Quantitative SWATH-Based Proteomic Profiling for Identification of Mechanism-Driven Diagnostic Biomarkers Conferring in the Progression of Metastatic Prostate Cancer

Unravelling the key steps impairing the metabolic state of Xanthomonas cells undergoing programmed cell death

Programmed cell death (PCD) has been reported in Xanthomonas axonopodis pv. glycines (Xag) wild type earlier and was indirectly shown to be induced by metabolic stress; however, deciphering the key proteins regulating the metabolic stress remained unrevealed. In this study, transcriptomic and proteomic analyses were performed to investigate the prominent pathways, having a role in the induction of metabolic stress in Xag cells undergoing PCD. A comprehensive analysis of transcriptome and proteome data revealed the major involvement of metabolic pathways related to branched chain amino acid degradation, such as acyl-CoA dehydrogenase and energy-yielding, ubiquinol:cytochrome c oxidoreductase complex, in Xag cells undergoing PCD. Consequently, oxidative stress response genes showed major upregulation in Xag cells in PCD-inducing medium; however, no such upregulation was observed at the protein level, indicative of depleted protein levels under excessive stress conditions. Activation of stress response and DNA repair proteins was also observed in Xag cells grown in PCD-inducing medium, which is indicative of excessive cellular damage. Thus, the findings indicate that programmed cell death in Xag is an outcome of metabolic stress in nutrient condition not suitable for a plant pathogen like Xanthomonas, which is more acclimatised with altogether a different nutritional requirement predominantly having an enriched carbohydrate source.

UBE2N promotes cell viability and glycolysis by promoting Axin1 ubiquitination in prostate cancer cells

BACKGROUND

Ubiquitin-conjugating enzyme E2 N (UBE2N) is recognized in the progression of some cancers; however, little research has been conducted to describe its role in prostate cancer. The purpose of this paper is to explore the function and mechanism of UBE2N in prostate cancer cells.

METHODS

UBE2N expression was detected in Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) data, prostate cancer tissue microarrays, and prostate cancer cell lines, respectively. UBE2N knockdown or overexpression was used to analyze its role in cell viability and glycolysis of prostate cancer cells and tumor growth. XAV939 or Axin1 overexpression was co-treated with UBE2N overexpression to detect the involvement of the Wnt/β-catenin signaling and Axin1 in the UBE2N function. UBE2N interacting with Axin1 was analyzed by co-immunoprecipitation assay.

RESULTS

UBE2N was upregulated in prostate cancer and the UBE2N-high expression correlated with the poor prognosis of prostate cancer. UBE2N knockdown inhibited cell viability and glycolysis in prostate cancer cells and restricted tumor formation in tumor-bearing mice. Wnt/β-catenin inhibition and Axin1 overexpression reversed the promoting viability and glycolysis function of UBE2N. UBE2N promoted Axin1 ubiquitination and decreased Axin1 protein level.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

Longitudinal change in castration-resistant prostate cancer biomarker AST/ALT ratio reflects tumor progression

We investigated whether aspartate transaminase (AST)-to-alanine aminotransferase (ALT) ratio and its change during the course of treatment in castration-resistant prostate cancer (CRPC) patients is associated with tumor condition and lethality. Clinical data from 130 CRPC patients were retrospectively evaluated. AST/ALT ratios at the time of prostate cancer (PC) diagnosis, androgen deprivation therapy (ADT), CRPC diagnosis, and the final follow-up examination after CRPC treatment were calculated for each. The prognostic capabilities of the AST/ALT ratio for overall survival (OS) were analyzed by use of the Kaplan-Meier method and Cox hazard models. The median AST/ALT ratio at PC diagnosis was 1.517 and the optimal value predicting lethality defined by the receiver operating curve was 1.467. The AST/ALT ratio decreased once during ADT and then elevated in a stepwise manner with cancer progression. In surviving patients, the median AST/ALT ratio at the time of PC diagnosis was 1.423, which did not change longitudinally, whereas that in patients later deceased was significantly higher (1.620) and further elevated after CRPC diagnosis. Kaplan-Meier curves indicated significantly worse OS in patients with an AST/ALT ratio ≥ 1.467, which was confirmed by multivariate analysis. These findings indicate AST/ALT ratio as a prognostic biomarker for CRPC with longitudinal changes reflecting tumor progression.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues

BACKGROUND

P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data.

