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

Deciphering the autoreactome: Massively parallelized methods for autoantibody detection in humans

Autoantibodies have a substantial impact on human health ranging from autoimmune diseases to cancer diagnostics. Knowledge of the antigens recognized can allow for more accurate diagnostics, a better understanding of pathogeneses and thus improved prevention, as well as laying the foundation for the development of new therapies. A critical step to acquire this knowledge is to detect the exact self-antigens targeted by autoantibodies out of the pool of 20,000 human proteins against which reactivities could be observed. Here, we review established and emerging methods for highly parallelized autoantigen detection such as human proteome microarrays, serological identification of antigens by screening of cDNA expression libraries (SEREX), serological proteome analysis (SERPA), phage display immunoprecipitation sequencing (PhIP-Seq), parallel analysis of translated ORFs (PLATO), and rapid extracellular antigen profiling (REAP). We highlight advantages and limitations of these methods, aiming to give a guideline to choose the appropriate method for a certain application.

Identifying Neurological Autoantibodies in COVID-19: mGluR2 as a Marker of Immune Dysregulation During the Omicron Outbreak in China

Aimed to comprehensively investigate the presence of neural autoantibodies in the cerebrospinal fluid (CSF) and plasma of COVID-19 patients experiencing neurological complications during the Omicron wave in China. Forty consecutive COVID-19 patients with severe neurological complications and 15 disease controls (DC) were enrolled. Neural autoantibodies were detected using both the indirect immunofluorescence assay (IFA) on mouse brain tissue and the Brain-neuronal-antigen microarray. Our results indicated a significantly higher prevalence of neural autoantibodies in the CSF (62.16% vs. 0.0%) and plasma (38.71% vs. 13.33%) of COVID-19 patients compared to DC. Additionally, we identified 12 upregulated intrathecal IgG autoantibodies with differential levels between COVID-19 patients and DC, as well as 51 upregulated IgG autoantibodies in plasma. A high prevalence of anti-mGluR2 antibodies (13.33%) in COVID-19 patients was confirmed by cell-based assays. Western blot analysis showed these antibodies cross-react with both the nucleocapsid (N) and spike (S) proteins of SARS-CoV-2. Notably, strong binding to both the S protein's RBD-Fc and mGluR2 was observed, an association that was substantiated by bioinformatics analysis evaluating the similarity between SARS-CoV-2 proteins and the targeted antigens on the microarray. This finding hints at a potential cross-reactivity between anti-mGluR2 antibodies and the S protein in COVID-19 patients.

Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer

Introduction

Tumor-associated antigens (TAA) are proteins expressed during the growth and development of tumor cells, and TAA autoantibodies (TAAbs) can be detected in the serum of lung cancer patients, which can be utilized in the early screening of lung cancer. Almost all the TAAbs applied for diagnosis are those elevated, however, there are still large numbers of autoantibodies detected to decrease in tumor serums, and their functions were rarely known. Diagnosing malignant small lung nodules (≤3cm) in CT scans remains a challenge in clinical practice.

Methods

In this study, we applied the HuProt array and the bioinformatics analysis to assess the diagnostic values of the decreased autoantibodies in lung cancers.

Results

In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. We combined with 4 elevated TAAbs, the sensitivity and the specificity of the 10-marker model can attain 80.0% and 87.0%, respectively, which is higher than that of the commonly used 7-TAAbs model in diagnosis for early-stage lung cancer. Moreover, 5 of the decreased autoantibodies can also be applied for supervising bone metastasis in lung adenocarcinoma. A follow-up process for 13 patients diagnosed with early-stage lung cancer revealed that 10 of the 15 decreased autoantibodies would recover to a higher level after the tumor was resected. Bioinformatic analysis indicated that the 15 biomarkers were strongly correlated with the prognosis of lung cancer patients.

Conclusion

We confirmed the importance of the decreased autoantibodies in lung cancer, providing new diagnostic and therapeutic strategies.

