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

Unraveling the causal relationship and underlying mechanisms between cathepsins on liver cancer: findings from mendelian randomization and bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) are two major types of primary liver cancer (PLC). Earlier research has indicated a potential link between cathepsins and liver cancer. Nonetheless, there have been limited clinical trials examining the connection between cathepsins and PLC. Therefore, we conducted a two-sample Mendelian randomization (MR) study to evaluate the causal relationship between cathepsins and PLC.

METHODS

Data from genome-wide association studies (GWAS) focusing on cathepsins was collected. Additionally, summary data for GCST90018803 (Hepatic bile duct cancer, HBDC), and GCST90018858 (related to hepatic cancer, HC), were employed in the discovery and validation phases of the study, respectively. The inverse variance weighted (IVW) method was served as the primary analytical method in our Mendelian randomization (MR) study, supplemented by the MR-Egger, weighted median, simple mode, and weighted mode methods. To assess heterogeneity and pleiotropy, we conducted the MR-Egger intercept test, Cochran's Q test, as well as the MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis, along with the leave-one-out analysis. After that, bioinformatic analysis based on the Gene Expression Omnibus (GEO) databases were utilized, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were utilized for exploring the underlying mechanisms. Additionally, protein-protein docking was employed to confirm the interaction between related proteins.

RESULTS

The results showed that cathepsin F (CTSF), was causally associated with HBDC. CTSF decrease the risk of HBDC (OR = 0.826, 95% CI 0.711-0.959, P = 0.012). CTSF may play protective roles in patients with HBDC. No heterogeneity or pleiotropy was observed. Additionally, the expression of CTSF genes is lower in patients with HBDC, GO and KEGG functional enrichment analysis revealed CTSF were mainly related to cell cycle, and P53 pathway in HBDC. Docking results showed that CTSF had good binding ability with MDM2, the most well-established negative regulator of p53.

CONCLUSION

This study provided new evidence of the relationship between CTSF and HBDC, suggesting that CTSF plays an inhibition role in HBDC progression. CTSF could be a novel and effective way to for HDBC treatment.

Screening of Cancer-Specific Biomarkers for Hepatitis B-Related Hepatocellular Carcinoma Based on a Proteome Microarray

Hepatocellular carcinoma (HCC) is associated with one of the highest mortality rates among cancers, rendering its early diagnosis clinically invaluable. Serum biomarkers, specifically alpha-fetoprotein (AFP), represent the most promising and widely used diagnostic biomarkers for HCC. However, its detection rate is low in the early stages of HCC progression, and distinguishing specific false positives for other liver-related diseases, such as cirrhosis and acute hepatitis, remains challenging. Therefore, this study was conducted to identify biomarkers for hepatitis B (HBV)-related liver diseases by screening differentially expressed autoantibodies against tumor-associated antigens (TAAbs). We designed a large-scale multistage investigation, encompassing initial screening, HCC-focused, and ELISA validation cohorts to identify potential TAAbs in HBV-related liver diseases, spanning from healthy control (HC) individuals to patients with chronic hepatitis B (CHB), hepatitis B-related cirrhosis (HBC), and HCC, using protein microarray technology. The differential biological characteristics of TAAbs were analyzed using bioinformatics analysis. Validation of tumor-specific biomarkers for HCC was performed using ELISA. In the screening cohort, 547 candidate TAAbs were identified in the HCC group compared to those in the HC group. In the HCC-focused cohort, 64, 61, and 65 candidate TAAbs were identified in the CHB, HBC, and HCC groups, respectively, compared to those in the HC group. Thirty-four proteins exhibited continuously elevated expression from HCs to patients with CHB, HBC, and HCC. Among these, nine were identified as cancer-specific proteins. In the validation cohort, UBE2Z, CNOT3, and EID3 were correlated with liver function indicators in patients with hepatitis B-related HCC. Overall, UBE2Z, CNOT3, and EID3 emerged as cancer-specific biomarkers for HBV-related liver disease, providing a scientific basis for clinical application.

The implicated role of GDF15 in gastrointestinal cancer

BACKGROUND

Growth differentiation factor 15 (GDF15), a stress-responsive cytokine from transforming growth factor superfamily, is highly expressed in mammalian tissues, including pancreas, stomach and intestine under pathological conditions. In particular, elevated levels of GDF15 might play an important role in the development and progression of various gastrointestinal cancers (GCs), suggesting its potential as a promising target for disease prediction and treatment.

METHODS

In this review, systematic reviews addressing the role of GDF15 in GCs were updated, along with the latest clinical trials focussing on the GDF15-associated digestive malignancies.

RESULTS

The multiple cellular pathways through which GDF15 is involved in the regulation of physiological and pathological conditions were first summarized. Then, GDF15 was also established as a valuable clinical index, functioning as a predictive marker in diverse GCs. Notably, latest clinical treatments targeting GDF15 were also highlighted, demonstrating its promising potential in mitigating and curing digestive malignancies.

CONCLUSIONS

This review unveils the pivotal roles of GDF15 and its potential as a promising target in the pathogenesis of GCs, which may provide insightful directions for future investigations.

PhIP-Seq: methods, applications and challenges

Phage-immunoprecipitation sequencing (PhIP-Seq) technology is an innovative, high-throughput antibody detection method. It enables comprehensive analysis of individual antibody profiles. This technology shows great potential, particularly in exploring disease mechanisms and immune responses. Currently, PhIP-Seq has been successfully applied in various fields, such as the exploration of biomarkers for autoimmune diseases, vaccine development, and allergen detection. A variety of bioinformatics tools have facilitated the development of this process. However, PhIP-Seq technology still faces many challenges and has room for improvement. Here, we review the methods, applications, and challenges of PhIP-Seq and discuss its future directions in immunological research and clinical applications. With continuous progress and optimization, PhIP-Seq is expected to play an even more important role in future biomedical research, providing new ideas and methods for disease prevention, diagnosis, and treatment.

Phage Immunoprecipitation and Sequencing-a Versatile Technique for Mapping the Antibody Reactome

Characterizing the antibody reactome for circulating antibodies provide insight into pathogen exposure, allergies, and autoimmune diseases. This is important for biomarker discovery, clinical diagnosis, and prognosis of disease progression, as well as population-level insights into the immune system. The emerging technology phage display immunoprecipitation and sequencing (PhIP-seq) is a high-throughput method for identifying antigens/epitopes of the antibody reactome. In PhIP-seq, libraries with sequences of defined lengths and overlapping segments are bioinformatically designed using naturally occurring proteins and cloned into phage genomes to be displayed on the surface. These libraries are used in immunoprecipitation experiments of circulating antibodies. This can be done with parallel samples from multiple sources, and the DNA inserts from the bound phages are barcoded and subjected to next-generation sequencing for hit determination. PhIP-seq is a powerful technique for characterizing the antibody reactome that has undergone rapid advances in recent years. In this review, we comprehensively describe the history of PhIP-seq and discuss recent advances in library design and applications.