RESULTS

We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors).

CONCLUSIONS

Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

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.

Upregulation of leucine-rich alpha-2 glycoprotein: A key regulator of inflammation and joint fibrosis in patients with severe knee osteoarthritis

Introduction

Osteoarthritis (OA) is a degenerative disease of the joints mainly affecting older individuals. Since the etiology behind the progression of OA is not well understood, several associated consequences, such as synovial joint stiffness and its progression due to joint fibrosis, are still poorly understood. Although a lot of developments have been achieved in the diagnosis and management of OA, synovial fibrosis remains one of the major challenging consequences. The present study was therefore focused on understanding the mechanism of synovial fibrosis, which may further contribute to improving symptomatic treatments, leading to overall improvements in the treatment outcomes of patients with OA.

Methods

We used advanced proteomic techniques including isobaric tag for relative and absolute quantitation and sequential window acquisition of all theoretical mass spectra for the identification of differentially expressed proteins in the plasma samples of patients with OA. An in silico study was carried out to evaluate the association of the identified proteins with their biological processes related to fibrosis and remodeling of the extracellular matrix (ECM). The most significantly upregulated protein was then validated by Western blot and enzyme-linked immunosorbent assay. The target protein was then further investigated for its role in inflammation and joint fibrosis using an in vitro study model.

Results

Leucine-rich alpha-2 glycoprotein (LRG1) was found to be the most highly differentially expressed upregulated (9.4-fold) protein in the plasma samples of patients with OA compared to healthy controls. The knockdown of LRG1 followed by in vitro studies revealed that this protein promotes the secretion of the ECM in synovial cells and actively plays a role in wound healing and cell migration. The knockdown of LRG1 further confirmed the reduction of the inflammatory- and fibrosis-related markers in primary cells.

Conclusion

LRG1 was identified as a highly significant upregulated protein in the plasma samples of patients with OA. It was found to be associated with increased fibrosis and cell migration, leading to enhanced inflammation and joint stiffness in OA pathogenesis.

Interactions between 14-3-3 Proteins and Actin Cytoskeleton and Its Regulation by microRNAs and Long Non-Coding RNAs in Cancer

14-3-3s are a family of structurally similar proteins that bind to phosphoserine or phosphothreonine residues, forming the central signaling hub that coordinates or integrates various cellular functions, thereby controlling many pathways important in cancer, cell motility, cell death, cytoskeletal remodeling, neuro-degenerative disorders and many more. Their targets are present in all cellular compartments, and when they bind to proteins they alter their subcellular localization, stability, and molecular interactions with other proteins. Changes in environmental conditions that result in altered homeostasis trigger the interaction between 14-3-3 and other proteins to retrieve or rescue homeostasis. In circumstances where these regulatory proteins are dysregulated, it leads to pathological conditions. Therefore, deeper understanding is needed on how 14-3-3 proteins bind, and how these proteins are regulated or modified. This will help to detect disease in early stages or design inhibitors to block certain pathways. Recently, more research has been devoted to identifying the role of MicroRNAs, and long non-coding RNAs, which play an important role in regulating gene expression. Although there are many reviews on the role of 14-3-3 proteins in cancer, they do not provide a holistic view of the changes in the cell, which is the focus of this review. The unique feature of the review is that it not only focuses on how the 14-3-3 subunits associate and dissociate with their binding and regulatory proteins, but also includes the role of micro-RNAs and long non-coding RNAs and how they regulate 14-3-3 isoforms. The highlight of the review is that it focuses on the role of 14-3-3, actin, actin binding proteins and Rho GTPases in cancer, and how this complex is important for cell migration and invasion. Finally, the reader is provided with super-resolution high-clarity images of each subunit of the 14-3-3 protein family, further depicting their distribution in HeLa cells to illustrate their interactions in a cancer cell.