A Novel IgG-IgM Autoantibody Panel Enhances Detection of Early-stage Lung Adenocarcinoma from Benign Nodules

Autoantibodies hold promise for diagnosing lung cancer. However, their effectiveness in early-stage detection needs improvement. In this study, we investigated novel IgG and IgM autoantibodies for detecting early-stage lung adenocarcinoma (Early-LUAD) by employing a multi-step approach, including Human Proteome Microarray (HuProtTM) discovery, focused microarray verification, and ELISA validation, on 1246 individuals consisting of 634 patients with Early-LUAD (stage 0-I), 280 patients with benign lung disease (BLD), and 332 normal healthy controls (NHCs). HuProtTM selected 417 IgG/IgM candidates, and focused microarray further verified 55 significantly elevated IgG/IgM autoantibodies targeting 32 tumor-associated antigens in Early-LUAD compared to BLD/NHC/BLD+NHC. A novel panel of 10 autoantibodies (ELAVL4-IgM, GDA-IgM, GIMAP4-IgM, GIMAP4-IgG, MGMT-IgM, UCHL1-IgM, DCTPP1-IgM, KCMF1-IgM, UCHL1-IgG, and WWP2-IgM) demonstrated a sensitivity of 70.5% and a specificity of 77.0% or 80.0% for distinguishing Early-LUAD from BLD or NHC in ELISA validation. Positive predictive values for distinguishing Early-LUAD from BLD with nodules ≤ 8 mm, 9-20 mm, and > 20 mm significantly increased from 47.27%, 52.00%, and 62.90% [low-dose computed tomography (LDCT) alone] to 79.17%, 71.13%, and 87.88% (10-autoantibody panel combined with LDCT), respectively. The combined risk score (CRS), based on the 10-autoantibody panel, sex, and imaging maximum diameter, effectively stratified the risk for Early-LUAD. Individuals with 10 ≤ CRS ≤ 25 and CRS > 25 indicated a higher risk of Early-LUAD compared to the reference (CRS < 10), with adjusted odds ratios of 5.28 [95% confidence interval (CI): 3.18-8.76] and 9.05 (95% CI: 5.40-15.15), respectively. This novel panel of IgG and IgM autoantibodies offers a complementary approach to LDCT in distinguishing Early-LUAD from benign nodules.

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers

Although blood autoantibodies were initially associated with autoimmune diseases, multiple evidence have been accumulated showing their presence in many types of cancer. This has opened their use in clinics, since cancer autoantibodies might be useful for early detection, prognosis, and monitoring of cancer patients. In this review, we discuss the different techniques available for their discovery and validation. Additionally, we discuss here in detail those autoantibody panels verified in at least two different reports that should be more likely to be specific of each of the four most incident cancers. We also report the recent developed kits for breast and lung cancer detection mostly based on autoantibodies and the identification of novel therapeutic targets because of the screening of the cancer humoral immune response. Finally, we discuss unsolved issues that still need to be addressed for the implementation of cancer autoantibodies in clinical routine for cancer diagnosis, prognosis, and/or monitoring.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

Humoral immune response to tumor-associated antigen Ubiquilin 1 (UBQLN1) and its tumor-promoting potential in lung cancer

BACKGROUND

This study aims to investigate the expression of UBQLN1 in lung cancer (LC) tissue and the diagnostic capability of autoantibody to UBQLN1 (anti-UBQLN1) in the detection of LC and the discrimination of pulmonary nodules (PNs).

METHODS

Sera from 798 participants were used to discover and validate the level of autoantibodies via HuProt microarray and Enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was applied to establish model. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic potential. Immunohistochemistry was performed to detect UBQLN1 expression in 88 LC tissues and 88 para-tumor tissues. qRT-PCR and western blotting were performed to detect the expression of UBQLN1 at the mRNA and protein levels, respectively. Trans-well assay and cell counting kit-8 (CCK-8) was used to investigate the function of UBQLN1.

RESULTS

Anti-UBQLN1 was identified with the highest fold change by protein microarray. The level of anti-UBQLN1 in LC patients was obviously higher than that in NC or patients with benign lung disease of validation cohort 1 (P<0.05). The area under the curve (AUC) of anti-UBQLN1 was 0.610 (95%CI: 0.508-0.713) while reached at 0.822 (95%CI: 0.784-0.897) when combining anti-UBQLN1 with CEA, CYFRA21-1, CA125 and three CT indicators (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) in the discrimination of PNs. UBQLN1 protein was overexpressed in lung adenocarcinoma (LUAD) tissues compared to para-tumor tissues. UBQLN1 knockdown remarkably inhibited the migration, invasion and proliferation of LUAD cell lines.

CONCLUSIONS

Anti-UBQLN1 might be a potential biomarker for the diagnosis of LC and the discrimination of PNs.

