Humoral immune response to epidermal growth factor receptor in lung cancer

Wang, Yulin; Liu, Fenghui; Ouyang, Songyun; Liu, Man; Zhang, Xue; Wang, Peng; Zhao, Chunling; Zhang, Liguo; Dai, Liping

doi:10.1007/s12026-021-09174-8

Cited by 7 publications

(7 citation statements)

References 37 publications

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“…Numerous studies have proved that combination of markers from different levels could improve signi cantly the accuracy of diagnostic test (4,18,19). Logistic regression is a common method used for the construction of diagnostic model and the combination of multiple level markers in various cancers (10,17,18). In our previous study, we employed logistic regression to combine CEA and anti-EGFR for gaining a higher diagnostic capability (17).…”

Section: Discussionmentioning

confidence: 99%

“…Logistic regression is a common method used for the construction of diagnostic model and the combination of multiple level markers in various cancers (10,17,18). In our previous study, we employed logistic regression to combine CEA and anti-EGFR for gaining a higher diagnostic capability (17). The AUC of CEA and anti-EGFR is 0.681 and 0.703, respectively, while the combination of CEA and anti-EGFR reached an AUC of 0.784 (17).…”

Section: Discussionmentioning

confidence: 99%

“…In our previous study, we employed logistic regression to combine CEA and anti-EGFR for gaining a higher diagnostic capability (17). The AUC of CEA and anti-EGFR is 0.681 and 0.703, respectively, while the combination of CEA and anti-EGFR reached an AUC of 0.784 (17). Similarly, logistic regression was utilized to improve the diagnostic capability of 4 TAAb panel (anti-ZPR1, anti-PRKCZ, anti-NSG1 and anti-CD84) with AUC of 0.747 in validation cohort 1 in our study.…”

Section: Discussionmentioning

confidence: 99%

“…The 11 puri ed recombinant proteins were diluted in coating buffer at optimal concentrations (SARS, ZPR1, FAM131A, GGA3, PRKCZ, HDAC1, GOLPH3, NSG1, DAB1, CD84, EEA1 at nal concentrations of 0.125, 0.25, 0.125, 0.25, 0.25, 0.25, 0.25, 0.25, 0.125, 0.25, and 0.25 µg/ml). The detailed experiment was described in the previous study (17)(18)(19)(20). All participants' serum samples were randomly dispensed on the plates.…”

Section: Indirect Enzyme-linked Immunosorbent Assay (Elisa)mentioning

confidence: 99%

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Autoantibody signatures discovered by HuProt protein microarray to enhance the diagnosis of lung cancer

Wang

Zhang

et al. 2023

Clinical Immunology

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Indirect Enzyme-linked Immunosorbent Assay (Elisa)mentioning

confidence: 99%

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Autoantibody signatures discovered by HuProt protein microarray to enhance the diagnosis of lung cancer

Wang

Zhang

et al. 2023

Clinical Immunology

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“…To date, numerous technologies such as serological analysis of recombination cDNA expression libraries (SEREX), serological proteome analysis (SERPA) and protein microarray were utilized to discovery novel TAAbs. Our previous studies have discovered several novel TAAbs of LC based on different screening approaches and further have proved that the combination of TAAbs and other traditional biomarkers can dramatically improve the diagnostic accuracy of LC [7][8][9][10][11].…”

mentioning

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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