Unveiling the Roles of Cysteine Proteinases F and W: From Structure to Pathological Implications and Therapeutic Targets

Cysteine cathepsins F and W are members of the papain-like cysteine protease family, which have distinct structural features and functional roles in various physiological and pathological processes. This review provides a comprehensive overview of the current understanding of the structure, biological functions, and pathological implications of cathepsins F and W. Beginning with an introduction to these proteases, we delve into their structural characteristics and elucidate their unique features that dictate their enzymatic activities and substrate specificity. We also explore the intricate involvement of cathepsins F and W in malignancies, highlighting their role as potential biomarkers and therapeutic targets in cancer progression. Furthermore, we discuss the emerging roles of these enzymes in immune response modulation and neurological disorders, shedding light on their implications in autoimmune and neurodegenerative diseases. Finally, we review the landscape of inhibitors targeting these proteases, highlighting their therapeutic potential and challenges in clinical translation. This review brings together the diverse facets of cysteine cathepsins F and W, providing insights into their roles in health and disease and guiding future investigations for therapeutic advances.

Proteome microarray identifies autoantibody biomarkers for diagnosis of hepatitis B-related hepatocellular carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) has the highest mortality rate among malignant tumors worldwide. This study aimed to analyze the biological characteristics of serum proteins in hepatitis B (HBV)-related liver diseases, identify diagnostic biomarkers for HBV-infected HCC, and provide a scientific basis for its prevention and treatment.

MATERIALS AND METHODS

We used HuProt arrays to identify candidate biomarkers for HBV-related liver diseases and verified the differential biomarkers by using an HCC-focused array. The biological characteristics of serum proteins were analyzed via bioinformatics. Serum biomarkers levels were validated by ELISA.

RESULTS

We identified 547 differentially expressed proteins from HBV-infected HCC in a screening cohort. After analyzing the biological characteristics of serum proteins, we identified 10 potential differential autoantibodies against tumor-associated antigens (TAAbs) and a candidate biomarker panel (APEX2, RCSD1, and TP53) for the diagnosis of HBV-associated HCC with 61.9% sensitivity and 81.7% specificity in an HCC-focused array validation cohort. Finally, the protein levels and diagnostic capability of the biomarker panel were confirmed in a large-sample validation cohort, and this panel was found to be superior to alpha-fetoprotein, the standard hallmark for the diagnosis of HCC.

CONCLUSION

The APEX2, RCSD1, and TP53 biomarker panels could be used for the diagnosis of HBV-associated HCC, providing a scientific basis for clinical practice.

ZPR1 is an immunodiagnostic biomarker and promotes tumor progression in esophageal squamous cell carcinoma

To evaluate the potential of zinc finger protein 1 (ZPR1) as a diagnostic biomarker and explore the underlying role for esophageal squamous cell carcinoma (ESCC). A human proteome microarray was customized to identify anti-ZPR1 autoantibody, and enzyme-linked immunosorbent assay (ELISA) was adopted to assess the diagnostic performance of anti-ZPR1 autoantibody in 294 patients with ESCC and 294 normal controls. The expression of ZPR1 protein was measured by immunohistochemistry. The effect of ZPR1 on the proliferation, migration, and invasion of ESCC cells was investigated through CCK-8, wound healing, and Transwell assays. The expression level of anti-ZPR1 autoantibody (fold change = 2.77) in ESCC patients was higher than that in normal controls. The receiver operating characteristic (ROC) analysis manifested anti-ZPR1 autoantibody achieved area under the ROC curve (AUC) of 0.726 and 0.734 to distinguish ESCC from normal controls with sensitivity of 50.0% and 42.3%, and specificity of 91.0% and 92.0% in the test group and validation group, respectively. The positive rate of ZPR1 protein was significantly higher in ESCC tissues (75.5%, 80/106) than paracancerous tissues (9.4%, 5/53). Compared with the human normal esophageal cell line, the expression level of ZPR1 mRNA and protein in ESCC lines (KYSE150, Eca109, and TE1) had an increased trend. The knockdown or overexpression of ZPR1 reduced and enhanced the proliferation, migration, and invasion of ESCC cell, respectively. ZPR1 was a potential immunodiagnostic biomarker for noninvasive detection and could be a promotional factor in tumor progression of ESCC.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

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

Using Proteome Microarray and Gene Expression Omnibus Database to Screen Tumour-Associated Antigens to Construct the Optimal Diagnostic Model of Oesophageal Squamous Cell Carcinoma

AIMS

Autoantibodies against tumour-associated antigens (TAAs) are promising biomarkers for early immunodiagnosis of cancers. This study was designed to screen and verify autoantibodies against TAAs in sera as diagnostic biomarkers for oesophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

The customised proteome microarray based on cancer driver genes and the Gene Expression Omnibus database were used to identify potential TAAs. The expression levels of the corresponding autoantibodies in serum samples obtained from 243 ESCC patients and 243 healthy controls were investigated by enzyme-linked immunosorbent assay (ELISA). In total, 486 serum samples were randomly divided into the training set and the validation set in the ratio of 2:1. Logistic regression analysis, recursive partition analysis and support vector machine were performed to establish different diagnostic models.

RESULTS

Five and nine candidate TAAs were screened out by proteome microarray and bioinformatics analysis, respectively. Among these 14 anti-TAAs autoantibodies, the expression level of nine (p53, PTEN, GNA11, SRSF2, CXCL8, MMP1, MSH6, LAMC2 and SLC2A1) anti-TAAs autoantibodies in the cancer patient group was higher than that in the healthy control group based on the results from ELISA. In the three constructed models, a logistic regression model including four anti-TAA autoantibodies (p53, SLC2A1, GNA11 and MMP1) was considered to be the optimal diagnosis model. The sensitivity and specificity of the model in the training set and the validation set were 70.4%, 72.8% and 67.9%, 67.9%, respectively. The area under the receiver operating characteristic curve for detecting early patients in the training set and the validation set were 0.84 and 0.85, respectively.

CONCLUSIONS

This approach to screen novel TAAs is feasible, and the model including four autoantibodies could pave the way for the diagnosis of ESCC.

Hydrophobicity and molecular mass-based separation method for autoantibody discovery from mammalian total cellular proteins

Serum autoantibody profiles are unique to individuals and reflect the level and history of autoimmunity and tumor immunity. The identification of autoantibody biomarkers is critical for the development of immune monitoring systems for immune-related disorders. Here, we present a practical method for large-scale autoantibody discovery using total cellular proteins from cultured mammalian cells. We found that nucleic acid-free and fully denatured water-soluble total cellular proteins from mammalian cells were superior, allowing precise separation by reversed-phase HPLC after preparing a large set of homogeneous total cellular proteins. After separating the proteins based on hydrophobicity, the fractionated samples were subjected to molecular mass analysis using conventional SDS-PAGE. The resulting two-dimensional gel electrophoresis was successfully employed for immune blotting and LC-MS/MS analysis. All procedures, including TRIzol-based total cellular protein extraction, solubilization of denatured proteins, reversed-phase HPLC separation, and SDS-PAGE, were highly reproducible and easily scalable. We propose this novel two-dimensional gel electrophoresis system as an alternative proteomics-based methodology suitable for large-scale autoantibody discovery.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach

PURPOSE

Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models.