Clinical and Paraclinical Characteristics of Endobronchial Pulmonary Squamous Cell Carcinoma-A Brief Review

BACKGROUND

Endobronchial squamous cell carcinoma is one of the most common types of tumors located inside the tracheobronchial tree. Patients often present in advanced stages of the disease, which most often leads to a targeted therapeutic attitude of pneumonectomy. Practicing lung parenchyma-preserving surgery led us to undertake this review.

MATERIALS AND METHODS

We used three search platforms-SCIENCE, MEDLINE, and PubMed-in order to identify studies presenting case reports, investigations, and reviews on endobronchial squamous cell carcinoma. We identified the clinical and paraclinical features of endobronchial squamous cell carcinoma. All the selected articles were in English and addressed the clinical criteria of endobronchial squamous cell carcinoma, autofluorescence bronchoscopy in endobronchial squamous cell carcinoma, imaging features of endobronchial squamous cell carcinoma, blood tumor markers specific to lung squamous cell carcinoma, and histopathological features of endobronchial squamous cell carcinoma.

RESULTS

In total, 73 articles were analyzed, from which 48 articles were selected as bibliographic references. We present the criteria used for the identification of endobronchial squamous cell carcinoma in order to highlight its main characteristics and the most reliable technologies that can be used for the detection of this type of cancer.

CONCLUSIONS

The current literature review highlights the clinical and paraclinical characteristics of endobronchial squamous cell carcinoma. It aims to open new paths for research and early detection with respect to the frequent practice of lung parenchymal preservation surgery.

Identification of Anti-SNRPA as a Novel Serological Biomarker for Systemic Sclerosis Diagnosis

Systemic sclerosis (SSc) is a systemic autoimmune disorder that leads to vasculopathy and tissue fibrosis. A lack of reliable biomarkers has been a challenge for clinical diagnosis of the disease. We employed a protein array-based approach to identify and validate SSc-specific autoantibodies. Phase I involved profiled autoimmunity using human proteome microarray (HuProt arrays) with 90 serum samples: 40 patients with SSc, 30 patients diagnosed with autoimmune diseases, and 20 healthy subjects. In Phase II, we constructed a focused array with candidates identified antigens and used this to profile a much larger cohort comprised of serum samples. Finally, we used a western blot analysis to validate the serum of validated proteins with high signal values. Bioinformatics analysis allowed us to identify 113 candidate autoantigens that were significantly associated with SSc. This two-phase strategy allowed us to identify and validate anti-small nuclear ribonucleoprotein polypeptide A (SNRPA) as a novel SSc-specific serological biomarker. The observed positive rate of anti-SNRPA antibody in patients with SSc was 11.25%, which was significantly higher than that of any disease control group (3.33%) or healthy controls (1%). In conclusion, anti-SNRPA autoantibody serves as a novel biomarker for SSc diagnosis and may be promising for clinical applications.

ETHE1 Accelerates Triple-Negative Breast Cancer Metastasis by Activating GCN2/eIF2α/ATF4 Signaling

Triple-negative breast cancer (TNBC) is the most fatal subtype of breast cancer; however, effective treatment strategies for TNBC are lacking. Therefore, it is important to explore the mechanism of TNBC metastasis and identify its therapeutic targets. Dysregulation of ETHE1 leads to ethylmalonic encephalopathy in humans; however, the role of ETHE1 in TNBC remains elusive. Stable cell lines with ETHE1 overexpression or knockdown were constructed to explore the biological functions of ETHE1 during TNBC progression in vitro and in vivo. Mass spectrometry was used to analyze the molecular mechanism through which ETHE1 functions in TNBC progression. ETHE1 had no impact on TNBC cell proliferation and xenograft tumor growth but promoted TNBC cell migration and invasion in vitro and lung metastasis in vivo. The effect of ETHE1 on TNBC cell migratory potential was independent of its enzymatic activity. Mechanistic investigations revealed that ETHE1 interacted with eIF2α and enhanced its phosphorylation by promoting the interaction between eIF2α and GCN2. Phosphorylated eIF2α in turn upregulated the expression of ATF4, a transcriptional activator of genes involved in cell migration and tumor metastasis. Notably, inhibition of eIF2α phosphorylation through ISRIB or ATF4 knockdown partially abolished the tumor-promoting effect of ETHE1 overexpression. ETHE1 has a functional and mechanistic role in TNBC metastasis and offers a new therapeutic strategy for targeting ETHE1-propelled TNBC using ISRIB.

