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

Proteomics Reveals Age as Major Modifier of Inflammatory CSF Signatures in Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Multiple sclerosis (MS) can start as relapsing or progressive. While their clinical features and treatment responses are distinct, it has remained uncertain whether their pathomechanisms differ. A notable age-related effect on MS phenotype and response to immunotherapies is well acknowledged, but the underlying pathophysiologic reasons are yet to be fully elucidated. We aimed to identify disease-specific and age-related proteomic signatures using a comprehensive targeted proteomic analysis.

METHODS

In our retrospective cohort study, we analyzed the CSF and serum proteome of age-matched individuals with treatment-naïve relapsing-remitting and primary progressive MS, neurologic controls (NC), and individuals with neuroborreliosis using targeted proteomics and validated findings in an independent cohort. Proteomic results were integrated with clinical and laboratory covariates.

RESULTS

Among 2,500 proteins, 47 CSF proteins were distinct between individuals with MS (n = 60) and NC (n = 20), with a subset also differing from those with neuroborreliosis (n = 8). We identified MS-associated proteins, including novel candidate biomarkers such as LY9 and JCHAIN, and putative treatment targets, such as SLAMF7, BCMA, and IL5RA, for which drugs are already licensed in other indications. The CSF proteome differences between relapsing and progressive MS were minimal, but major changes were noted in individuals older than 50 years, indicating a shift from MS-associated inflammatory to age-related protein signature. NEFL was the only serum protein that differed between individuals with MS and controls.

DISCUSSION

This study unveils a unique CSF proteomic signature in MS, providing new pathophysiologic insights and identifying novel biomarker candidates and potential therapeutic targets. Our findings highlight similar immunologic mechanisms in relapsing and progressive MS and underscore aging's profound effect on the intrathecal immune response. This aligns with the observed lower efficacy of immunotherapies in the elderly, thus emphasizing the necessity for alternative therapeutic approaches in treating individuals with MS beyond the age of 50.

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Proteomic Signatures of Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with COVID-19: A Narrative Review

BACKGROUND/OBJECTIVES

Multisystem Inflammatory Syndrome in Children (MIS-C) is a post-infectious complication of COVID-19. MIS-C has overlapping features with other pediatric inflammatory disorders including Kawasaki Disease (KD), Macrophage Activation Syndrome (MAS), Toxic Shock Syndrome and sepsis. The exact mechanisms responsible for the clinical overlap between MIS-C and these conditions remain unclear, and biomarkers that could distinguish MIS-C from its clinical mimics are lacking. This study aimed to provide an overview of how proteomic methods, like Mass Spectrometry (MS) and affinity-based proteomics, can offer a detailed understanding of pathophysiology and aid in the diagnosis and prognosis of MIS-C.

METHODS

A narrative review of relevant studies published up to July 2024 was conducted.

RESULTS

We identified 15 studies and summarized their key proteomic findings. These studies investigated the serum or plasma proteome of MIS-C patients using MS, Proximity Extension, or Aptamer-based assays. The studies associated the proteomic profile of MIS-C with laboratory and clinical parameters and/or compared it with that of other diseases including acute COVID-19, KD, MAS, pediatric rheumatic diseases, sepsis and myocarditis or pericarditis following COVID-19 mRNA immunization. Depending on the method and the control group, different proteins were increased or decreased in the MIS-C group. The limitations and challenges in MIS-C proteomic research are also discussed, and future research recommendations are provided.

CONCLUSIONS

Although proteomics appear to be a promising approach for understanding the pathogenesis and uncovering candidate biomarkers in MIS-C, proteomic studies are still needed to recognize and validate biomarkers that could accurately discriminate MIS-C from its clinical mimics.

Proteomic analysis of STEAP1 knockdown in human LNCaP prostate cancer cells

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. The six transmembrane epithelial antigen of the prostate 1 (STEAP1) protein is overexpressed in several types of human tumors, particularly in PCa. Our research group has demonstrated that STEAP1 overexpression is associated with PCa progression and aggressiveness. Therefore, understanding the cellular and molecular mechanisms triggered by STEAP1 overexpression will provide important insights to delineate new strategies for PCa treatment. In the present work, a proteomic strategy was used to characterize the intracellular signaling pathways and the molecular targets downstream of STEAP1 in PCa cells. A label-free approach was applied using an Orbitrap LC-MS/MS system to characterize the proteome of STEAP1-knockdown PCa cells. More than 6700 proteins were identified, of which a total of 526 proteins were found differentially expressed in scramble siRNA versus STEAP1 siRNA (234 proteins up-regulated and 292 proteins down-regulated). Bioinformatics analysis allowed us to explore the mechanism through which STEAP1 exerts influence on PCa, revealing that endocytosis, RNA transport, apoptosis, aminoacyl-tRNA biosynthesis, and metabolic pathways are the main biological processes where STEAP1 is involved. By immunoblotting, it was confirmed that STEAP1 silencing induced the up-regulation of cathepsin B, intersectin-1, and syntaxin 4, and the down-regulation of HRas, PIK3C2A, and DIS3. These findings suggested that blocking STEAP1 might be a suitable strategy to activate apoptosis and endocytosis, and diminish cellular metabolism and intercellular communication, leading to inhibition of PCa progression.

Biosensors for prostate cancer detection

Prostate cancer (PC) is one of the most common tumors and a leading cause of mortality among men, resulting in ~375 000 deaths annually worldwide. Various analytical methods have been designed for quantitative and rapid detection of PC biomarkers. Electrochemical (EC), optical, and magnetic biosensors have been developed to detect tumor biomarkers in clinical and point-of-care (POC) settings. Although POC biosensors have shown potential for detection of PC biomarkers, some limitations, such as the sample preparation, should be considered. To tackle such shortcomings, new technologies have been utilized for development of more practical biosensors. Here, biosensing platforms for the detection of PC biomarkers such as immunosensors, aptasensors, genosensors, paper-based devices, microfluidic systems, and multiplex high-throughput platforms, are discussed.

