Identification of Serum Biomarkers for Gastric Cancer Diagnosis Using a Human Proteome Microarray

Identification of novel autoantibodies based on the protein chip encoded by cancer-driving genes in detection of esophageal squamous cell carcinoma

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAbs) and explore the optimal diagnosis model based on the protein chip for detecting esophageal squamous cell carcinoma (ESCC). The human protein chip based on cancer-driving genes was customized to discover candidate TAAbs. Enzyme-linked immunosorbent assay was applied to verify and validate the expression levels of candidate TAAbs in the training cohort (130 ESCC and 130 normal controls) and the validation cohort (125 ESCC and 125 normal controls). Logistic regression analysis was adopted to construct the diagnostic model based on the expression levels of autoantibodies with diagnostic value. Twelve candidate autoantibodies were identified based on the protein chip according to the corresponding statistical methods. In both the training cohort and validation cohort, the expression levels of 10 TAAbs (GNA11, PTEN, P53, SRSF2, GNAS, ACVR1B, CASP8, DAXX, PDGFRA, and MEN1) in ESCC patients were higher than that in normal controls. The panel consisting of GNA11, ACVR1B and P53 demonstrated favorable diagnostic power. The sensitivity, specificity and accuracy of the model in the train cohort and the validation cohort were 71.5%, 93.8%, 79.6% and 77.6%, 81.6%, 70.8%, respectively. In either cohort, there was no correlation between positive rate of the autoantibody panel and clinicopathologic features for ESCC patients. Protein chip technology is an effective method to identify novel TAAbs, and the panel of 3 TAAbs (GNA11, ACVR1B, and P53) is promising for distinguishing ESCC patients from normal individuals.

Discovery of new serum biomarker panels for systemic lupus erythematosus diagnosis

OBJECTIVE

Clinical diagnosis of SLE is currently challenging due to its heterogeneity. Many autoantibodies are associated with SLE and are considered potential diagnostic markers, but systematic screening and validation of such autoantibodies is lacking. This study aimed to systematically discover new autoantibodies that may be good biomarkers for use in SLE diagnosis.

METHODS

Sera from 15 SLE patients and 5 healthy volunteers were analysed using human proteome microarrays to identify candidate SLE-related autoantibodies. The results were validated by screening of sera from 107 SLE patients, 94 healthy volunteers and 60 disease controls using focussed arrays comprised of autoantigens corresponding to the identified candidate antibodies. Logistic regression was used to derive and validate autoantibody panels that can discriminate SLE disease. Extensive ELISA screening of sera from 294 SLE patients and 461 controls was performed to validate one of the newly discovered autoantibodies.

RESULTS

A total of 31, 11 and 18 autoantibodies were identified to be expressed at significantly higher levels in the SLE group than in the healthy volunteers, disease controls and healthy volunteers plus disease control groups, respectively, with 25, 7 and 13 of these differentially expressed autoantibodies being previously unreported. Diagnostic panels comprising anti-RPLP2, anti-SNRPC and anti-PARP1, and anti-RPLP2, anti-PARP1, anti-MAK16 and anti- RPL7A were selected. Performance of the newly discovered anti-MAK16 autoantibody was confirmed by ELISA. Some associations were seen with clinical characteristics of SLE patients, such as disease activity with the level of anti-PARP1 and rash with the level of anti-RPLP2, anti-MAK16 and anti- RPL7A.

CONCLUSION

The combined autoantibody panels identified here show promise for the diagnosis of SLE and for differential diagnosis of other major rheumatic immune diseases.

Autoantibody signature in hepatocellular carcinoma using seromics

Background

Alpha-fetoprotein (AFP) is a widely used biomarker for hepatocellular carcinoma (HCC) early detection. However, low sensitivity and false negativity of AFP raise the requirement of more effective early diagnostic approaches for HCC.

Methods

We employed a three-phase strategy to identify serum autoantibody (AAb) signature for HCC early diagnosis using protein array-based approach. A total of 1253 serum samples from HCC, liver cirrhosis, and healthy controls were prospectively collected from three liver cancer centers in China. The Human Proteome Microarray, comprising 21,154 unique proteins, was first applied to identify AAb candidates in discovery phase (n = 100) and to further fabricate HCC-focused arrays. Then, an artificial neural network (ANN) model was used to discover AAbs for HCC detection in a test phase (n = 576) and a validation phase (n = 577), respectively.

Results

Using HCC-focused array, we identified and validated a novel 7-AAb panel containing CIAPIN1, EGFR, MAS1, SLC44A3, ASAH1, UBL7, and ZNF428 for effective HCC detection. The ANN model of this panel showed improvement of sensitivity (61.6–77.7%) compared to AFP (cutoff 400 ng/mL, 28.4–30.7%). Notably, it was able to detect AFP-negative HCC with AUC values of 0.841–0.948. For early-stage HCC (BCLC 0/A) detection, it outperformed AFP (cutoff 400 ng/mL) with approximately 10% increase in AUC.

Conclusions

The 7-AAb panel provides potentially clinical value for non-invasive early detection of HCC, and brings new clues on understanding the immune response against hepatocarcinogenesis.

Developments and Applications of Functional Protein Microarrays

Protein microarrays are crucial tools in the study of proteins in an unbiased, high-throughput manner, as they allow for characterization of up to thousands of individually purified proteins in parallel. The adaptability of this technology has enabled its use in a wide variety of applications, including the study of proteome-wide molecular interactions, analysis of post-translational modifications, identification of novel drug targets, and examination of pathogen-host interactions. In addition, the technology has also been shown to be useful in profiling antibody specificity, as well as in the discovery of novel biomarkers, especially for autoimmune diseases and cancers. In this review, we will summarize the developments that have been made in protein microarray technology in both in basic and translational research over the past decade. We will also introduce a novel membrane protein array, the GPCR-VirD array, and discuss the future directions of functional protein microarrays.

