major vault protein as a prognostic biomarker for fatal prostate cancer

The demographic shift toward an older population will increase the number of prostate cancer cases. A challenge in the treatment of prostate cancer is to avoid undertreatment of patients at high risk of progression following curative treatment. These men can benefit from early salvage treatment. An explorative cohort consisting of tissue from 16 patients who underwent radical prostatectomy, and were either alive or had died from prostate cancer within 10 years postsurgery, was analyzed by mass spectrometry analysis. Following proteomic and bioinformatic analyses, major vault protein (MVP) was identified as a putative prognostic biomarker. A publicly available tissue proteomics dataset and a retrospective cohort of 368 prostate cancer patients were used for validation. The prognostic value of the MVP was verified by scoring immunohistochemical staining of a tissue microarray. High level of MVP was associated with more than 4-fold higher risk for death from prostate cancer (hazard ratio = 4.41, 95% confidence interval: 1.45–13.38; P = 0.009) in a Cox proportional hazard models, adjusted for Cancer of the Prostate Risk Assessments Post-surgical (CAPRA-S) score and perineural invasion. Decision curve analyses suggested an improved standardized net benefit, ranging from 0.06 to 0.18, of adding MVP onto CAPRA-S score. This observation was confirmed by receiver operator characteristics curve analyses for the CAPRA-S score versus CAPRA-S and MVP score (area under the curve: 0.58 versus 0.73). From these analyses, one can infer that MVP levels in combination with CAPRA-S score might add onto established risk parameters to identify patients with lethal prostate cancer.

SWATH-MS Based Proteomic Profiling of Prostate Cancer Cells Reveals Adaptive Molecular Mechanisms in Response to Anti-Androgen Therapy

Prostate cancer (PCa) is the second most common cancer affecting men worldwide. PCa shows a broad-spectrum heterogeneity in its biological and clinical behavior. Although androgen targeted therapy (ATT) has been the mainstay therapy for advanced PCa, it inevitably leads to treatment resistance and progression to castration resistant PCa (CRPC). Thus, greater understanding of the molecular basis of treatment resistance and CRPC progression is needed to improve treatments for this lethal phenotype. The current study interrogated both proteomics and transcriptomic alterations stimulated in AR antagonist/anti-androgen (Bicalutamide and Enzalutamide) treated androgen-dependent cell model (LNCaP) in comparison with androgen-independent/castration-resistant cell model (C4-2B). The analysis highlighted the activation of MYC and PSF/SFPQ oncogenic upstream regulators in response to the anti-androgen treatment. Moreover, the study revealed anti-androgen induced genes/proteins related to transcription/translation regulation, energy metabolism, cell communication and signaling cascades promoting tumor growth and proliferation. In addition, these molecules were found dysregulated in PCa clinical proteomic and transcriptomic datasets, suggesting their potential involvement in PCa progression. In conclusion, our study provides key molecular signatures and associated pathways that might contribute to CRPC progression despite treatment with anti-androgens. Such molecular signatures could be potential therapeutic targets to improve the efficacy of existing therapies and/or predictive/prognostic value in CRPC for treatment response.

Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI

Background

Prostate cancer tissues are inherently heterogeneous, which presents a challenge for metabolic profiling using traditional bulk analysis methods that produce an averaged profile. The aim of this study was therefore to spatially detect metabolites and lipids on prostate tissue sections by using mass spectrometry imaging (MSI), a method that facilitates molecular imaging of heterogeneous tissue sections, which can subsequently be related to the histology of the same section.

Methods

Here, we simultaneously obtained metabolic and lipidomic profiles in different prostate tissue types using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MSI. Both positive and negative ion mode were applied to analyze consecutive sections from 45 fresh-frozen human prostate tissue samples (N = 15 patients). Mass identification was performed with tandem MS.

Results

Pairwise comparisons of cancer, non-cancer epithelium, and stroma revealed several metabolic differences between the tissue types. We detected increased levels of metabolites crucial for lipid metabolism in cancer, including metabolites involved in the carnitine shuttle, which facilitates fatty acid oxidation, and building blocks needed for lipid synthesis. Metabolites associated with healthy prostate functions, including citrate, aspartate, zinc, and spermine had lower levels in cancer compared to non-cancer epithelium. Profiling of stroma revealed higher levels of important energy metabolites, such as ADP, ATP, and glucose, and higher levels of the antioxidant taurine compared to cancer and non-cancer epithelium.

Conclusions

This study shows that specific tissue compartments within prostate cancer samples have distinct metabolic profiles and pinpoint the advantage of methodology providing spatial information compared to bulk analysis. We identified several differential metabolites and lipids that have potential to be developed further as diagnostic and prognostic biomarkers for prostate cancer. Spatial and rapid detection of cancer-related analytes showcases MALDI-TOF MSI as a promising and innovative diagnostic tool for the clinic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00242-z.

Precision Medicine Approach in Prostate Cancer

Prostate cancer is the most prevalent type of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-grade or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patients' survival and to improve their quality of life. Precision medicine is a novel emerging field that plays an essential role in identifying new sub-classifications of diseases and in providing guidance in treatment that is based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with highthroughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.