Neddylation-mediated degradation of hnRNPA2B1 contributes to hypertriglyceridemia pancreatitis

Hypertriglyceridemia-induced acute pancreatitis (HTGP) is characterized by the acute and excessive release of FFA produced by pancreatic lipases. However, the underlying mechanisms of this disease remain poorly understood. In this study, we describe the involvement of the RNA binding protein hnRNPA2B1 in the development of HTGP. We used palmitic acid (PA) and AR42J cells to create a model of HTGP in vitro. RT-PCR and western blot analyses revealed a decrease in the level of hnRNPA2B1 protein but not mRNA expression in PA-treated cells. Further analyses revealed that hnRNPA2B1 expression was regulated at the post-translational level by neddylation. Restoration of hnRNPA2B1 expression using the neddylation inhibitor MLN4924 protected AR42J cells from PA-induced inflammatory injury by preventing NF-κB activation and restoring fatty acid oxidation and cell proliferation. Furthermore, RNA immunoprecipitation studies demonstrated that hnRNPA2B1 orchestrates fatty acid oxidation by regulating the expression of the mitochondrial trifunctional protein-α (MTPα). Administration of MLN4924 in vivo restored hnRNPA2B1 protein expression in the pancreas of hyperlipidemic mice and ameliorated HTGP-associated inflammation and pancreatic tissue injury. In conclusion, we show that hnRNPA2B1 has a central regulatory role in preventing HTGP-induced effects on cell metabolism and viability. Furthermore, our findings indicate that pharmacological inhibitors that target neddylation may provide therapeutic benefits to HTGP patients.

Functions and mechanisms of N6‑methyladenosine in prostate cancer (Review)

Prostate cancer (PCa) has long been a major public health problem affecting men worldwide. Even with treatment, it can develop into castration-resistant PCa. With the continuous advancement in epigenetics, researchers have explored N6-methyladenosine (m⁶A) in search of a more effective and lasting treatment for PCa. m⁶A is widely distributed in mammalian cells and influences various aspects of mRNA metabolism. Recently, it has been associated with the development or suppression of various types of cancer, including PCa. This review summarizes the recent findings on m⁶A regulation and its functions and mechanisms in cells, focusing on the various functional proteins operating within m⁶A in PCa cells. Moreover, the potential clinical value of exploiting m⁶A modification as an early diagnostic marker in PCa diagnosis and therapeutics was discussed. m⁶A may also be used as an indicator to evaluate treatment outcome and prognosis.

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.

Glutamic oxaloacetic transaminase 1 as a potential target in human cancer

Glutamic Oxaloacetic Transaminase 1 (GOT1) is one distinct isoenzyme of glutamic oxaloacetic transaminase in eukaryotic cells, which is located in the cytoplasm. To date, several studies have shown that GOT1 plays a critical role in regulating cell proliferation by participating in amino acid metabolism, especially in glutamine metabolism. In addition, GOT1 is overexpressed in many cancer, so GOT1 has been identified as a potentially therapeutic target. Herein, this review summarizes the structure and function of GOT1 and the important roles of GOT1 in some tumor progress, as well as the characterization of GOT1 inhibitors. It may provide new insight into the discovery of small compounds as potential anti-GOT1 drugs for treatment of cancer.

Construction of a risk prediction model using m6A RNA methylation regulators in prostate cancer: comprehensive bioinformatic analysis and histological validation

Background

Epigenetic reprogramming reportedly has a crucial role in prostate cancer (PCa) progression. RNA modification is a hot topic in epigenetics, and N6-methyladenosine (m6A) accounts for approximately 60% of RNA chemical modifications. The aim of this study was to evaluate the m6A modification patterns in PCa patients and construct a risk prediction model using m6A RNA regulators.