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.

A Bayesian hierarchical model for signal extraction from protein microarrays

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

Autoantibody screening of plasma and peritoneal fluid of patients with endometriosis

STUDY QUESTION

Are there specific autoantibody profiles in patients with endometriosis that are different from those in controls?

SUMMARY ANSWER

This study did not reveal a significantly higher prevalence of autoantibodies in the studied groups of patients.

WHAT IS KNOWN ALREADY

Various inflammatory factors are postulated to be involved in the pathomechanisms of endometriosis, and a potential link exists with autoimmune diseases, which may also play an important role. As the diagnosis of endometriosis remains invasive, it can only be confirmed using laparoscopy with histopathological examination of tissues. Numerous studies have focused on identifying useful biomarkers to confirm the disease, but without unequivocal effects. Autoantibodies are promising molecules that serve as potential prognostic factors.

STUDY DESIGN, SIZE, DURATION

A multicentre, cross-sectional study was conducted over 18 months (between 2018 and 2019), at eight Departments of Obstetrics and Gynaecology in several cities across Poland on 137 patients undergoing laparoscopic examination for the diagnosis of endometriosis.

PARTICIPANTS/MATERIALS, SETTINGS, METHODS

During laparoscopy, we obtained plasma samples from 137 patients and peritoneal fluid (PF) samples from 98 patients. Patients with autoimmune diseases were excluded from the study. Autoantibody profiling was performed using HuProt v3.1 human proteome microarrays.

MAIN RESULTS AND THE ROLE OF CHANCE

We observed no significant differences in the expression of autoantibodies in the plasma or PF between the endometriosis and control groups. The study revealed that in the PF of women with Stage II endometriosis, compared with other stages, there were significantly higher reactivity signals for ANAPC15 and GABPB1 (adj. P < 0.016 and adj. P < 0.026, respectively; logFC > 1 in both cases). Comparison of the luteal and follicular phases in endometriosis patients revealed that levels of NEIL1 (adj. P < 0.029), MAGEB4 (adj. P < 0.029), and TNIP2 (adj. P < 0.042) autoantibody signals were significantly higher in the luteal phase than in the follicular phase in PF samples of patients with endometriosis. No differences were observed between the two phases of the cycle in plasma or between women with endometriosis and controls. Clustering of PF and plasma samples did not reveal unique autoantibody profiles for endometriosis; however, comparison of PF and plasma in the same patient showed a high degree of concordance.

LIMITATIONS, REASONS FOR CAUTION

Although this study was performed using the highest-throughput protein array available, it does not cover the entire human proteome and cannot be used to study potentially promising post-translational modifications. Autoantibody levels depend on numerous factors, such as infections; therefore the autoantibody tests should be repeated for more objective results.

WIDER IMPLICATIONS OF THE FINDINGS

Although endometriosis has been linked to different autoimmune diseases, it is unlikely that autoimmune responses mediated by specific autoantibodies play a pivotal role in the pathogenesis of this inflammatory disease. Our study shows that in searching for biomarkers of endometriosis, it may be more efficient to use higher-throughput proteomic microarrays, which may allow the detection of potentially new biomarkers. Only research on such a scale, and possibly with different technologies, can help discover biomarkers that will change the method of endometriosis diagnosis.

STUDY FUNDING/COMPETING INTEREST(S)

This study was funded by a grant from the Polish Ministry of Health (grant no. 6/6/4/1/NPZ/2017/1210/1352). It was also funded by the Estonian Research Council (grant PRG1076) and the Horizon 2020 Innovation Grant (ERIN; grant no. EU952516), Enterprise Estonia (grant no. EU48695), and MSCA-RISE-2020 project TRENDO (grant no. 101008193). The authors declare that there is no conflict of interest.

TRIAL REGISTRATION NUMBER

N/A.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

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.

