Discovery of gastric cancer specific biomarkers by the application of serum proteomics

Progress in research of proteomics related to digestive system tumor markers

The incidence and mortality of digestive system tumors are high. Even though the number of methods for tumor diagnosis and treatment is increasing, most of these tumors still cannot be diagnosed early, and their prognosis is poor. The lack of effective biomarkers and therapeutic targets is the reason why they cannot be diagnosed early and treated effectively. With the continuous development of proteomics technology, proteomics has become increasingly valuable in exploring the mechanisms of tumorigenesis and searching for biomarkers and drug targets. This article reviews the application progress of proteomics technology in screening of biomarkers for digestive system tumors, with an aim to provide new ideas for early diagnosis, prognosis, and treatment of digestive system tumors.

Protein Biomarkers of Gastric Preneoplasia and Cancer Lesions in Blood: A Comprehensive Review

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide. It is often associated with a bad prognosis because of its asymptomatic phenotype until advanced stages, highlighting the need for its prevention and early detection. GC development is preceded by the emergence of gastric preneoplasia lesions (GPNLs), namely atrophic gastritis (AG), intestinal metaplasia (IM), and dysplasia (DYS). GC is currently diagnosed by endoscopy, which is invasive and costly and has limited effectiveness for the detection of GPNLs. Therefore, the discovery of non-invasive biomarkers in liquid biopsies, such as blood samples, in order to identify the presence of gastric preneoplasia and/or cancer lesions at asymptomatic stages is of paramount interest. This comprehensive review provides an overview of recently identified plasma/serum proteins and their diagnostic performance for the prediction of GPNLs and early cancer lesions. Autoantibodies appear to be promising biomarkers for AG, IM and early gastric cancer detection, along with inflammation and immunity-related proteins and antibodies against H. pylori virulence factors. There is a lack of specific protein biomarkers with which to detect DYS. Despite the need for further investigation and validation, some emerging candidates could pave the way for the development of reliable, non-invasive diagnostic tests for the detection and prevention of GC.

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multiomics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

Seeking a better understanding of the non-receptor tyrosine kinase, SRMS

SRMS (Src-Related kinase lacking C-terminal regulatory tyrosine and N-terminal Myristoylation Sites) is a non-receptor tyrosine kinase first reported in a 1994 screen for genes regulating murine neural precursor cells. SRMS, pronounced "Shrims", lacks the C-terminal regulatory tyrosine critical for the regulation of the enzymatic activity of Src-family kinases (SFKs). Another remarkable characteristic of SRMS is its localization into distinct SRMS cytoplasmic punctae (SCPs) or GREL (Goel Raghuveera-Erique Lukong) bodies, a pattern not observed in the SFKs. This unique subcellular localization of SRMS could dictate its cellular targets, proteome, and potentially, substrates. However, the function of SRMS is still relatively unknown. Further, how is its activity regulated and by what cellular targets? Studies have emerged highlighting the potential role of SRMS in autophagy and in regulating the activation of BRK/PTK6. Potential novel cellular substrates have also been identified, including DOK1, vimentin, Sam68, FBKP51, and OTUB1. Recent studies have also demonstrated the potential role of the kinase in various cancers, including gastric and colorectal cancers and platinum resistance in ovarian cancer. This review discusses the advancements made in SRMS-related biology to date and the path to understanding the cellular and physiological significance of the kinase.

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer

BACKGROUND

Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.

METHODS

A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.

RESULTS

Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.

CONCLUSION

The study identified some hub genes that could be useful in predicting prognosis and management in GC.

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R² > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

The possible role of SRMS in colorectal cancer by bioinformatics analysis

BACKGROUND

Src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites (SRMS) is a non-receptor tyrosine kinase that has been found to be overexpressed in various tumors. However, the role of SRMS in colorectal cancer (CRC) has not been well established.

METHODS

We evaluated the expression levels of SRMS in CRC using GEPIA, Oncomine, and HPA datasets. Survival information and gene expression data of CRC were obtained from The Cancer Genome Atlas (TCGA). Then, the association between SRMS and clinicopathological features was analyzed using UALCAN dataset. LinkedOmics was used to determine co-expression and functional networks associated with SRMS. Besides, we used TISIDB to assess the correlation between SRMS and immune signatures, including tumor-infiltrating immune cells and immunomodulators. Lastly, protein-protein interaction network (PPI) was established and the function enrichment analysis of the SRMS-associated immunomodulators and immune cell marker genes were performed using the STRING portal.