METHODS

A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data.

RESULTS

FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917 to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis.

CONCLUSION

A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.

Circulating tumor-associated autoantibodies as novel diagnostic biomarkers in pancreatic adenocarcinoma

To develop a superior diagnostic approach for pancreatic adenocarcinoma (PAAC), the present study prospectively included 338 PAAC patients, 294 normal healthy volunteers (NHV), 122 chronic pancreatitis (CP) patients and 100 patients with non-PAAC malignancies. In the identification phase, HuProt Human Proteome Microarray, comprising 21 065 proteins, was used to identify serum tumor-associated autoantibodies (TAAbs) candidates differentiating PAAC (n = 30) from NHV (n = 30). A PAAC-focused array containing 165 differentially expressed TAAbs identified was subsequently adopted in the validation phase (n = 712) for specificity and sensitivities. The multivariate TAAbs signature for differentiation PAAC from controls (NHV + CP) identified five candidates, namely the IgG-type TAAbs against CLDN17, KCNN3, SLAMF7, SLC22A11 and OR51F2. Multivariate logistic performance model of y = (22.893 × CA19-9 + 0.68 × CLDN17 - 4.012) showed a significant better diagnostic accuracy than that of CA19-9 and CLDN17 in differentiating PAAC from controls (NHV + CP) (AUC = 0.97, 0.92 and 0.82, respectively, P-value < .0001). We further tested the autoantigen level of CLDN17 by ELISA in 82 sera samples from PAAC (n = 42), CP (n = 24) and NHV (n = 16). Similarly, the model showed superior diagnostic performance than that of CA19-9 and CLDN17 (AUC = 0.93, 0.83 and 0.81, respectively, P-value < .0001) in differentiating PAAC from controls. In conclusion, our study is the first to characterize the circulating TAAbs signatures in PAAC. The results showed that CLDN17 combined with CA19-9 provided potentially clinical value and may serve as noninvasive novel biomarkers for PAAC diagnosis.

Research progress in protein microarrays: Focussing on cancer research

Although several effective treatment modalities have been developed for cancers, the morbidity and mortality associated with cancer continues to increase every year. As one of the most exciting emerging technologies, protein microarrays represent a powerful tool in the field of cancer research because of their advantages such as high throughput, small sample usage, more flexibility, high sensitivity and direct readout of results. In this review, we focus on the research progress in four types of protein microarrays (proteome microarray, antibody microarray, lectin microarray and reversed protein array) with emphasis on their application in cancer research. Finally, we discuss the current challenges faced by protein microarrays and directions for future developments. We firmly believe that this novel systems biology research tool holds immense potential in cancer research and will become an irreplaceable tool in this field.

Human Proteome Microarray identifies autoantibodies to tumor-associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma

The identification of the high-efficiency and non-invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor-associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which eight passed multiple-stage validations by ELISA with a total of 1625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer, and colorectal cancer. Finally, an immunodiagnostic model including six TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of six TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers.

Identification of novel serological autoantibodies in Chinese prostate cancer patients using high-throughput protein arrays

Autoantibody (AAb) has a prominent role in prostate cancer (PCa), with few studies profiling the AAb landscape in Chinese patients. Therefore, the AAb landscape in Chinese patients was characterized using protein arrays. First, in the discovery phase, Huprot arrays outlined autoimmune profiles against ~ 21,888 proteins from 57 samples. In the verification phase, the PCa-focused arrays detected 25 AAbs selected from the discovery phase within 178 samples. Then, PCa was detected using a backpropagation artificial neural network (BPANN) model. In the validation phase, an enzyme-linked immunosorbent assay (ELISA) was used to validate four AAb biomarkers from 196 samples. Huprot arrays profiled distinct PCa, benign prostate diseases (BPD), and health AAb landscapes. PCa-focused array depicted that IFIT5 and CPOX AAbs could distinguish PCa from health with an area under curve (AUC) of 0.71 and 0.70, respectively. PAH and FCER2 AAbs had AUCs of 0.86 and 0.88 in discriminating PCa from BPD. Particularly, PAH AAb detected patients in the prostate-specific antigen (PSA) gray zone with an AUC of 0.86. Meanwhile, the BPANN model of 4-AAb (IFIT5, PAH, FCER2, CPOX) panel attained AUC of 0.83 among the two cohorts for detecting patients with gray-zone PSA. In the validation cohort, the IFIT5 AAb was upregulated in PCa compared to health (p < 0.001). Compared with BPD, PAH and FCER2 AAbs were significantly elevated in PCa (p = 0.012 and 0.039). We have demonstrated the first extensive profiling of autoantibodies in Chinese PCa patients, identifying novel diagnostic AAb biomarkers, especially for identification of gray-zone-PSA patients.

Recognition and release of uridine and hCNT3: From multivariate interactions to molecular design

As a vital target for the development of novel anti-cancer drugs, human concentrative nucleoside transporter 3 (hCNT3) has been widely concerned. Nevertheless, the lack of a comprehensive understanding of molecular interactions and motion mechanism has greatly hindered the development of novel inhibitors against hCNT3. In this paper, molecular recognition of hCNT3 with uridine was investigated with molecular docking, conventional molecular dynamics (CMD) simulations and adaptive steered molecular dynamics (ASMD) simulations; and then, the uridine derivatives with possibly highly inhibitory activity were designed. The result of CMD showed that more water-mediated H-bonds and lower binding free energy both explained higher recognition ability and transported efficiency of hCNT3. While during the ASMD simulation, nucleoside transport process involved the significant side-chain flip of residues F321 and Q142, a typical substrate-induced conformational change. By considering electronegativity, atomic radius, functional group and key H-bonds factors, 25 novel uridine derivatives were constructed. Subsequently, the receptor-ligand binding free energy was predicted by solvated interaction energy (SIE) method to determine the inhibitor c8 with the best potential performance. This work not only revealed molecular recognition and release mechanism of uridine with hCNT3, but also designed a series of uridine derivatives to obtain lead compounds with potential high activity.

Screening of autoantibodies as biomarkers in the serum of renal cancer patients based on human proteome microarray

The autoantibody in patients' serum can act as a biomarker for diagnosing cancer, and the differences in autoantibodies are significantly correlated with the changes in their target proteins. In this study, 16 renal cancer (RC) patients were assigned to the disease group, and 16 healthy people were assigned to the healthy control (HC) group. The human proteome microarray consisting of>19,500 proteins was used to examine the differences in IgG and IgM autoantibodies in sera between RC and HC. The comparative analysis of the microarray results shows that 101 types of IgG and 25 types of IgM autoantibodies are significantly higher in RC than in HC. Highly responsive autoantibodies can be candidate biomarkers (e.g., anti-KCNAB2 IgG and anti-RCN1 IgM). Extensive enzyme-linked immunosorbent assay (ELISA) was performed to screen sera in 72 RC patients and 66 healthy volunteers to verify the effectiveness of the new autoantibodies. The AUCs of anti-KCNAB2 IgG and anti-GAPDH IgG were 0.833 and 0.753, respectively. KCNAB2 achieves high protein expression, and its high mRNA level is confirmed to be an unfavorable prognostic marker in clear cell renal cell carcinoma (ccRCC) tissues. This study suggests that the high-throughput human proteome microarray can effectively screen autoantibodies in serum as candidate biomarkers, and their corresponding target proteins can lay a basis for the in-depth investigation into renal cancer.

