Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

The Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

Systematic molecular analysis of the human secretome and membrane proteome in gastrointestinal adenocarcinomas

The human secretome and membrane proteome are a large source of cancer biomarkers. Membrane‐bound and secreted proteins are promising targets for many clinically approved drugs, including for the treatment of tumours. Here, we report a deep systematic analysis of 957 adenocarcinomas of the oesophagus, stomach, colon and rectum to examine the cancer‐associated human secretome and membrane proteome of gastrointestinal tract adenocarcinomas (GIACs). Transcriptomic data from these GIACs were applied to an innovative majority decision‐based algorithm. We quantified significantly expressed protein‐coding genes. Interestingly, we found a consistent pattern in a small group of genes found to be overexpressed in GIACs, which were associated with a cytokine–cytokine interaction pathway (CCRI) in all four cancer subtypes. These CCRI associated genes, which spanned both one secretory and one membrane isoform were further analysed, revealing a putative biomarker, interleukin‐1 receptor accessory protein (IL1RAP), which indicated a poor overall survival, a positive correlation with cancer stemness and a negative correlation with several kinds of T cells. These results were further validated in vitro through the knockdown of IL1RAP in two human gastric carcinoma cell lines, which resulted in a reduced indication of cellular proliferation, migration and markers of invasiveness. Following IL1RAP silencing, RNA seq results showed a consistent pattern of inhibition related to CCRI, proliferation pathways and low infiltration of regulatory T cells (Tregs) and CD8 naive cells. The significance of the human secretome and membrane proteome is elucidated by these findings, which indicate IL1RAP as a potential candidate biomarker for cytokine‐mediated cancer immunotherapy in gastric carcinoma.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Mass Spectrometry-Based Proteomics Reveal Alcohol Dehydrogenase 1B as a Blood Biomarker Candidate to Monitor Acetaminophen-Induced Liver Injury

Acute liver injury (ALI) is a severe disorder resulting from excessive hepatocyte cell death, and frequently caused by acetaminophen intoxication. Clinical management of ALI progression is hampered by the dearth of blood biomarkers available. In this study, a bioinformatics workflow was developed to screen omics databases and identify potential biomarkers for hepatocyte cell death. Then, discovery proteomics was harnessed to select from among these candidates those that were specifically detected in the blood of acetaminophen-induced ALI patients. Among these candidates, the isoenzyme alcohol dehydrogenase 1B (ADH1B) was massively leaked into the blood. To evaluate ADH1B, we developed a targeted proteomics assay and quantified ADH1B in serum samples collected at different times from 17 patients admitted for acetaminophen-induced ALI. Serum ADH1B concentrations increased markedly during the acute phase of the disease, and dropped to undetectable levels during recovery. In contrast to alanine aminotransferase activity, the rapid drop in circulating ADH1B concentrations was followed by an improvement in the international normalized ratio (INR) within 10–48 h, and was associated with favorable outcomes. In conclusion, the combination of omics data exploration and proteomics revealed ADH1B as a new blood biomarker candidate that could be useful for the monitoring of acetaminophen-induced ALI.

Proof of Gene Doping in a Mouse Model with a Human Erythropoietin Gene Transferred Using an Adenoviral Vector

Despite the World Anti-Doping Agency (WADA) ban on gene doping in the context of advancements in gene therapy, the risk of EPO gene-based doping among athletes is still present. To address this and similar risks, gene-doping tests are being developed in doping control laboratories worldwide. In this regard, the present study was performed with two objectives: to develop a robust gene-doping mouse model with the human EPO gene (hEPO) transferred using recombinant adenovirus (rAdV) as a vector and to develop a detection method to identify gene doping by using this model. The rAdV including the hEPO gene was injected intravenously to transfer the gene to the liver. After injection, the mice showed significantly increased whole-blood red blood cell counts and increased expression of hematopoietic marker genes in the spleen, indicating successful development of the gene-doping model. Next, direct and potentially indirect proof of gene doping were evaluated in whole-blood DNA and RNA by using a quantitative PCR assay and RNA sequencing. Proof of doping could be detected in DNA and RNA samples from one drop of whole blood for approximately a month; furthermore, the overall RNA expression profiles showed significant changes, allowing advanced detection of hEPO gene doping.