Materials and methods

Analyses were based on the levels of 25 m6A regulators in The Cancer Genome Atlas (TCGA). Differentially expressed gene (DEG) and survival analyses were performed according to TCGA-PRAD clinicopathologic and follow-up information. To detect the influences of m6A regulators and their DEGs, consensus clustering analysis was performed, and tumor mutational burden (TMB) estimation and tumor microenvironment (TME) cell infiltration were assessed. mRNA levels of representative genes were verified using clinical PCa data.

Results

Diverse expression patterns of m6A regulators between tumor and normal (TN) tissues were detected regarding Gleason score (GS), pathological T stage (pT), TP53 mutation, and survival comparisons, with HNRNPA2B1 and IGFBP3 being intersecting genes. HNRNPA2B1 was upregulated in advanced stages (GS > 7, pT3, HR > 1, and TP53 mutation), as verified using clinical PCa tissue. Three distinct m6A modification patterns were identified through consensus clustering analysis, but no significant difference was found among these groups in recurrence-free survival (RFS) analysis. Six DEGs of m6A clusters (m6Aclusters) were screened through univariate Cox regression analysis. MMAB and PAIAP2 were intersecting genes for the five clinical factors. MMAB, which was upregulated in PCa compared with TN, was verified using clinical PCa samples. Three distinct subgroups were established according to the 6 DEGs. Cluster A involved the most advanced stages and had the poorest RFS. The m6A score (m6Ascore) was calculated based on the 6 genes, and the low m6Ascore group showed poor RFS with a negative association with infiltration for 16 of 23 immune-related cells.

Conclusion

We screened DEGs of m6Aclusters and identified 6 genes (BAIAP2, TEX264, MMAB, JAGN1, TIMM8AP1, and IMP3), with which we constructed a highly predictive model with prognostic value by dividing TCGA-PRAD into three distinct subgroups and performing m6Ascore analysis. This study helps to elucidate the integral effects of m6A modification patterns on PCa progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02438-1.

Detection of melanogenesis- and anti-apoptosis-associated melanoma factors: Array CGH and PPI mapping integrating study

BACKGROUND

We investigated melanogenesis- and anti-apoptosis-related melanoma factors in melanoma cells (TXM1, TXM18, A375P, and A375SM).

OBJECTIVE

To find melanoma associated hub factor, high-throughput screening-based techniques integrating with bioinformatics were investigated.

METHODS

Array CGH analysis was conducted with a commercial system. Total genomic DNAs prepared individually from each cell line with control DNA were properly labeled with Cy3-dCTP and Cy5-dCTP and hybridizations and subsequently performed data treatment by the log2 green (G; test) to red (R; reference) fluorescence ratios (G/R). Gain or loss of copy number was judged by spots with log2-transformed ratios. PPI mapping analysis of detected candidate genes based on the array CGH results was conducted using the human interactome in the STRING database. Energy minimization and a short molecular dynamics (MD) simulation using the implicit solvation model in CHARMM were performed to analyze the interacting residues between YWHAZ and YWHAB.

RESULTS

Three genes (BMP-4, BFGF, LEF-1) known to be involved in melanogenesis were found to lose chromosomal copy numbers, and Chr. 6q23.3 was lost in all tested cell lines. Ten hub genes (CTNNB1, PEX13, PEX14, PEX5, IFNG, EXOSC3, EXOSC1, EXOSC8, UBC, and PEX10) were predicted to be functional interaction factors in the network of the 6q23.3 locus. The apoptosis-associated genes E2F1, p50, BCL2L1, and BIRC7 gained, and FGF2 lost chromosomal copy numbers in the tested melanoma cell lines. YWHAB, which gained chromosomal copy numbers, was predicted to be the most important hub protein in melanoma cells. Molecular dynamics simulations for binding YWHAB and YWHAZ were conducted, and the complex was predicted to be energetically and structurally stable through its 3 hydrogen-bond patterns. The number of interacting residues is 27.

CONCLUSION

Our study compares genome-wide screening interactomics predictions for melanoma factors and offers new information for understanding melanogenesis- and anti-apoptosis-associated mechanisms in melanoma. Especially, YWHAB was newly detected as a core factor in melanoma cells.