Current advances in antibody-based serum biomarker studies: From protein microarray to phage display

PURPOSE

This review aims to summarize the technological advances in the field of antibody-based biomarker studies by proteome microarray and phage display. In addition, the possible development directions of this field are also discussed.

EXPERIMENTAL DESIGN

We have focused on the antibody profiling by proteome microarray and phage display, including the technological advances, the tools/resources constructed, and the characteristics of both platforms.

RESULTS

With the help of tools/resources and technological advances in proteome microarray and phage display, the efficiency of profiling antibody-based biomarkers in serum samples has been greatly improved.

CONCLUSIONS

In the past few years, proteome microarray and phage display, especially the latter one, have already demonstrated their capacity and efficiency for biomarker identification. In the near future, we believe that more antibody-based biomarkers could be identified, and some of them could eventually be developed into real clinical applications.

Development of a protein microarray for profiling circulating autoantibodies in human diseases

PURPOSE

To develop a robust microarray platform to detect thousands of serological autoantibodies (AAbs) simultaneously in different diseases.

EXPERIMENTAL DESIGN

An AAbMap microarray was prepared by printing a total of 4032 purified His-tagged human proteins and peptide probes on a chemically-modified slide. The sensitivity, dynamic range, and the inter- and intra-array reproducibility of the AAb microarray were then systematically tested and optimized. Finally, the large-scale profiling of AAbs in the serum of patients with different human diseases using the AAbMap microarray was demonstrated.

RESULTS

The dynamic range of antibody (Ab) detection was 2 to 3 orders of magnitude with the lowest limit of detection (LOD) of 68 pg/mL. The intra-array (r) correlation of duplicate spots was 1.00, whereas the inter-array correlations between different arrays and batches were 0.99 and 0.97 to 0.98, respectively. Notably, 132, 266, 171, and 84 AAbs were detected in pooled serum from healthy controls (HCs) or patients with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), or lung cancer (LC), respectively. These AAbs included antibodies that target well-known disease biomarkers, such as anti-cyclic citrullinated peptide, anti-ribonucleoprotein, and anti-nucleosome.

CONCLUSIONS AND CLINICAL RELEVANCE

We developed a microarray platform to measure thousands of serological AAbs simultaneously with high sensitivity and reproducibility. The array can help study autoimmunity and complement genomics, proteomics, and metabolomics data for systematic investigations of human diseases.

A new biomarker for the early diagnosis of gastric cancer: gastric juice- and serum-derived SNCG

Aim: To explore the possibility of gastric juice (GJ)- and serum-derived SNCG as a potential biomarker for the early diagnosis of gastric cancer (GC). Materials & methods: GJ and serum samples were collected from 87 patients with GC, 38 patients with gastric precancerous lesions and 44 healthy volunteers. The levels of SNCG in GJ and serum samples were detected by ELISA. Results: The levels of SNCG in GJ and serum were significantly higher in the GC group when compared with the GPL group or the control group. The expression of SNCG in GJ and serum was associated with tumor node metastasis stage, lymph node metastasis, tumor size and drinking, and it is important for the diagnosis and prognosis of GC (p < 0.05). Conclusion: The findings highlight the significance of SNCG in GC diagnosis and prognosis and implicate SNCG as a promising candidate for GC treatment.

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Background

Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods.

Methods

Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method.

Results

The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control.

Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R² = 0.20, p < 0.001).

Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741.

As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively.

Conclusion

Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

Differential Long Non-Coding RNA Expression Analysis in Chronic Non-Atrophic Gastritis, Gastric Mucosal Intraepithelial Neoplasia, and Gastric Cancer Tissues

Gastric cancer (GC) has a high incidence worldwide, and when detected, the majority of patients have already progressed to advanced stages. Long non-coding RNAs (lncRNAs) have a wide range of biological functions and affect tumor occurrence and development. However, the potential role of lncRNAs in GC diagnosis remains unclear. We selected five high-quality samples from each group of chronic non-atrophic gastritis, gastric mucosal intraepithelial neoplasia, and GC tissues for analysis. RNA-seq was used to screen the differentially expressed lncRNAs, and we identified 666 differentially expressed lncRNAs between the chronic non-atrophic gastritis and GC groups, 13 differentially expressed lncRNAs between the gastric mucosal intraepithelial neoplasia and GC groups, and 507 differentially expressed lncRNAs between the chronic non-atrophic gastritis and gastric mucosal intraepithelial neoplasia groups. We also identified six lncRNAs (lncRNA H19, LINC00895, lnc-SRGAP2C-16, lnc-HLA-C-2, lnc-APOC1-1, and lnc-B3GALT2-1) which not only differentially expressed between the chronic non-atrophic gastritis and GC groups, but also differentially expressed between the gastric mucosal intraepithelial neoplasia and GC groups. Furthermore, RT-qPCR was used to verify the differentially co-expressed lncRNAs. LncSEA was used to conduct a functional analysis of differentially expressed lncRNAs. We also predicted the target mRNAs of the differentially expressed lncRNAs through bioinformatics analysis and analyzed targeting correlations between three differentially co-expressed lncRNAs and mRNAs (lncRNA H19, LINC00895, and lnc-SRGAP2C-16). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used to explore the functions of target mRNAs of differentially expressed lncRNAs. In conclusion, our study provides a novel perspective on the potential functions of differentially expressed lncRNAs in GC occurrence and development, indicating that the differentially expressed lncRNAs might be new biomarkers for early GC diagnosis.

Temporal reproducibility of IgG and IgM autoantibodies in serum from healthy women

Autoantibodies are present in healthy individuals and altered in chronic diseases. We used repeated samples collected from participants in the NYU Women's Health Study to assess autoantibody reproducibility and repertoire stability over a one-year period using the HuProt array. We included two samples collected one year apart from each of 46 healthy women (92 samples). We also included eight blinded replicate samples to assess laboratory reproducibility. A total of 21,211 IgG and IgM autoantibodies were interrogated. Of those, 86% of IgG (n = 18,303) and 34% of IgM (n = 7,242) autoantibodies showed adequate lab reproducibility (coefficient of variation [CV] < 20%). Intraclass correlation coefficients (ICCs) were estimated to assess temporal reproducibility. A high proportion of both IgG and IgM autoantibodies with CV < 20% (76% and 98%, respectively) showed excellent temporal reproducibility (ICC > 0.8). Temporal reproducibility was lower after using quantile normalization suggesting that batch variability was not an important source of error, and that normalization removed some informative biological information. To our knowledge this study is the largest in terms of sample size and autoantibody numbers to assess autoantibody reproducibility in healthy women. The results suggest that for many autoantibodies a single measurement may be used to rank individuals in studies of autoantibodies as etiologic markers of disease.