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

BACKGROUND

Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.

RESULTS

In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.

CONCLUSIONS

PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics

Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.

Increased DSG2 plasmatic levels identified by transcriptomic-based secretome analysis is a potential prognostic biomarker in laryngeal carcinoma

OBJECTIVES

The tumor secretome deconvolution is a promising strategy to identify diagnostic and prognostic biomarkers. Here, transcriptomic-based secretome analysis was performed aiming to discover laryngeal squamous cell carcinomas (LSCC) biomarkers from potentially secreted proteins (PSPs).

MATERIAL AND METHODS

The tumor expression profile (35 LSCC biopsies compared with surrounding normal tissues - SN) revealed 589 overexpressed genes. This gene list was used for secretome analysis based on laryngeal tumors and related secretome databases.

RESULTS

Forty-nine (Laryngeal tumor secretome database) and 50 (Human Protein Atlas and Cancer Secretome Database) PSPs presented an association with worse overall survival. Specifically, DSG2 overexpression was strongly correlated with poor survival and distant metastasis. DSG2 increased expression was confirmed in the LSCC dataset (LSCC = 111; SN = 12) from TCGA. A significant association between shorter survival and DSG2 overexpression was also detected. In an independent cohort of cases, we analyzed and confirmed high protein levels of DSG2 in plasma from LSCC patients.

CONCLUSION

A set of PSPs including the circulating DSG2, were associated with shorter overall survival in LSCC. DSG2 overexpression was also correlated with distant metastasis. The high plasmatic protein levels of DSG2 suggest its potential to be tested in liquid biopsies and applied as prognostic biomarker of LSCC.

Deep proteome profiling of the hippocampus in the 5XFAD mouse model reveals biological process alterations and a novel biomarker of Alzheimer's disease

Alzheimer’s disease (AD), which is the most common type of dementia, is characterized by the deposition of extracellular amyloid plaques. To understand the pathophysiology of the AD brain, the assessment of global proteomic dynamics is required. Since the hippocampus is a major region affected in the AD brain, we performed hippocampal analysis and identified proteins that are differentially expressed between wild-type and 5XFAD model mice via LC-MS methods. To reveal the relationship between proteomic changes and the progression of amyloid plaque deposition in the hippocampus, we analyzed the hippocampal proteome at two ages (5 and 10 months). We identified 9,313 total proteins and 1411 differentially expressed proteins (DEPs) in 5- and 10-month-old wild-type and 5XFAD mice. We designated a group of proteins showing the same pattern of changes as amyloid beta (Aβ) as the Aβ-responsive proteome. In addition, we examined potential biomarkers by investigating secretory proteins from the Aβ-responsive proteome. Consequently, we identified vitamin K-dependent protein S (PROS1) as a novel microglia-derived biomarker candidate in the hippocampus of 5XFAD mice. Moreover, we confirmed that the PROS1 level in the serum of 5XFAD mice increases as the disease progresses. An increase in PROS1 is also observed in the sera of AD patients and shows a close correlation with AD neuroimaging markers in humans. Therefore, our quantitative proteome data obtained from 5XFAD model mice successfully predicted AD-related biological alterations and suggested a novel protein biomarker for AD.

A protein newly implicated in Alzheimer’s disease could serve as a diagnostic biomarker or therapeutic target. A team led by Youngsoo Kim and Inhee Mook-Jung from Seoul National University, South Korea, analyzed all the proteins expressed in the hippocampus, the brain’s memory center, in mice with and without Alzheimer’s-like disease. They identified more than 1,400 proteins differentially expressed between the mouse model of Alzheimer’s and the normal mice. Among these were 36 secretory proteins that tended to increase their levels along with build-up of amyloid-beta, the protein found in clumps in the brains of people with Alzheimer’s. Many already had known links to Alzheimer’s, but the researchers also identified a novel protein called PROS1. Blood samples from Alzheimer’s patients also showed an increase in PROS1 levels, with a close correlation with amyloid-beta build-up in the brain.

