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Immunoglobulin M (IgM) autoantibodies, as the early appearing antibodies in humoral immunity when stimulated by antigens, might be excellent biomarkers for the early detection of lung cancer (LC). We aimed to develop a multi-analyte integrative model combining IgM autoantibodies and a traditional tumor biomarker that could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of lung adenocarcinoma (LUAD). A customized protein array based on cancer driver genes was constructed and applied in the discovery cohort consisting of 68 LUAD patients and 68 normal controls (NCs); 31 differentially expressed IgM autoantibodies were identified. The top 5 candidate IgM autoantibodies [based on the area under the receiver operating characteristic curve (AUC) ranking], namely, TSHR, ERBB2, survivin, PIK3CA, and JAK2, were validated in the validation cohort using enzyme-linked immunosorbent assay (ELISA), which included 147 LUAD samples, 72 lung squamous cell carcinoma (LUSC) samples, 44 small cell lung carcinoma (SCLC) samples, and 147 NCs. These indicators presented diagnostic capacity for LUAD, with AUCs of 0.599, 0.613, 0.579, 0.601, and 0.633, respectively ( p  < 0.05). However, none of them showed a significant difference between the SCLC and NC groups, and only the IgM autoantibody against JAK2 showed a higher expression in LUSC than in NC ( p  = 0.046). Through logistic regression analysis, with the five IgM autoantibodies and carcinoembryonic antigen (CEA), one diagnostic model was constructed for LUAD. The model yielded an AUC of 0.827 (sensitivity = 56.63%, specificity = 93.98%). The diagnostic efficiency was superior to that of either CEA (AUC = 0.692) or IgM autoantibodies alone (AUC = 0.698). Notably, the accuracy of this model in early-stage LUAD reached 83.02%. In conclusion, we discovered and identified five novel IgM indicators and developed a multi-analyte model combining IgM autoantibodies and CEA, which could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of LUAD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Li, Wang, Liu, Liu, Zhang, Pei, Wang, Jiang, Wang, Zhang and Dai.)

Background: Serum autoantibodies (AAbs) against tumor-associated antigens (TAAs) could be useful biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody against PDLIM1 for improving the detection of ovarian cancer (OC)., Methods: Immunohistochemistry (IHC) test in tissue array containing 280 OC tissues, 20 adjacent tissues, and 8 normal ovarian tissues was performed to analyze the expression of PDLIM1 in tissues. Enzyme-linked immunosorbent assay (ELISA) was employed to measure the autoantibody to PDLIM1 in 545 sera samples from 182 patients with OC, 181 patients with ovarian benign diseases, and 182 healthy controls., Results: The results of IHC indicated that 84.3% (236/280) OC tissues were positively stained with PDLIM1, while no positive staining was found in adjacent or normal ovarian tissues. The frequency of anti-PDLIM1 autoantibody was significantly higher in OC patients than that in healthy and ovarian benign controls in both training (n=122) and validation (n=423) sets. The area under the curves (AUCs) of anti-PDLIM1 autoantibody for discriminating OC from healthy controls were 0.765 in training set and 0.740 in validation set, and the AUC of anti-PDLIM1 autoantibody for discriminating OC from ovarian benign controls was 0.757 in validation set. Overall, it was able to distinguish 35.7% of OC, 40.6% of patients with early-stage, and 39.5% of patients with late-stage. When combined with CA125, the AUC increased to 0.846, and 79.2% of OC were detected, which is statistically higher than CA125 (61.7%) or anti-PDLIM1(35.7%) alone ( p <0.001). Also, anti-PDLIM1 autoantibody could identify 15% (18/120) of patients that were negative with CA125 (CA125 <35 U/ml)., Conclusions: The anti-PDLIM1 autoantibody response in OC patients was positively correlated with PDLIM1 high expression in OC tissues, suggesting that the autoantibody against PDLIM1 might have the potential to be a novel serological biomarker of OC, serving as a complementary measure of CA125, which could improve the power of OC detection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Qiu, Duan, Wang, Shi, Wang, Ye, Dai, Zhang and Wang.)

