Discovery of PTN as a serum-based biomarker of pro-metastatic prostate cancer

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data

BACKGROUND

Concurrent chemo-radiotherapy (CCRT) is the preferred non-surgical treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Unfortunately, some patients respond poorly, which leads to inappropriate or excessive treatment and affects patient survival. To accurately predict the response of ESCC patients to CCRT, we developed classification models based on the clinical, serum proteomic and radiomic data.

METHODS

A total of 138 ESCC patients receiving CCRT were enrolled in this study and randomly split into a training cohort (n = 92) and a test cohort (n = 46). All patients were classified into either complete response (CR) or incomplete response (non-CR) groups according to RECIST1.1. Radiomic features were extracted by 3Dslicer. Serum proteomic data was obtained by Olink proximity extension assay. The logistic regression model with elastic-net penalty and the R-package "rms" v6.2-0 were applied to construct classification and nomogram models, respectively. The area under the receiver operating characteristic curves (AUC) was used to evaluate the predictive performance of the models.

RESULTS

Seven classification models based on multi-omics data were constructed, of which Model-COR, which integrates five clinical, five serum proteomic, and seven radiomic features, achieved the best predictive performance on the test cohort (AUC = 0.8357, 95 % CI: 0.7158-0.9556). Meanwhile, patients predicted to be CR by Model-COR showed significantly longer overall survival than those predicted to be non-CR in both cohorts (Log-rank P = 0.0014 and 0.027, respectively). Furthermore, two nomogram models based on multi-omics data also performed well in predicting response to CCRT (AUC = 0.8398 and 0.8483, respectively).

CONCLUSION

We developed and validated a multi-omics based classification model and two nomogram models for predicting the response of ESCC patients to CCRT, which achieved the best prediction performance by integrating clinical, serum Olink proteomic, and radiomic data. These models could be useful for personalized treatment decisions and more precise clinical radiotherapy and chemotherapy for ESCC patients.

Single-cell RNA combined with bulk RNA analysis to explore oxidative stress and energy metabolism factors and found a new prostate cancer oncogene MXRA8

BACKGROUND

Prostate cancer is the most common malignancy among men worldwide, and its diagnosis and treatment are challenging due to its heterogeneity.

METHODS

Integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we identified two molecular subtypes of prostate cancer based on dysregulated genes involved in oxidative stress and energy metabolism. We constructed a risk score model (OMR) using common differentially expressed genes, which effectively evaluated prostate cancer prognosis.

RESULTS

Our analysis demonstrated a significant correlation between the risk score model and various factors, including tumor immune microenvironment, genomic variations, chemotherapy resistance, and immune response. Notably, patients with low-risk scores exhibited increased sensitivity to chemotherapy and immunotherapy compared to those with high-risk scores, indicating the model's potential to predict patient response to treatment. Additionally, our investigation of MXRA8 in prostate cancer showed significant upregulation of this gene in the disease as confirmed by PCR and immunohistochemistry. Functional assays including CCK-8, transwell, plate cloning, and ROS generation assay demonstrated that depletion of MXRA8 reduced the proliferative, invasive, migratory capabilities of PC-3 cells, as well as their ROS generation capacity.

CONCLUSIONS

Our study highlights the potential of oxidative stress and energy metabolism-related genes as prognostic markers and therapeutic targets in prostate cancer. The integration of scRNA-seq and bulk RNA-seq data enables a better understanding of prostate cancer heterogeneity and promotes personalized treatment development. Additionally, we identified a novel oncogene MXRA8 in prostate cancer.

Radiotranscriptomics identified new mRNAs and miRNA markers for distinguishing prostate cancer from benign prostatic hyperplasia

The present study investigated ultrasound (US) phenotypes reflecting prostate cancer (PCa)-related genetic mutations. Herein, integration of radiotranscriptomic data, US and contrast-enhanced ultrasound (CEUS) radiomic images, and RNA sequencing was performed with the aim of significantly improving the accuracy of PCa prognosis. We performed radiotranscriptomic analysis of clinical, imaging, and two genomic (mRNA and microRNA expression) datasets from 48 and 22 men with PCa and benign prostatic hyperplasia (BPH), respectively. Twenty-three US texture features and four microvascular perfusion features were associated with various patterns of 52 differentially expressed genes related to PCa (p < 0.05); 17 overexpressed genes were associated with two key texture features. Twelve overexpressed genes were identified using microvascular perfusion features. Furthermore, mRNA and miRNA biomarkers could be used to distinguish between PCa and BPH. Compared with RNA sequencing, B-mode and CEUS features reflected genomic alterations associated with hormone receptor status, angiogenesis, and prognosis in patients with PCa. These findings indicate the potential of US to assess biomarker levels in patients with PCa.