Autoantibody signatures discovered by HuProt protein microarray to enhance the diagnosis of lung cancer

This study aims to discover novel autoantibodies against tumor-associated antigens (TAAs) and establish diagnostic models for assisting in the diagnosis of lung cancer and discrimination of pulmonary nodules (PNs). Ten autoantibodies to TAAbs (TAAbs) were discovered by means of protein microarray and their serum level was also higher in 212 LC patients than that in 212 NC of validation cohort 1 (P < 0.05). The model 1 comprising 4 TAAbs and CEA reached an AUC of 0.813 (95%CI: 0.762-0.864) for diagnosing LC from normal individuals. Five TAAbs existed a significant difference between 105 malignant pulmonary nodules (MPNs) and 105 benign pulmonary nodules (BPNs) patients in validation cohort 2 (P < 0.05). Model 2 could distinguish MPNs from BPNs with an AUC of 0.845. High-throughput protein microarray is an efficient approach in discovering novel TAAbs which could be used as biomarkers in lung cancer diagnosis.

Diagnostic value of anti-Kaiso autoantibody in axial spondyloarthritis

Objective

Axial spondyloarthritis (axSpA) is a chronic rheumatic disease predominantly characterized by inflammation and progressive structural damage. Patients are often diagnosed very late, which delays the optimal treatment period. Early diagnosis of axSpA, especially non-radiographic axSpA (nr-axSpA), remains a major challenge. This study aimed to investigate the diagnostic value of anti-Kaiso autoantibodies in axSpA and their correlation with clinical disease indicators.

Methods

Two pooled serum samples (seven patients with nr-axSpA and seven healthy controls) were profiled using HuProt arrays to investigate the diagnostic value of autoantibodies in nr-axSpA. Levels of anti-Kaiso autoantibodies in patients with axSpA and controls were determined using the Meso Scale Discovery assay system. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of anti-Kaiso autoantibodies in axSpA. Pearson's correlation was used to assess the correlation between anti-Kaiso autoantibodies and clinical parameters.

Results

Seven candidate autoantibodies were present in the serum of patients with nr-axSpA. The levels of anti-Kaiso autoantibodies were significantly higher in the nr-axSpA group than in the other groups. It can differentiate nr-axSpA from ankylosing spondylitis (AS), healthy controls, and rheumatoid arthritis. The level of early-stage AS among patients with nr-axSpA decreased when they progressed to the late stage. Of all patients with axSpA, serum anti-Kaiso autoantibody levels were positively correlated with the C-reactive protein level and the Bath Ankylosing Spondylitis Disease Activity Index score and negatively correlated with disease duration.

Conclusion

Anti-Kaiso autoantibody may be a valuable diagnostic biomarker for early-stage AS in the nr-axSpA period and may be a potential therapeutic target.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

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.

Advances in the application of proteomics in lung cancer

Although the incidence and mortality of lung cancer have decreased significantly in the past decade, it is still one of the leading causes of death, which greatly impairs people's life and health. Proteomics is an emerging technology that involves the application of techniques for identifying and quantifying the overall proteins in cells, tissues and organisms, and can be combined with genomics, transcriptomics to form a multi-omics research model. By comparing the content of proteins between normal and tumor tissues, proteomics can be applied to different clinical aspects like diagnosis, treatment, and prognosis, especially the exploration of disease biomarkers and therapeutic targets. The applications of proteomics have promoted the research on lung cancer. To figure out potential applications of proteomics associated with lung cancer, we summarized the role of proteomics in studies about tumorigenesis, diagnosis, prognosis, treatment and resistance of lung cancer in this review, which will provide guidance for more rational application of proteomics and potential therapeutic strategies of lung cancer.

Recent Advances in DNA Vaccines against Lung Cancer: A Mini Review

Lung cancer is regarded as the major causes of patient death around the world. Although the novel tumor immunotherapy has made great progress in the past decades, such as utilizing immune checkpoint inhibitors or oncolytic viruses, the overall 5-year survival of patients with lung cancers is still low. Thus, development of effective vaccines to treat lung cancer is urgently required. In this regard, DNA vaccines are now considered as a promising immunotherapy strategy to activate the host immune system against lung cancer. DNA vaccines are able to induce both effective humoral and cellular immune responses, and they possess several potential advantages such as greater stability, higher safety, and being easier to manufacture compared to conventional vaccination. In the present review, we provide a global overview of the mechanism of cancer DNA vaccines and summarize the innovative neoantigens, delivery platforms, and adjuvants in lung cancer that have been investigated or approved. Importantly, we highlight the recent advance of clinical studies in the field of lung cancer DNA vaccine, focusing on their safety and efficacy, which might accelerate the personalized design of DNA vaccine against lung cancer.