Hsa_circ_0007967 promotes gastric cancer proliferation through the miR-411-5p/MAML3 axis

Circular RNAs are an important kind of noncoding RNAs and involved in cancerogenesis, but the specific mechanism between gastric cancer and circRNAs needs further study. Hsa_circ_0007967 was selected by RNA sequencing. Here, hsa_circ_0007967 was highly expressed in gastric cancer tissues than adjacent normal tissues. Overexpressing hsa_circ_0007967 promoted gastric cancer cell proliferation in vitro and in vivo, while suppression of hsa_circ_0007967 inhibited gastric cancer cell proliferation in vitro and in vivo. Mechanistically, hsa_circ_0007967 sponged miR-411-5p to increase MAML3 expression. Overall, hsa_circ_0007967 is a promising biomarker for gastric cancer diagnosis and a potential molecule for gastric cancer treatment.

Targeting the COP9 signalosome for cancer therapy

The COP9 signalosome (CSN) is a highly conserved protein complex composed of 8 subunits (CSN1 to CSN8). The individual subunits of the CSN play essential roles in cell proliferation, tumorigenesis, cell cycle regulation, DNA damage repair, angiogenesis, and microenvironmental homeostasis. The CSN complex has an intrinsic metalloprotease that removes the ubiquitin-like activator NEDD8 from cullin-RING ligases (CRLs). Binding of neddylated CRLs to CSN is sensed by CSN4 and communicated to CSN5 with the assistance of CSN6, thus leading to the activation of deneddylase. Therefore, CSN is a crucial regulator at the intersection between neddylation and ubiquitination in cancer progression. Here, we summarize current understanding of the roles of individual CSN subunits in cancer progression. Furthermore, we explain how the CSN affects tumorigenesis through regulating transcription factors and the cell cycle. Finally, we discuss individual CSN subunits as potential therapeutic targets to provide new directions and strategies for cancer therapy.

Autoantibody to GNAS in Early Detection of Hepatocellular Carcinoma: A Large-Scale Sample Study Combined with Verification in Serial Sera from HCC Patients

The aim of this study was to explore the value of autoantibody to GNAS in the early detection of hepatocellular carcinoma (HCC). In a large-scale sample set of 912 participants (228 cases in each of HCC, liver cirrhosis (LC), chronic hepatitis B (CHB), and normal controls (NCs) groups), autoantibody to GNAS was detected with a positive result in 47.8% of HCC patients, which was significantly higher than that in patients with LC (35.1%), CHB (19.7%), and NCs (19.7%). Further analysis showed that the frequency of autoantibody to GNAS started increasing in compensated cirrhosis patients (37.0%) with a jump in decompensated cirrhosis patients (53.2%) and reached a peak in early HCC patients (62.4%). The increasing autoantibody response to GNAS in patients at different stages was closely associated with the progression of chronic liver lesions. The result from 44 human serial sera demonstrated that 5 of 11 (45.5%) HCC patients had elevated autoantibody to GNAS before and/or at diagnosis of HCC. Moreover, 46.1% and 62.4% of high positive rates in alpha-fetoprotein (AFP) negative and early-stage HCC patients can supplement AFP in early detection of HCC. These findings suggest that autoantibody to GNAS could be used as a potential biomarker for the early detection of HCC.

Proteomic profiling identifies signatures associated with progression of precancerous gastric lesions and risk of early gastric cancer

Background

Molecular features underlining the multistage progression of gastric lesions and development of early gastric cancer (GC) are poorly understood, restricting the ability to GC prevention and management.

Methods

We portrayed proteomic landscape and explored proteomic signatures associated with progression of gastric lesions and risk of early GC. Tissue proteomic profiling was conducted for a total of 324 subjects. A case-control study was performed in the discovery stage (n=169) based on populations from Linqu, a known high-risk area for GC in China. We then conducted two-stage validation, including a cohort study from Linqu (n = 56), with prospective follow-up for progression of gastric lesions (280–473 days), and an independent case-control study from Beijing (n = 99).

Findings

There was a clear distinction in proteomic features for precancerous gastric lesions and GC. We derived four molecular subtypes of gastric lesions and identified subtype-S4 with the highest progression risk. We found 104 positively-associated and 113 inversely-associated proteins for early GC, with APOA1BP, PGC, HPX and DDT associated with the risk of gastric lesion progression. Integrating these proteomic signatures, the ability to predict progression of gastric lesions was significantly strengthened (areas-under-the-curve=0.88 (95%CI: 0.78–0.99) vs. 0.56 (0.36–0.76), Delong's P = 0.002). Immunohistochemistry assays and examination at mRNA level validated the findings for four proteins.

Interpretation

We defined proteomic signatures for progression of gastric lesions and risk of early GC, which may have translational significance for identifying particularly high-risk population and detecting GC at an early stage, improving potential for targeted GC prevention.

Funding

The funders are listed in the Acknowledgement.

Analysis of expression profiles and prognostic value of COP9 signalosome subunits for patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) has been associated with poor prognosis, due to its strong invasive ability and resistance to chemotherapy. Thus, there is an urgent requirement to identify effective biomarkers for the early diagnosis and prognostic evaluation of HNSCC. COP9 signalosome (COPS) regulates numerous cancer-associated biological processes in various malignancies. The aim of the present study was to investigate the association between COPS and HNSCC. The mRNA expression profiles of COPS in HNSCC were analyzed using UALCAN, Oncomine and UCSC Xena databases. The association between overall survival time in patients with HNSCC and the COPS genes was investigated using the Kaplan-Meier plotter database. The CERES score was obtained and evaluated to determine the importance of the COPS genes for survival of the HNSCC cell lines. Functional analysis for Gene Ontology and Gene Set Enrichment Analysis (GSEA) was performed using The Database for Annotation, Visualization and Integrated Discovery and GSEA software, respectively. After knocking down COPS5 and COPS6, cell Counting Kit-8 and wound healing assays were used to detect cell growth and migration of the CAL27 and SCC25 cell lines, respectively. Among the 10 COPS genes examined, most COPS subunits were upregulated in HNSCC samples compared with that in normal tissues, except for COPS9. Increased mRNA expression level of COPS5, COPS6, COPS7B, COPS8 and COPS9 was associated with TNM stage in patients with HNSCC. High mRNA expression level of COPS2, COPS5, COPS6, COPS7A, COPS7B, COPS8 and COPS9 had prognostic significance of patients with HNSCC. Knockdown of COPS5 and COPS6 inhibited cell growth and migration of the CAL27 and SCC25 cell lines. The results from the present study suggested that COPS subunits could be potential biomarkers in patients with HNSCC. COPS5 and COPS6 were important for cell survival and migration of the HNSCC cells.