Whole transcriptome analysis of canine pheochromocytoma and paraganglioma

Pheochromocytomas and paragangliomas (PPGLs) are neuroendocrine tumors arising from the chromaffin cells in the adrenal medulla and extra-adrenal paraganglia, respectively. Local invasion, concurrent disorders, and metastases prevent surgical removal, which is the most effective treatment to date. Given the current lack of effective medical treatment, there is a need for novel therapeutic strategies. To identify druggable pathways driving PPGL development, we performed RNA sequencing on PPGLs (n = 19) and normal adrenal medullas (NAMs; n = 10) of dogs. Principal component analysis (PCA) revealed that PPGLs clearly clustered apart from NAMs. In total, 4,218 genes were differentially expressed between PPGLs and NAMs. Of these, 232 had a log2 fold change of >3 or < -3, of which 149 were upregulated in PPGLs, and 83 were downregulated. Compared with NAMs, PPGLs had increased expression of genes related to the cell cycle, tumor development, progression and metastasis, hypoxia and angiogenesis, and the Wnt signaling pathway, and decreased expression of genes related to adrenal steroidogenesis. Our data revealed several overexpressed genes that could provide targets for novel therapeutics, such as Ret Proto-Oncogene (RET), Dopamine Receptor D2 (DRD2), and Secreted Frizzled Related Protein 2 (SFRP2). Based on the PCA, PPGLs were classified into 2 groups, of which group 1 had significantly higher Ki67 scores (p = 0.035) and shorter survival times (p = 0.04) than group 2. Increased expression of 1 of the differentially expressed genes between group 1 and 2, pleiotrophin (PTN), appeared to correlate with a more aggressive tumor phenotype. This study has shed light on the transcriptomic profile of canine PPGL, yielding new insights into the pathogenesis of these tumors in dogs, and revealed potential novel targets for therapy. In addition, we identified 2 transcriptionally distinct groups of PPGLs that had significantly different survival times.

Exploration of diagnostic biomarkers, microenvironment characteristics, and ursolic acid's therapeutic effect for benign prostate hyperplasia

Benign prostatic hyperplasia (BPH) and early-stage prostate cancer (PC) have similar symptoms, making it challenging to differentially diagnose these two conditions. The study used Weighted Gene Co-Expression Network Analysis, as well as two machine learning strategies to identify BPH-specific biomarkers based on an integrated transcriptome data from 922 samples. Eight prognostic genes (ALCAM, COL6A2, CRISP2, FOXF2, IGF1, PTN, SCN7A, and UAP1) were identified to be BPH-specific biomarkers with high accuracy and specificity. Moreover, we constructed a seven-gene diagnostic classifier to distinguish BPH from PC. The infiltrations of plasmacytoid dendritic cells and neutrophil cells showed distinct differences between BPH and non-BPH groups. Additionally, ursolic acid can reverse transcriptional features associated with the occurrence and progression of BPH. Both in vivo and in vitro experiments have confirmed that it induces apoptosis of BPH cells and inhibits cell proliferation by promoting cell cycle S-phase arrest. The diagnostic biomarkers, microenvironment characteristics, and therapeutic effect of ursolic acid explored in this study offer new diagnostic and therapeutic strategies for BPH.