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.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

High-throughput and multi-phases identification of autoantibodies in diagnosing early-stage breast cancer and subtypes

Autoantibodies (AAbs) targeted tumor‐associated antigens (TAAs) have the potential for early detection of breast cancer. Here, 574 early‐stage breast cancer (ES‐BC) patients containing 4 subtypes (Luminal A, Luminal B, HER2+, TN), 126 benign breast disease (BBD) patients, and 199 normal healthy controls (NHC) were separated into three‐phases to discover, verify, and validate AAbs. In discovery phase using high‐throughput protein microarray, 37 AAbs with sensitivity of 31.25%‐86.25% and specificity over 73% in ES‐BC, and 40 AAbs with different positive rates between subtypes were identified as candidates. In verification phase, 18 AAbs were significantly increased compared with the Control (BBD and NHC) in focused array. Ten out of 18 AAbs exhibited a significant difference between subtypes (P < .05). In ELISA validation phase, 5 novel AAbs (anti‐KJ901215, ‐FAM49B, ‐HYI, ‐GARS, ‐CRLF3) exhibited significantly higher levels in ES‐BC compared with BBD/NHC (P < .05). The sensitivities of individual AAb and a 5‐AAbs panel were 20.41%‐28.57% and 38.78%, whereas the specificities were over 90% and 85.94%. Simultaneously, 4 AAbs except anti‐GARS differed significantly between TN and non‐TN subtype (P < .05). We constructed 3 random forest classifier models based on AAbs to discriminant ES‐BC from Control or BBD, and to discern TN subtype, which yielded an area under the curve of 0.870, 0.860, and 0.875, respectively. Biological interaction analysis revealed 4 TAAs, except for KJ901215, that were associated with well known proteins of BC. This study discovered and stepwise validated 5 novel AAbs with the potential to diagnose ES‐BC and discern TN subtype, indicating easy‐to‐detect and minimally invasive diagnostic value of serum AAbs ahead of biopsy for future application.

The isocitrate dehydrogenase 1 is a potential prognostic indicator for non-small cell lung cancer patients

BACKGROUND

The serum isocitrate dehydrogenase 1(IDH1) level is significantly elevated in patients with non-small cell lung cancer (NSCLC) and has important clinical value as a marker for early diagnosis. This study examined the dynamic changes of serum IDH1 levels of patients with NSCLC undergoing surgery or medical treatment, to evaluate its potential prognostic value.

METHODS

The study cohort included 83 NSCLC patients who underwent surgery, 37 NSCLC patients who underwent medical treatment, 50 healthy controls, and 52 disease controls. Serum levels of IDH1 were assayed by enzyme-linked immunoassay. Tumor biomarkers including carcinoembryonic antigen, squamous cell carcinoma, neuron-specific enolase, CYFRA21-1, and pro-gastrin-releasing peptide-which are currently used in clinical practice-were measured by automatic immunoanalyzers.

RESULTS

Serum IDH1 was significantly higher in patients with NSCLC compared with healthy people or patients with benign lung diseases (p < 0.001). The area under the receiver operating characteristic curve for diagnosis and differential diagnosis were 0.897 and 0.879, respectively, which were superior to the five tumor markers. Serum IDH1 levels decreased in most patients after surgery, with the most dramatic changes in patients with stage I tumors compared with stage II and III. Analyses of changes in the serum IDH1 level of patients after receiving chemotherapy or targeted therapy revealed that for patients with progressive disease, serum IDH1 increased significantly after treatment; for patients with partial response or stable disease, it decreased steadily.

CONCLUSION

IDH1 has potential prognostic value and may be used as a marker for the monitoring of treatment efficacy.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

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.

Integrative Metabolomic and Lipidomic Profiling of Lung Squamous Cell Carcinoma for Characterization of Metabolites and Intact Lipid Species Related to the Metastatic Potential

SQCC is a major type of NSCLC, which is a major cause of cancer-related deaths, and there were no reports regarding the prediction of metastatic potential of lung SQCC by metabolomic and lipidomic profiling. In this study, metabolomic and lipidomic profiling of lung SQCC were performed to predict its metastatic potential and to suggest potential therapeutic targets for the inhibition of lung SQCC metastasis. Human bronchial epithelial cells and four lung SQCC cell lines with different metastatic potentials were analyzed using gas chromatography-mass spectrometry and direct infusion-mass spectrometry. Based on the obtained metabolic and lipidomic profiles, we constructed models to predict the metastatic potential of lung SQCC; glycerol, putrescine, β-alanine, hypoxanthine, inosine, myo-inositol, phosphatidylinositol (PI) 18:1/18:1, and PI 18:1/20:4 were suggested as characteristic metabolites and intact lipid species associated with lung SQCC metastatic potential. In this study, we established predictive models for the metastatic potential of lung SQCC; furthermore, we identified metabolites and intact lipid species relevant to lung SQCC metastatic potential that may serve as potential therapeutic targets for the inhibition of lung SQCC metastasis.