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC., (© 2021 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

The aim of this study is to identify novel tumor-associated antigens (TAAs) of lung cancer by using serological analysis of recombinant cDNA expression library (SEREX) and bioinformatics analysis as well as to explore their humoral immune response. SEREX and pathway enrichment analysis were used to immunoscreen TAAs of lung cancer and elaborate their function in biological pathways, respectively. Subsequently, the sera level of autoantibodies against the selected TAAs (TOP2A, TRIM37, HSP90AB1, EEF1G and TPP1) was detected by immunoserological analysis to explore the immune response of these antigens. The Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) database were applied to explore the mRNA and protein expression level of TOP2A, TRIM37 and HSP90AB1 in tissues, respectively. Seventy positive clones were identified by SEREX which contain 63 different genes, and 35 genes of them have been reported. These 35 genes were mainly related to regulation of different transcription factor and performed enrichment in legionellosis, RNA transport, IL-17 signaling pathway via enrichment analysis. Additionally, the positive rate of autoantibodies against TOP2A, TRIM37 and HSP90AB1 in lung cancer patients were typically higher than normal control (NC; P < 0.05). Moreover, the combination of the autoantibodies against TOP2A, TRIM37 and HSP90AB1 possessed an excellent diagnostic performance with sensitivity of 84% and specificity of 60%. The mRNA expression level of TOP2A was obviously unregulated in squamous cell carcinoma (SCC) tissues and adenocarcinoma (ADC) tissues compared to normal tissues (P < 0.05). In addition, TRIM37 and HSP90AB1 also showed a significant difference between SCC and NC at the mRNA expression level (P < 0.05). This study combining comprehensive autoantibody and gene expression assays has added to the growing list of lung cancer antigens, which may aid the development of diagnostic and immunotherapeutic targets for lung cancer patients., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jiang, Zhang, Liu, Wang, Wang, Pei, Wang, Ye, Shi, Song, Wang, Wang, Dai and Zhang.)