A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome

The collection of proteins secreted from a cell-the secretome-is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or cancer type or focus on biomarker prediction without exploring the associated functions. We therefore conducted a pan-cancer analysis of secretome gene expression changes to identify candidate diagnostic biomarkers and to investigate the underlying biological function of these changes. Using transcriptomic data spanning 32 cancer types and 30 healthy tissues, we quantified the relative diagnostic potential of secretome proteins for each cancer. Furthermore, we offer a potential mechanism by which cancer cells relieve secretory pathway stress by decreasing the expression of tissue-specific genes, thereby facilitating the secretion of proteins promoting invasion and proliferation. These results provide a more systematic understanding of the cancer secretome, facilitating its use in diagnostics and its targeting for therapeutic development.

Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework

Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery.

Upregulated circular RNA circ_0007534 indicates an unfavorable prognosis in pancreatic ductal adenocarcinoma and regulates cell proliferation, apoptosis, and invasion by sponging miR-625 and miR-892b

Circular RNAs (circRNAs) have been regarded as critical regulators of human diseases and biological markers in some types of malignancies, including pancreatic ductal adenocarcinoma (PDAC). Recently, circ_0007534 has been identified as a novel cancer-related circRNA. Nevertheless, its clinical relevance, functional roles, and mechanism have not been studied in PDAC. In the current study, real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of circ_0007534 in 60-paired PDAC tissue samples and different cell lines. Loss-of-function and gain-of-function assays were performed to detect cell proliferation, apoptosis, and metastatic properties affected by circ_0007534. An animal study was also carried out. The luciferase reporter assay was performed to uncover the underlying mechanism of circ_0007534. As a result, circ_0007534 was overexpressed not only in PDAC tissues but also in a panel of PDAC cell lines, and this overexpression is closely associated with advanced tumor stage and positive lymph node invasion. In addition, circ_0007534 may be regarded as an independent prognostic factor for patients with PDAC. For the part of functional assays, circ_0007534 significantly increased cell proliferation, migratory, and invasive potential of PDAC cells. Circ_0007534 could inhibit cell apoptosis partly via a Bcl-2/caspase-3 pathway. The xenograft study further confirmed the cell growth promoting the role of circ_0007534. Mechanistically, miR-625 and miR-892b were sponged by circ_0007534. The oncogenic functions of circ_0007534 is partly dependent on its regulation of miR-625 and miR-892b. In conclusion, our study illuminates a novel circRNA that confers an oncogenic function in PDAC.

Biomarkers for detection, prognosis and therapeutic assessment of neurological disorders

Neurological disorders have aroused a significant concern among the health scientists globally, as diseases such as Parkinson's, Alzheimer's and dementia lead to disability and people have to live with them throughout the life. Recent evidence suggests that a number of environmental chemicals such as pesticides (paraquat) and metals (lead and aluminum) are also the cause of these diseases and other neurological disorders. Biomarkers can help in detecting the disorder at the preclinical stage, progression of the disease and key metabolomic alterations permitting identification of potential targets for intervention. A number of biomarkers have been proposed for some neurological disorders based on laboratory and clinical studies. In silico approaches have also been used by some investigators. Yet the ideal biomarker, which can help in early detection and follow-up on treatment and identifying the susceptible populations, is not available. An attempt has therefore been made to review the recent advancements of in silico approaches for discovery of biomarkers and their validation. In silico techniques implemented with multi-omics approaches have potential to provide a fast and accurate approach to identify novel biomarkers.

Upregulated long noncoding RNA LINC01296 indicates a dismal prognosis for pancreatic ductal adenocarcinoma and promotes cell metastatic properties by affecting EMT

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease that responds poorly to chemotherapy and radiotherapy and whose incidence has increased worldwide. Long noncoding RNAs have been demonstrated to play important roles in cancer initiation and progression. Long intergenic non-coding RNA 01296 (LINC01296) has been reported to be upregulated in several malignancies, but the clinical relevance and biological role of LINC01296 in PDAC are still unclear.

METHODS

RT-qPCR was performed to evaluate the expression of LINC01296 in 85 pared PDAC tissue samples and a panel of PDAC cell lines. The clinical value and prognostic role of LINC01296 in patients with PDAC were further explored. Furthermore, we explored the functional roles of LINC01296 depletion in PANC-1 and SW1990 cells, including cell proliferation, apoptosis, migration, invasion, and epithelial-to-mesenchymal transition (EMT).