Prognostic value of cell division cycle-associated protein-3 in prostate cancer

BACKGROUND

The study was aimed to explore cell division cycle-associated protein-3 (CDCA3) expression and its correlation with clinicopathological characteristics, and identification of co-expressed genes of CDCA3 in prostate cancer (PCa).

METHODS

Data for CDCA3 mRNA expression in PCa were obtained from The Cancer Genome Atlas database. Furtherly, CDCA3 protein expression was examined by immunohistochemistry in 80 cases, including 20 normal prostate samples and 60 PCa samples. Then we used "survival" package to obtain the differentially expressed CDCA3 mRNA associated with prognosis of PCa patients. "pROC" package was used to analyze receiver operating characteristic of CDCA3. We used chi-square test, Kruskal-Wallis test and Wilcoxon rank sum test to identify clinicopathological parameters that correlated with CDCA3 expression. In order to determine the effects of CDCA3 expression and clinicopathological parameters on survival, univariate cox regression analysis was performed. Finally, the co-expressed genes of CDCA3 in PCa were explored by search tool for the retrieval of interacting genes, Kyoto encyclopedia of genes and genomes enrichment analysis and Spearman correlation analysis.

RESULTS

In this study, we found that CDCA3 expression was increased in PCa. PCa patients with higher CDCA3 expression had poor outcomes. In terms of receiver operating characteristic, CDCA3 had an area under the curve of 0.857. High CDCA3 expression was positively correlated with advanced T stage, N stage, Gleason score, and served as an independent predictor of progress free interval in PCa patients. Then 20 proteins closely related to CDCA3 were screened through STRING website. Functional enrichment analysis revealed that, Kyoto encyclopedia of genes and genomes pathway was mainly enriched in cell cycle, including 6 genes, BUB1, CCNA2, CDK1, CDC20, TTK, and CCNB2.

CONCLUSION

CDCA3 is significantly associated with the prognosis of PCa, which may be an indicator of the diagnosis and prognosis of PCa and a new therapeutic target.

Immune response and barrier dysfunction-related proteomic signatures in preclinical phase of Crohn's disease highlight earliest events of pathogenesis

OBJECTIVE

The measure of serum proteome in the preclinical state of Crohn's disease (CD) may provide insight into biological pathways involved in CD pathogenesis. We aimed to assess associations of serum proteins with future CD onset and with other biomarkers predicting CD risk in a healthy at-risk cohort.

DESIGN

In a nested case-control study within the Crohn's and Colitis Canada Genetics Environment Microbial Project (CCC-GEM) cohort, which prospectively follows healthy first-degree relatives (FDRs), subjects who developed CD (n=71) were matched with four FDRs remaining healthy (n=284). Using samples at recruitment, serum protein profiles using the Olink Proximity Extension Assay platform was assessed for association with future development of CD and with other baseline biomarkers as follows: serum antimicrobial antibodies (AS: positive antibody sum) (Prometheus); faecal calprotectin (FCP); gut barrier function using the fractional excretion of lactulose-to-mannitol ratio (LMR) assay.

RESULTS

We identified 25 of 446 serum proteins significantly associated with future development of CD. C-X-C motif chemokine 9 (CXCL9) had the highest OR with future risk of CD (OR=2.07 per SD, 95% CI 1.58 to 2.73, q=7.9e-5), whereas matrix extracellular phosphoglycoprotein had the lowest OR (OR 0.44, 95% CI 0.29 to 0.66, q=0.02). Notably, CXCL9 was the only analyte significantly associated with all other CD-risk biomarkers with consistent direction of effect (FCP: OR=2.21; LMR: OR=1.67; AS: OR=1.59) (q<0.05 for all).

CONCLUSION

We identified serum proteomic signatures associated with future CD development, reflecting potential early biological processes of immune and barrier dysfunction.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

Transcriptome Data Reveal Geographic Heterogeneity in Gene Expression in Patients with Prostate Cancer

Background

The incidence of prostate cancer (PC) exhibits geographical heterogeneity. However, the metabolic mechanisms underlying this geographic heterogeneity remain unclear. This study aimed to reveal the metabolic mechanism of the geographic heterogeneity in the incidence of PC.This study aimed to investigate the anti-cancer effects of different gum extracts on metabolic changes and their impact on gene expression in HT-29 cell.

Methods

Transcriptomic data from public databases were obtained and analyzed to screen geographic-differentially expressed genes and metabolic pathways. Associations between these differentially expressed genes and the incidence of PC were determined to identify genes that were highly associated with PC incidence. A co-expression network analysis was performed to identify geographic-specific regulatory pathways.

Results

A total of 175 differentially expressed genes were identified in four countries and were associated with the regulation of DNA replication and the metabolism of pyrimidine, nucleotides, purines, and galactose.Additionally, the expression of the genes CLVS2, SCGB1A1, KCNK3, HHIPL2, MMP26, KCNJ15, and PNMT was highly correlated with the incidence of PC. Geographic-specific differentially expressed genes in low-incidence areas were highly correlated with KCNJ15, MMP26, KCNK3, and SCCB1A1, which play a major role in ion channel-related functions.

Conclusions

This study suggests that geographic heterogeneity in PC incidence is associated with the expression levels of genes associated with amino acid metabolism, lipid metabolism, and ion channels.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.