The diversity and breadth of cancer cell fatty acid metabolism

Tumor cellular metabolism exhibits distinguishing features that collectively enhance biomass synthesis while maintaining redox balance and cellular homeostasis. These attributes reflect the complex interactions between cell-intrinsic factors such as genomic-transcriptomic regulation and cell-extrinsic influences, including growth factor and nutrient availability. Alongside glucose and amino acid metabolism, fatty acid metabolism supports tumorigenesis and disease progression through a range of processes including membrane biosynthesis, energy storage and production, and generation of signaling intermediates. Here, we highlight the complexity of cellular fatty acid metabolism in cancer, the various inputs and outputs of the intracellular free fatty acid pool, and the numerous ways that these pathways influence disease behavior.

The Complexity and Dynamics of the Tissue Glycoproteome Associated With Prostate Cancer Progression

The complexity and dynamics of the immensely heterogeneous glycoproteome of the prostate cancer (PCa) tumor microenvironment remain incompletely mapped, a knowledge gap that impedes our molecular-level understanding of the disease. To this end, we have used sensitive glycomics and glycoproteomics to map the protein-, cell-, and tumor grade-specific N- and O-glycosylation in surgically removed PCa tissues spanning five histological grades (n = 10/grade) and tissues from patients with benign prostatic hyperplasia (n = 5). Quantitative glycomics revealed PCa grade-specific alterations of the oligomannosidic-, paucimannosidic-, and branched sialylated complex-type N-glycans, and dynamic remodeling of the sialylated core 1- and core 2-type O-glycome. Deep quantitative glycoproteomics identified ∼7400 unique N-glycopeptides from 500 N-glycoproteins and ∼500 unique O-glycopeptides from nearly 200 O-glycoproteins. With reference to a recent Tissue and Blood Atlas, our data indicate that paucimannosidic glycans of the PCa tissues arise mainly from immune cell-derived glycoproteins. Furthermore, the grade-specific PCa glycosylation arises primarily from dynamics in the cellular makeup of the PCa tumor microenvironment across grades involving increased oligomannosylation of prostate-derived glycoproteins and decreased bisecting GlcNAcylation of N-glycans carried by the extracellular matrix proteins. Furthermore, elevated expression of several oligosaccharyltransferase subunits and enhanced N-glycoprotein site occupancy were observed associated with PCa progression. Finally, correlations between the protein-specific glycosylation and PCa progression were observed including increased site-specific core 2-type O-glycosylation of collagen VI. In conclusion, integrated glycomics and glycoproteomics have enabled new insight into the complexity and dynamics of the tissue glycoproteome associated with PCa progression generating an important resource to explore the underpinning disease mechanisms.

The Mitochondrial Proteome of Tumor Cells: A SnapShot on Methodological Approaches and New Biomarkers

Simple Summary

Mitochondria are central hubs of cellular signaling, energy metabolism, and redox balance. The plasticity of these cellular organelles is an essential requisite for the cells to cope with different stimuli and stress conditions. Cancer cells are characterized by changes in energy metabolism, mitochondrial signaling, and dynamics. These changes are driven by alterations in the mitochondrial proteome. For this reason, in the last years a focus of basic and cancer research has been the implementation and optimization of technologies to investigate changes in the mitochondrial proteome during cancer initiation and progression. This review presents an overview of the most used technologies to investigate the mitochondrial proteome and recent evidence on changes in the expression levels and delocalization of certain proteins in and out the mitochondria for shaping the functional properties of tumor cells.

Abstract

Mitochondria are highly dynamic and regulated organelles implicated in a variety of important functions in the cell, including energy production, fatty acid metabolism, iron homeostasis, programmed cell death, and cell signaling. Changes in mitochondrial metabolism, signaling and dynamics are hallmarks of cancer. Understanding whether these modifications are associated with alterations of the mitochondrial proteome is particularly relevant from a translational point of view because it may contribute to better understanding the molecular bases of cancer development and progression and may provide new potential prognostic and diagnostic biomarkers as well as novel molecular targets for anti-cancer treatment. Making an inventory of the mitochondrial proteins has been particularly challenging given that there is no unique consensus targeting sequence that directs protein import into mitochondria, some proteins are present at very low levels, while other proteins are expressed only in some cell types, in a particular developmental stage or under specific stress conditions. This review aims at providing the state-of-the-art on methodologies used to characterize the mitochondrial proteome in tumors and highlighting the biological relevance of changes in expression and delocalization of proteins in and out the mitochondria in cancer biology.

Convergent network effects along the axis of gene expression during prostate cancer progression

Background

Tumor-specific genomic aberrations are routinely determined by high-throughput genomic measurements. It remains unclear how complex genome alterations affect molecular networks through changing protein levels and consequently biochemical states of tumor tissues.

Results

Here, we investigate the propagation of genomic effects along the axis of gene expression during prostate cancer progression. We quantify genomic, transcriptomic, and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis reveals the convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devise a network-based approach that integrates perturbations across different molecular layers, which identifies a sub-network consisting of nine genes whose joint activity positively correlates with increasingly aggressive tumor phenotypes and is predictive of recurrence-free survival. Further, our data reveal a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers.

