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

ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection.

METHODS

The study encompassed three phases: discovery, verification, and validation, comprising a total of 1309 individuals. Serum autoantibodies were profiled using the HuProtTM human proteome microarray, and autoantibody levels were measured using the enzyme-linked immunosorbent assay (ELISA). Twelve machine learning algorithms were employed to construct diagnostic models, and evaluated using the area under the receiver operating characteristic curve (AUC). The model application was facilitated through R Shiny, providing a graphical interface.

RESULTS

Thirteen autoantibodies targeting TAAs (CAST, FAM131A, GABPA, HDAC1, HDGFL1, HSF1, ISM2, PTMS, RNF219, SMARCE1, SNAP25, SRPK2, and ZPR1) were identified in the discovery phase. Subsequent verification and validation phases identified five TAAbs (anti-CAST, anti-HDAC1, anti-HSF1, anti-PTMS, and anti-ZPR1) that exhibited significant differences between ESCC and control subjects (P < 0.05). The support vector machine (SVM) model demonstrated robust performance, with AUCs of 0.86 (95% CI: 0.82-0.89) in the training set and 0.83 (95% CI: 0.78-0.88) in the test set. For early-stage ESCC, the SVM model achieved AUCs of 0.83 (95% CI: 0.79-0.88) in the training set and 0.83 (95% CI: 0.77-0.90) in the test set. Notably, promising results were observed for high-grade intraepithelial neoplasia, with an AUC of 0.87 (95% CI: 0.77-0.98). The web-based implementation of the early ESCC diagnostic tool is publicly accessible at https://litdong.shinyapps.io/ESCCPred/ .

CONCLUSION

This study provides a promising and easy-to-use diagnostic prediction model for early ESCC detection. It holds promise for improving early detection strategies and has potential implications for public health.

Proliferative verrucous and homogeneous Leukoplakias exhibit differential methylation patterns

OBJECTIVE

Proliferative verrucous leukoplakia (PVL) is considered a clinically distinct entity from other oral leucoplakias (OLs) due to its clinical presentation and evolution. However, molecular differences between them remain unclear. We aimed to determine whether there are methylation differences between PVL and other forms of OLs.

MATERIALS AND METHODS

Oral biopsies from 12 patients with PVL, eight patients with homogeneous leucoplakia (HL), and 10 healthy individuals were obtained for a genome-wide DNA methylation analysis via the Infinium EPIC Platform.

RESULTS

A total of 1815 differentially methylated CpGs were found between PVL and HL, with a prominent state of hypermethylation in HL patients. CpGs covered 813 genes with distinct roles, including cell adhesion, extracellular matrix organization, and cell and synaptic signaling. 43% of these genes had been previously described in cancer and associated with prognosis. We developed a multinomial logistic regression model able to differentiate HL, PVL, and control samples. The model had a cross-validated estimate of 73% and included differentially methylated cancer-related genes between the pathological conditions and the healthy donors, including ADNP, BRCA2, CDK13, GNB1, NIN, NUMB, PIK3C2B, PTK2, SHISA4, THSD7B, WWP1, and ZNF292. It also included CpGs covering differentially methylated genes in HL (MEN1 and TNRC6B) and PVL (ACOXL, ADH1B, CAMTA1, CBFA2T3, CPXM2, LRFN2, SORCS2, and SPN).

CONCLUSIONS

PVL and HL present differential methylation patterns that could be linked to their differential clinical behavior. Our findings show the potential of methylation markers and suggest novel diagnostic biomarkers.

Association of elevated autoantibody to high expression of GNAS in hepatocellular carcinoma

Purpose

This study was based on hepatocellular carcinoma (HCC) patients of early-stage to explore the diagnostic capability and possible production causes of anti-GNAS autoantibody.

Methods

We evaluated the frequency of anti-GNAS autoantibody in sera from patients with early-stage HCC by enzyme-linked immunosorbent assay (ELISA) and the expression of GNAS protein in early-stage HCC tissues by immunohistochemistry. Western blotting (WB) and real-time polymerase chain reaction (RT-PCR) were utilized to examine the expressions of GNAS protein and mRNA in cell lines. GEO and International Cancer Genome Consortium (ICGC) databases were inquired to explore mRNA expression and mutation of GNAS in HCC tissues.

Results

The positive rates of anti-GNAS autoantibody in HCC patients at clinical stage I (78.1 %) and clinical stage II (57.1 %) were all significantly higher than that in healthy control (20 %). There was also a significant difference in GNAS protein expression between HCC and its adjacent normal liver tissues. The results from WB and RT-PCR showed a significant difference at the mRNA level but no statistical difference at the protein level between HCC and normal liver cell lines. The difference in mRNA level between HCC and adjacent normal liver tissues was verified to be significant. Furthermore, the ICGC database demonstrated a 10.6 % mutation frequency for GNAS in HCC patients.

Conclusion

The coordination of elevated anti-GNAS autoantibody, high expression of GNAS in the mRNA and protein levels in HCC, and high frequency of GNAS mutation indicates that anti-GNAS autoantibody may be used as an early indicator of HCC.