Exercise training reverses cancer-induced oxidative stress and decrease in muscle COPS2/TRIP15/ALIEN

OBJECTIVE

We tested the hypothesis that exercise training would attenuate metabolic impairment in a model of severe cancer cachexia.

METHODS

We used multiple in vivo and in vitro methods to explore the mechanisms underlying the beneficial effects induced by exercise training in tumor-bearing rats.

RESULTS

Exercise training improved running capacity, prolonged lifespan, reduced oxidative stress, and normalized muscle mass and contractile function in tumor-bearing rats. An unbiased proteomic screening revealed COP9 signalosome complex subunit 2 (COPS2) as one of the most downregulated proteins in skeletal muscle at the early stage of cancer cachexia. Exercise training normalized muscle COPS2 protein expression in tumor-bearing rats and mice. Lung cancer patients with low endurance capacity had low muscle COPS2 protein expression as compared to age-matched control subjects. To test whether decrease in COPS2 protein levels could aggravate or be an intrinsic compensatory mechanism to protect myotubes from cancer effects, we performed experiments in vitro using primary myotubes. COPS2 knockdown in human myotubes affected multiple cellular pathways, including regulation of actin cytoskeleton. Incubation of cancer-conditioned media in mouse myotubes decreased F-actin expression, which was partially restored by COPS2 knockdown. Direct repeat 4 (DR4) response elements have been shown to positively regulate gene expression. COPS2 overexpression decreased the DR4 activity in mouse myoblasts, and COPS2 knockdown inhibited the effects of cancer-conditioned media on DR4 activity.

CONCLUSIONS

These studies demonstrated that exercise training may be an important adjuvant therapy to counteract cancer cachexia and uncovered novel mechanisms involving COPS2 to regulate myotube homeostasis in cancer cachexia.

Prognostic role and therapeutic susceptibility of cathepsin in various types of solid tumor and leukemia: A systematic review

Cathepsins (CTSs) are multifunctional proteins that can play prominent roles in cancer progression and metastasis. In this systematic review, we compared the prognosis of CTS subtypes overexpression in leukemia and solid tumors, and investigated the effect of different factors on CTS prognosis. We systematically searched published articles indexed in PubMed, Scopus, Cochrane library, ISI Web of Science, and EmBase databases from February 2000 until January 2020. Among the selected leukemia and solid tumors studies, overexpression of CTS subtypes in newly diagnosed and treated patients were with poor prognosis in 43 studies (79.6%) and with good prognosis in 9 studies (16.6%). However, there were 2 studies (3.8%) with either good or poor prognosis, depending on conditions and caner stage and host cell. The relation between CTS and human leukocyte antigen (HLA) in leukemia and solid tumors was mentioned in 7 studies (13%). Overexpression of CTS subtypes in all new case patients had contributed to the induction of poor prognosis. It seems that CTS subtypes, based on the type of cancer and its stage, the type of host cells, and the probable relation with HLA, breed good or poor prognosis in patients with cancer. Therefore, monitoring the overexpression of CTS subtypes and determining the effect of each of these factors on CTS prognosis could be helpful in predicting cancer prognosis both in newly diagnosed or under treatment patients. They could also be useful in finding ways for improving the efficiency of contemporary therapeutic strategies in various types of leukemia and solid tumors.

Integration of IgA and IgG Autoantigens Improves Performance of Biomarker Panels for Early Diagnosis of Lung Cancer

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. In our previous study, we surveyed both IgG and IgM-bound serological biomarkers and validated a panel of IgG-bound autoantigens for early LC diagnosis with 50% sensitivity at 90% specificity. To further improve the performance of these serological biomarkers, we surveyed HuProt arrays, comprised of 20,240 human proteins, for IgA-bound autoantigens because IgAs are a major immunoglobulin isotype in the lung. Integrating with IgG-bound autoantigens, we discovered and validated a combined biomarker panel using ELISA-format tests. Specifically, in Phase I, we obtained IgA-based autoimmune profiles of 69 early stage LC patients, 30 healthy subjects and 25 patients with lung benign lesions (LBL) on HuProt arrays and identified 28 proteins as candidate autoantigens that were significantly associated with early stage LC. In Phase II, we re-purified the autoantigens and converted them into an ELISA-format testing to profile an additional large cohort, comprised of 136 early stage LC patients, 58 healthy individuals, and 29 LBL patients. Integration of IgG autoimmune profiles allowed us to identify and validate a biomarker panel of three IgA autoantigens (i.e. BCL7A, and TRIM33 and MTERF4) and three IgG autoantigens (i.e. CTAG1A, DDX4 and MAGEC2) for diagnosis of early stage LC with 73.5% sensitivity at >85% specificity. In Phase III, the performance of this biomarker panel was confirmed with an independent cohort, comprised of 88 early stage LC patients, 18 LBL patients, and 36 healthy subjects. Finally, a blind test on 178 serum samples was conducted to confirm the performance of the biomarker panel. In summary, this study demonstrates for the first time that an integrated panel of IgA/IgG autoantigens can serve as valuable biomarkers to further improve the performance of early diagnosis of LC.

