Serum anti-EIF3A autoantibody as a potential diagnostic marker for hepatocellular carcinoma

Heo, Chang-Kyu; Hwang, Hai-Min; Lee, Hyejung; Kwak, Sang-Seob; Yoo, Jong‐Shin; Yu, Dae‐Yeul; Lim, Kok‐Geng; Lee, Soojin; Cho, Eun‐Wie

doi:10.1038/s41598-019-47365-4

Cited by 26 publications

(35 citation statements)

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“…We constructed a B-cell hybridoma pool using tumor-bearing transgenic mice, enriched with B cell hybridomas producing HCC-associated autoantibodies. Several TA autoantibodies from these B cell hybridoma cells have been characterized and proposed as biomarkers for cancer diagnosis [ 19 , 22 , 23 , 24 ]. In this study, another monoclonal TA autoantibody, named XC154 mAb, which bound specifically to human tumor cells, was purified, and its antigenic characteristics were characterized.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we have identified an anti-ATIC autoantibody as a novel autoantibody biomarker for liver cancer. HBx-transgenic HCC-model mouse [ 19 , 20 ] was used to obtain B cell hybridomas producing TA autoantibodies, and a monoclonal TA autoantibody, XC154, was purified and characterized. Its specific TA antigen was identified as ATIC, a purine synthesis protein overexpressed in human tumor cells.…”

Section: Introductionmentioning

confidence: 99%

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Cyclic Peptide Mimotopes for the Detection of Serum Anti–ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma

Heo

Hwang

Lim

et al. 2020

IJMS

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Tumor-associated (TA) autoantibodies have been identified at the early tumor stage before developing clinical symptoms, which holds hope for early cancer diagnosis. We identified a TA autoantibody from HBx-transgenic (HBx-tg) hepatocellular carcinoma (HCC) model mouse, characterized its target antigen, and examined its relationship to human HCC. The mimotopes corresponding to the antigenic epitope of TA autoantibody were screened from a random cyclic peptide library and used for the detection of serum TA autoantibody. The target antigen of the TA autoantibody was identified as an oncogenic bi-functional purine biosynthesis protein, ATIC. It was upregulated in liver cancer tissues of HBx-tg mouse as well as human HCC tissues. Over-expressed ATIC was also secreted extracellularly via the cancer-derived exosomes, which might cause auto-immune responses. The cyclic peptide mimotope with a high affinity to anti-ATIC autoantibody, CLPSWFHRC, distinguishes between serum samples from HCC patients and healthy subjects with 70.83% sensitivity, 90.68% specificity (AUC = 0.87). However, the recombinant human ATIC protein showed a low affinity to anti-ATIC autoantibody, which may be incompatible as a capture antigen for serum TA autoantibody. This study indicates that anti-ATIC autoantibody can be a potential HCC-associated serum biomarker and suggests that autoantibody biomarker’s efficiency can be improved by using antigenic mimicry to native antigens present in vivo.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cyclic Peptide Mimotopes for the Detection of Serum Anti–ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma

Heo

Hwang

Lim

et al. 2020

IJMS

Self Cite

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“…eIF3a is upregulated in HCC and facilitates the translation of HIF-1α, which, in turn, regulates glycolytic metabolism (22). Additionally, a serum anti-eIF3a autoantibody has been identified as a potential diagnostic biomarker for HCC, further supporting the role of eIF3a in HCC (37). However, the mechanism underlying the upregulation of eIF3a and the functions of eIF3a on the proliferation and metastasis in HCC remains unclear.…”

Section: Discussionmentioning

confidence: 96%

MicroRNA‑875‑5p inhibits tumor growth and metastasis of hepatocellular carcinoma by targeting eukaryotic translation initiation factor 3 subunit a