Prediction of Blood miRNA-mRNA Regulatory Network in Gastric Cancer

Background

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

Methods

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

Results

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

Conclusion

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

Identification and Evaluation of Autoantibody to a Novel Tumor-Associated Antigen GNA11 as a Biomarker in Esophageal Squamous Cell Carcinoma

The study aims to explore the diagnostic value of anti-GNA11 autoantibody in esophageal squamous cell carcinoma (ESCC) from multiple levels. Autoantibody against GNA11 with the highest diagnostic performance was screened out from the customized protein microarray. A total of 486 subjects including ESCC patients and matched normal controls were recruited in the verification and validation phases by using enzyme-linked immunosorbent assay (ELISA). Western blotting analysis was used to verify the ELISA results. Immunohistochemistry (IHC) was used to evaluate GNA11 expression in ESCC tissues and para-tumor tissues. In addition, a bioinformatics approach was adopted to investigate the mRNA expression of GNA11 in ESCC. Results indicated that the level of anti-GNA11 autoantibody in ESCC patients was significantly higher than that in the normal controls, and it can be used to distinguish ESCC patients from normal individuals in clinical subgroups (p < 0.05), as revealed by both ELISA and Western blotting. The receiver operating characteristic (ROC) curve analysis showed that anti-GNA11 autoantibody could distinguish ESCC patients from normal controls with an area under the ROC curve (AUC) of 0.653, sensitivity of 10.96%, and specificity of 98.63% in the verification cohort and with an AUC of 0.751, sensitivity of 38.24%, and specificity of 88.82% in the validation cohort. IHC manifested that the expression of GNA11 can differentiate ESCC tissues with para-tumor tissues (p < 0.05), but it cannot be used to differentiate different pathological grades and clinical stages (p > 0.05). The mRNA expression of GNA11 in ESCC patients and normal controls was different with a bioinformatics mining with The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data in Gene Expression Profiling Interactive Analysis (GEPIA). In summary, anti-GNA11 autoantibody has the potential to be a new serological marker in the diagnosis of ESCC.

Elevated SYNC Expression Is Associated with Gastric Tumorigenesis and Infiltration of M2-Polarized Macrophages in the Gastric Tumor Immune Microenvironment

Aims: To assess the expression and epigenetic regulation of Syncoilin, intermediate filament protein (SYNC) in gastric cancer tissues, and to determine its associations with clinicopathological features; immune infiltration of macrophages in tumors; and patient survival. Materials and Methods: Clinicopathological features, expression profiles, and methylation data of the SYNC gene were obtained from multi-institutional real-world public datasets. A total of 1601 samples from patients with gastric cancer were examined. The associations between clinicopathological features and SYNC expression levels were assessed by the chi-square test; survival was assessed using the Kaplan-Meier analysis. The infiltration levels of M1, 2-polarized tumor-associated macrophages (TAMs) in a gastric tumor immune microenvironment were quantified using deconvolution, and the correlation between SYNC expression level and M1, 2-polarized macrophages' infiltration was examined using the Pearson correlation test. SYNC gene methylation data were analyzed to investigate epigenetic control of its expression. Results: SYNC expression was elevated in gastric cancer tissues (p < 0.01), and was associated with a poorer overall survival (p < 0.01) and poorer postprogression survival (p = 0.01). Higher SYNC levels were significantly associated with more aggressive clinicopathological features in gastric cancer patients (p < 0.05). SYNC was also associated with the infiltration of M2-polarized TAMs in the gastric tumor immune microenvironment (p < 0.001). Hypomethylation was shown to be associated with SYNC's upregulation (p < 0.05). Conclusion: SYNC is highly expressed in gastric cancer tissues and has the potential to be a therapeutic target and to serve as a prognostic marker.

Microarray-based selection of a serum biomarker panel that can discriminate between latent and active pulmonary TB

Bacterial culture of M. tuberculosis (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change > 1.2; p < 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.

Mapping Autoantibodies in Children With Acute Rheumatic Fever

Background

Acute rheumatic fever (ARF) is a serious sequela of Group A Streptococcus (GAS) infection associated with significant global mortality. Pathogenesis remains poorly understood, with the current prevailing hypothesis based on molecular mimicry and the notion that antibodies generated in response to GAS infection cross-react with cardiac proteins such as myosin. Contemporary investigations of the broader autoantibody response in ARF are needed to both inform pathogenesis models and identify new biomarkers for the disease.

Methods

This study has utilised a multi-platform approach to profile circulating autoantibodies in ARF. Sera from patients with ARF, matched healthy controls and patients with uncomplicated GAS pharyngitis were initially analysed for autoreactivity using high content protein arrays (Protoarray, 9000 autoantigens), and further explored using a second protein array platform (HuProt Array, 16,000 autoantigens) and 2-D gel electrophoresis of heart tissue combined with mass spectrometry. Selected autoantigens were orthogonally validated using conventional immunoassays with sera from an ARF case-control study (n=79 cases and n=89 matched healthy controls) and a related study of GAS pharyngitis (n=39) conducted in New Zealand.

Results

Global analysis of the protein array data showed an increase in total autoantigen reactivity in ARF patients compared with controls, as well as marked heterogeneity in the autoantibody profiles between ARF patients. Autoantigens previously implicated in ARF pathogenesis, such as myosin and collagens were detected, as were novel candidates. Disease pathway analysis revealed several autoantigens within pathways linked to arthritic and myocardial disease. Orthogonal validation of three novel autoantigens (PTPN2, DMD and ANXA6) showed significant elevation of serum antibodies in ARF (p < 0.05), and further highlighted heterogeneity with patients reactive to different combinations of the three antigens.

Conclusions

The broad yet heterogenous elevation of autoantibodies observed suggests epitope spreading, and an expansion of the autoantibody repertoire, likely plays a key role in ARF pathogenesis and disease progression. Multiple autoantigens may be needed as diagnostic biomarkers to capture this heterogeneity.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

The converging roles of Batten disease proteins in neurodegeneration and cancer

Epidemiological studies have reported an inverse correlation between cancer and neurodegenerative disorders, and increasing evidence shows that similar genes and pathways are dysregulated in both diseases but in a contrasting manner. Given the genetic convergence of the neuronal ceroid lipofuscinoses (NCLs), a family of rare neurodegenerative disorders commonly known as Batten disease, and other neurodegenerative diseases, we sought to explore the relationship between cancer and the NCLs. In this review, we survey data from The Cancer Genome Atlas and available literature on the roles of NCL genes in different oncogenic processes to reveal links between all the NCL genes and cancer-related processes. We also discuss the potential contributions of NCL genes to cancer immunology. Based on our findings, we propose that further research on the relationship between cancer and the NCLs may help shed light on the roles of NCL genes in both diseases and possibly guide therapy development.