Longitudinal serum autoantibody repertoire profiling identifies surgery-associated biomarkers in lung adenocarcinoma

•

Longitudinal sera were globally analyzed for identification of surgery-associated serum biomarker for the first time.

•

Autoantibody repertories are stable for a single individual at different time points but highly variable among individuals.

•

Surgery-associated serum biomarkers are prevalent in lung adenocarcinoma patients.

Background

Autoantibodies against tumor associated antigens are highly related to cancer progression. Autoantibodies could serve as indicators of tumor burden, and have the potential to monitor the response of treatment and tumor recurrence. However, how the autoantibody repertoire changes in response to cancer treatment are largely unknown.

Methods

Sera of five lung adenocarcinoma patients before and after surgery, were collected longitudinally. These sera were analyzed on a human proteome microarray of 20,240 recombinant proteins to acquire dynamic autoantibody repertoire in response to surgery, as well as to identify the antigens with decreased antibody response after tumor excision or surgery, named as surgery-associated antigens. The identified candidate antigens were then used to construct focused microarray and validated by longitudinal sera collected from a variety of time points of the same patient and a larger cohort of 45 sera from lung adenocarcinoma patients.

Findings

The autoantibody profiles are highly variable among patients. Meanwhile, the autoantibody profiles of the sera from the same patient were surprisingly stable for at least 3 months after surgery. Six surgery-associated antigens were identified and validated. All the five patients have at least one surgery-associated antigen, demonstrating this type of biomarkers is prevalent, while specific antigens are poorly shared among individuals. The prevalence of each antigen is 2%–14% according to the test with a larger cohort.

Interpretation

To our knowledge, this is the first study of dynamically profiling of autoantibody repertoires before/after surgery of cancer patients. The high prevalence of surgery-associated antigens implies the possible broad application for monitoring of tumor recurrence in population, while the low prevalence of specific antigens allows personalized medicine. After the accumulation and analysis of more longitudinal samples, the surgery-associated serum biomarkers, combined as a panel, may be applied to alarm the recurrence of tumor in a personalized manner.

Funding

Research supported by grants from National Key Research and Development Program of China Grant (No. 2016YFA0500600), National Natural Science Foundation of China (No. 31970130, 31600672, 31670831, and 31370813), Open Foundation of Key Laboratory of Systems Biomedicine (No. KLSB2017QN-01), Science and Technology Commission of Shanghai Municipality Medical Guidance Science &Technology Support Project (16411966100), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20172005), Shanghai Municipal Commission of Health and Family Planning Outstanding Academic Leaders Training Program (2017BR055) and National Natural Science Foundation of China (81871882).

FGA isoform as an indicator of targeted therapy for EGFR mutated lung adenocarcinoma

Epidermal growth factor receptor (EGFR) gene is frequently mutated in non-small cell lung cancer (NSCLC), which can be targeted by EGFR tyrosine kinase inhibitors (TKIs). It is hard, however, to monitor the performance of EGFR-TKI therapy dynamically. Therefore, therapeutic indicators are urgently needed. Novel antibody microarray, containing 41,472 antibodies, was used for comprehensive analyzing of serum samples from 9 normal subjects and 9 EGFR mutated lung adenocarcinoma patients at three EGFR-TKI treatment time points, including before treatment (Baseline), partial response (PR) during treatment, and disease progression (PD) after resistance. Through microarray data analysis, five candidate antibodies were screened out for confirmation in serum samples and the verified one was utilized for candidate protein identification through immunoprecipitation-mass spectrometry strategy. A novel protein, isoform 2 of fibrinogen alpha chain (FGA2), was revealed and verified in the discovery sample set. Its performance as therapy indicator was further evaluated in another pre-validation sample set (n = 60). Our data confirmed that serum FGA2 level was correlated with EGFR-TKI response (p < 0.05). The expression and secretion of FGA2 in hepatocytes were inhibited by EGFR-TKI, partially explaining the downregulation of FGA2 in serum. Our results demonstrate that FGA2 is an indicator of targeted therapy for EGFR mutated lung adenocarcinoma. KEY MESSAGES: Antibody microarray was coupled with mass spectrometry for proteomics research. FGA2 was discovered as an indicator of EGFR-TKI targeted therapy. FGA2's expression/secretion in hepatocytes was dramatically inhibited by EGFR-TKI.

Establishment and validation of an immunodiagnostic model for prediction of breast cancer

Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919–0.967) in training cohort and 0.916 (95% CI, 0.886–0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848–0.914) and 0.849 (95% CI, 0.803–0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer.

Proteome microarray technology and application: higher, wider, and deeper

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.

Identification of Serum Biomarkers for Systemic Lupus Erythematosus Using a Library of Phage Displayed Random Peptides and Deep Sequencing

Systemic lupus erythematosus (SLE) is one of the most serious autoimmune diseases, characterized by highly diverse clinical manifestations. A biomarker is still needed for accurate diagnostics. SLE serum autoantibodies were discovered and validated using serum samples from independent sample cohorts encompassing 306 participants divided into three groups, i.e. healthy, SLE patients, and other autoimmune-related diseases. To discover biomarkers for SLE, a phage displayed random peptide library (Ph.D. 12) and deep sequencing were applied to screen specific autoantibodies in a total of 100 serum samples from 50 SLE patients and 50 healthy controls. A statistical analysis protocol was set up for the identification of peptides as potential biomarkers. For validation, 10 peptides were analyzed using enzyme-linked immunosorbent assays (ELISA). As a result, four peptides (SLE2018Val001, SLE2018Val002, SLE2018Val006, and SLE2018Val008) were discovered with high diagnostic power to differentiate SLE patients from healthy controls. Among them, two peptides, i.e. SLE2018Val001 and SLE2018Val002, were confirmed between SLE with other autoimmune patients. The procedure we established could be easily adopted for the identification of autoantibodies as biomarkers for many other diseases.