The aim of this study was to develop a noninvasive serological diagnostic approach in identifying and evaluating a panel of candidate autoantibodies to tumor-associated antigens (TAAs) based on protein microarray technology for early detection of ovarian cancer (OC). Protein microarray based on 154 proteins encoded by 138 cancer driver genes was used to screen candidate anti-TAA autoantibodies in a discovery cohort containing 17 OC and 27 normal controls (NC). Indirect enzyme-linked immunosorbent assay (ELISA) was used to detect the content of candidate anti-TAA autoantibodies in sera from 140 subjects in the training cohort. Differential anti-TAA autoantibodies were further validated in the validation cohort with 328 subjects. Subsequently, 112 sera from the patients with ovarian benign diseases with 104 OC sera and 104 NC sera together were recruited to identify the specificity of representative autoantibodies to OC among ovarian diseases. Five TAAs (GNAS, NPM1, FUBP1, p53, and KRAS) were screened out in the discovery phase, in which four of them presented higher levels in OC than controls (P < .05) in the training cohort, which was consistent with the result in the subsequent validation cohort. An optimized panel of three anti-TAA (GNAS, p53, and NPM1) autoantibodies was identified to have relatively high sensitivity (51.2%), specificity (86.0%), and accuracy (68.6%), respectively. This panel can identify 51% of OC patients with CA125 negative. This study supports our assumption that anti-TAA autoantibodies can be considered as potential diagnostic biomarkers for detection of OC; especially a panel of three anti-TAA autoantibodies could be a good tool in immunodiagnosis of OC., (© 2020 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Introduction: Previous studies have demonstrated that autoantibodies against tumor-associated antigens (TAAs) in patients with cancer can be used as sensitive immunodiagnostic biomarkers for the detection of cancer. Most of these TAAs are involved in the tumorigenesis pathway. Cancer driver genes with intragenic mutations can promote tumorigenesis. This study aims to identify autoantibodies against TAAs encoded by cancer driver genes in sera as potential immunodiagnostic biomarkers for gastric adenocarcinoma (GAC)., Methods: Protein arrays based on cancer driver genes were customized for screening candidate TAAs in 100 GAC sera and 50 normal control (NC) sera. Autoantibodies against candidate TAAs were assessed by enzyme-linked immunosorbent assay in both training group (205 GAC sera and 205 NC sera) and independent validation group (126 GAC sera and 126 NC sera). Moreover, the immunodiagnostic models were respectively established and validated in the training group and validation group., Results: A panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti-serine/arginine-rich splicing factor 2, and anti-SMARCB1 was selected by the Fisher linear discriminant analysis model with an areas under receiver operating characteristic curve (AUC) of 0.928 (95% confidence interval [CI]: 0.888-0.967) in the training cohort and an AUC of 0.885 (95% CI: 0.852-0.918) in the validation cohort. Besides, the panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti-PBRM1, and anti-ACVR1B which were selected by the binary logistic regression model showed an AUC of 0.885 (95% CI: 0.852-0.919) in the training cohort and 0.884 (95% CI: 0.842-0.925) in the validation cohort., Discussion: Two panels which were selected in this study could boost the detection of anti-TAA autoantibodies in sera as biomarkers for the detection of GAC., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Objective: The identification of tumor-associated antigens (TAAs) and their corresponding autoantibodies in lung cancer (LC) may expand our vision of cancer immunity. This study aims to screen novel TAAs to distinguish LC from the healthy population., Methods: In our previous study, 35 genes encoding LC-associated TAAs were identified from the serological analysis of recombinant cDNA expression libraries (SEREX), and Oncomine database was further used to identify potential genes in cancer progression. Autoantibody to TAAs were tested by enzyme-linked immunosorbent assay (ELISA) in sera from 1379 participants in validation set and verification set., Findings: Based on analysis of three independent microarrays in Oncomine, ten genes were consistently dysregulated in LC. The sera level and positive frequency of the anti-TOP2A, anti-ACTR3, anti-RPS6KA5 and anti-PSIP1 from LC patients were higher than normal control in validation set. The area under curve (AUC) of anti-TOP2A, anti-ACTR3, anti-RPS6KA5 and anti-PSIP1 was respectively 0.758, 0.787, 0.707, 0.668. The sensitivity of these four autoantibodies for LC detection ranged from 26.63 % to 32.07 % with the specificity over 90 %. Data from the verification set confirmed the results. Except that, the frequency of serum autoantibody against TOP2A (43.3 %) and ACTR3 (50.0 %) was significantly higher in early stage LC than late stage (23.6 % and 22.3 %, respectively)., Conclusion: TOP2A, ACTR3, RPS6KA5 and PSIP1 can elicit humoral immune response in LC and their autoantibodies have relationship with the tumorigenesis of LC. Anti-TOP2A and anti-ACTR3 have the potential to serve as a serological biomarkers in early stage LC., Competing Interests: Declaration of Competing Interest The Authors declare that there is no conflict of interest., (Copyright © 2019 Elsevier GmbH. All rights reserved.)

Esophageal carcinoma (EC) is a common malignant disease worldwide, especially in China. There is currently no specific blood test for detecting EC. Autoantibodies against tumor-associated antigens (TAAbs) and microRNAs (miRNAs) are promising markers for cancer diagnosis and this study focuses on combining TAAbs and miRNAs to evaluate the diagnostic value in esophageal squamous cell carcinoma (ESCC). The expression levels of seven TAAbs and five microRNAs in plasmas from 125 patients diagnosed with ESCC and 125 healthy individuals were detected by enzyme-linked immunosorbent assay (ELISA) and reverse transcription quantitative-polymerase chain reaction (RT-qPCR), respectively. The receiver operating characteristic (ROC) analysis was applied to estimate the diagnostic value of these markers for distinguishing ESCC patients from normal individuals. Logistic regression analysis was performed to generate prediction model and calculate the probability of individuals being diagnosed with ESCC. Three panels were established including four TAAbs, three miRNAs, and three TAAbs combined with three miRNAs. The panel consisting of three TAAbs (HCCR, C-myc, and MDM2) and three miRNAs (miR-21, miR-223, and miR-375) attained great diagnostic value for ESCC, with an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.85-0.93) with the sensitivity of 69%, the specificity of 90%, the PPV of 83%, the NPV of 79%, and the coincidence rate of 81%. The optimal panel of six-member markers was able to effectively discriminate the patients with ESCC from normal individuals, especially for early esophageal squamous cell carcinoma., (© 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Objective: Lung cancer (LC) is one of the most common malignant tumors worldwide with low five-year survival rate due to lack of effective diagnosis. This study aims to find an optimal combination of autoantibodies for detecting of early-stage LC., Methods: Nine relatively novel autoantibodies against tumor-associated (TAAs) (PSIP1, TOP2A, ACTR3, RPS6KA5, HMGB3, MMP12, GREM1, ZWINT and NUSAP1) were detected by using ELISA. Diagnostic models were developed by using the training set (n = 644) and further validated in another independent set (n = 248). We also evaluated the diagnostic accuracy of the model to detect benign lung diseases (BLD) from the early-stage lung cancer., Results: The areas under the receiver operating characteristic curve (AUC) for the model with three TAAs panel (GREM1, HMGB3 and PSIP1) was 0.711(95% CI 0.674-0.746) in the training set and 0.858 (95% CI 0.808-0.899) in the validation set, which demonstrated a higher diagnostic capability. The AUC of this three TAAs model was 0.833 (95%CI 0.780-0.878) in discriminating LC from BLD. This model could identify early-stage LC patients from normal control (NC) individuals, with AUC of 0.687(95% CI 0.634-0.736) in training set and AUC of 0.920(95% CI 0.860-0.960) in validation set, and the overall AUC for early-stage LC was 0.779(95% CI 0.739-0.816) when the training set and validation set were combined., Conclusions: The model with three TAAs panel would detect LC with higher effectiveness, and might be potential screening method for the early LC., Competing Interests: Declaration of Competing Interest The Authors declare that there is no conflict of interest., (Copyright © 2019 Elsevier GmbH. All rights reserved.)

