Autoantibodies against tumor-associated antigens in sputum as biomarkers for lung cancer.
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ABSTRACT:
SUBMITTER: Li N
PROVIDER: S-EPMC7736713 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Serum autoantibodies against tumor-associated antigens (TAAs) have received much attention as potential biomarkers for early detection of cancers, since they can be detected in the early stages of cancers. Autoantibodies against Cancer Antigen 15-3 (CA15-3), carcinoembryonic antigen (CEA), Cancer Antigen 19-9 (CA19-9), c-Myc, p53, heat shock protein (Hsp)27 and Hsp70 have been suggested as potential markers for detecting several types of cancer. In the present study, the seven types of antibody listed above were evaluated for detecting cervical lesions. Enzyme-linked immunosorbent assays (ELISAs) were used to measure IgG levels of the autoantibodies in women with normal cytology, cervical intraepithelial neoplasia (CIN) I, CIN II, CIN III and cervical cancer. The increases of anti-CA15-3 and anti-CEA IgG in cervical cancer were more pronounced than the increases of the other markers, and the level of anti-CA19-9 IgG in CIN III stage was higher than in normal CIN I, CIN II or cervical cancer. A combination of ELISAs detecting anti-CA15-3, anti-CEA and anti-CA19-9 IgGs was found to reliably discriminate CINs from normal and to strongly differentiate cancer from normal (90.3% of sensitivity and 82.1% of specificity). We suggest that the combination of three ELISA may be useful for detecting cervical lesions.
| S-EPMC5739648 | biostudies-literature
Project description:Gastric cancer is the second leading cause of cancer death in the world, and effective diagnosis is extremely important for good outcome. We assessed the diagnostic potential of an autoantibody panel that may provide a novel tool for the early detection of gastric cancer. We analyzed data from patients with gastric cancer and normal controls in test and validation cohorts. Autoantibody levels were measured against a panel of six tumor-associated antigens (TAAs) by ELISA: p53, heat shock protein 70, HCC-22-5, peroxiredoxin VI, KM-HN-1, and p90 TAA. We assessed serum autoantibodies in 100 participants in the test cohort. The validation cohort comprised 248 participants. Autoantibodies to at least one of the six antigens showed a sensitivity/specificity of 49.0% (95% confidence interval [CI], 39.2-58.8%)/92.4% (95% CI, 87.2-97.6%), and 52.0% (95% CI, 42.2-61.8%)/90.5% (95% CI, 84.8-96.3%) in the test and validation cohorts, respectively. In the validation cohort, no significant differences were seen when patients were subdivided based on age, sex, depth of tumor invasion, lymph node metastasis, distant metastasis, peritoneal dissemination, or TNM stage. Patients who were positive for more than two antibodies in the panel tended to have a worse prognosis than those who were positive for one or no antibody. Measurement of autoantibody response to multiple TAAs in an optimized panel assay to discriminate patients with early stage gastric cancer from normal controls may aid in the early detection of gastric cancer.
| S-EPMC5378227 | biostudies-literature
Project description: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.
| S-EPMC7280966 | biostudies-literature
Project description:Background: The aim of this study was to identify a panel of candidate autoantibodies against tumor-associated antigens in the detection of osteosarcoma (OS) so as to provide a theoretical basis for constructing a non-invasive serological diagnosis method in early immunodiagnosis of OS. Methods: The serological proteome analysis (SERPA) approach was used to select candidate anti-TAA autoantibodies. Then, indirect enzyme-linked immunosorbent assay (ELISA) was used to verify the expression levels of eight candidate autoantibodies in the serum of 51 OS cases, 28 osteochondroma (OC), and 51 normal human sera (NHS). The rank-sum test was used to compare the content of eight autoantibodies in the sera of three groups. The diagnostic value of each indicator for OS was analyzed by an ROC curve. Differential autoantibodies between OS and NHS were screened. Then, a binary logistic regression model was used to establish a prediction logistical regression model. Results: Through ELISA, the expression levels of seven autoantibodies (ENO1, GAPDH, HSP27, HSP60, PDLIM1, STMN1, and TPI1) in OS patients were identified higher than those in healthy patients (p < 0.05). By establishing a binary logistic regression predictive model, the optimal panel including three anti-TAAs (ENO1, GAPDH, and TPI1) autoantibodies was screened out. The sensitivity, specificity, Youden index, accuracy, and AUC of diagnosis of OS were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively. Conclusion: The results proved that through establishing a predictive model, an optimal panel of autoantibodies could help detect OS from OC or NHS at an early stage, which could be used as a promising and powerful tool in clinical practice.
| S-EPMC9081566 | biostudies-literature
Project description:IntroductionPrevious 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).MethodsProtein 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.ResultsA 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.DiscussionTwo panels which were selected in this study could boost the detection of anti-TAA autoantibodies in sera as biomarkers for the detection of GAC.