RESULTS

Compared to normal colorectal tissues, SRMS was found to be overexpressed in CRC tissues, which was correlated with a poor prognosis. In colon adenocarcinoma (COAD), the expression levels of SRMS are significantly correlated with pathological stages and nodal metastasis status. Functional network analysis suggested that SRMS regulates intermediate filament-based processes, protein autophosphorylation, translational initiation, and elongation signaling through pathways involving ribosomes, proteasomes, oxidative phosphorylation, and DNA replication. In addition, SRMS expression was correlated with infiltrating levels of CD4+ T cells, CD56dim, MEM B, Neutrophils, Th2, Th17, and Act DC. The gene ontology (GO) analysis of SRMS-associated immunomodulators and immune cell marker genes showed that they were mainly enriched in the immune microenvironment molecule-related signals. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these genes indicated that they are involved in multiple cancer-related pathways.

CONCLUSIONS

SRMS is a promising prognostic biomarker and potential therapeutic target for CRC patients. In particular, SRMS regulates CRC progression by modulating cytokine-cytokine receptor interaction, chemokines, IL-17, and intestinal immune networks for IgA production signaling pathways among others. However, more studies are needed to validate these findings.

Prediction of Blood miRNA-mRNA Regulatory Network in Gastric Cancer

Background

The aim of the study was to suggest a high specific and sensitive blood biomarker for early GC diagnosis.

Methods

the expression data of miRNAs and mRNAs were collected from the blood samples of the GC patients based on literature mining. Bioinformatics tools and databases (PANTHER, TargetScan, miRTarBase, miRDB, STRING, and Cytoscape) were used to predict the regulatory relationship. Subsequently, expression level of the selected miRNA was evaluated in the blood samples of gastritis patients to recognize the common miRNA between the GC and gastritis patients.

Results

Analysis of 40 target genes by MCODE (installed in Cytoscape software) indicated 4 hub genes (WWP1, SKP2, KLHL42, and FBXO11) as a significant cluster in the PPI network related to miR-21, with Node Score Cutoff: 0.2, Degree Cutoff: 2 and K-Core: 2. In addition, the miRNA RT-qPCR results showed that, the expression level of miR-21 was significantly higher in gastritis group compared to the healthy group (p< 0.05).

Conclusion

the present study clearly demonstrated the increasing level of blood miR-21 among the gastritis patients infected by H. pylori. Therefore, the altered miRNAs, especially overexpression of onco-miRs, may identify a potential link between miRNAs and pathogenesis of the H. pylori-related complications.

Over-expression of vitronectin correlates with impaired survival in gastric cancers

Over-expression of vitronectin (VN) is associated with tumorigenesis. The present study aimed to evaluate the prognostic value of VN expression in gastric cancer.The least absolute shrinkage and selection operator analysis was performed to screen the hub gene from The Cancer Genome Atlas gastric cancer patients with complete follow-up data, and 347 patients were finally included. Moreover, 102 patients were enrolled from the Affiliated Fuzhou First Hospital of Fujian Medical University. VN expression in paired gastric cancer and adjacent gastric normal tissues was detected using immunohistochemistry, and the clinicopathological significance of VN expression was evaluated. The prognostic significance of VN expression in gastric cancer patients was evaluated using by Kaplan-Meier method and Cox regression analysis and confirmed using Oncomine.VN was the prognosis relative gene which screened by The Cancer Genome Atlas dataset. Moreover, we identified the VN expression in an external dataset by immunohistochemistry. The result demonstrated that VN expression was remarkedly elevated in gastric cancer tissues (P < .001). High VN expression correlated with higher pathological Tumor-Node-Metastasis stage, and poorer survival outcomes. Cox regression analysis showed that VN expression was independently predictive of overall survival (OS) and disease-free survival (P = .004, P < .001, respectively). A prognostic risk score for OS was built based on VN expression. A meta-analysis from Oncomine datasets revealed that significantly lower VN mRNA levels in gastric cancer correlated with poorer OS.VN expression could be a prognostic marker of gastric cancer.