Purpose: Autoantibody to 14-3-3 zeta was identified in gastric cancer (GC) by serological proteome analysis (SERPA) in our previous study. We comprehensively evaluated its ability to detect GC, determined its association with clinical characteristics, and explored its temporal change in GC patients before and after gastrectomy resection in this study., Methods: Anti-14-3-3 zeta antibody was examined by immunoassay in sera from 465 GC patients and 465 normal individuals, and also in 69 serial sera from 26 GC patients before and after resection., Results: The frequency of anti-14-3-3 zeta were significantly higher in GC group than in control group, with AUC of 0.627. The appearance of anti-14-3-3 zeta showed no difference in different tumor stage, tumor size, tumor differentiation, and lymphatic metastasis, but was higher in GC patients with family tumor history than without family tumor history. When anti-14-3-3 zeta was combined with clinical markers (CEA, CA199 and CA724), the sensitivity increased to 52.7%. In the follow-up analysis, the titer of anti-14-3-3 zeta was higher in post-resection sera than pre-resection sera, and no difference was observed in CEA, CA199 and CA724. Anti-14-3-3 zeta showed an increase from negative status to positive status in six patients after resection, while other three clinical markers presented different change in GC patients after resection., Conclusions: Autoantibody against 14-3-3 zeta could be a potential diagnostic biomarker and improve the sensitivity of CEA, CA199 and CA724 in diagnosis of GC. Further largescale studies will be needed to validate its performance in GC patients after resection.

Background: Ovarian cancer (OC) is a major malignancy affecting a large population over the world, and a biomarker that holds diagnostic potential is of critical importance. Recently, autoantibodies have been indicated as biomarkers in multiple cancer research. The current study was designed to explore the practice of using autoantibodies in diagnostic settings by the enzyme-linked immunosorbent assay of sera with a panel of tumor-associated antigens (TAAs)., Methods: A panel of 12 TAAs was selected to detect the corresponding autoantibodies in sera sampled from 132 OC patients as case group and 147 normal healthy individuals as the control group. The diagnostic potential of this panel was evaluated by conventional evaluation, receiver operating characteristic (ROC) curve analyses, and classification tree analysis., Results: When the cutoff values were set as mean ± 2 SD for normal healthy individuals, the positive rates of antibodies to any single TAA were less than 20% both in OC and in normal healthy individuals. In a parallel screening approach, a panel of nine TAAs (p53, C-myc, p90, p62, AHSG, 14-3-3zeta, RalA, Koc, and p16), obtained optimal diagnostic performance in OC with the sensitivity of 61.4% at the 85.0% specificity. In addition, when the nine TAAs were combined with CA125, the sensitivity and specificity were improved to 94.7% and 78.2%, respectively. The ROC curve analyses showed that only the area under the receiver operating characteristic curves (AUCs) of antibodies against C-myc, Koc, and RalA was beyond 0.6, which were 0.732, 0.668, and 0.665, respectively. The AUC of the combination was up to 0.914 (P < 0.05). Decision tree analysis showed that C-myc, HCC1.3, RalA, and CA125 held high potential in the detection of OC. The panel of nine TAAs also identified 78.8% of OC patients who had normal CA125 levels in their serum samples, indicating that elevated CA125 and anti-TAA antibodies appeared to be independent but supplementary biomarkers for diagnosing OC., Conclusions: In summary, the current study further supports that a customized TAA panel can serve as a promising and powerful tool for immunodiagnosis of OC and may be particularly useful in patients with normal CA125 levels., (© 2018 Wiley Periodicals, Inc.)