Circulating lncRNA DANCR as a potential auxillary biomarker for the diagnosis and prognostic prediction of colorectal cancer

Studies have shown that long non-coding RNAs (lncRNAs) play vital roles in the development of cancer, including colorectal cancer (CRC). Our purpose is to validate the diagnostic value of serum differentiation antagonizing non-protein coding RNA (DANCR) in CRC by focusing on its expression and clinical application. lncRNA expression profiles of CRC patients were obtained and analyzed by repurposing the publically available microarray data. Tissue or serum specimens were obtained from 40 patients with primary CRC, 10 patients with recurrent CRC, 40 patients with colorectal polyps, and 40 healthy controls. It was found that DANCR level in the CRC tissue and serum was significantly increased, and serum DANCR expression was decreased in post-operative patients as compared with that in pre-treatment patients and recurrent patients. In addition, serum DANCR expression was significantly correlated with different TNM stages. Correlation analysis of DANCR and other diagnostic indicators showed that the serum DANCR expression level was significantly correlated with CA199 but not with CEA in CRC patients. As for diagnostic efficiency by ROC analysis, the area under the curve (AUC) of serum DANCR was higher than that of CEA and CA199 in CRC group vs. colorectal polyp group. Simultaneous detection of DANCR, CEA and CA199 yielded the highest sensitivity and AUC as compared with either of them alone. Taken together, serum DANCR was up-regulated in CRC patients and high expression of DANCR may prove to be a potential biomarker for the diagnosis of CRC.

Systematic evaluation of IgG responses to SARS-CoV-2 spike protein-derived peptides for monitoring COVID-19 patients

Serological tests play an essential role in monitoring and combating the COVID-19 pandemic. Recombinant spike protein (S protein), especially the S1 protein, is one of the major reagents used for serological tests. However, the high cost of S protein production and possible cross-reactivity with other human coronaviruses pose unavoidable challenges. By taking advantage of a peptide microarray with full spike protein coverage, we analyzed 2,434 sera from 858 COVID-19 patients, 63 asymptomatic patients and 610 controls collected from multiple clinical centers. Based on the results, we identified several S protein-derived 12-mer peptides that have high diagnostic performance. In particular, for monitoring the IgG response, one peptide (aa 1148-1159 or S2-78) exhibited a sensitivity (95.5%, 95% CI 93.7-96.9%) and specificity (96.7%, 95% CI 94.8-98.0%) comparable to those of the S1 protein for the detection of both symptomatic and asymptomatic COVID-19 cases. Furthermore, the diagnostic performance of the S2-78 (aa 1148-1159) IgG was successfully validated by ELISA in an independent sample cohort. A panel of four peptides, S1-93 (aa 553-564), S1-97 (aa 577-588), S1-101 (aa 601-612) and S1-105 (aa 625-636), that likely will avoid potential cross-reactivity with sera from patients infected by other coronaviruses was constructed. The peptides identified in this study may be applied independently or in combination with the S1 protein for accurate, affordable, and accessible COVID-19 diagnosis.

Identification of Novel Autoantibodies Based on the Human Proteomic Chips and Evaluation of Their Performance in the Detection of Gastric Cancer

Autoantibodies against tumor-associated antigens (TAAbs) can be used as potential biomarkers in the detection of cancer. Our study aims to identify novel TAAbs for gastric cancer (GC) based on human proteomic chips and construct a diagnostic model to distinguish GC from healthy controls (HCs) based on serum TAAbs. The human proteomic chips were used to screen the candidate TAAbs. Enzyme-linked immunosorbent assay (ELISA) was used to verify and validate the titer of the candidate TAAbs in the verification cohort (80 GC cases and 80 HCs) and validation cohort (192 GC cases, 128 benign gastric disease cases, and 192 HCs), respectively. Then, the diagnostic model was established by Logistic regression analysis based on OD values of candidate autoantibodies with diagnostic value. Eleven candidate TAAbs were identified, including autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, RRAS2, RGS4, RHOG, SRARP, RAC1, and TMEM243 by proteomic chips. The titer of autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, and RRAS2 were significantly higher in GC cases while the titer of autoantibodies against RGS4, RHOG, SRARP, RAC1, and TMEM243 showed no difference in the verification group. Next, six potential TAAbs were validated in the validation cohort. The titer of autoantibodies against F8, NRAS, MFGE8, RRAS2, and PTP4A1 was significantly higher in GC cases. Finally, an optimal prediction model with four TAAbs (anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2) showed an optimal diagnostic performance of GC with AUC of 0.87 in the training group and 0.83 in the testing group. The proteomic chip approach is a feasible method to identify TAAbs for the detection of cancer. Moreover, the panel consisting of anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2 may be useful to distinguish GC cases from HCs.

Exosomal miR-590-5p in Serum as a Biomarker for the Diagnosis and Prognosis of Gastric Cancer

The purpose of this study is to explore the expression of miRNA-590-5p, an exosome of gastric cancer (GC), and to evaluate the suitability of miR-590-5p, an exosome with its own clinical characteristics. Serum samples from 168 gastric cancer patients and 50 matched controls were collected and exosomal RNAs were extracted. After that, miR-590-5p is analyzed by quantitative polymerase chain reaction (qRT-PCR), which is more related to clinical and pathological parameters and patient monitoring data. MGC-803 and HGC-27 cells were treated by miR-590-5p mimics, and then the changes of cell fluidity and invasiveness were monitored. The results showed that the expression level of miR-590-5p in exosomes of healthy observation group, early (I and II) stage group, and late stage (III) group was 30.34 ± 6.35, 6.19 ± 0.81, and 2.9 ± 0.19, respectively (all p < 0.05). ROC (receiver-operating characteristic curve) showed that the AUC (area under the curve) of exosomal miR-590-5p was 0.810 with 63.7% sensitivity and 86% specificity. The expression of exosomal miR-590-5p in serum was related to clinical stage (p = 0.008), infiltration depth, and the expression level of ki-67 (p < 0.001). In addition, Kaplan-Meier analysis showed that the decrease of explicit level of exosomal miR-590-5p was related to the decrease of overall survival rate (p < 0.001). Cox regression analysis showed that miR-590-5p can be used as an independent predictor. Furthermore, upregulation of miR-590-5p inhibited cell migration and invasion in MGC-803 cells and HGC-27 cells. The serum expression level of exosomal miR-590-5p may be a biomarker, which is potentially useful and noninvasive for early detection and prediction of GC. In addition, miR-590-5p can play a role in eliminating carcinogens by actively regulating the malignant potential of gastric cancer.