RESULTS

LINC01296 was enhanced in PDAC tissues and cell lines, and this overexpression was correlated with advanced tumor stages and positive lymph node metastasis in patients with PDAC. In addition, upregulation of LINC01296 was an independent prognostic predictor for patients with PDAC after surgery. Moreover, silencing of LINC01296 followed by treatment with small interfering RNAs suppressed cell proliferation and promoted cell apoptosis by affecting the Bcl-2/caspase-3 pathway. Importantly, LINC01296 attenuation impaired the migratory and invasive potential partly by reversing EMT.

CONCLUSIONS

Overall, our work may help to develop a novel prognostic biomarker and therapeutic target for PDAC.

LncRNA TUG1 promoted viability and associated with gemcitabine resistant in pancreatic ductal adenocarcinoma

OBJECTIVE

To investigate the underlying mechanism of lncRNA TUG1 in pancreatic ductal adenocarcinoma (PDAC).

METHODS

The expression of TUG1 was defined by qRT-PCR. The apoptotic cells were detected by flow cytometry assay. The cell migration and invasion were measured by scratch assay and Transwell assay. The level of ERK pathway was detected using Western blot.

RESULTS

Compared with normal tissues and cells, the expression of TUG1 was up-regulated in pancreatic cancer tissue and cells. Meanwhile, knockdown of TUG1 could promote PDAC cells apoptosis and inhibit PDAC cells viability, migration and invasion. In addition, overexpression of TUG1 enhanced the gemcitabine chemoresistance of PDAC cells. Surprisingly, gemcitabine combined with SCH772984 (a suppressor of ERK pathway) could reverse the drug resistance resulted from overexpression of TUG1.

CONCLUSION

TUG1 promoted the viability of PDAC cells and enhanced its resistance of gemcitabine.

Upregulated long non-coding RNA SPRY4-IT1 predicts dismal prognosis for pancreatic ductal adenocarcinoma and regulates cell proliferation and apoptosis

Recently, long noncoding RNAs (lncRNAs) have been emerged as pivotal regulators in various human cancers, including pancreatic ductal adenocarcinoma (PDAC). SPRY4-intronic transcript 1 (SPRY4-IT1) was reported to be upregulated in some kind of human cancers. Here, we elucidated the biological functions and possible clinical values of SPRY4-IT1 on PDAC. In present study, expression of SPRY4-IT1 in PDAC tissues and corresponding normal tissues were explored by qRT-PCR experiments. The link between SPRY4-IT1 expression levels and clinicopathological significance was further analyzed. In addition, the oncogenic role of SPRY4-IT1 was detected both in vitro and in vivo. The results demonstrated that SPRY4-IT1 was abnormally upregulated in PDAC tissues and cell lines. Tumor stage and differentiation grade was closely correlated with SPRY4-IT1 expression. Additionally, decreased SPRY4-IT1 contributed to tumor suppressive effect through attenuating cell growth, clonogenic ability and facilitating apoptosis via Bcl-2/caspase-3 pathway in PANC1 and Capan-2 cells. Furthermore, the xenograft study confirmed the tumor proliferation-promoting role of SPRY4-IT1 in PANC1 cells. Taken together, these findings indicated that SPRY4-IT1 is a potential therapeutic target and prognosis biomarker for the patients with PDAC.

Generation of monoclonal antibodies and development of an immunofluorometric assay for the detection of CUZD1 in tissues and biological fluids

BACKGROUND

CUB and zona pellucida-like domain-containing protein 1 (CUZD1) was identified as a pancreas-specific protein and was proposed as a candidate biomarker for pancreatic related disorders. CUZD1 protein levels in tissues and biological fluids have not been extensively examined. The purpose of the present study was to generate specific antibodies targeting CUZD1 to assess CUZD1 expression within tissues and biological fluids.

METHODS

Mouse monoclonal antibodies against CUZD1 were generated and used to perform immunohistochemical analyses and to develop a sensitive and specific enzyme-linked immunosorbent assay (ELISA). CUZD1 protein expression was assessed in various human tissue extracts and biological fluids and in gel filtration chromatography-derived fractions of pancreatic tissue extract, pancreatic juice and recombinant protein.