HNRNPA2B1 regulates tamoxifen- and fulvestrant-sensitivity and hallmarks of endocrine resistance in breast cancer cells

Despite new combination therapies improving survival of breast cancer patients with estrogen receptor α (ER+) tumors, the molecular mechanisms for endocrine-resistant disease remain unresolved. Previously we demonstrated that expression of the RNA binding protein and N6-methyladenosine (m6A) reader HNRNPA2B1 (A2B1) is higher in LCC9 and LY2 tamoxifen (TAM)-resistant ERα breast cancer cells relative to parental TAM-sensitive MCF-7 cells. Here we report that A2B1 protein expression is higher in breast tumors than paired normal breast tissue. Modest stable overexpression of A2B1 in MCF-7 cells (MCF-7-A2B1 cells) resulted in TAM- and fulvestrant- resistance whereas knockdown of A2B1 in LCC9 and LY2 cells restored TAM and fulvestrant, endocrine-sensitivity. MCF-7-A2B1 cells gained hallmarks of TAM-resistant metastatic behavior: increased migration and invasion, clonogenicity, and soft agar colony size, which were attenuated by A2B1 knockdown in MCF-7-A2B1 and the TAM-resistant LCC9 and LY2 cells. MCF-7-A2B1, LCC9, and LY2 cells have a higher proportion of CD44+/CD24-/low cancer stem cells (CSC) compared to MCF-7 cells. MCF-7-A2B1 cells have increased ERα and reduced miR-222-3p that targets ERα. Like LCC9 cells, MCF-7-A2B1 have activated AKT and MAPK that depend on A2B1 expression and are growth inhibited by inhibitors of these pathways. These data support that targeting A2B1 could provide a complimentary therapeutic approach to reduce acquired endocrine resistance.

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.

Proteomics Analysis Identified ASNS as a Novel Biomarker for Predicting Recurrence of Skull Base Chordoma

Background

The prognostic factors of skull base chordoma associated with outcomes of patients after surgery remain inadequately identified. This study was designed to identify a novel prognostic factor for patients with skull base chordoma.

Method

Using a proteomic technique, the tumor biomarkers that were upregulated in the rapid-recurrence group of chordoma were screened and then narrowed down by bioinformatic analysis. Finally one potential biomarker was chosen for validation by immunohistochemistry using tissue microarray (TMA). A total of 187 patients included in TMA were randomly divided into two cohorts, the training cohort included 93 patients and the validation cohort included 94 patients. Kaplan-Meier survival analysis was used to assess the patients’ survival. Univariable and multivariable Cox regression analysis were used to identify prognostic factors predicting recurrence-free survival (RFS). CCK-8 assay, clonal formation assay and transwell assay were used to test the effect of asparagine synthetase (ASNS) on the proliferation, migration and invasion in chordoma cell lines.

Results

Among 146 upregulated proteins, ASNS was chosen as a potential prognostic biomarker after bioinformatics analysis. The H-scores of ASNS ranged from 106.27 to 239.58 in TMA. High expression of ASNS was correlated with shorter RFS in both the training cohort (p = 0.0093) and validation cohort (p < 0.001). Knockdown of ASNS by small interfering RNA (siRNA) inhibited the growth, colony formation, migration and invasion of chordoma cells in vitro.

Conclusion

This study indicates that high expression of ASNS is correlated with poor prognosis of patients with skull base chordoma. ASNS may be a useful prognostic factor for patients with skull base chordoma.

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.