Purpose: Lung cancer (LC) is the leading cause of cancer-related deaths for both male and female worldwide. Early detection of LC could improve five-year survival rate up to 48.8% compared to 3.3% of late/distant stage. Autoantibodies to tumor-associated antigens (TAAs) have been described as being present before clinical symptoms in lung and other cancers. We aimed to identify more TAAs to improve the performance for discovering non-small cell lung cancer (NSCLC) patients from healthy individuals., Methods: Two independent sets were included in this study. Serological proteome analysis (SERPA) was used to identify TAAs from NSCLC cell line H1299 in a discovery set. In validation study, anti-ENO1 autoantibody was examined by immunoassay in sera from 242 patients with NSCLC and 270 normal individuals., Results: A 47 KDa protein was identified to be alpha-enolase (ENO1) by using SERPA. Analysis of sera from 512 participants by ELISA showed significantly higher frequency of anti-ENO1 autoantibodies in NSCLC sera compared with the sera from normal individuals, with AUC (95%CI) of 0.589 (0.539-0.638, P=0.001). There was no significant difference in frequency of anti-ENO1 in different stages, histological or metastasis status of NSCLC. When anti-ENO1 detection was combined with other two tumor protein biomarkers (CEA and CYFRA 21-1), the sensitivity of NSCLC increased to 84%., Conclusions: ENO1 can elicit humoral immune response in NSCLC and its autoantibody has association with the tumorigenesis of NSCLC. Furthermore, these intriguing results suggest the possibility of autoantibody against ENO1 serving as a potential diagnostic biomarker in NSCLC and have implications for defining novel histological determinants of NSCLC.

Antibodies against tumor-associated antigens (TAAs) have been found in serum of patients with various types of cancers and may serve as biomarkers for early detection of esophageal cancer as well. This systematic review aims to give an overview about known autoantibodies and their diagnostic value in esophageal cancer. We conducted a systematic literature search in two databases to identify studies which performed serological testing for autoantibodies in esophageal cancer patients and controls. Data on study characteristics and results were extracted independently by two reviewers. Overall, 45 articles reporting the detection of 35 different autoantibodies met the inclusion criteria of this review. The most common antibody detection method was enzyme-linked immunosorbent assay (ELISA), and the most frequently assessed autoantibody was anti-p53, which was tested in 17 studies and for 15 studies of which a meta-analysis was conducted to comprehensively evaluate the diagnostic value. Most antibodies were assessed in only one study, and only few authors have evaluated the diagnostic value of combinations of multiple autoantibodies. For single autoantibodies, specificity was generally very high (median 98.3 %), but sensitivity was mostly rather low (median 26.7 %). For some autoantibody combinations, substantially higher sensitivity at reasonably high levels of specificity could be achieved. Development of extended and optimized multimarker panels of autoantibodies might be a promising approach for esophageal cancer early detection.

Introduction. Systemic lupus erythematosus (SLE) is one of the systemic autoimmune diseases characterized by the polyclonal autoantibody production. The human homologue of the mouse double minute 2 (MDM2) is well known as the negative regulator of p53. MDM2 has been reported to be overexpressed in SLE animal model and to promote SLE. Since abnormally expressed proteins can induce autoimmune response, anti-MDM2 autoantibody was examined in SLE patients. Methods. Anti-MDM2 antibody in sera from 43 SLE patients and 69 healthy persons was investigated by ELISA. Positive samples were further confirmed by western blotting. The immunological feathers of anti-MDM2 positive sera were analyzed by indirect immunofluorescence assay. Anti-p53 was also investigated in SLE patients by ELISA, and the correlation of anti-MDM2 and anti-p53 was analyzed. Results. The presence of anti-MDM2 in SLE patients was 23.30%, much higher than normal healthy persons (4.30%). These anti-MDM2 positive sera present a nuclear staining pattern. The presence of anti-p53 in SLE patients was 39.50%, and the titer of anti-MDM2 was positively correlated with anti-p53 in SLE patients. Conclusions. Anti-MDM2 autoantibody was detected at high prevalence in SLE patients. The detection of anti-MDM2 in SLE patients should be clinically useful.