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.

Applications of Protein Microarrays in Biomarker Discovery for Autoimmune Diseases

Dysregulated autoantibodies and cytokines were deemed to provide important cues for potential illnesses, such as various carcinomas and autoimmune diseases. Increasing biotechnological approaches have been applied to screen and identify the specific alterations of these biomolecules as distinctive biomarkers in diseases, especially autoimmune diseases. As a versatile and robust platform, protein microarray technology allows researchers to easily profile dysregulated autoantibodies and cytokines associated with autoimmune diseases using various biological specimens, mainly serum samples. Here, we summarize the applications of protein microarrays in biomarker discovery for autoimmune diseases. In addition, the key issues in the process of using this approach are presented for improving future studies.

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Substantial studies indicate that autoantibodies to tumor-associated antigens (TAAbs) arise in early stage of lung cancer (LC). However, since single TAAbs as non-invasive biomarkers reveal low diagnostic performances, a panel approach is needed to provide more clues for early detection of LC. In the present research, potential TAAbs were screened in 150 serum samples by focused protein array based on 154 proteins encoded by cancer driver genes. Indirect enzyme-linked immunosorbent assay (ELISA) was used to verify and validate TAAbs in two independent datasets with 1,054 participants (310 in verification cohort, 744 in validation cohort). In both verification and validation cohorts, eight TAAbs were higher in serum of LC patients compared with normal controls. Moreover, diagnostic models were built and evaluated in the training set and the test set of validation cohort by six data mining methods. In contrast to the other five models, the decision tree (DT) model containing seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1), built in the training set, yielded the highest diagnostic value with the area under the receiver operating characteristic curve (AUC) of 0.897, the sensitivity of 94.4% and the specificity of 84.9%. The model was further assessed in the test set and exhibited an AUC of 0.838 with the sensitivity of 89.4% and the specificity of 78.2%. Interestingly, the accuracies of this model in both early and advanced stage were close to 90%, much more effective than that of single TAAbs. Protein array based on cancer driver genes is effective in screening and discovering potential TAAbs of LC. The TAAbs panel with TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1 is excellent in early detection of LC, and they might be new target in LC immunotherapy.

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.

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.

The multifaceted roles of sulfane sulfur species in cancer-associated processes

Sulfane sulfur species comprise a variety of biologically relevant hydrogen sulfide (H₂S)-derived species, including per- and poly-sulfidated low molecular weight compounds and proteins. A growing body of evidence suggests that H₂S, currently recognized as a key signaling molecule in human physiology and pathophysiology, plays an important role in cancer biology by modulating cell bioenergetics and contributing to metabolic reprogramming. This is accomplished through functional modulation of target proteins via H₂S binding to heme iron centers or H₂S-mediated reversible per- or poly-sulfidation of specific cysteine residues. Since sulfane sulfur species are increasingly viewed not only as a major source of H₂S but also as key mediators of some of the biological effects commonly attributed to H₂S, the multifaceted role of these species in cancer biology is reviewed here with reference to H₂S, focusing on their metabolism, signaling function, impact on cell bioenergetics and anti-tumoral properties.

Autoantibodies to tumor-associated antigens in lung cancer diagnosis

Lung cancer (LC) accounts for the majority of cancer-related deaths worldwide. Although screening the high-risk population by low-dose CT (LDCT) has reduced mortality, the cost and high false positivity rate has prevented its general diagnostic use. As such, better and more specific minimally invasive biomarkers are needed in general and for early LC detection, specifically. Autoantibodies produced by humoral immune response to tumor-associated antigens (TAA) are emerging as a promising noninvasive biomarker for LC. Given the low sensitivity of any one single autoantibody, a panel approach could provide a more robust and promising strategy to detect early stage LC. In this review, we summarize the background of TAA autoantibodies (TAAb) and the techniques currently used for identifying TAA, as well as recent findings of LC specific antigens and TAAb. This review provides guidance toward the development of accurate and reliable TAAb as immunodiagnostic biomarkers in the early detection of LC.