Using recursive partitioning approach to select tumor-associated antigens in immunodiagnosis of gastric adenocarcinoma

The present study aimed to select anti-tumor-associated antigen (TAA) autoantibodies as biomarkers in the immunodiagnosis of gastric adenocarcinoma (GAC) by the recursive partitioning approach (RPA) and further construct and evaluate a predictive model. A case-control study was designed including 407 GAC patients as the case group and 407 normal controls. In addition, 67 serial serum samples from 25 GAC patients were collected at different time points before and after gastrectomy treatment. Autoantibodies against 14 TAA were measured in sera from all subjects by enzyme immunoassay. Finally, RPA resulted in the selection of nine-panel TAA (c-Myc, p16, HSPD1, PTEN, p53, NPM1, ENO1, p62, HCC1.4) from all detected TAA in the case-control study; the classification tree based on this nine-TAA panel had area under curve (AUC) of 0.857, sensitivity of 71.5% and specificity of 71.3%; The optimal panel also can identify GAC patients at an early stage from normal individuals, with AUC of 0.737, sensitivity of 64.9% and specificity of 70.5%. However, frequencies of the nine autoantibodies showed no correlation with GAC stage, tumor size, lymphatic metastasis or differentiation. GAC patients positive for more than two autoantibodies in the nine-TAA panel had a worse prognosis than that of the GAC patients positive for no or one antibody. Titers of 10 autoantibodies in serial serum samples were significantly higher in GAC patients after surgical resection than before. In conclusion, this study showed that the panel of nine multiple TAAs could enhance the detection of anti-TAA antibodies in GAC, and may be potential prognostic biomarkers in GAC.

Plasma proteomic and autoantibody profiles reveal the proteomic characteristics involved in longevity families in Bama, China

BACKGROUND

Chinese Bama Yao Autonomous County is a well-known longevity region in the world. In the past 30 years, population and genome studies were undertaken to investigate the secret of longevity and showed that longevity is the result of a combination of multiple factors, such as genetic, environmental and other causes. In this study, characteristics of the blood plasma proteomic and autoantibody profiles of people from Bama longevity family were investigated.

METHODS

Sixty-six plasma donors from Chinese Bama longevity area were recruited in this study. Thirty-three offsprings of longevous families were selected as case studies (Longevous group) and 33 ABO (blood type), age, and gender-matched subjects from non-longevous families were selected as controls (Normal group). Each group contains 3 biological replicates. Tandem mass tag-based proteomic technique was used to investigate the differentially expressed plasma proteins between the two groups. The auto-reactive IgG antibody profiles of the 3 pooled samples in each group were revealed by human proteome microarrays with 17,000 recombinant human proteins.

RESULTS

Firstly, 525 plasma proteins were quantified and 12 proteins were discovered differentially expressed between the two groups. Secondly, more than 500 proteins were recognized by plasma antibodies, 14 proteins ware differentially reacted with the autoantibodies in the two groups. Bioinformatics analysis showed some of the differential proteins and targeted autoantigens were involved in cancer, cardiovascular disease and immunity.

CONCLUSIONS

Proteomic and autoantibody profiles varied between the offspring of longevous and normal families which are from the same area and shared the same environmental factors. The identified differences were reported to be involved in several physiological and pathological pathways. The identified proteins will contribute to a better understanding of the proteomic characteristics of people from Bama longevous area and a revelation of the molecular mechanisms of longevity.

NT5E is associated with unfavorable prognosis and regulates cell proliferation and motility in gastric cancer

Ecto-5′-nucleotidase (NT5E) is a glycosylphosphatidylinositol anchored cell surface protein, and has been suggested to be dysregulated in most types of human cancer including gastric cancer. The aim of the present study was to present more evidence about the clinical and prognostic value of Ecto-5′-nucleotidase in gastric cancer patients, and preliminarily explore the biological function of Ecto-5′-nucleotidase in gastric cancer cells. In our study, high Ecto-5′-nucleotidase expression was observed in gastric cancer tissues and cell lines, respectively, compared with normal gastric mucosa tissues cells. Meanwhile, TCGA database also indicated that Ecto-5′-nucleotidase expression levels were notably elevated in gastric cancer tissues compared with normal gastric mucosa tissues. Furthermore, high-expression of Ecto-5′-nucleotidase was obviously associated with advanced clinical stage, deep tumor invasion, lymph node metastasis and distant metastasis in gastric cancer patients. The survival analyses of TCGA database and our study consistent suggested high Ecto-5′-nucleotidase expression was negatively correlated with overall survival time in gastric cancer patients. The univariate and multivariate Cox proportional hazards regression model showed high Ecto-5′-nucleotidase expression was an independent poor prognostic factor for gastric cancer patients. Moreover, silencing of Ecto-5′-nucleotidase expression suppressed cell proliferation, migration and invasion in vitro in gastric cancer. In conclusion, Ecto-5′-nucleotidase is a credible prognostic biomarker, and serves as a potential therapeutic target in gastric cancer.