RESULTS

Immunohistochemical staining of CUZD1 in pancreatic tissue showed that the protein is localized to the acinar cells and the lumen of the acini. Western blot analysis detected the protein in pancreatic tissue extract and pancreatic juice. The newly developed ELISA measured CUZD1 in high levels in pancreas and in much lower but detectable levels in several other tissues. In the biological fluids tested, CUZD1 expression was detected exclusively in pancreatic juice. The analysis of gel filtration chromatography-derived fractions of pancreatic tissue extract, pancreatic juice and recombinant CUZD1 suggested that the protein exists in high molecular weight protein complexes.

CONCLUSION

This study describes the development of tools targeting CUZD1 protein, its tissue expression pattern and levels in several biological fluids. These new tools will facilitate future investigations aiming to delineate the role of CUZD1 in physiology and pathobiology.

Pattern Genes Suggest Functional Connectivity of Organs

Human organ, as the basic structural and functional unit in human body, is made of a large community of different cell types that organically bound together. Each organ usually exerts highly specified physiological function; while several related organs work smartly together to perform complicated body functions. In this study, we present a computational effort to understand the roles of genes in building functional connection between organs. More specifically, we mined multiple transcriptome datasets sampled from 36 human organs and tissues, and quantitatively identified 3,149 genes whose expressions showed consensus modularly patterns: specific to one organ/tissue, selectively expressed in several functionally related tissues and ubiquitously expressed. These pattern genes imply intrinsic connections between organs. According to the expression abundance of the 766 selective genes, we consistently cluster the 36 human organs/tissues into seven functional groups: adipose &gland, brain, muscle, immune, metabolism, mucoid and nerve conduction. The organs and tissues in each group either work together to form organ systems or coordinate to perform particular body functions. The particular roles of specific genes and selective genes suggest that they could not only be used to mechanistically explore organ functions, but also be designed for selective biomarkers and therapeutic targets.

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate biomarkers in rheumatoid and psoriatic arthritis, evaluated the evidence for their potential as biomarkers of bone (joint) damage, and outlined how mass spectrometry-based targeted and multiplexed measurement of candidate biomarker proteins is likely to accelerate their clinical validation and the development of clinical diagnostic tests.

The long non-coding RNA HOTTIP promotes progression and gemcitabine resistance by regulating HOXA13 in pancreatic cancer

Background

The human genome encodes many long non-coding RNAs (lncRNAs). However, their biological functions, molecular mechanisms, and the prognostic value associated with pancreatic ductal adenocarcinoma (PDAC) remain to be elucidated. Here, we identify a fundamental role for the lncRNA HOXA transcript at the distal tip (HOTTIP) in the progression and chemoresistance of PDAC.

Methods

High-throughput microarrays were performed to detect the expression profiles of lncRNAs and messenger RNAs in eight human PDAC tissues and four pancreatic tissues. Quantitative real-time PCR was used to determine the levels of HOTTIP and HOXA13 transcripts in PDAC cell lines and 90 PDAC samples from patients. HPDE6 cells (immortalized human pancreatic ductal epithelial cells) and corresponding adjacent non-neoplastic tissues were used as controls, respectively. The functions of HOTTIP and HOXA13 in cell proliferation, invasion, and epithelial-mesenchymal transition were evaluated by targeted knockdown in vitro. CCK-8 assays, colony formation assays, and xenografts in nude mice were used to investigate whether targeted silencing of HOTTIP could sensitize pancreatic cancer cells to gemcitabine. Immunohistochemistry was performed to investigate the relationship between HOXA13 expression and patient outcome.

Results

Microarray analyses revealed that HOTTIP was one of the most significantly upregulated lncRNAs in PDAC tissues compared with pancreatic tissues. Quantitative PCR further verified that HOTTIP levels were increased in PDAC cell lines and patient samples compared with controls. Functionally, HOTTIP silencing resulted in proliferation arrest by altering cell-cycle progression, and impaired cell invasion by inhibiting epithelial-mesenchymal transition in pancreatic cancer. Additionally, inhibition of HOTTIP potentiated the antitumor effects of gemcitabine in vitro and in vivo. Furthermore, knockdown of HOXA13 by RNA interference (siHOXA13) revealed that HOTTIP promoted PDAC cell proliferation, invasion, and chemoresistance, at least partly through regulating HOXA13. Immunohistochemistry results revealed that higher HOXA13 expression was correlated with lymph node metastasis, poor histological differentiation, and decreased overall survival in PDAC patients.