Establishment of cancer-associated fibroblasts-related subtypes and prognostic index for prostate cancer through single-cell and bulk RNA transcriptome

Current evidence indicate that cancer-associated fibroblasts (CAFs) play an important role in prostate cancer (PCa) development and progression. In this study, we identified CAF-related molecular subtypes and prognostic index for PCa patients undergoing radical prostatectomy through integrating single-cell and bulk RNA sequencing data. We completed analyses using software R 3.6.3 and its suitable packages. Through single-cell and bulk RNA sequencing analysis, NDRG2, TSPAN1, PTN, APOE, OR51E2, P4HB, STEAP1 and ABCC4 were used to construct molecular subtypes and CAF-related gene prognostic index (CRGPI). These genes could clearly divide the PCa patients into two subtypes in TCGA database and the BCR risk of subtype 1 was 13.27 times higher than that of subtype 2 with statistical significance. Similar results were observed in MSKCC2010 and GSE46602 cohorts. In addtion, the molucular subtypes were the independent risk factor of PCa patients. We orchestrated CRGPI based on the above genes and divided 430 PCa patients in TCGA database into high- and low- risk groups according to the median value of this score. We found that high-risk group had significant higher risk of BCR than low-risk group (HR: 5.45). For functional analysis, protein secretion was highly enriched in subtype 2 while snare interactions in vesicular transport was highly enriched in subtype 1. In terms of tumor heterogeneity and stemness, subtype 1 showd higher levels of TMB than subtype 2. In addition, subtype 1 had significant higher activated dendritic cell score than subtype 2. Based on eight CAF-related genes, we developed two prognostic subtypes and constructed a gene prognostic index, which could predict the prognosis of PCa patients very well.

Molecular predictors of metastasis in patients with prostate cancer

INTRODUCTION

Prostate cancer is a serious threat to the health of older adults worldwide. The quality of life and survival time of patients sharply decline once metastasis occurs. Thus, early screening for prostate cancer is very advanced in developed countries. The detection methods used include Prostate-specific antigen (PSA) detection and digital rectal examination. However, the lack of universal access to early screening in some developing countries has resulted in an increased number of patients presenting with metastatic prostate cancer. In addition, the treatment methods for metastatic and localized prostate cancer are considerably different. In many patients, early-stage prostate cancer cells often metastasize due to delayed observation, negative PSA results, and delay in treatment time. Therefore, the identification of patients who are prone to metastasis is important for future clinical studies.

AREAS COVERED

this review introduced a large number of predictive molecules related to prostate cancer metastasis. These molecules involve the mutation and regulation of tumor cell genes, changes in the tumor microenvironment, and the liquid biopsy.

EXPERT OPINION

In next decade, PSMA PET/CT and liquid biopsy will be the excellent predicting tools, while ¹⁷⁷ Lu- PSMA-RLT will be showed excellent anti-tumor efficacy in mPCa patients.

Loco-Regional Control and Sustained Difference in Serum Immune Protein Expression in Patients Treated for p16-Positive and p16-Negative Head and Neck Squamous Cell Carcinoma

The main prognostic factors for patients with head and neck cancer are the tumour site and stage, yet immunological and metabolic factors are certainly important, although knowledge is still limited. Expression of the biomarker p16INK4a (p16) in oropharyngeal cancer tumour tissue is one of the few biomarkers for the diagnosis and prognosis of head and neck cancer. The association between p16 expression in the tumour and the systemic immune response in the blood compartment has not been established. This study aimed to assess whether there is a difference in serum immune protein expression profiles between patients with p16+ and p16- head and squamous cell carcinoma (HNCC). The serum immune protein expression profiles, using the Olink^® immunoassay, of 132 patients with p16+ and p16- tumours were compared before treatment and one year after treatment. A significant difference in the serum immune protein expression profile was observed both before and one year after treatment. In the p16- group, a low expression of four proteins: IL12RB1, CD28, CCL3, and GZMA before treatment conferred a higher rate of failure. Based on the sustained difference between serum immune proteins, we hypothesise that the immunological system is still adapted to the tumour p16 status one year after tumour eradication or that a fundamental difference exists in the immunological system between patients with p16+ and p16- tumours.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Association of Neighborhood Deprivation With Prostate Cancer and Immune Markers in African American and European American Men

Importance

Neighborhood variables may be factors in the excessive burden of prostate cancer among African American men.

Objective

To examine associations between neighborhood deprivation, circulating immune-oncology markers, and prostate cancer among African American and European American men.

Design, Setting, and Participants

A case-control study was conducted between January 1, 2005, and January 1, 2016. Participants included men with prostate cancer and age- and race-frequency-matched population controls. Participants were recruited at the Baltimore Veterans Affairs Medical Center and University of Maryland Medical Center; controls were obtained through the Maryland Motor Vehicle Administration database. National Death Index follow-up was performed through December 31, 2020, and data analysis was conducted from February 1, 2022, through October 31, 2022.