Insulin-like growth factor-binding protein-2 (IGFBP-2) is considered to be a human tumor antigen, and the tumor-specific immunity of IGFBP-2 has been reported in several types of cancer. The purpose of this study was to evaluate whether autoantibodies to IGFBP-2 can be used as diagnostic markers in lung cancer. The results demonstrated that serum anti-IGFBP-2 autoantibody levels were significantly elevated in lung cancer (mean, 1,633.318 ng/ml; median, 1,651.462 ng/ml; range, 342.732-4932.582 ng/ml) compared with benign lung disease (1,210.139, 1,035.900, 547.596‑2,331.167 ng/ml) and normal controls (1,303.369, 1,194.800, 528.200-2140.500 ng/ml). The sensitivity and specificity of anti-IGFBP-2 autoantibodies in diagnosing lung cancer was 73.2 and 60.6%, respectively. When serum IGFBP-2 and anti-IGFBP-2 autoantibody were used together in the diagnosis of lung cancer, it can increase the discriminative power for lung cancer with a sensitivity of 85.7% and a specificity of 57.5%. In conclusion, this study demonstrates that circulating anti-IGFBP-2 autoantibodies can be used as a potential biomarker in diagnosing lung cancer.

Hepatocellular carcinoma (HCC) is a type of cancer with a very poor prognosis. Although α-fetoprotein (AFP) is the most effective marker available to detect HCC, the sensitivity and specificity are not optimal. Therefore, there is a need for the development of more sensitive and specific methods that can supplement AFP in the early detection of this cancer. In this study, autoantibody responses to glucose-regulated protein 78 (GRP78) were evaluated by enzyme-linked immunosorbent assay (ELISA), western blotting and indirect immunofluorescence assay in sera from patients with HCC, liver cirrhosis (LC) and chronic hepatitis (CH), as well as from normal human individuals. Immunohistochemistry (IHC) with tissue array slides was also preformed to analyze protein expression profiles of GRP78 in HCC and control tissues. The prevalence of autoantibodies against GRP78 was 35.5% (27/76) in HCC, which was significantly higher than that in LC, CH and normal human sera (NHS; P<0.01). The average titer of autoantibodies against GRP78 in HCC sera was higher compared to that in LC, CH and NHS(P<0.01). When both autoantibodies against GRP78 and AFP were used simultaneously as diagnostic markers, sensitivity reached 71.4%. Our data indicate that anti-GRP78 autoantibodies may be potential diagnostic markers for HCC, especially in conjunction with AFP.

Although autoantibodies have been recognized as participants in pathogenesis of tissue injury, the collateral role of autoantibodies as reporters from the immune system identifying cellular participants in tumorigenesis has not been fully appreciated. The immune system appears to be capable of sensing aberrant structure, distribution, and function of certain cellular components involved in tumorigenesis and making autoantibody responses to the tumor-associated antigens (TAAs). Autoantibodies to TAAs can report malignant transformation before standard clinical studies and may be useful as early detection biomarkers. The autoantibody response also provides insights into factors related to how cellular components may be rendered immunogenic. As diagnostic biomarkers, specific TAA miniarrays for identifying autoantibody profiles could have sufficient sensitivity in differentiating between types of tumors. Such anti-TAA profiles could also be used to monitor response to therapy. The immune system of cancer patients reveals the immune interactive sites or the autoepitopes of participants in tumorigenesis, and this information should be used in the design of immunotherapy.