Conclusions

As a crucial tumor promoter, HOTTIP promotes cell proliferation, invasion, and chemoresistance by modulating HOXA13. Therefore, the HOTTIP/HOXA13 axis is a potential therapeutic target and molecular biomarker for PDAC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0442-z) contains supplementary material, which is available to authorized users.

Expression and prognostic significance of lncRNA MALAT1 in pancreatic cancer tissues

BACKGROUND

Long non-coding RNAs (lncRNAs) have been recently observed in various human cancers. However, the role of lncRNAs in pancreatic duct adenocarcinoma (PDAC) remains unclarified. The aim of this study was to detect the expression of lncRNA MALAT1 in PDAC formalin-fixed, paraffin embedded (FFPE) tissues and to investigate the clinical significance of the MALAT1 level.

METHODS

The expression of MALAT1 was examined in 45 PDAC and 25 adjacent non-cancerous FFPE tissues, as well as in five PDAC cell lines and a normal pancreatic epithelium cell line HPDE6c-7, using qRT-PCR. The relationship between MALAT1 level and clinicopathological parameters of PDAC was analyzed with the Kaplan-Meier method and Cox proportional hazards model.

RESULTS

The relative level of MALAT1 was significantly higher in PDAC compared to the adjacent normal pancreatic tissues (p=0.009). When comparing the MALAT1 level in the cultured cell lines, remarkably higher expression of MALAT1 was found in aspc-1 PDAC cells compared with the immortal pancreatic duct epithelial cell line HPDE6c-7 (q=7.573, p<0.05). Furthermore, MALAT1 expression level showed significant correlation with tumor size (r=0.35, p=0.018), tumor stage (r=0.439, p=0.003) and depth of invasion (r=0.334, p=0.025). Kaplan-Meier analysis revealed that patients with higher MALAT1 expression had a poorer disease free survival (p=0.043). Additionally, multivariate analysis indicated that overexpression of MALAT1, as well as the tumor location and nerve invasion, was an independent predictor of disease-specific survival of PDAC.

CONCLUSION

MALAT1 might be considered as a potential prognostic indicator and may be a target for diagnosis and gene therapy for PDAC.

Validation of biomarkers that complement CA19.9 in detecting early pancreatic cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a significant cause of cancer mortality. Carbohydrate antigen 19.9 (CA19.9), the only tumor marker available to detect and monitor PDAC, is not sufficiently sensitive and specific to consistently differentiate early cancer from benign disease. In this study, we aimed to validate recently discovered serum protein biomarkers for the early detection of PDAC and ultimately develop a biomarker panel that could discriminate PDAC from other benign disease better than the existing marker CA19.9.

PATIENTS AND METHODS

We performed a retrospective blinded evaluation of 400 serum samples collected from individuals recruited on a consecutive basis. The sample population consisted of 250 individuals with PDAC at various stages, 130 individuals with benign conditions and 20 healthy individuals. The serum levels of each biomarker were determined by ELISAs or automated immunoassay.

RESULTS

By randomly splitting matched samples into a training (n = 186) and validation (n = 214) set, we were able to develop and validate a biomarker panel consisting of CA19.9, CA125, and LAMC2 that significantly improved the performance of CA19.9 alone. Improved discrimination was observed in the validation set between all PDAC and benign conditions (AUCCA19.9 = 0.80 vs. AUCCA19.9+CA125+LAMC2 = 0.87; P < 0.005) as well as between early-stage PDAC and benign conditions (AUCCA19.9 = 0.69 vs. AUCCA19.9+CA125+LAMC2 = 0.76; P < 0.05) and between early-stage PDAC and chronic pancreatitis (CP; AUCCA19.9 = 0.59 vs. AUCCA19.9+CA125+LAMC2 = 0.74; P < 0.05).

CONCLUSIONS

The data demonstrate that a serum protein biomarker panel consisting of CA125, CA19.9, and LAMC2 is able to significantly improve upon the performance of CA19.9 alone in detecting PDAC.