Exposures

2000 Census-tract Neighborhood Deprivation Index as a standardized score.

Main Outcomes and Measures

Primary outcomes included prostate cancer, all-cause mortality, and disease-specific mortality. Secondary outcomes included the National Comprehensive Cancer Network risk score and serum proteomes for 82 immune-oncology markers with pathway annotation.

Results

Participants included men with prostate cancer (n = 769: 405 African American, 364 European American men) and age- and race-frequency-matched population controls (n = 1023: 479 African American, 544 European American men). The median survival follow-up was 9.70 years (IQR, 5.77 years), with 219 deaths. Among 884 African American men, mean (SD) age at recruitment was 63.8 (7.6) years; mean (SD) age at recruitment among 908 European American men was 66.4 (8.1) years. In the multivariable logistic regression analysis with individual socioeconomic status adjustment, neighborhood deprivation was associated with 55% increased odds of prostate cancer among African American men (odds ratio [OR], 1.55; 95% CI, 1.33-1.81), but was not associated with the disease among European American men. Residing in the most-deprived vs least-deprived neighborhoods corresponded to 88% higher disease odds (OR, 1.88; 95% CI, 1.30-2.75) among all men and an approximate 3-fold increase among African American men (OR, 3.58; 95% CI, 1.72-7.45), but no association was noted among European American men. In Cox proportional hazard regression analyses, socioeconomic status-adjusted neighborhood deprivation was associated with an increased all-cause mortality only among African American men (hazard ratio [HR], 1.28; 95% CI, 1.08-1.53), whereas it was associated with metastatic disease and a 50% increased hazard of a prostate cancer-specific death among all men (HR, 1.50; 95% CI, 1.07-2.09). In analyses restricted to controls, neighborhood deprivation was associated with increased activity scores of serum proteome-defined chemotaxis, inflammation, and tumor immunity suppression.

Conclusions and Relevance

The findings of this study suggest that deprived neighborhood residency may increase the risk of African American men for prostate cancer and a related mortality, potentially through its association with systemic immune function and inflammation.

SFRP2 Overexpression Induces an Osteoblast-like Phenotype in Prostate Cancer Cells

Prostate cancer bone metastasis is still one of the most fatal cancer diagnoses for men. Survival of the circulating prostate tumor cells and their adaptation strategy to survive in the bone niche is the key point to determining metastasis in early cancer stages. The promoter of SFRP2 gene, encoding a WNT signaling modulator, is hypermethylated in many cancer types including prostate cancer. Moreover, SFRP2 can positively regulate osteogenic differentiation in vitro and in vivo. Here, we showed SFRP2 overexpression in the prostate cancer cell line PC3 induces an epithelial mesenchymal transition (EMT), increases the attachment, and modifies the transcriptome towards an osteoblast-like phenotype (osteomimicry) in a collagen 1-dependent manner. Our data reflect a novel molecular mechanism concerning how metastasizing prostate cancer cells might increase their chance to survive within bone tissue.

Determination of temporal reproducibility and variability of cancer biomarkers in serum and EDTA plasma samples using a proximity extension assay

Background

Proximity extension assay (PEA) is a novel antibody-based proteomic technology. Sparse data have been published concerning the matrix effect of serum vs. ethylenediamine tetraacetic acid (EDTA) plasma and the reproducibility of results obtained using PEA technology.

Methods

We analyzed samples with the PEA-based 92-plex Olink® immuno-oncology (I-O) assay. To estimate the matrix effect, we analyzed paired serum and EDTA plasma samples from 12 patients with biliary tract cancer. To evaluate the reproducibility, we used data from 7 studies, where 6–8 serum samples from patients with pancreatic cancer were used as bridging samples on 3 versions of the panel over a 2.5-years period.

Results

For the study of serum vs. plasma, 80 proteins were evaluable. The mean serum to EDTA plasma ratio ranged from 0.41–3.01. For 36 proteins, the serum and plasma values were not comparable due to high variability of the ratio, poor correlation, or possible concentration effect. For the bridging samples, the mean intra-study inter-assay coefficient of variation (CV) ranged from 11.3% to 26.1%. The mean inter-study CV was 42.0% before normalization and 26.2% after normalization. Inter-study results were well correlated (r ≥ 0.93), especially for studies using the same version of the panel (r ≥ 0.99).