Identification of differentially expressed proteins and clinicopathological significance of HMGB2 in cervical cancer

To investigate the complexity of proteomics in cervical cancer tissues, we used isobaric tags for relative and absolute quantitation (iTRAQ)-based mass spectrometry analysis on a panel of normal cervical tissues (N), high-grade squamous intraepithelial lesion tissues (HSIL) and cervical cancer tissues (CC). Total 72 differentially expressed proteins were identified both in CC vs N and CC vs HSIL. The expression of HMGB2 was markedly higher in CC than that in HSIL and N. High HMGB2 expression was significantly correlated with primary tumor size, invasion and tumor stage. The up-regulated HMGB2 was discovered to be associated with human cervical cancer. These findings suggest that HMGB2 may be a potentially prognostic biomarker and a target for the therapy of cervical cancer.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics.

Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer

Early diagnosis of gastric cancer (GC) provides patients opportunities for minimally invasive endoscopic resection. Here, we developed a new strategy integrated the state-of-the-art sequential windowed acquisition of all theoretical fragment ion (SWATH) mass spectra (MS) with multi-dataset joint analysis to screen for the stage-I GC plasma biomarker. In SWATH-MS assays, we identified 37 upregulated and 21 downregulated proteins in GC plasma. In the mRNA database analysis, 633 genes were identified as differentially expressed genes in at least 4 out of 5 datasets, but there were only 94 genes identified as upregulated. Only 1 gene, CHI3L1, was characterized as upregulated in both the dataset consensus list and the SWATH-MS list. Then, we detected the CHI3L1 level in the plasma of a large cohort consisting of 200 participants. The area under the ROC curve (AUC) of CHI3L1 in distinguishing GC from others was 0.788. Integrating the plasma CHI3L1 level with clinical factors further boosted the AUC to 0.887. In conclusion, we provide a novel strategy for biomarker screening, combining recent MS techniques with public database analysis, and identified plasma CHI3L1 as a potential biomarker for patients with endoscopically resectable GC.

Structure, Function, and Regulation of the SRMS Tyrosine Kinase

Src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites (SRMS) is a tyrosine kinase that was discovered in 1994. It is a member of a family of nonreceptor tyrosine kinases that also includes Brk (PTK6) and Frk. Compared with other tyrosine kinases, there is relatively little information about the structure, function, and regulation of SRMS. In this review, we summarize the current state of knowledge regarding SRMS, including recent results aimed at identifying downstream signaling partners. We also present a structural model for the enzyme and discuss the potential involvement of SRMS in cancer cell signaling.

Serum proteome profiling reveals SOX3 as a candidate prognostic marker for gastric cancer

Searching for the novel tumour biomarkers is pressing for gastric cancer diagnostication and prognostication. The serum specimens from patients diagnosed with locally advanced gastric carcinoma before operation and 4 week after surgery were collected, respectively, and serum proteome profiling was conducted by liquid chromatography–mass spectrometry (MS)/MS. Fifty‐five proteins were identified to be up‐regulated and 16 proteins were down‐regulated, and these differentially expressed proteins participated in various biological processes. Serum levels of SOX3, one of down‐regulated proteins, in stomach cancer patients were higher than in healthy controls. SOX3 levels in cancer tissues were remarkably related to tumour differentiation, lymph node metastasis, primary tumour invasion and pTNM (pathological TNM) stage. Analysis with The Cancer Genome Atlas database indicated that SOX3 level and pTNM stage were the independent risk factors for the patient survival and that the overall survival was negatively associated with the SOX3 levels. Loss‐of‐function showed that SOX3 promoted gastric cancer cell invasion and migration in vitro and in vivo. SOX3 silence inhibits the expression of MMP9, and SOX3 is responsible for MMP9 expression transcriptionally. Our study highlights the potentiality of the paired pre‐ and post‐operation serum proteome signatures for the detection of biomarkers and reveals that SOX3 may serve as a candidate prognosis marker for gastric cancer.

Proteomic Profiles and Biological Processes of Relapsed vs. Non-Relapsed Pediatric Hodgkin Lymphoma