Identification of psoriatic arthritis mediators in synovial fluid by quantitative mass spectrometry

Background

Synovial fluid (SF) is a dynamic reservoir for proteins originating from the synovial membrane, cartilage, and plasma, and may therefore reflect the pathophysiological conditions that give rise to arthritis. Our goal was to identify and quantify protein mediators of psoriatic arthritis (PsA) in SF.

Methods

Age and gender-matched pooled SF samples from 10 PsA and 10 controls [early osteoarthritis (OA)], were subjected to label-free quantitative proteomics using liquid chromatography coupled to mass spectrometry (LC-MS/MS), to identify differentially expressed proteins based on the ratios of the extracted ion current of each protein between the two groups. Pathway analysis and public database searches were conducted to ensure these proteins held relevance to PsA. Multiplexed selected reaction monitoring (SRM) assays were then utilized to confirm the elevated proteins in the discovery samples and in an independent set of samples from patients with PsA and controls.

Results

We determined that 137 proteins were differentially expressed between PsA and control SF, and 44 were upregulated. The pathways associated with these proteins were acute-phase response signalling, granulocyte adhesion and diapedesis, and production of nitric oxide and reactive oxygen species in macrophages. The expression of 12 proteins was subsequently quantified using SRM assays.

Conclusions

Our in-depth proteomic analysis of the PSA SF proteome identified 12 proteins which were significantly elevated in PsA SF compared to early OA SF. These proteins may be linked to the pathogenesis of PsA, as well serve as putative biomarkers and/or therapeutic targets for this disease.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

Reconstruction and analysis of human kidney-specific metabolic network based on omics data

With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

False biomarker discovery due to reactivity of a commercial ELISA for CUZD1 with cancer antigen CA125

BACKGROUND

By using proteomics and bioinformatics, we have previously identified a group of highly pancreas-specific proteins as candidate pancreatic ductal adenocarcinoma (PDAC) biomarkers. With the use of commercially available ELISAs, the performance of some of these candidates was initially evaluated in a relatively small serum cohort (n = 100 samples). This phase revealed that CUB and zona pellucida-like domains protein 1 (CUZD1) may represent a new, promising PDAC biomarker.

METHODS

We performed detailed experiments to investigate the specificity of the commercial CUZD1 ELISA assay. CUZD1 was expressed in house in both bacteria and yeast expression systems. Recombinant CUZD1 and biological samples containing CUZD1, as well as commercial CUZD1 ELISA standards, were analyzed by Western blot, size exclusion HPLC, and mass spectrometry (LC-MS Orbitrap).

RESULTS

We confirmed that instead of CUZD1, the commercial assay is recognizing a nonhomologous, known cancer antigen [cancer antigen 125 (CA125)].

CONCLUSIONS

We conclude that poor characterization of commercial ELISA assays is a factor that could lead to false biomarker discovery. To our knowledge, this is the first report documenting that a commercial ELISA marketed for one analyte (CUZD1) may, in fact, recognize a different, nonhomologous antigen (CA125).

Semiquantitative proteomic analysis of human hippocampal tissues from Alzheimer's disease and age-matched control brains

BACKGROUND

Alzheimer's disease (AD) is the most common type of dementia affecting people over 65 years of age. The hallmarks of AD are the extracellular deposits known as amyloid β plaques and the intracellular neurofibrillary tangles, both of which are the principal players involved in synaptic loss and neuronal cell death. Tau protein and Aβ fragment 1-42 have been investigated so far in cerebrospinal fluid as a potential AD biomarkers. However, an urgent need to identify novel biomarkers which will capture disease in the early stages and with better specificity remains. High-throughput proteomic and pathway analysis of hippocampal tissue provides a valuable source of disease-related proteins and biomarker candidates, since it represents one of the earliest affected brain regions in AD.

RESULTS

In this study 2954 proteins were identified (with at least 2 peptides for 1203 proteins) from both control and AD brain tissues. Overall, 204 proteins were exclusively detected in AD and 600 proteins in control samples. Comparing AD and control exclusive proteins with cerebrospinal fluid (CSF) literature-based proteome, 40 out of 204 AD related proteins and 106 out of 600 control related proteins were also present in CSF. As most of these proteins were extracellular/secretory origin, we consider them as a potential source of candidate biomarkers that need to be further studied and verified in CSF samples.

