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 th...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2021-04-01
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Series: | Frontiers in Immunology |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2021.658922/full |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Di Jiang Di Jiang Di Jiang Xue Zhang Xue Zhang Xue Zhang Man Liu Man Liu Man Liu Yulin Wang Yulin Wang Yulin Wang Tingting Wang Lu Pei Peng Wang Peng Wang Hua Ye Hua Ye Jianxiang Shi Jianxiang Shi Chunhua Song Chunhua Song Kaijuan Wang Kaijuan Wang Xiao Wang Xiao Wang Liping Dai Liping Dai Liping Dai Jianying Zhang Jianying Zhang |
spellingShingle |
Di Jiang Di Jiang Di Jiang Xue Zhang Xue Zhang Xue Zhang Man Liu Man Liu Man Liu Yulin Wang Yulin Wang Yulin Wang Tingting Wang Lu Pei Peng Wang Peng Wang Hua Ye Hua Ye Jianxiang Shi Jianxiang Shi Chunhua Song Chunhua Song Kaijuan Wang Kaijuan Wang Xiao Wang Xiao Wang Liping Dai Liping Dai Liping Dai Jianying Zhang Jianying Zhang Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array Frontiers in Immunology lung cancer protein array tumor-associated antigen autoantibody diagnostic model |
author_facet |
Di Jiang Di Jiang Di Jiang Xue Zhang Xue Zhang Xue Zhang Man Liu Man Liu Man Liu Yulin Wang Yulin Wang Yulin Wang Tingting Wang Lu Pei Peng Wang Peng Wang Hua Ye Hua Ye Jianxiang Shi Jianxiang Shi Chunhua Song Chunhua Song Kaijuan Wang Kaijuan Wang Xiao Wang Xiao Wang Liping Dai Liping Dai Liping Dai Jianying Zhang Jianying Zhang |
author_sort |
Di Jiang |
title |
Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array |
title_short |
Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array |
title_full |
Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array |
title_fullStr |
Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array |
title_full_unstemmed |
Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array |
title_sort |
discovering panel of autoantibodies for early detection of lung cancer based on focused protein array |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Immunology |
issn |
1664-3224 |
publishDate |
2021-04-01 |
description |
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. |
topic |
lung cancer protein array tumor-associated antigen autoantibody diagnostic model |
url |
https://www.frontiersin.org/articles/10.3389/fimmu.2021.658922/full |
work_keys_str_mv |
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doaj-04a7e95575de484991814f14cc3fcb2d2021-04-23T06:05:00ZengFrontiers Media S.A.Frontiers in Immunology1664-32242021-04-011210.3389/fimmu.2021.658922658922Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein ArrayDi Jiang0Di Jiang1Di Jiang2Xue Zhang3Xue Zhang4Xue Zhang5Man Liu6Man Liu7Man Liu8Yulin Wang9Yulin Wang10Yulin Wang11Tingting Wang12Lu Pei13Peng Wang14Peng Wang15Hua Ye16Hua Ye17Jianxiang Shi18Jianxiang Shi19Chunhua Song20Chunhua Song21Kaijuan Wang22Kaijuan Wang23Xiao Wang24Xiao Wang25Liping Dai26Liping Dai27Liping Dai28Jianying Zhang29Jianying Zhang30Department of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaSchool of Basic Medical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaSchool of Basic Medical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaSchool of Basic Medical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaSchool of Basic Medical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Clinical Laboratory, Fuwai Central China Cardiovascular Hospital, Zhengzhou, ChinaDepartment of Clinical Laboratory, Zhengzhou Hospital of Traditional Chinese Medicine, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Epidemiology and Biostatistics in School of Public Health, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Epidemiology and Biostatistics in School of Public Health, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Epidemiology and Biostatistics in School of Public Health, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Epidemiology and Biostatistics in School of Public Health, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaSchool of Basic Medical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou, ChinaSubstantial 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.https://www.frontiersin.org/articles/10.3389/fimmu.2021.658922/fulllung cancerprotein arraytumor-associated antigenautoantibodydiagnostic model |