The Identification of Critical m6A RNA Methylation Regulators as Malignant Prognosis Factors in Prostate Adenocarcinoma

RNA methylation accounts for over 60% of all RNA modifications, and N⁶-methyladenosine (m⁶A) is the most common modification on mRNA and lncRNA of human beings. It has been found that m⁶A modification occurs in microRNA, circRNA, rRNA, and tRNA, etc. The m⁶A modification plays an important role in regulating gene expression, and the abnormality of its regulatory mechanism refers to many human diseases, including cancers. Pitifully, as it stands there is a serious lack of knowledge of the extent to which the expression and function of m⁶A RNA methylation can influence prostate cancer (PC). Herein, we systematically analyzed the expression levels of 35 m⁶A RNA methylation regulators mentioned in literatures among prostate adenocarcinoma patients in the Cancer Genome Atlas (TCGA), finding that most of them expressed differently between cancer tissues and normal tissues with the significance of p < 0.05. Utilizing consensus clustering, we divided PC patients into two subgroups based on the differentially expressed m⁶A RNA methylation regulators with significantly different clinical outcomes. To appraise the discrepancy in total transcriptome between subgroups, the functional enrichment analysis was conducted for differential signaling pathways and cellular processes. Next, we selected five critical genes by the criteria that the regulators had a significant impact on prognosis of PC patients from TCGA through the last absolute shrinkage and selection operator (LASSO) Cox regression and obtained a risk score by weighted summation for prognosis prediction. The survival analysis curve and receiver operating characteristic (ROC) curve showed that this signature could excellently predict the prognosis of PC patients. The univariate and multivariate Cox regression analyses proved the independent prognostic value of the signature. In summary, our effort revealed the significance of m⁶A RNA methylation regulators in prostate cancer and determined a m⁶A gene expression classifier that well predicted the prognosis of prostate cancer.

Proteomics analysis identified TPI1 as a novel biomarker for predicting recurrence of intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is the second most common tumor in primary liver cancer, but the prognostic factors associated with long-term outcomes after surgical resection remain poorly defined. This study aimed to develop a novel prognostic classifier for patients with ICC after surgery.

METHODS

Using a proteomics approach, we screened tumor markers that up-regulated in ICC tissues, and narrowed down by bioinformatics analysis, western blot and immunohistochemistry. Prognostic markers were identified using Cox regression analyses in primary training cohort and the predictive models for time to recurrence (TTR) were established. The predictive accuracy of predictive model was validated in external validation cohort and prospective validation cohort. MTT assay, clonal formation assay and trans-well assays were used to verify the effect on the proliferation and migration in ICC cell line.

RESULTS

Triosephosphate isomerise (TPI1) was significantly up-regulated in ICC tissues and Kaplan-Meier analysis reveals that higher TPI1 expression was strongly correlated with higher recurrence rate of ICC patients. In the primary training cohort, mean TTR was significantly longer (p < 0.0001) than in the low-risk group (26.9 months for TTR, 95% CI 22.4-31.5) than in the high-risk group (14.5 months for TTR, 95% CI 10.6-18.4). Similar results were observed in two validation cohorts. In addition, a nomogram to predict recurrence was developed. Moreover, Knockdown of TPI1 by shRNA inhibited ICC cell growth, colony information, migration, invasion in vitro.

CONCLUSIONS

Current prognostic models were accurate in predicting recurrence for ICC patients after surgical resection.

The mechanobiome: a goldmine for cancer therapeutics

Cancer progression is dependent on heightened mechanical adaptation, both for the cells' ability to change shape and to interact with varying mechanical environments. This type of adaptation is dependent on mechanoresponsive proteins that sense and respond to mechanical stress, as well as their regulators. Mechanoresponsive proteins are part of the mechanobiome, which is the larger network that constitutes the cell's mechanical systems that are also highly integrated with many other cellular systems, such as gene expression, metabolism, and signaling. Despite the altered expression patterns of key mechanobiome proteins across many different cancer types, pharmaceutical targeting of these proteins has been overlooked. Here, we review the biochemistry of key mechanoresponsive proteins, specifically nonmuscle myosin II, α-actinins, and filamins, as well as the partnering proteins 14-3-3 and CLP36. We also examined a wide range of data sets to assess how gene and protein expression levels of these proteins are altered across many different cancer types. Finally, we determined the potential of targeting these proteins to mitigate invasion or metastasis and suggest that the mechanobiome is a goldmine of opportunity for anticancer drug discovery and development.