Discovering novel lung cancer associated antigens and the utilization of their autoantibodies in detection of lung cancer

Lü, Peng; Liu, Hongchun; Ouyang, Songyun; Zhao, Chunling; Liu, Man; Wang, Tingting; Wang, Peng; Ye, Hua; Wang, Kaijuan; Song, Chi; Zhang, Jianying; Dai, Liping

doi:10.1016/j.imbio.2019.11.026

Cited by 22 publications

(9 citation statements)

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“…Park et al (36) also found the significance of anti-TP53 in the diagnosis of LC. Besides, it was regrettably found that the majority single TAAbs had lower diagnostic performance for LC, which was similar to the results shown in previous studies (37). In order to improve the diagnostic value, we combined different TAAbs by using diverse data mining methods.…”

Section: Discussionsupporting

confidence: 80%

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 80%

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

et al. 2021

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“…In LC, also TOP2A, ACTR3, RPS6KA5, and PSIP1 can elicit a humoral immune response and consequently have the potential to serve as a serological biomarkers in early stage tumors [80].…”

Section: Lung Cancermentioning

confidence: 99%

Anti-Cancer Auto-Antibodies: Roles, Applications and Open Issues

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Iamele

Maggi

et al. 2021

Cancers

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Auto-antibodies are classically associated with autoimmune diseases, where they are an integral part of diagnostic panels. However, recent evidence is accumulating on the presence of auto-antibodies against single or selected panels of auto-antigens in many types of cancer. Auto-antibodies might initially represent an epiphenomenon derived from the inflammatory environment induced by the tumor. However, their effect on tumor evolution can be crucial, as is discussed in this paper. It has been demonstrated that some of these auto-antibodies can be used for early detection and cancer staging, as well as for monitoring of cancer regression during treatment and follow up. Interestingly, certain auto-antibodies were found to promote cancer progression and metastasis, while others contribute to the body’s defense against it. Moreover, auto-antibodies are of a polyclonal nature, which means that often several antibodies are involved in the response to a single tumor antigen. Dissection of these antibody specificities is now possible, allowing their identification at the genetic, structural, and epitope levels. In this review, we report the evidence available on the presence of auto-antibodies in the main cancer types and discuss some of the open issues that still need to be addressed by the research community.

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“…It is worth noting that in this study, 8 (ACTR3, ARF4, PGRMC1, RPS23, WDR12, ACTR2, SIAH1, and RPS27L) of 12 hub genes are related to cancers, such as lung cancer, epithelial ovarian cancer, gastric cancer, glioblastoma, breast cancer, and esophageal cancer (51,54,(57)(58)(59)(60)(61)(62)(63). A last year's research showed pulmonary adenocarcinoma cells around the ossification expressed bone morphogenetic protein-2 and osteopontin, which generally induce and stimulate bone formation (64,65).…”

Section: Discussionmentioning

confidence: 75%

Identification of the Biomarkers and Pathological Process of Heterotopic Ossification: Weighted Gene Co-Expression Network Analysis

et al. 2020

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Heterotopic ossification (HO) is the formation of abnormal mature lamellar bone in extra-skeletal sites, including soft tissues and joints, which result in high rates of disability. The understanding of the mechanism of HO is insufficient. The aim of this study was to explore biomarkers and pathological processes in HO+ samples. The gene expression profile GSE94683 was downloaded from the Gene Expression Omnibus database. Sixteen samples from nine HO- and seven HO+ subjects were analyzed. After data preprocessing, 3,529 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 13 modules. Finally, the cyan and purple modules were selected for further study. Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the cyan module was enriched in a variety of components, including protein binding, membrane, nucleoplasm, cytosol, poly(A) RNA binding, biosynthesis of antibiotics, carbon metabolism, endocytosis, citrate cycle, and metabolic pathways. In addition, the purple module was enriched in cytosol, mitochondrion, protein binding, structural constituent of ribosome, rRNA processing, oxidative phosphorylation, ribosome, and non-alcoholic fatty liver disease. Finally, 10 hub genes in the cyan module [actin related protein 3 (ACTR3), ADP ribosylation factor 4 (ARF4), progesterone receptor membrane component 1 (PGRMC1), ribosomal protein S23 (RPS23), mannose-6-phosphate receptor (M6PR), WD repeat domain 12 (WDR12), synaptosome associated protein 23 (SNAP23), actin related protein 2 (ACTR2), siah E3 ubiquitin protein ligase 1 (SIAH1), and glomulin (GLMN)] and 2 hub genes in the purple module [proteasome 20S subunit alpha 3 (PSMA3) and ribosomal protein S27 like (RPS27L)] were identified. Hub genes were validated through quantitative real-time polymerase chain reaction. In summary, 12 hub genes were identified in two modules that were associated with HO. These hub genes could provide new biomarkers, therapeutic ideas, and targets in HO.

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Discovering novel lung cancer associated antigens and the utilization of their autoantibodies in detection of lung cancer

Cited by 22 publications

References 50 publications

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Anti-Cancer Auto-Antibodies: Roles, Applications and Open Issues

Identification of the Biomarkers and Pathological Process of Heterotopic Ossification: Weighted Gene Co-Expression Network Analysis

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