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

Cui, Chenchen; Duan, Yaru; Qiu, Cuipeng; Wang, Peng; Sun, Guiying; Ye, Hua; Dai, Liping; Han, Ze‐Guang; Song, Chunhua; Wang, Kaijuan; Shi, Jianxiang; Zhang, Jianying

doi:10.3389/fonc.2021.637871

Cited by 13 publications

(9 citation statements)

References 53 publications

(60 reference statements)

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“…In this study, the range of the AUC of a single autoantibody was 0.617–0.716, which was difficult to meet the requirements for early diagnosis in clinical practice. By establishing the logistic regression predictive model, which was widely used to classify diseases, especially in cancers ( Wang S et al, 2018 ; Cui et al, 2021 ), it proved to be a conventional analytical method. This model contained three biomarkers finally, and the sensitivity, specificity, Youden index, accuracy, and AUC of this model were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively, between OS and NHS.…”

Section: Discussionmentioning

confidence: 99%

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Luo

et al. 2022

Front. Genet.

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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.

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Section: Discussionmentioning

confidence: 99%

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Luo

et al. 2022

Front. Genet.

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“…Most of the previous studies showed that combining multiple biomarkers can remarkably improve the diagnostic accuracy and speci city in different cancerous diseases, such as OC, BC, ESCC, gastric cancer (GC) and LC [17,[21][22][23][24][25]. Jiang et al utilized decision tree method to construct a diagnostic panel consist of seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1) with the AUC of 0.897 and sensitivity and speci city of 94.4% and 84.9% [17].…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Ouyang

Liu

et al. 2022

Preprint

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Background The aim of this study is 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, as well as discriminating malignant and benign pulmonary nodules (PNs). Methods Sera from 798 participants of three independent cohorts 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 combining anti-UBQLN1, CT characteristics and traditional serum biomarkers. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic potential. Immunohistochemistry of tissue array 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 in cell lines, respectively. Trans-well assay and cell counting kit-8 (CCK-8) was used to investigate the functions of UBQLN1 in two lung cancer cell lines (CALU3 and H358). Results Anti-UBQLN1 was identified with the highest fold change by means of protein microarray in the discovery cohort. 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-UBQLN1with CEA, CYFRA21-1, CA125 and three CT indicators (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) in the discrimination of malignant from benign 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 two lung cancer cell lines. Conclusions UBQLN1 can elicit the humoral immune response as tumor-associated antigen in LC. The autoantibody to UBQLN1 might be a potential biomarker for LC diagnosis and might be useful to improve the discrimination of malignant from benign PNs.

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“…Among them, WGCNA is an excellent algorithm in screening the biological markers, and it has been used in many studies 26 . The human proteomics chip contains 21 000 human recombinant proteins in this study, which can comprehensively screen TAAs 53‐56 . In summary, bioinformatics can screen for genes that play important roles in the occurrence and development of HCC.…”

Section: Discussionmentioning

confidence: 99%

“… 26 The human proteomics chip contains 21 000 human recombinant proteins in this study, which can comprehensively screen TAAs. 53 , 54 , 55 , 56 In summary, bioinformatics can screen for genes that play important roles in the occurrence and development of HCC. Hence, those two methods were used together to screen valuable TAAs as much as possible.…”

Section: Discussionmentioning

confidence: 99%

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody‐antigen system

Wang

Han

et al. 2021

Cancer Science

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Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen‐antibody system) were taken as candidate tumor‐associated antigens (TAAs). Enzyme‐linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha‐fetoprotein (AFP)‐negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early‐HCC cases.

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

Cited by 13 publications

References 53 publications

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

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

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody‐antigen system

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