Diagnostic performance of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma

MAGEA family proteins are immunogenic and can produce corresponding autoantibodies, and we aim to evaluate the diagnostic value of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma (ESCC). Protein chip was used to detect the expression level of anti-MAGEA autoantibodies (IgG and IgM) in 20 mixed serum samples. Enzyme linked immunosorbent assay was adopted to determine the expression level of autoantibodies in 1019 serum samples (423 ESCC, 423 healthy control (HC), 173 benign esophageal disease (BED)), and stepwise logistic regression analysis was used for developing a diagnostic model. Eight anti-MAGEA autoantibodies were screened out based on the protein chip. The levels of 7 autoantibodies (MAGEA1-IgG, MAGEA3-IgG, MAGEA3-IgM, MAGEA4-IgG, MAGEA6-IgG, MAGEA10-IgG, MAGEA12-IgG) in ESCC were significantly higher than that in HC, and the levels of anti-MAGEA1 IgG, anti-MAGEA3-IgG, anti-MAGEA4-IgG, anti-MAGEA10-IgG and anti-MAGEA12-IgG autoantibodies in ESCC group were significantly higher than those in BED group. The area under curve (AUC), sensitivity and specificity of the logistic regression model (MAGEA1-IgG, MAGEA4-IgG, MAGEA6-IgG, MAGEA12-IgG) in the training set and the validation set were 0.725 and 0.698, 55.2% and 51.8%, 80.4% and 84.5%, respectively, in distinguishing ESCC and HC. The model also could distinguish between ESCC and BED, with the AUC of 0.743, sensitivity of 55.4% and specificity of 89.0%. The positive rate of the model combined with cytokeratin 19 fragment to diagnose ESCC reached 78.0%. The study identified anti-MAGEA autoantibodies with potential diagnostic value for ESCC, which may provide new promising for the detection of the disease.

Identification of novel tumor-associated antigens and evaluation of a panel of autoantibodies in detecting oral cancer

BACKGROUND

We aimed to identify tumor-associated antigen (TAA) biomarkers through bioinformatic analysis and experimental verification, and to evaluate a panel of autoantibodies against tumor-associated antigens (TAAbs) for the detection of oral cancer (OC).

METHODS

GEO and TCGA databases were used to screen significantly up-regulated genes related to OC, and protein-protein interaction (PPI) analysis and Cystoscope software were used to identify key genes. Enzyme-linked immunosorbent assay (ELISA) was used to detect the expression levels of autoantibodies in 173 OC patients and 173 normal controls, and binary logistic regression analysis was used to build a diagnostic model.

RESULTS

Using bioinformatics, we identified 10 key genes (AURKA, AURKB, CXCL8, CXCL10, COL1A1, FN1, FOXM1, MMP9, SPP1 and UBE2C) that were highly expressed in OC. Three autoantibodies (anti-AURKA, anti-CXCL10, anti-FOXM1) were proven to have diagnostic value for OC in the verification set and the validation set. The combined assessment of these three autoantibodies improved the diagnostic value for OC, with an area under the curve (AUC), sensitivity and specificity of 0.741(95%CI:0.690-0.793),58.4% and 80.4%, respectively. In addition, the combination of these three autoantibodies also had high diagnostic value for oral squamous cell carcinoma (OSCC), with an AUC, sensitivity and specificity of 0.731(95%CI:0.674,0.786), 53.8% and 82.1%, respectively.

CONCLUSIONS

Our study revealed that AURKA, CXCL10 and FOXM1 may be potential biomarkers and the panel of three autoantibodies (anti-AURKA, anti-CXCL10 and anti-FOXM1) had good diagnostic value for OC.

Construction and Evaluation of a Novel MAP Immunoassay for 9 Nutrition-and-Health-Related Protein Markers Based on Multiplex Liquid Protein Chip Technique

We attempted to construct and evaluate a novel detection method to realize simultaneous detection based on a multiplex liquid protein chip technique for nine nutrition-and-health-related protein markers to meet the requirement of an accurate, simultaneous and comprehensive analysis of the proteomics of nutrition and health. The lower limits of detection, biological limits of detection and regression equations of serum ferritin (SF), soluble transferrin receptor (sTfR), c-reactive protein (CRP), retinol-binding protein4 (RBP4), apolipoprotein B (ApoB), alpha-fetoprotein (AFP), prealbumin (PA), carcino-embryonic antigen (CEA) and D-Dimmer (D-D) were determined after a series of optimal experiments. Then, the results of the methodological evaluation for this novel method indicated that the accuracies were between 70.12% and 127.07%, the within-run precisions were between 0.85% and 7.31%, the between-run precisions were between 3.53% and 19.07%, the correlation coefficients between this method and other methods were above 0.504 (p < 0.05), and the direct bilirubin (DBIL) of low concentration and the indirect bilirubin (IBIL) of high concentration could not interfere with the detected results of nine indicators. The novel multiplex detection method, which can increase accuracy and improve the ability of comprehensive analysis, can basically meet the requirement of detection and the diagnosis of the proteomics of nutrition and health.

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.

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.

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.