Identification of Novel Serological Autoantibodies in Takayasu Arteritis Patients Using HuProt Arrays

To identify novel autoantibodies of Takayasu arteritis (TAK) using HuProt array-based approach, a two-phase approach was adopted. In Phase I, serum samples collected from 40 TAK patients, 15 autoimmune disease patients, and 20 healthy subjects were screened to identify TAK-specific autoantibodies using human protein (HuProt) arrays. In phase II, the identified candidate autoantibodies were validated with TAK-focused arrays using an additional cohort comprised of 109 TAK patients, 110 autoimmune disease patients, and 96 healthy subjects. Subsequently, the TAK-specific autoantibodies validated in phase II were further confirmed using western blot analysis. We identified and validated eight autoantibodies as potential TAK-specific diagnostic biomarkers, including anti-SPATA7, -QDPR, -SLC25A2, -PRH2, -DIXDC1, -IL17RB, -ZFAND4, and -NOLC1 antibodies, with AUC of 0.803, 0.801, 0.780, 0.696, 0.695, 0.678, 0.635, and 0.613, respectively. SPATA7 could distinguish TAK from healthy and disease controls with 73.4% sensitivity at 85.4% specificity, while QDPR showed 71.6% sensitivity at 86.4% specificity. SLC25A22 showed the highest sensitivity of 80.7%, but at lower specificity of 67.0%. In addition, PRH2, IL17RB, and NOLC1 showed good specificities of 88.3%, 85.9%, and 86.9%, respectively, but at lower sensitivities (<50%). Finally, DIXDC1 and ZFAND4 showed moderate performance as compared with the other autoantibodies. Using a decision tree model, we could reach a specificity of 94.2% with AUC of 0.843, a significantly improved performance as compared with that by each individual biomarker. The performances of three autoantibodies, namely anti-SPATA7, -QDPR, and -PRH2, were successfully confirmed with western blot analysis. Using this two-phase strategy, we identified and validated eight novel autoantibodies as TAK-specific biomarker candidates, three of which could be readily adopted in a clinical setting.

Long Non-coding RNA LINC01503 Promotes Gastric Cancer Cell Proliferation and Invasion by Regulating Wnt Signaling

BACKGROUND

Previous studies have indicated that the dysregulation of long non-coding RNAs plays an important role in tumors. LINC01503 is a newly discovered lncRNA that promotes development of various tumor types. However, the function of LINC01503 in gastric cancer has not been reported yet.

AIMS

To explore the function of LINC01503 in gastric cancer development and the underlying molecular biological regulatory mechanisms.

METHODS

LINC01503 expression in tissues and cell lines of gastric cancer were determined through qRT-PCR. Transwell assay and cell number counting experiments were employed to detect the cell invasion and proliferation. C-myc, cyclin D1, and β-catenin expressions were analyzed through Western blot and qRT-PCR.

RESULTS

LINC01503 was highly expressed in gastric cancer tissues and cell lines, which was correlated with poor prognosis. Knockdown of LINC01503 suppressed gastric cancer cell proliferation and invasion, whereas overexpression of LINC01503 showed a reverse trend. Silencing LINC01503 significantly inhibited the expression of c-myc, cyclin D1, and β-catenin. Overexpressing β-catenin rescued the inhibitory effects, induced by LINC01503 silencing, on gastric cancer cell proliferation and metastasis.

CONCLUSIONS

This research reported that the elevated expression of LINC01503 could promote proliferation and metastasis of gastric cancer through positively regulating the Wnt/β-catenin pathway.

Serum Sp17 Autoantibody Serves as a Potential Specific Biomarker in Patients with SAPHO Syndrome

SAPHO (synovitis, acne, pustulosis, hyperostosis, and osteitis) syndrome shows a wide variability in musculoskeletal and cutaneous manifestations, and it is therefore underrecognized and misdiagnosed in the clinic due to a lack of specific markers. In this study, we aimed to identify specific biomarkers by screening serum autoantibodies in SAPHO patients with a 17K human whole-proteome microarray. The serum anti-Sp17 autoantibody was identified and verified to be a specific biomarker in patients with SAPHO syndrome. Indeed, the level of the anti-Sp17 autoantibody was significantly increased in patients with active SAPHO compared to patients with an inactive disease and healthy controls (P < 0.05). Additionally, serum anti-Sp17 autoantibody levels correlated with those of serum hypersensitive C-reactive protein (hsCRP), the erythrocyte sedimentation rate (ESR), and β-crosslaps (β-CTx) in patients with active SAPHO disease. Moreover, anti-Sp17 autoantibody levels were markedly decreased after anti-inflammatory treatment with pamidronate disodium, which downregulated levels of hsCRP and ESR in patients with active SAPHO. Thus, serum levels of the anti-Sp17 autoantibody might serve as a specific biomarker for the diagnosis of SAPHO syndrome or for monitoring the disease status.

Protein array-based companion diagnostics in precision medicine

INTRODUCTION

The development of companion diagnostics (CDx) will increase efficacy and cost-benefit markedly, compared to the currently prevailing trial-and-error approach for treatment. Recent improvements in high-throughput protein technology have resulted in large amounts of predictive biomarkers that are potentially useful components of future CDx assays. Current high multiplex protein arrays are suitable for discovery-based approaches, while low-density and more simple arrays are suitable for use in point-of-care facilities.

AREA COVERED

This review discusses the technical platforms available for protein array focused CDx, explains the technical details of the platforms and provide examples of clinical use, ranging from multiplex arrays to low-density clinically applicable arrays. We thereafter highlight recent predictive biomarkers within different disease areas, such as oncology and autoimmune diseases. Lastly, we discuss some of the challenges connected to the implementation of CDx assays as point-of-care tests.

EXPERT OPINION

Recent advances in the field of protein arrays have enabled high-density arrays permitting large biomarker discovery studies, which are beneficial for future CDx assays. The density of protein arrays range from a single protein to proteome-wide arrays, allowing the discovery of protein signatures that may correlate with drug response. Protein arrays will undoubtedly play a key role in future CDx assays.

High ELK3 expression is associated with the VEGF-C/VEGFR-3 axis and gastric tumorigenesis and enhances infiltration of M2 macrophages

Aim: To assess the expression and effect of ELK3 in gastric cancer, along with its associations with the VEGF-C/VEGFR-3 axis, IC₅₀ of the VEGFR-3 inhibitor axitinib and immune infiltration of M2-polarized macrophages in gastric cancer, and to analyze the possible epigenetic regulation mechanism. Materials & methods: Expression profiles and methylation data from 1645 samples were obtained and examined from multi-institutional public datasets. The associations were assessed by multiple analysis methods. Results: Elevated ELK3 is associated with the VEGF-C/VEGFR-3 axis and tumorigenesis, reduces the effect of axitinib in vitro, enhances immune infiltration of M2 macrophages and affects clinical outcomes. Hypomethylation contributes to the upregulation of ELK3 in gastric cancer. Conclusions: ELK3 is a potential therapeutic target which reduces the effect of axitinib and enhances infiltration of M2-polarized macrophages.

Autoantibodies in Early Detection of Breast Cancer

In spite of the progress made in treatment and early diagnosis, breast cancer remains a major public health issue worldwide. Although modern image-based screening modalities have significantly improved early diagnosis, around 15% to 20% of breast cancers still go undetected. In underdeveloped countries, lack of resources and cost concerns prevent implementing mammography for routine screening. Noninvasive, low-cost, blood-based markers for early breast cancer diagnosis would be an invaluable alternative that would complement mammography screening. Tumor-specific autoantibodies are excellent biosensors that could be exploited to monitor disease-specific changes years before disease onset. Although clinically informative autoantibody markers for early breast cancer screening have yet to emerge, progress has been made in the development of tools to discover and validate promising autoantibody signatures. This review focuses on the current progress toward the development of autoantibody-based early screening markers for breast cancer.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

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.