Conclusion

For 44 of 92 proteins included in the Olink® I-O panel, the variation between results obtained using serum and EDTA plasma was constant and results were well correlated. Furthermore, samples could be stored for several years and used on different versions of the same PEA panel without it effecting results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09380-y.

Phosphorylated Proteins from Serum: A Promising Potential Diagnostic Biomarker of Cancer

Cancer is a fatal disease worldwide. Each year ten million people are diagnosed around the world, and more than half of patients eventually die from it in many countries. A majority of cancer remains asymptomatic in the earlier stages, with specific symptoms appearing in the advanced stages when the chances of adequate treatment are low. Cancer screening is generally executed by different imaging techniques like ultrasonography (USG), mammography, CT-scan, and magnetic resonance imaging (MRI). Imaging techniques, however, fail to distinguish between cancerous and non-cancerous cells for early diagnosis. To confirm the imaging result, solid and liquid biopsies are done which have certain limitations such as invasive (in case of solid biopsy) or missed early diagnosis due to extremely low concentrations of circulating tumor DNA (in case of liquid biopsy). Therefore, it is essential to detect certain biomarkers by a noninvasive approach. One approach is a proteomic or glycoproteomic study which mostly identifies proteins and glycoproteins present in tissues and serum. Some of these studies are approved by the Food and Drug Administration (FDA). Another non-expensive and comparatively easier method to detect glycoprotein biomarkers is by ELISA, which uses lectins of diverse specificities. Several of the FDA approved proteins used as cancer biomarkers do not show optimal sensitivities for precise diagnosis of the diseases. In this regard, expression of phosphoproteins is associated with a more specific stage of a particular disease with high sensitivity and specificity. In this review, we discuss the expression of different serum phosphoproteins in various cancers. These phosphoproteins are detected either by phosphoprotein enrichment by immunoprecipitation using phosphospecific antibody and metal oxide affinity chromatography followed by LC-MS/MS or by 2D gel electrophoresis followed by MALDI-ToF/MS analysis. The updated knowledge on phosphorylated proteins in clinical samples from various cancer patients would help to develop these serum phophoproteins as potential diagnostic/prognostic biomarkers of cancer.

Binding of pleiotrophin to cell surface nucleolin mediates prostate cancer cell adhesion to osteoblasts

Pleiotrophin (PTN) is a growth factor that appears to play an important role in prostate cancer growth and angiogenesis. We have previously shown that decreased PTN expression in human prostate cancer PC3 cells leads to decreased adhesion of prostate cancer cells to osteoblasts, suggesting that PTN mediates this interaction. In the current work, using peptides that correspond to different regions of the PTN protein, we identified that a domain responsible for the adhesion of prostate cancer cells to osteoblasts corresponds to amino acids 16-24 of the mature PTN protein. Given that a synthetic PTN16-24 peptide which disturbs the interaction of PTN with nucleolin (NCL) was found to inhibit prostate cancer cells' adhesion to osteoblasts, it seems that NCL mediates the cellular interactions involved in the adhesion process. Two pseudopeptides that bind to cell surface NCL and an anti-NCL antibody also decrease prostate cancer cell adhesion to osteoblasts to the same degree as PTN16-24, further supporting the involvement of cell surface NCL in this interaction. Collectively, our data suggest that NCL on the cell surface of osteoblasts may mediate adhesion of prostate cancer cells through PTN and identify peptides that could be exploited therapeutically to target this component of prostate cancer bone metastases.

Serum proteomics links suppression of tumor immunity to ancestry and lethal prostate cancer

There is evidence that tumor immunobiology and immunotherapy response may differ between African American and European American prostate cancer patients. Here, we determine if men of African descent harbor a unique systemic immune-oncological signature and measure 82 circulating proteins in almost 3000 Ghanaian, African American, and European American men. Protein signatures for suppression of tumor immunity and chemotaxis are elevated in men of West African ancestry. Importantly, the suppression of tumor immunity protein signature associates with metastatic and lethal prostate cancer, pointing to clinical importance. Moreover, two markers, pleiotrophin and TNFRSF9, predict poor disease survival specifically among African American men. These findings indicate that immune-oncology marker profiles differ between men of African and European descent. These differences may contribute to the disproportionate burden of lethal prostate cancer in men of African ancestry. The elevated peripheral suppression of tumor immunity may have important implication for guidance of cancer therapy which could particularly benefit African American patients.