CONCLUSIONS

Our semiquantitative proteomic analysis provides one of the largest human hippocampal proteome databases. The lists of AD and control related proteins represent a panel of proteins potentially involved in AD pathogenesis and could also serve as prospective AD diagnostic biomarkers.

The role of proteomics in prostate cancer research: biomarker discovery and validation

PURPOSE

Prostate Cancer (PCa) represents the second most frequent type of tumor in men worldwide. Incidence increases with patient age and represents the most important risk factor. PCa is mostly characterized by indolence, however in a small percentage of cases (3%) the disease progresses to a metastatic state. To date, the most important issue concerning PCa research is the difficulty in distinguishing indolent from aggressive disease. This problem frequently results in low-grade PCa patient overtreatment and, in parallel; an effective treatment for distant and aggressive disease is not yet available.

RESULT

Proteomics represents a promising approach for the discovery of new biomarkers able to improve the management of PCa patients. Markers more specific and sensitive than PSA are needed for PCa diagnosis, prognosis and response to treatment. Moreover, proteomics could represent an important tool to identify new molecular targets for PCa tailored therapy. Several possible PCa biomarkers sources, each with advantages and limitations, are under investigation, including tissues, urine, serum, plasma and prostatic fluids. Innovative high-throughput proteomic platforms are now identifying and quantifying new specific and sensitive biomarkers for PCa detection, stratification and treatment. Nevertheless, many putative biomarkers are still far from being applied in clinical practice.

CONCLUSIONS

This review aims to discuss the recent advances in PCa proteomics, emphasizing biomarker discovery and their application to clinical utility for diagnosis and patient stratification.

Strategies for discovering novel pancreatic cancer biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related deaths in both men and women in Canada and the United States and has the most dismal survival rates among any solid malignancy. Most patients are diagnosed with pancreatic cancer once the disease has progressed into an advanced or metastatic stage, making the only curative approach of resection surgery impossible. The persistent delayed or missed diagnosis of pancreatic cancer can be attributed to the absence of early symptoms and the lack of efficient non-invasive screening or diagnostic tests in clinical practice. Given that earlier diagnosis is critical for ameliorating patients' survival rates, there is an urgent need for biomarkers with enough sensitivity and specificity to help diagnose pancreatic cancer early. Serological biomarkers provide a minimally invasive and efficient way of detecting pancreatic cancer, however, there is currently no marker with sufficient diagnostic sensitivity and specificity to identify early cancer patients. This review focuses on the classical tumor markers for PDAC as well as emerging markers. In addition, we will discuss an integrative proteomic approach used in our lab to identify a panel of biomarkers that have the potential to allow the early detection of PDAC.This article is part of a Special Issue entitled: From protein structures to clinical applications.

CUB and zona pellucida-like domain-containing protein 1 (CUZD1): a novel serological biomarker for ovarian cancer

OBJECTIVES

To measure the levels of serum CUB and zona pellucida-like domain-containing protein 1 (CUZD1) in patients with ovarian cancer (OvCa), benign gynecological conditions and healthy women and in a number of other cancer types (breast, colorectal, lung, prostate and testicular).

DESIGN AND METHODS

Serum CUZD1 levels were measured with a commercial enzyme-linked immunosorbent assay (ELISA). All specimens were analyzed in duplicate. Preliminary verification was performed in serum using 9 healthy women and 20 late stage (III-IV) OvCa patients. An independent cohort of serum samples was used to validate the verification results (18 late stage OvCa, 8 benign gynecological conditions and 8 healthy controls). The following specimens were used for the other cancer types of unknown stage-breast (n=11), colorectal (n=10), lung (n=10), prostate (n=15) and testicular (n=10).

RESULTS

Serum CUZD1 was significantly elevated in ovarian cancer patients (range 95-668 μg/L) as compared to healthy controls (range 0.7-2.5 μg/L). The independent cohort of OvCa samples confirmed the preliminary verification results. CUZD1 was also elevated in breast and lung cancer specimens and not in colorectal, prostate and testicular cancer specimens.

CONCLUSIONS

CUZD1 appears to be a highly promising novel serum biomarker for OvCa diagnosis. Its performance in the 2 independent cohorts examined, and in lung and breast cancer patients warrants further investigation.