Long Noncoding RNAs Serve as Potential Diagnostic Biomarkers for Colorectal Cancer

Background: Mounting evidence has indicated that long noncoding RNAs (lncRNAs) are promising candidates for tumor diagnosis and prognosis. Nonetheless, the significance of lncRNAs in colorectal cancer (CRC) diagnosis remains to be clarified. Here, we performed a comprehensive meta-analysis to evaluate the utility of lncRNAs as diagnostic indicators for CRC. Materials and Methods: Pertinent studies were searched using PubMed, PMC, Web of Science, Cochrane, and EMBASE database up to September 2018. Study quality was assessed with the Quality Assessment for Studies of Diagnostic Accuracy-2. Subgroup analyses by sample size and publication year were conducted. Threshold effect and meta-regression were performed to find the origin of heterogeneity. Statistical analyses were conducted using Stata and Meta-Disc. Results: A total of 19 studies with 3,114 individuals were enrolled in the current analysis. The overall sensitivity and specificity of lncRNAs in the diagnosis of CRC were 0.83 [95% confidence interval (CI): 0.76-0.87] and 0.84 (95% CI: 0.77-0.89), respectively. The pooled positive likelihood ratio was 5.11 (95% CI: 3.57-7.31), and the pooled negative likelihood ratio was 0.21 (95% CI: 0.15-0.28). The overall area under the curve was 0.90 (95% CI: 0.87-0.92), with a diagnostic odds ratio of 24.57 (95% CI: 14.67-41.17). Conclusions: The accuracy of lncRNAs for CRC diagnosis is high, and lncRNAs could be functioned as promising candidates for CRC diagnosis.

Immunoproteomics Methods and Techniques

The varied landscape of the adaptive immune response is determined by the peptides presented by immune cells, derived from viral or microbial pathogens or cancerous cells. The study of immune biomarkers or antigens is not new, and classical methods such as agglutination, enzyme-linked immunosorbent assay, or Western blotting have been used for many years to study the immune response to vaccination or disease. However, in many of these traditional techniques, protein or peptide identification has often been the bottleneck. Recent progress in genomics and mass spectrometry have led to many of the rapid advances in proteomics approaches. Immunoproteomics describes a rapidly growing collection of approaches that have the common goal of identifying and measuring antigenic peptides or proteins. This includes gel-based, array-based, mass spectrometry-based, DNA-based, or in silico approaches. Immunoproteomics is yielding an understanding of disease and disease progression, vaccine candidates, and biomarkers. This review gives an overview of immunoproteomics and closely related technologies that are used to define the full set of protein antigens targeted by the immune system during disease.

Global Profiling of PknG Interactions Using a Human Proteome Microarray Reveals Novel Connections with CypA

Mycobacterium tuberculosis (Mtb) serine/threonine kinase PknG plays an important role in the Mtb-host interaction by facilitating the survival of Mtb in macrophages. However, the human proteins with which the PknG interacts, and the underlying molecular mechanisms are still largely unknown. In this study, a HuProt array is been applied to globally identify the host proteins to which PknG binds. In this way, 125 interactors are discovered, including a cyclophilin protein, CypA. This interaction between PknG and CypA is validated both in vitro and in vivo, and functional studies show that PknG significantly reduces the protein levels of CypA through phosphorylation, which consequently inhibit the inflammatory response through downregulation of NF-κB and ERK1/2 pathways. Phenotypically, overexpression of PknG reduces cytokine levels and promotes the survival of Mycobacterium smegmatis (Msm) in macrophages. Overall, it is expected that the PknG interactors identified in this study will serve as a useful resource for further systematic studies of the roles that PknG plays in the Mtb-host interactions.

Prognostic Significance of CSN2, CD8, and MMR Status-Associated Nomograms in Patients with Colorectal Cancer

BACKGROUND

COP9 signalosome subunit 2 (CSN2) is believed to be involved in human cancer, but its prognostic significance in colorectal cancer (CRC) has not been elucidated.

PATIENTS AND METHODS

We retrospectively analyzed the expression of CSN2 andCD8+ tumor-infiltrating lymphocytes (TILs), and mismatch repair (MMR) status in 267 paraffin-embedded specimens using immunohistochemistry in a training cohort. A number of risk factors were used to form nomograms to evaluate survival, and Harrell's concordance index (C-index) was used to evaluate the predictive accuracy. Further validation was performed in an independent cohort of 238cases.

RESULTS

Low CSN2 expression and a low number of CD8 + TILs were significantly associated with diminished disease-free survival (DFS) and overall survival (OS) in CRC patients, and patients with MMR-deficient CRC had enhanced DFS and OS. Moreover, the multivariate Cox analysis identified CSN2, CD8 + TILs, and MMR status as independent prognostic factors for DFS and OS. Using these three markers and four clinicopathological risk variables, two nomograms were constructed and validated for predicting DFS and OS (C-index: training cohort, 0.836 (95% CI:0.804-0.868) and 0.841 (0.808-0.874), respectively; validation cohort, 0.801 (0.760-843) and 0.843 (0.806-0.881), respectively).

CONCLUSIONS

CSN2, CD8+ TILs, and MMR status were independent prognostic factors. The nomograms could be used to generate individualized predictions for DFS and OS.