Development of a panel of serum IgG and IgA autoantibodies for early diagnosis of colon cancer

Purpose: Our pilot study in a small cohort by ELISA showed that the levels and positive rates of serum IgG autoantibodies against p53, HRAS and NSG1, and IgA autoantibody against TIF1γ in early colon cancer (CC) group were significantly higher than that of colon benign lesion (CBL) group / healthy control (HC) group (P <0.01), which suggested that four autoantibodies might be valuable for the diagnosis of patients with CC at early stage. On the basis of pilot study, we intend to comprehensively elucidate the performance of four autoantibodies for the early diagnosis of CC in a large sample cohort, and explore the optimal panel of autoantibodies in the diagnosis of patients with CC at early stage. Methods: Western blot was used to define the ELISA results of serum anti-p53, HRAS, NSG1-IgG and anti-TIF1γ-IgA. The performances of anti-p53, HRAS, NSG1-IgG and anti-TIF1γ-IgA were evaluated by ELISA for the early diagnosis of CC with 601 serum samples of 157 patients with CC at early stage, 144 patients with CC at advanced stage, 130 patients with CBL, and 170 HC, and then the performances of different combinations of four autoantibodies were analyzed for the development of an optimal panel for the early diagnosis of CC. Results: The results of anti-p53, HRAS, NSG1-IgG and anti-TIF1γ-IgA in western blotting were consistent with that in ELISA. The levels and positive rates of anti-p53, HRAS, NSG1-IgG and anti-TIF1γ-IgA in early CC group were significantly higher than that in CBL group/HC group (P <0.01), while had no significant difference from that in advanced CC group (P >0.05), of which anti-TIF1γ-IgA showed the highest area under the receiver operating characteristic curve (AUC) of 0.716 for the patients with CC at early stage, with 25.5% sensitivity and specificity at 96.7%. Additionally, a panel of anti-p53, HRAS-IgG and anti-TIF1γ-IgA showed the highest AUC among all possible combinations of four autoantibodies, up to 0.737, with 47.1% sensitivity at 92.0% specificity. Conclusions: Serum IgG autoantibodies against p53, HRAS and NSG1, and IgA autoantibody against TIF1γ show the diagnostic value for the patients with CC at early stage, of which anti-TIF1γ-IgA is demonstrated to be a preferable biomarker, and an optimal panel comprised of anti-p53, HRAS-IgG and anti-TIF1γ-IgA might contribute to the further improvement of early diagnosis for CC.

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.

Clinical Application of Mass Spectrometry-Based Proteomics in Lung Cancer Early Diagnosis

The current knowledge on proteomic biomarker analysis for the early diagnosis of lung cancer is summarized, underlining the diversity among the results and the current interest in translating research results into clinical practice. A MEDLINE/PubMed literature search to retrieve all the papers published in the last 10 years is performed. Proteomics studies on lung cancer have gathered evidence on the potential role of biomarkers in early diagnosis. Although promising, none of them have proved to be sufficiently reliable to achieve validation. Future research should evolve toward a multipanel analysis of proteins, considering the possibility that individual biomarkers might not be specific enough to diagnose lung cancer, but could be related to oncological conditions.

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.

Development of a machine learning-based multimode diagnosis system for lung cancer

As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broad-spectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

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

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

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

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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).

Proteome Profiling Uncovers an Autoimmune Response Signature That Reflects Ovarian Cancer Pathogenesis

Harnessing the immune response to tumor antigens in the form of autoantibodies, which occurs early during tumor development, has relevance to the detection of cancer at early stages. We conducted an initial screen of antigens associated with an autoantibody response in serous ovarian cancer using recombinant protein arrays. The top 25 recombinants that exhibited increased reactivity with cases compared to controls revealed TP53 and MYC, which are ovarian cancer driver genes, as major network nodes. A mass spectrometry based independent analysis of circulating immunoglobulin (Ig)-bound proteins in ovarian cancer and of ovarian cancer cell surface MHC-II bound peptides also revealed a TP53–MYC related network of antigens. Our findings support the occurrence of a humoral immune response to antigens linked to ovarian cancer driver genes that may have utility for early detection applications.