Background: Cancer patients produce autoantibodies to self-proteins called tumor-associated antigens (TAA). These autoantibodies represent potentially valuable tools for identifying novel biomarkers and therapeutic targets. This study was designed to identify TAA in prostate cancer (PCa)., Methods: Serum autoantibodies to the survival protein lens epithelium-derived growth factor p75 (LEDGF/p75) were detected by immunofluorescence microscopy, ELISA, and immunoblotting. Expression of LEDGF/p75 in prostate cells and tumors was evaluated by immunoblotting or immunohistochemistry. Apoptotic cleavage of LEDGF/p75 was detected by immunoblotting., Results: Anti-LEDGF/p75 autoantibodies were detected by ELISA in 18.4% of PCa patients and 5.5% of matched controls (P < 0.001) but not in patients with benign prostatic hyperplasia (BPH). LEDGF/p75 expression was detected in 93% of prostate tumors but not in normal prostate. Strong expression of the protein was observed in 61% of prostate tumors. Moderate to high expression was also detected in BPH tissue. Cleavage of LEDGF/p75 was detected in apoptotic prostate cells., Conclusions: The high expression of LEDGF/p75 in prostate tumors and BPH could be induced by inflammation and oxidative stress. LEDGF/p75 cleavage fragments generated during prostate tumor cell death might trigger autoantibodies under inflammatory conditions in certain patients., (Copyright 2004 Wiley-Liss, Inc.)

Studies of autoantibodies in systemic rheumatic diseases have provided abundant evidence suggesting that autoimmune responses are antigen-driven and that autoantibodies often can be viewed as reporters of the immune system revealing the identity of antigens which might be playing roles in the pathophysiology of the disease process. Recent data from our laboratory suggest a similar mechanistic process may be involved in humoral immune responses in certain cancers such as hepatocellular carcinoma (HCC). HCC is unique in that one can follow a cohort of patients with chronic liver disease who will likely progress to develop malignancy over a period of 10 or more years. It has been observed that during transition from chronic liver disease to HCC, novel autoantibodies can appear which are not detected prior to pre-malignant conditions. The hypothesis is that these novel antibody responses may be stimulated by cellular proteins which are involved in carcinogenesis. By immunoscreening an expression library to isolate cDNA clones of autoantigens, a RNA-binding autoantigen p62 has been identified in HCC recently and autoantibodies to p62 were found in 21% of a cohort of HCC patients. p62 is a cytoplasmic protein which binds to mRNA encoding insulin-like growth factor II (IGF-II), a growth factor which is known to be overexpressed in HCC and is tumorigenic in transgenic animals. The expression of p62 is developmentally regulated, and expressed in fetal, but not in adult liver. Our recent observations showing that p62 was aberrantly expressed in 30% of unselected HCC suggest that it could play a role in HCC and other tumors by upregulating expression of growth factor IGF-II in the milieu of other oncogenic factors.

Background and Objectives: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies. Anti-tumor associated antigen autoantibodies (TAAbs) can be used as biomarkers for tumor detection. The aim of this study was to identify a reliable TAAb as the diagnostic marker for ESCC. Materials and Methods: The Cancer Genome Atlas (TCGA) database was used to screen candidate genes. The mRNA expression of the key gene was then verified by micro array dataset GSE44021 from the Gene Expression Omnibus (GEO) database and the diag nostic value of the corresponding autoantibody to the key gene in ESCC was detected by enzyme-linked im muno sorbent assay (ELISA). Results: CXCL8 was identified as the key gene. The dataset GSE44021 showed that CXCL8 mRNA expression was prominently over-expressed in ESCC tissues compared with normal tissues. ELISA results showed that the level of anti-CXCL8 autoantibody in ESCC patients was significantly higher than in normal controls and the receiver operating char ac teristic (ROC) curve indicated that anti-CXCL8 autoantibody could discriminate ESCC patients from normal controls, with the area under the ROC curve (AUC) for the verification cohort, and the validation cohort were 0.713 and 0.751, respectively. Conclusions: Our study illustrated that anti-CXCL8 autoantibody had good diagnostic value, and may become a candidate biomarker for ESCC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Antibodies against tumor-associated antigens (TAAs) have been found in serum of patients with various types of cancers and may serve as biomarkers for early detection of esophageal cancer as well. This systematic review aims to give an overview about known autoantibodies and their diagnostic value in esophageal cancer. We conducted a systematic literature search in two databases to identify studies which performed serological testing for autoantibodies in esophageal cancer patients and controls. Data on study characteristics and results were extracted independently by two reviewers. Overall, 45 articles reporting the detection of 35 different autoantibodies met the inclusion criteria of this review. The most common antibody detection method was enzyme-linked immunosorbent assay (ELISA), and the most frequently assessed autoantibody was anti-p53, which was tested in 17 studies and for 15 studies of which a meta-analysis was conducted to comprehensively evaluate the diagnostic value. Most antibodies were assessed in only one study, and only few authors have evaluated the diagnostic value of combinations of multiple autoantibodies. For single autoantibodies, specificity was generally very high (median 98.3 %), but sensitivity was mostly rather low (median 26.7 %). For some autoantibody combinations, substantially higher sensitivity at reasonably high levels of specificity could be achieved. Development of extended and optimized multimarker panels of autoantibodies might be a promising approach for esophageal cancer early detection. [ABSTRACT FROM AUTHOR]