et al. 2020

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Accumulating evidence has demonstrated that aberrant microRNA (miRNA) expression is involved in hepatocellular carcinoma (HCC) progression. Previous findings suggested that miRNA (miR)-875-5p participates in the development of various types of cancer. However, the expression and function of miR-875-5p in HCC remains largely unclear. The analysis of clinical samples in the present study demonstrated that miR-875-5p expression was downregulated in HCC tissues compared to adjacent non-tumor tissues, which was associated with a large tumor size, venous infiltration, advanced tumor-node-metastasis stage and unfavorable overall survival. In vitro experiments revealed that ectopic expression of miR-875-5p suppressed, whereas inhibition of miR-875-5p promoted HCC cell proliferation, migration, invasion and epithelial-to-mesenchymal transition (EMT) progression. Overexpression of miR-875-5p restrained HCC tumor growth and metastasis in vivo. Mechanistically, eukaryotic translation initiation factor 3 subunit a (eIF3a) was identified as the downstream target of miR-875-5p in HCC. Further experiments demonstrated that the expression of eIF3a was upregulated and negatively correlated with that of miR-875-5p in HCC tissues. In addition, miR-875-5p negatively regulated the luciferase activity of wild-type, but not mutant 3'-untranslated region (3'UTR) of eIF3a mRNA. miR-875-5p suppressed eIF3a expression at the mRNA and protein level in HCC cells. Additionally, eIF3a exerted an oncogenic role, and knockdown of eIF3a inhibited the proliferation, motility and EMT of HCC cells. In addition, eIF3a overexpression abolished the inhibitory effects of miR-875-5p on the proliferation, motility and EMT in HCC cells. In conclusion, miR-875-5p, which was downregulated in HCC, may inhibit tumor growth and metastasis by eIF3a downregulation via targeting its 3'UTR and may be a promising prognostic and therapeutic strategy in HCC.

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“…A routine task for proteomics laboratories and core facilities has long been the identification and characterization of the proteins from specific regions of a gel-based separation [27]. Despite the age of these gel-based methods, these types of analyses are still valuable for answering specific biological questions [28–31]. In these settings, a common goal is to detect the proteins—and potentially the post-translational modifications—that are present within an excised band from a 1-dimensional SDS-PAGE gel.…”

Section: Resultsmentioning

confidence: 99%

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Fondrie

Noble

2019

Preprint

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1Machine learning methods have proven invaluable for increasing the sensitivity of peptide de-2 tection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, 3 use semi-supervised algorithms to learn models directly from the datasets that they analyze. 4 Although these methods are effective for many proteomics experiments, we suspected that they 5 may be suboptimal for experiments of smaller scale. In this work, we found that the power and 6 consistency of Percolator results was reduced as the size of the experiment was decreased. As 7 an alternative, we propose a different operating mode for Percolator: learn a model with Per-8 colator from a large dataset and use the learned model to evaluate the small-scale experiment. 9 We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" 10 that is trained anew for each dataset. We applied this static modeling approach to two settings: 11 small, gel-based experiments and single-cell proteomics. In both cases, static models increased 12 the yield of detected peptides and eliminated the model-induced variability of the standard dy-13 namic approach. These results suggest that static models are a powerful tool for bringing the 14 full benefits of Percolator and other semi-supervised algorithms to small-scale experiments. 15 1 Introduction 16The assignment of peptide sequences to tandem mass spectra is a fundamental task in any pro-17 teomics experiment [1]. This task is most often performed by a database search algorithm, which 18 was first introduced with the SEQUEST search engine in 1994 [2]. Database search algorithms score 19 the theoretical mass spectra of peptides from a selected sequence database against the acquired 20 mass spectra from the experiment, yielding a set of peptide-spectrum matches (PSMs). Although 21 the score functions of individual search engines may differ greatly, the scores reported by each are 22 intended to reflect the quality of PSMs. 23 2 Machine learning strategies to re-score PSMs were first introduced due to their ability to inte-24 grate multiple, orthogonal scores and features from database search engines, thereby improving the 25 sensitivity of peptide detection. Two such methods were proposed simultaneously: one based on 26 linear discriminant analysis [3] and another that used support vector machine (SVM) models to a 27 similar end [4]. Critically, both methods were examples of supervised learning: they relied on fully 28 labeled datasets-where the correct and incorrect PSMs could be determined a priori -to train 29 their respective models. These trained models were then used to predict a new score for the PSMs 30 of a new dataset. We refer to the models used by these methods as static, meaning their parameters 31 do not change when the dataset is changed. Critically, the success of these static models depended 32 on the quality of the labeled dataset that was used for training and how well it reflected the datasets 33 that were subsequently analyzed. The origina...

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Serum anti-EIF3A autoantibody as a potential diagnostic marker for hepatocellular carcinoma

Cited by 26 publications

References 53 publications

Cyclic Peptide Mimotopes for the Detection of Serum Anti–ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma

Cyclic Peptide Mimotopes for the Detection of Serum Anti–ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma

MicroRNA‑875‑5p inhibits tumor growth and metastasis of hepatocellular carcinoma by targeting eukaryotic translation initiation factor 3 subunit a

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

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