Ancestry-related differences in immunobiology may explain the health disparities observed in prostate cancer patients, with men of African origin bearing the highest prostate cancer burden. By measuring immune-related proteins in serum samples, here the authors report that systemic cytokines linked to suppression of tumor immunity are upregulated in men of African ancestry and associated with reduced survival.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

Purpose

The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF).

Methods

Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map.

Results

Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF.

Conclusion

We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

Metastasis Model to Test the Role of Notch Signaling in Prostate Cancer

Distant metastasis is the main cause of death in prostate cancer patients. Notch signaling plays an important role in driving prostate cancer aggressiveness and metastasis. In this chapter, we describe a protocol to measure prostate cancer metastatic colonization, incidences of metastasis, accurately quantify the burden of metastasis, and test the role of NOTCH1 receptor on prostate cancer metastatic colonization and homing to distant sites. The metastasis model presented here is established by intracardiac injection of control human prostate cancer cells and NOTCH1 downregulated cells. The cells are engineered to express both red fluorescent protein (RFP) and luciferase. In this model, whole body bioluminescence imaging, high-resolution, and quantitative fluorescence imaging are utilized for quantitative assessment of metastatic colonization and metastasis burden. Further, histopathology analyses of diverse metastatic organs are performed. This model is a powerful and versatile tool to investigate the mechanisms underlying the function of NOTCH receptors in metastatic colonization in prostate cancer.

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.

A Review of Prostate Organogenesis and a Role for iPSC-Derived Prostate Organoids to Study Prostate Development and Disease

The prostate is vulnerable to two major age-associated diseases, cancer and benign enlargement, which account for significant morbidity and mortality for men across the globe. Prostate cancer is the most common cancer reported in men, with over 1.2 million new cases diagnosed and 350,000 deaths recorded annually worldwide. Benign prostatic hyperplasia (BPH), characterised by the continuous enlargement of the adult prostate, symptomatically afflicts around 50% of men worldwide. A better understanding of the biological processes underpinning these diseases is needed to generate new treatment approaches. Developmental studies of the prostate have shed some light on the processes essential for prostate organogenesis, with many of these up- or downregulated genes expressions also observed in prostate cancer and/or BPH progression. These insights into human disease have been inferred through comparative biological studies relying primarily on rodent models. However, directly observing mechanisms of human prostate development has been more challenging due to limitations in accessing human foetal material. Induced pluripotent stem cells (iPSCs) could provide a suitable alternative as they can mimic embryonic cells, and iPSC-derived prostate organoids present a significant opportunity to study early human prostate developmental processes. In this review, we discuss the current understanding of prostate development and its relevance to prostate-associated diseases. Additionally, we detail the potential of iPSC-derived prostate organoids for studying human prostate development and disease.

Discovery of CASP8 as a potential biomarker for high-risk prostate cancer through a high-multiplex immunoassay

Prostate cancer remains the most common non-cutaneous malignancy among men in the United States. To discover potential serum-based biomarkers for high-risk prostate cancer, we performed a high-multiplex immunoassay utilizing patient-matched pre-operative and post-operative serum samples from ten men with high-grade and high-volume prostate cancer. Our study identified six (CASP8, MSLN, FGFBP1, ICOSLG, TIE2 and S100A4) out of 174 proteins that were significantly decreased after radical prostatectomy. High levels of CASP8 were detected in pre-operative serum samples when compared to post-operative serum samples and serum samples from patients with benign prostate hyperplasia (BPH). By immunohistochemistry, CASP8 protein was expressed at higher levels in prostate cancer tissues compared to non-cancerous and BPH tissues. Likewise, CASP8 mRNA expression was significantly upregulated in prostate cancer when compared to benign prostate tissues in four independent clinical datasets. In addition, mRNA levels of CASP8 were higher in patients with recurrent prostate cancer when compared to patients with non-recurrent prostate cancer and high expression of CASP8 was associated with worse disease-free survival and overall survival in renal cancer. Together, our results suggest that CASP8 may potentially serve as a biomarker for high-risk prostate cancer and possibly renal cancer.