Comprehensive analysis of marker gene detection and computed tomography for the diagnosis of human lung cancer

Non-small cell lung cancer (NSCLC) is one of leading causes of cancer-associated mortality, with a high number of cases caused by metastasis. The early diagnosis of cancer contributes to the successful treatment of patients with lung cancer. The aim of the present study was to analyze the efficacy of marker gene detection and computed tomography (CT) in diagnosing human lung cancer. Lung cancer marker genes, including carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), tissue polypeptide antigen (TPA), pro-gastrin-releasing peptide (ProGRB), cytokeratin fragment 21-1 (Cyfra21-1) and neuron-specific enolase (NSE), were analyzed in patients with lung cancer. The tumor size was evaluated using CT, and the association between lung serum levels of marker gene protein expression and tumor size was investigated. A total of 328 patients with lung cancer were identified, including 204 adenocarcinoma, 75 large cell carcinoma and 49 squamous cell carcinoma cases. All patients were indicated to have a high serum level of CEA, CA125, TPA, ProGRB, Cyfra21-1 and NSE, compared with the normal range. Immunohistochemistry demonstrated higher expression levels of CEA, CA125, TPA, ProGRB, Cyfra21-1 and NSE in lung tumor tissues, compared with the normal range. Results indicated that CT was able to diagnose tumor size for patients with lung cancer. The CEA and CA125 expression levels were associated with CT-diagnosed adenocarcinoma tumor size. Large cell carcinoma tumor size was associated with serum levels of CEA, TPA and ProGRB. Results indicated that Cyfra21-1 and NSE were associated with the squamous cell carcinoma cases, as demonstrated using CT. In conclusion, these results indicated that comprehensive analysis of marker gene detection and CT results may be used to diagnose human lung cancer.

TERT expression in gastric carcinogenesis: Correlation with clinical features

AIM

To investigate the expression of telomerase reverse transcriptase (TERT) mRNA in gastric cancer and precancerous lesions, and to analyze the relationship between TERT expression and clinicopathologic features and prognosis in gastric cancer.

METHODS

From September 2011 to October 2016, 102 patients with gastric cancer, 32 patients with precancerous lesions, and 30 patients with chronic non-atrophic gastritis were treated at the First Affiliated Hospital of Jiamus University. The expression of TERT mRNA in tissues was detected by real-time quantitative PCR, and the correlation of expression of TERT with clinicopathologic features and prognosis in gastric cancer was analyzed.

RESULTS

The expression of TERT in gastric cancer and precancerous lesions was significantly higher than that in chronic non-atrophic gastritis (P < 0.05). TERT expression was significantly correlated with the depth of invasion, TNM stage, vascular invasion, and lymph node metastasis (P < 0.05). The overall survival rate of gastric cancer patients with high expression of TERT was significantly lower than that of patients with low expression of TERT (HR = 0.420, 95%CI: 0.264-0.668, P < 0.001). The progression-free survival rate of gastric cancer patients with high expression of TERT was also significantly lower than that of patients with low TERT expression (HR = 0.649, 95%CI: 0.421-0.999, P = 0.049). Cox multivariate survival analysis showed that TERT expression was an independent prognostic factor for overall survival in gastric cancer.

CONCLUSION

TERT is highly expressed in gastric cancer and precancerous lesions, and high expression of TERT is significantly associated with the clinicopathologic features and prognosis of gastric cancer patients.

The search for drug-targetable diagnostic, prognostic and predictive biomarkers in chronic graft-versus-host disease

INTRODUCTION

Chronic graft-versus-host disease (cGVHD) continues to be the leading cause of late morbidity and mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT), which is an increasingly applied curative method for both benign and malignant hematologic disorders. Biomarker identification is crucial for the development of noninvasive and cost-effective cGVHD diagnostic, prognostic, and predictive test for use in clinic. Furthermore, biomarkers may help to gain a better insight on ongoing pathophysiological processes. The recent widespread application of omics technologies including genomics, transcriptomics, proteomics and cytomics provided opportunities to discover novel biomarkers. Areas covered: This review focuses on biomarkers identified through omics that play a critical role in target identification for drug development, and that were verified in at least two independent cohorts. It also summarizes the current status on omics tools used to identify these useful cGVHD targets. We briefly list the biomarkers identified and verified so far. We further address challenges associated to their exploitation and application in the management of cGVHD patients. Finally, insights on biomarkers that are drug targetable and represent potential therapeutic targets are discussed. Expert commentary: We focus on biomarkers that play an essential role in target identification.

Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer

Autoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assay (ELISA). A logistic regression model predicting the risk of being diagnosed with GC in the training cohort (n = 558) was generated and then validated in an independent cohort (n = 372). Area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Finally, an optimal prediction model with 6 TAAs (p62, c-Myc, NPM1, 14-3-3ξ, MDM2 and p16) showed a great diagnostic performance of GC with AUC of 0.841 in the training cohort and 0.856 in the validation cohort. The proportion of subjects being correctly defined were 78.49% in the training cohort and 81.99% in the validation cohort. This prediction model could also differentiate early-stage (stage I-II) GC patients from healthy controls with sensitivity/specificity of 76.60%/72.34% and 80.56%/79.17% in the training and validation cohort, respectively, and the overall sensitivity/specificity for early-stage GC were 78.92%/74.70% when being combined with two cohorts. This prediction model presented no significant difference for the diagnostic accuracy between early-stage and late-stage (stage III - IV) GC patients. The model with 6 TAAs showed a high diagnostic performance for GC detection, particularly for early-stage GC. This study further supported the hypothesis that a customized array of multiple TAAs was able to enhance autoantibody detection in the immunodiagnosis of GC.