Presents a study which characterized 90kDA protein, auto-antibodies to which were found associated with anti-p62 responses in the same hepatocellular carcinoma patient group. Review of related literature; Materials and methods used; Results.

Substantial evidence manifests the occurrence of autoantibodies to tumor-associated antigens (TAAs) in the early stage of hepatocellular carcinoma (HCC), and previous studies have mainly focused on known TAAs. In the present study, protein microarrays based on cancer driver genes were customized to screen TAAs. Subsequently, autoantibodies against selected TAAs in sera were tested by enzyme-linked immunosorbent assays (ELISA) in 1175 subjects of three independent datasets (verification dataset, training dataset, and validation dataset). The verification dataset was used to verify the results from the microarrays. A logistic regression model was constructed within the training dataset; seven TAAs were included in the model and yielded an area under the receiver operating characteristic curve (AUC) of 0.831. The validation dataset further evaluated the model, exhibiting an AUC of 0.789. Remarkably, as the aggravation of HCC increased, the prediction probability (PP) of the model tended to decrease, the trend of which was contrary to alpha-fetoprotein (AFP). For AFP-negative HCC patients, the positive rate of this model reached 67.3% in the training dataset and 50.9% in the validation dataset. Screening TAAs with protein microarrays based on cancer driver genes is the latest, fast, and effective method for finding indicators of HCC. The identified anti-TAA autoantibodies can be potential biomarkers in the early detection of HCC. [ABSTRACT FROM AUTHOR]

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

The purpose of this study is to discover novel tumor-associated antigens (TAAs) to improve the diagnosis of lung cancer (LC). Oncomine database was used to discover potential TAAs from LC tissues, enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of autoantibodies against TAAs in two independent sets (identification set, n = 368; validation set, n = 1011). Analyses of sera from identification set showed that the sensitivity of autoantibodies against five TAAs (HMGB3, ZWINT, GREM1, NUSAP1 and MMP12) reached 57.1%, 42.4%, 38.0%, 36.4% and 20.7%, with area under ROC curve (AUC) of 0.85, 0.75, 0.71, 0.73 and 0.70, respectively. It also validated the diagnostic performances of these autoantibodies with AUC of 0.72, 0.65, 0.61, 0.64 and 0.64, respectively. Autoantibody against HMGB3 exhibited significantly increased frequency in early LC (53.3%) compared to advanced LC (29.3%) (P <.05). The positive rates of autoantibody against HMGB3 and NUSAP1 in serum of LC patients without distant metastasis were significantly higher than that of distant metastatic LC (P <.05). When each of the three protein biomarkers (CEA, CA125 and CYFRA21-1) was combined with anti-HMGB3 autoantibody, the sensitivity of early LC increased to 72.7%, 63.3% and 75.9% from 36.4%, 13.3% and 27.6%, respectively. Autoantibodies against 5 TAAs (HMGB3, ZWINT, GREM1, NUSAP1 and MMP12) might have favorable diagnostic values in LC detection, and autoantibody against HMGB3 has the potential to serve as a serological biomarker in early-stage LC. The combination of protein biomarkers and anti-HMGB3 might contribute to detection of early-stage LC. • Autoantibodies to tumor-associated antigen are expected to be markers for early lung cancer. • mRNA profiling is useful for screening of tumor-associated antigen. • HMGB3, MMP12, GREM1, ZWINT and NUSAP1 might elicit immune response in lung cancer. [ABSTRACT FROM AUTHOR]