The identification of circulating proteins associated with relapse in pediatric Hodgkin lymphoma (HL) may help develop predictive biomarkers. We previously identified a set of predictive biomarkers by difference gel electrophoresis. Here we used label-free quantitative liquid chromatography-mass spectrometry (LC-MS/MS) on plasma collected at diagnosis from 12 children (age 12–16 years) with nodular sclerosis HL, including six in whom the disease relapsed within 5 years of treatment in the LH2004 trial. Plasma proteins were pooled in groups of three, separately for non-relapsing and relapsing HL, and differentially abundant proteins between the two disease states were identified by LC-MS/MS in an explorative and validation design. Proteins with a fold change in abundance >1.2 or ≤0.8 were considered “differentially abundant”. LC-MS/MS identified 60 and 32 proteins that were more abundant in non-relapsing and relapsing HL plasma, respectively, in the explorative phase; these numbers were 39 and 34 in the validation phase. In both analyses, 11 proteins were more abundant in non-relapsing HL (e.g., angiotensinogen, serum paraoxonase/arylesterase 1, transthyretin), including two previously identified by difference gel electrophoresis (antithrombin III and α-1-antitrypsin); seven proteins were more abundant in relapsing HL (e.g., fibronectin and thrombospondin-1), including two previously identified proteins (fibrinogen β and γ chains). The differentially abundant proteins participated in numerous biological processes, which were manually grouped into 10 biological classes and 11 biological regulatory subclasses. The biological class Lipid metabolism, and its regulatory subclass, included angiotensinogen and serum paraoxonase/arylesterase 1 (more abundant in non-relapsing HL). The biological classes Immune system and Cell and extracellular matrix architecture included fibronectin and thrombospondin-1 (more abundant in relapsing HL). These findings deepen our understanding of the molecular scenario underlying responses to therapy and provide new evidence about these proteins as possible biomarkers of relapse in pediatric HL.

Multiplex profiling of peritoneal metastases from gastric adenocarcinoma identified novel targets and molecular subtypes that predict treatment response

OBJECTIVE

Peritoneal carcinomatosis (PC) occurs frequently in patients with gastric adenocarcinoma (GAC) and confers a poor prognosis. Multiplex profiling of primary GACs has been insightful but the underpinnings of PC's development/progression remain largely unknown. We characterised exome/transcriptome/immune landscapes of PC cells from patients with GAC aiming to identify novel therapeutic targets.

DESIGN

We performed whole-exome sequencing (WES) and whole transcriptome sequencing (RNA-seq) on 44 PC specimens (43 patients with PC) including an integrative analysis of WES, RNA-seq, immune profile, clinical and pathological phenotypes to dissect the molecular pathogenesis, identifying actionable targets and/or biomarkers and comparison with TCGA primary GACs.

RESULTS

We identified distinct alterations in PC versus primary GACs, such as more frequent CDH1 and TAF1 mutations, 6q loss and chr19 gain. Alterations associated with aggressive PC phenotypes emerged with increased mutations in TP53, CDH1, TAF1 and KMT2C, higher level of 'clock-like' mutational signature, increase in whole-genome doublings, chromosomal instability (particularly, copy number losses), reprogrammed microenvironment, enriched cell cycle pathways, MYC activation and impaired immune response. Integrated analysis identified two main molecular subtypes: 'mesenchymal-like' and 'epithelial-like' with discriminating response to chemotherapy (31% vs 71%). Patients with the less responsive 'mesenchymal-like' subtype had high expression of immune checkpoint T-Cell Immunoglobulin And Mucin Domain-Containing Protein 3 (TIM-3), its ligand galectin-9, V-domain Ig suppressor of T cell activation (VISTA) and transforming growth factor-β as potential therapeutic immune targets.

CONCLUSIONS

We have uncovered the unique mutational landscape, copy number alteration and gene expression profile of PC cells and defined PC molecular subtypes, which correlated with PC therapy resistance/response. Novel targets and immune checkpoint proteins have been identified with a potential to be translated into clinics.

Investigation Of Small Molecular Substances As Potential Biomarkers For Discrimination Of Gastric Tumor

Background

Gastric tumor (GT) is associated with high morbidity and mortality, with surgery among the most effective treatment methods. Accurate interoperative determination of the tumor margin is crucial.

Methods

In this study, using internal extractive electrospray ionization-MS, mass spectral data of GT and gastric normal (GN) tissues from 36 patients were collected.

Results

In positive ion detection mode, the relative abundances of m/z 132, 147, 170, and 175 were increased, while the relative abundances of m/z 55, 83, 154, and 203 were decreased in GT tissue. Using partial least squares analysis, the mass spectral data of GT and GN tissues were discriminated, and differential ions (P≤0.01), including m/z 55, 83, 154, 170, and 203, were obtained from loading plots. After receiver operating characteristic curve analysis, peaks at m/z 83 and 203 showed high accuracy for distinguishing GT from GN tissue. These two peaks were then preliminarily attributed to 5-aminoimidazole and serylproline, respectively, which might be useful molecular biomarkers associated with GT development.

Conclusion

Further investigations of the functions of 5-aminoimidazole and serylproline might provide a better understanding of the underlying mechanisms involved in GT.