Advances in the development of human protein microarrays

INTRODUCTION

High-content protein microarrays in principle enable the functional interrogation of the human proteome in a broad range of applications, including biomarker discovery, profiling of immune responses, identification of enzyme substrates, and quantifying protein-small molecule, protein-protein and protein-DNA/RNA interactions. As with other microarrays, the underlying proteomic platforms are under active technological development and a range of different protein microarrays are now commercially available. However, deciphering the differences between these platforms to identify the most suitable protein microarray for the specific research question is not always straightforward. Areas covered: This review provides an overview of the technological basis, applications and limitations of some of the most commonly used full-length, recombinant protein and protein fragment microarray platforms, including ProtoArray Human Protein Microarrays, HuProt Human Proteome Microarrays, Human Protein Atlas Protein Fragment Arrays, Nucleic Acid Programmable Arrays and Immunome Protein Arrays. Expert commentary: The choice of appropriate protein microarray platform depends on the specific biological application in hand, with both more focused, lower density and higher density arrays having distinct advantages. Full-length protein arrays offer advantages in biomarker discovery profiling applications, although care is required in ensuring that the protein production and array fabrication methodology is compatible with the required downstream functionality.

Advances in endometrial cancer protein biomarkers for use in the clinic

INTRODUCTION

Endometrial cancer (EC) is the fourth most common cancer in women in developed countries. The identification of sensitive and specific biomarkers to improve early detection of EC is crucial for an appropriate management of this disease, in which 30% of patients are diagnosed only at advanced stages, which is associated with high levels of morbidity and mortality. Despite major efforts and investments made to identify EC biomarkers, no protein has yet reached the stage of clinical application. Areas covered: This review gathers the numerous candidate biomarkers for EC diagnosis proposed in proteomic studies published from 1978 to 2017. Additionally, we summarize limitations associated with the proteomic technologies and study designs employed in those articles. Finally, we address new perspectives in EC biomarker research, including the comprehensive knowledge of previously suggested candidate biomarkers in conjunction with novel mass spectrometry-based proteomic technologies with enhanced sensitivity and specificity not yet applied to EC studies and a directed clinical perspective in the study design. Expert commentary: These ingredients could be the recipe to accelerate the application of protein biomarkers in the clinic.

Identification of Serological Biomarkers for Early Diagnosis of Lung Cancer Using a Protein Array-Based Approach

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a protein array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt arrays, and identified 170 candidate proteins significantly associated with LC. In Phase II, we constructed a LC focused array with the 170 proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.

Multiplexed Nucleic Acid Programmable Protein Arrays

Rationale: Cell-free protein microarrays display naturally-folded proteins based on just-in-time in situ synthesis, and have made important contributions to basic and translational research. However, the risk of spot-to-spot cross-talk from protein diffusion during expression has limited the feature density of these arrays. Methods: In this work, we developed the Multiplexed Nucleic Acid Programmable Protein Array (M-NAPPA), which significantly increases the number of displayed proteins by multiplexing as many as five different gene plasmids within a printed spot. Results: Even when proteins of different sizes were displayed within the same feature, they were readily detected using protein-specific antibodies. Protein-protein interactions and serological antibody assays using human viral proteome microarrays demonstrated that comparable hits were detected by M-NAPPA and non-multiplexed NAPPA arrays. An ultra-high density proteome microarray displaying > 16k proteins on a single microscope slide was produced by combining M-NAPPA with a photolithography-based silicon nano-well platform. Finally, four new tuberculosis-related antigens in guinea pigs vaccinated with Bacillus Calmette-Guerin (BCG) were identified with M-NAPPA and validated with ELISA. Conclusion: All data demonstrate that multiplexing features on a protein microarray offer a cost-effective fabrication approach and have the potential to facilitate high throughput translational research.

A meta-analysis: The diagnostic values of long non-coding RNA as a biomarker for gastric cancer

Long non-coding RNAs (lncRNAs) have been identified as novel biomarkers for the diagnosis, staging and prognosis for gastric cancer. However, various studies have reported a series of significances based on different diagnostic values. Therefore, the current study performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of lncRNAs for gastric cancer, and to discuss lncRNA types and sources of heterogeneity. The Cochrane Central Register of Controlled Trials, MEDLINE, PubMed, EMBASE, the Chinese Biomedical Literature Database, the China Academic Journals Full-text Database and the Chinese Scientific Journals Database were systematically searched for potential studies. Studies were included if they were associated with lncRNAs, gastric cancer and reported diagnostic outcomes. Analysis of diagnostic values was used to summarize the overall test performance of lncRNAs. Ten studies were included in this meta-analysis. The ranges of the diagnostic value of lncRNAs for gastric cancer were as follows: Sensitivity was 0.45–0.83, and pooled sensitivity was 0.63; specificity was 0.60–0.93, and pooled specificity was 0.75; positive likelihood ratio was 1.80–6.92, and pooled positive likelihood ratio was 2.51; negative likelihood ratio was 0.23–0.67, and pooled negative likelihood ratio was 0.50; diagnostic odds ratio was 3.33–13.75, and pooled diagnostic odds ratio was 5.47. An overall area under the curve value of the summary receiver operating characteristic curve was 0.7550. LncRNAs did not have a high accuracy for identifying gastric cancer at present, but may be a useful screening tool for diagnosing gastric cancer due to their correlation with gastric cancer biological features. LncRNAs are potential biomarkers for gastric cancer if the screening strategy is altered, or they are combined with other biomarkers to diagnose gastric cancer.