Conclusions

This study uncovers molecular networks with considerable convergent alterations across tumor sites and patients. It also exposes a diversity of network effects: we could not identify a single sub-network that is perturbed in all high-grade tumor regions.

A Novel Nanoproteomic Approach for the Identification of Molecular Targets Associated with Thyroid Tumors

A thyroid nodule is the most common presentation of thyroid cancer; thus, it is extremely important to differentiate benign from malignant nodules. Within malignant lesions, classification of a thyroid tumor is the primary step in the assessment of the prognosis and selection of treatment. Currently, fine-needle aspiration biopsy (FNAB) is the preoperative test most commonly used for the initial thyroid nodule diagnosis. However, due to some limitations of FNAB, different high-throughput “omics” approaches have emerged that could further support diagnosis based on histopathological patterns. In the present work, formalin-fixed paraffin-embedded (FFPE) tissue specimens from normal (non-neoplastic) thyroid (normal controls (NCs)), benign tumors (follicular thyroid adenomas (FTAs)), and some common types of well-differentiated thyroid carcinoma (follicular thyroid carcinomas (FTCs), conventional or classical papillary thyroid carcinomas (CV-PTCs), and the follicular variant of papillary thyroid carcinomas (FV-PTCs)) were analyzed. For the first time, FFPE thyroid samples were deparaffinized using an easy, fast, and non-toxic method. Protein extracts from thyroid tissue samples were analyzed using a nanoparticle-assisted proteomics approach combined with shotgun LC-MS/MS. The differentially regulated proteins found to be specific for the FTA, FTC, CV-PTC, and FV-PTC subtypes were analyzed with the bioinformatic tools STRING and PANTHER showing a profile of proteins implicated in the thyroid cancer metabolic reprogramming, cancer progression, and metastasis. These proteins represent a new source of potential molecular targets related to thyroid tumors.

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.

FABP5 as a novel molecular target in prostate cancer

Emerging evidence suggests that dysregulated lipid signaling is a key factor in prostate cancer (PC), through fatty acid activation of the nuclear receptors peroxisome proliferator-activated receptors (PPARs), leading to the upregulation of protumoral genes. Fatty acid-binding proteins (FABPs) are intracellular lipid-binding proteins that transport fatty acid to PPARs, facilitating their activation. FABP5 is overexpressed in PC, and correlates with poor patient prognosis and survival. Genetic knockdown or silencing of FABP5 decreases the proliferation and invasiveness of PC cells in vitro, and reduces tumor growth and metastasis in vivo. Pharmacological FABP5-specific inhibitors also reduce tumor growth and metastases, and produce synergistic effects with taxanes. In this review, we present current data supporting FABP5 as a novel molecular target for PC.

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.

Omics Derived Biomarkers and Novel Drug Targets for Improved Intervention in Advanced Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies, and the fifth leading cause of cancer related mortality in men. For advanced PCa, radical prostatectomy, radiotherapy, and/or long-term androgen deprivation therapy are the recommended treatment options. However, subsequent progression to metastatic disease after initial therapy results in low 5-year survival rates (29%). Omics technologies enable the acquisition of high-resolution large datasets that can provide insights into molecular mechanisms underlying PCa pathology. For the purpose of this article, a systematic literature search was conducted through the Web of Science Database to critically evaluate recent omics-driven studies that were performed towards: (a) Biomarker development and (b) characterization of novel molecular-based therapeutic targets. The results indicate that multiple omics-based biomarkers with prognostic and predictive value have been validated in the context of PCa, with several of those being also available for commercial use. At the same time, omics-driven potential drug targets have been investigated in pre-clinical settings and even in clinical trials, holding the promise for improved clinical management of advanced PCa, as part of personalized medicine pipelines.

Human DECR1 is an androgen-repressed survival factor that regulates PUFA oxidation to protect prostate tumor cells from ferroptosis

Fatty acid β-oxidation (FAO) is the main bioenergetic pathway in human prostate cancer (PCa) and a promising novel therapeutic vulnerability. Here we demonstrate therapeutic efficacy of targeting FAO in clinical prostate tumors cultured ex vivo, and identify DECR1, encoding the rate-limiting enzyme for oxidation of polyunsaturated fatty acids (PUFAs), as robustly overexpressed in PCa tissues and associated with shorter relapse-free survival. DECR1 is a negatively-regulated androgen receptor (AR) target gene and, therefore, may promote PCa cell survival and resistance to AR targeting therapeutics. DECR1 knockdown selectively inhibited β-oxidation of PUFAs, inhibited proliferation and migration of PCa cells, including treatment resistant lines, and suppressed tumor cell proliferation and metastasis in mouse xenograft models. Mechanistically, targeting of DECR1 caused cellular accumulation of PUFAs, enhanced mitochondrial oxidative stress and lipid peroxidation, and induced ferroptosis. These findings implicate PUFA oxidation via DECR1 as an unexplored facet of FAO that promotes survival of PCa cells.