| S-EPMC7752677 | biostudies-literature
Project description:ObjectiveWe performed a comprehensive review and meta-analysis to evaluate the diagnostic values of serum single and multiplex tumor-associated autoantibodies (TAAbs) in patients with lung cancer (LC).MethodsWe searched the MEDLINE and EMBASE databases for relevant studies investigating serum TAAbs for the diagnosis of LC. The primary outcomes included sensitivity, specificity and accuracy of the test.ResultsThe systematic review and meta-analysis included 31 articles with single autoantibody and 39 with multiplex autoantibodies. Enzyme-linked immunosorbent assay (ELISA) was the most common detection method. For the diagnosis of patients with all stages and early-stage LC, different single or combinations of TAAbs demonstrated different diagnostic values. Although individual TAAbs showed low diagnostic sensitivity, the combination of multiplex autoantibodies offered relatively high sensitivity. For the meta-analysis of a same panel of autoantibodies in patients at all stages of LC, the pooled results of the panel of 6 TAAbs (p53, NY-ESO-1, CAGE, GBU4-5, Annexin 1 and SOX2) were: sensitivity 38% (95% CI 0.35-0.40), specificity 89% (95% CI 0.86-0.91), diagnostic accuracy 65.9% (range 62.5-81.8%), AUC 0.52 (0.48-0.57), while the summary estimates of 7 TAAbs (p53, CAGE, NY-ESO-1, GBU4-5, SOX2, MAGE A4 and Hu-D) were: sensitivity 47% (95% CI 0.34-0.60), specificity 90% (95% CI 0.89-0.92), diagnostic accuracy 78.4% (range 67.5-88.8%), AUC 0.90 (0.87-0.93). For the meta-analysis of the same panel of autoantibodies in patients at early-stage of LC, the sensitivities of both panels of 7 TAAbs and 6 TAAbs were 40% and 29.7%, while their specificities were 91% and 87%, respectively.ConclusionsSerum single or combinations of multiplex autoantibodies can be used as a tool for the diagnosis of LC patients at all stages or early-stage, but the combination of multiplex autoantibodies shows a higher detection capacity; the diagnostic value of the panel of 7 TAAbs is higher than the panel of 6 TAAbs, which may be used as potential biomarkers for the early detection of LC.
| S-EPMC5547718 | biostudies-literature
Project description: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.
| S-EPMC7894002 | biostudies-literature
Project description:BackgroundTumor-associated antigens (TAAs) have been investigated for many years as potential early diagnosis tools, especially for hepatocellular carcinoma (HCC). Nonetheless, very few studies have focused on the Hispanic HCC group that may be associated with distinct etiological risk factors. In the present study, we investigated novel anti-TAA autoantibodies as diagnostic biomarkers for Hispanic HCC patients.MethodsNovel TAA targets were identified by the serological proteome analysis (SERPA) and from differentially expressed HCC driver genes via bioinformatics. The autoantibody levels were validated by enzyme-linked immunosorbent assay (ELISA).ResultsAmong 19 potential TAA targets, 4 anti-TAA autoantibodies were investigated as potential diagnostic biomarkers with significantly high levels in Hispanic HCC sera, including DNA methyltransferase 3A (DNMT3A), p16, Hear shock protein 60 (Hsp60), and Heat shock protein A5 (HSPA5). The area under the ROC curve (AUC) value of the single autoantibodies varies from 0.7505 to 0.8885. After combining all 4 autoantibodies, the sensitivity of the autoantibody panel increased to 75% compared to the single one with the highest value of 45.8%. In a separate analysis of the Asian cohort, autoantibodies against HSPA5 and p16 showed significantly elevated levels in HCC compared to normal healthy controls, but not for DNMT3A or HSP60.ConclusionAnti-DNMT3A, p16, HSPA5, and HSP60 autoantibodies have the potential to be diagnostic biomarkers for Hispanic HCC patients, of which DNMT3A and HSP60 might be exclusive for Hispanic HCC diagnosis.
| S-EPMC9600682 | biostudies-literature
Project description:The aim of this study was to test whether autoantibodies against neurologic surface Ags are found in nonneurologic autoimmune diseases, indicating a broader loss of tolerance. Patient and matched healthy donor (HD) sera were derived from four large cohorts: 1) rheumatoid arthritis (RA) (n = 194, HD n = 64), 2) type 1 diabetes (T1D) (n = 200, HD n = 200), 3) systemic lupus erythematosus (SLE) (n = 200, HD n = 67; neuro-SLE n = 49, HD n = 33), and 4) a control cohort of neurologic autoimmunity (relapsing-remitting multiple sclerosis [MS] n = 110, HD n = 110; primary progressive MS n = 9; secondary progressive MS n = 10; neuromyelitis optica spectrum disorders n = 15; and other neurologic disorders n = 26). Screening of 1287 unique serum samples against four neurologic surface Ags (myelin oligodendrocyte glycoprotein, aquaporin 4, acetylcholine receptor, and muscle-specific kinase) was performed with live cell-based immunofluorescence assays using flow cytometry. Positive samples identified in the screening were further validated using autoantibody titer quantification by serial dilutions or radioimmunoassay. Autoantibodies against neurologic surface Ags were not observed in RA and T1D patients, whereas SLE patients harbored such autoantibodies in rare cases (2/200, 1%). Within the CNS autoimmunity control cohort, autoantibodies against aquaporin 4 and high-titer Abs against myelin oligodendrocyte glycoprotein were, as expected, specific for neuromyelitis optica spectrum disorders. We conclude that neurologic autoantibodies do not cross disease barriers in RA and T1D. The finding of mildly increased neurologic autoantibodies in SLE may be consistent with a broader loss of B cell tolerance in this form of systemic autoimmunity.
| S-EPMC6452031 | biostudies-literature
Project description:BackgroundWe 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).MethodsGEO 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.ResultsUsing 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.ConclusionsOur 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.
| S-EPMC10464482 | biostudies-literature