Cathepsin F Knockdown Induces Proliferation and Inhibits Apoptosis in Gastric Cancer Cells

Gastric cancer (GC) is one of the most common cancers in the world. The cathepsin F (CTSF) gene has recently been found to participate in the progression of several types of cancer. However, the clinical characteristics and function of CTSF in GC as well as its molecular mechanisms are not clear. Six GC cell lines and 44 paired adjacent noncancerous and GC tissue samples were used to assess CTSF expression by quantitative polymerase chain reaction (qPCR). We used lentivirus-mediated small hairpin RNA (Lenti-shRNA) against CTSF to knock down the expression of CTSF in GC cells. Western blot and qPCR were used to analyze the mRNA and related protein expression. The biological phenotypes of gastric cells were examined by cell proliferation and apoptosis assays. Microarray-based mRNA expression profile screening was also performed to evaluate the potential molecular pathways in which CTSF may be involved. The CTSF mRNA level was associated with tumor differentiation, depth of tumor invasion, and lymph node metastasis. Downregulation of CTSF expression efficiently inhibited apoptosis and promoted the proliferation of GC cells. Moreover, a total of 1,117 upregulated mRNAs and 1,143 downregulated mRNAs were identified as differentially expressed genes (DEGs). Further analysis identified the involvement of these mRNAs in cancer-related pathways and various other biological processes. Nine DEGs in cancer-related pathways and three downstream genes in the apoptosis pathway were validated by Western blot, which was mainly in agreement with the microarray data. To our knowledge, this is the first report investigating the effect of CTSF on the growth and apoptosis in GC cells and its clinical significance. The CTSF gene may function as a tumor suppressor in GC and may be a potential therapeutic target in the treatment of GC.

Protein Array-based Approaches for Biomarker Discovery in Cancer

Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, protein array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of protein arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.

Current Understanding of the TCTP Interactome

Evolutionarily conserved and pleiotropic, the translationally controlled tumor protein (TCTP) is a housekeeping protein present in eukaryotic organisms. It plays an important role in regulating many fundamental processes, such as cell proliferation, cell death, immune responses, and apoptosis. As a result of the pioneer work by Adam Telerman and Robert Amson, the critical role of TCTP in tumor reversion was revealed. Moreover, TCTP has emerged as a regulator of cell fate determination and a promising therapeutic target for cancers. The multifaceted action of TCTP depends on its ability to interact with different proteins. Through this interaction network, TCTP regulates diverse physiological and pathological processes in a context-dependent manner. Complete mapping of the entire sets of TCTP protein interactions (interactome) is essential to understand its various cellular functions and to lay the foundation for the rational design of TCTP-based therapeutic approaches. So far, the global profiling of the interacting partners of TCTP has rarely been performed, but many interactions have been identified in small-scale studies in a specific biological system. This chapter, based on information from protein interaction databases and the literature, illustrates current knowledge of the TCTP interactome.

Rapid Production of Virus Protein Microarray Using Protein Microarray Fabrication through Gene Synthesis (PAGES)

The high genetic variability of RNA viruses is a significant factor limiting the discovery of effective biomarkers, the development of vaccines, and characterizations of the immune response during infection. Protein microarrays have been shown to be a powerful method in biomarker discovery and the identification of novel protein–protein interaction networks, suggesting that this technique could also be very useful in studies of infectious RNA viruses. However, to date, the amount of genetic material required to produce protein arrays, as well as the time- and labor-intensive procedures typically needed, have limited their more widespread application. Here, we introduce a method, protein microarray fabrication through gene synthesis (PAGES), for the rapid and efficient construction of protein microarrays particularly for RNA viruses. Using dengue virus as an example, we first identify consensus sequences from 3,604 different strains and then fabricate complete proteomic microarrays that are unique for each consensus sequence. To demonstrate their applicability, we show that these microarrays can differentiate sera from patients infected by dengue virus, related pathogens, or from uninfected patients. We anticipate that the microarray and expression library constructed in this study will find immediate use in further studies of dengue virus and that, more generally, PAGES will become a widely applied method in the clinical characterization of RNA viruses.

Abnormal amphiregulin expression correlates with gastric cancer prognosis

Gastric cancer (GC) is a global health issue with a high mortality rate. Early diagnosis and tracking of GC is a challenge due to a lack of reliable tools. Amphiregulin (AREG) is a member of the epidermal growth factor (EGF) family that activates growth signaling upon binding of EGF receptors. Elevated AREG expression is associated with various pathological conditions, including cancer. Here, we investigated whether increased AREG expression is a disease indicator and/or prognostic biomarker for GC. We used tissue microarray and quantitative real-time polymerase chain reaction to assess AREG expression in clinical tissue specimens at various stages of GC and a conducted bioinformatics analysis to evaluate the value of AREG over-expression as a GC biomarker. We found that both mRNA and protein expression of AREG were increased in the tissues of GC patients when compared to tissues from non-cancer patients or normal tissues. High expression of AREG was also associated with GC clinicopathological characteristics and poor survival. Thus, over-expression of AREG could serve as a novel GC biomarker, and active surveillance of its expression could be a novel approach to GC diagnosis and monitoring.

Protein microarray applications: Autoantibody detection and posttranslational modification

The discovery of DNA microarrays was a major milestone in genomics; however, it could not adequately predict the structure or dynamics of underlying protein entities, which are the ultimate effector molecules in a cell. Protein microarrays allow simultaneous study of thousands of proteins/peptides, and various advancements in array technologies have made this platform suitable for several diagnostic and functional studies. Antibody arrays enable researchers to quantify the abundance of target proteins in biological fluids and assess PTMs by using the antibodies. Protein microarrays have been used to assess protein-protein interactions, protein-ligand interactions, and autoantibody profiling in various disease conditions. Here, we summarize different microarray platforms with focus on its biological and clinical applications in autoantibody profiling and PTM studies. We also enumerate the potential of tissue microarrays to validate findings from protein arrays as well as other approaches, highlighting their significance in proteomics.