Network Reconstruction and Significant Pathway Extraction Using Phosphoproteomic Data from Cancer Cells

Protein phosphorylation acts as an efficient switch controlling deregulated key signaling pathway in cancer. Computational biology aims to address the complexity of reconstructed networks but overrepresents well-known proteins and lacks information on less-studied proteins. A bioinformatic tool to reconstruct and select relatively small networks that connect signaling proteins to their targets in specific contexts is developed. It enables to propose and validate new signaling axes of the Syk kinase. To validate the potency of the tool, it is applied to two phosphoproteomic studies on oncogenic mutants of the well-known phosphatidyl-inositol 3-kinase (PIK3CA) and the unfamiliar Src-related tyrosine kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites (SRMS) kinase. By combining network reconstruction and signal propagation, comprehensive signaling networks from large-scale experimental data are built and multiple molecular paths from these kinases to their targets are extracted. Specific paths from two distinct PIK3CA mutants are retrieved, and their differential impact on the HER3 receptor kinase is explained. In addition, to address the missing connectivities of the SRMS kinase to its targets in interaction pathway databases, phospho-tyrosine and phospho-serine/threonine proteomic data are integrated. The resulting SRMS-signaling network comprises casein kinase 2, thereby validating its currently suggested role downstream of SRMS. The computational pipeline is publicly available, and contains a user-friendly graphical interface (http://doi.org/10.5281/zenodo.3333687).

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Significance and Diagnostic Role of Antimicrobial Cathelicidins (LL-37) Peptides in Oral Health

Cathelicidins are a group of oral antimicrobial peptides that play multiple vital roles in the human body, such as their antimicrobial (broad spectrum) role against oral microbes, wound healing, and angiogenesis, with recent evidences about their role in cancer regulation. Cathelicidins are present in humans and other mammals as well. By complex interactions with the microenvironment, it results in pro-inflammatory effects. Many in vitro and in vivo experiments have been conducted to ultimately conclude that these unique peptides play an essential role in innate immunity. Peptides are released in the precursor form (defensins), which after cleavage results in cathelicidins formation. Living in the era where the major focus is on non-invasive and nanotechnology, this ultimately leads to further advancements in the field of salivaomics. Based on current spotlight innovations, we have highlighted the biochemistry, mode of action, and the importance of cathelicidins in the oral cavity.

Panomics for Precision Medicine

Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.

The cytoprotective protein clusterin is overexpressed in hypergastrinemic rodent models of oxyntic preneoplasia and promotes gastric cancer cell survival

The cytoprotective protein clusterin is often dysregulated during tumorigenesis, and in the stomach, upregulation of clusterin marks emergence of the oxyntic atrophy (loss of acid-producing parietal cells)-associated spasmolytic polypeptide-expressing metaplasia (SPEM). The hormone gastrin is important for normal function and maturation of the gastric oxyntic mucosa and hypergastrinemia might be involved in gastric carcinogenesis. Gastrin induces expression of clusterin in adenocarcinoma cells. In the present study, we examined the expression patterns and gastrin-mediated regulation of clusterin in gastric tissue from: humans; rats treated with proton pump (H+/K+-ATPase) inhibitors and/or a gastrin receptor (CCK2R) antagonist; H+/K+-ATPase β-subunit knockout (H/K-β KO) mice; and Mongolian gerbils infected with Helicobacter pylori and given a CCK2R antagonist. Biological function of secretory clusterin was studied in human gastric cancer cells. Clusterin was highly expressed in neuroendocrine cells in normal oxyntic mucosa of humans and rodents. In response to hypergastrinemia, expression of clusterin increased significantly and its localization shifted to basal groups of proliferative cells in the mucous neck cell-chief cell lineage in all animal models. That shift was partially inhibited by antagonizing the CCK2R in rats and gerbils. The oxyntic mucosa of H/K-β KO mice contained areas with clusterin-positive mucous cells resembling SPEM. In gastric adenocarcinomas, clusterin mRNA expression was higher in diffuse tumors containing signet ring cells compared with diffuse tumors without signet ring cells, and clusterin seemed to be secreted by tumor cells. In gastric cancer cell lines, gastrin increased secretion of clusterin, and both gastrin and secretory clusterin promoted survival after starvation- and chemotherapy-induced stress. Overall, our results indicate that clusterin is overexpressed in hypergastrinemic rodent models of oxyntic preneoplasia and stimulates gastric cancer cell survival.