Literature DB >> 28112944

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

Su Jong Yu1,2, Hyunsoo Kim1,2, Hophil Min1,2, Areum Sohn1,2, Young Youn Cho1,2, Jeong-Ju Yoo1,2, Dong Hyeon Lee1,2, Eun Ju Cho1,2, Jeong-Hoon Lee1,2, Jungsoo Gim1,2, Taesung Park1,2, Yoon Jun Kim1,2, Chung Yong Kim1,2, Jung-Hwan Yoon1,2, Youngsoo Kim1,2.   

Abstract

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

Entities:  

Keywords:  HCC; TACE; multiple reaction monitoring-mass spectrometry (MRM-MS); prognostic biomarker; proteomics

Mesh:

Substances:

Year:  2017        PMID: 28112944     DOI: 10.1021/acs.jproteome.6b00833

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  6 in total

Review 1.  Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Authors:  Bradley Spieler; Carl Sabottke; Ahmed W Moawad; Ahmed M Gabr; Mustafa R Bashir; Richard Kinh Gian Do; Vahid Yaghmai; Radu Rozenberg; Marielia Gerena; Joseph Yacoub; Khaled M Elsayes
Journal:  Abdom Radiol (NY)       Date:  2021-03-31

2.  A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.

Authors:  Ali Morshid; Khaled M Elsayes; Ahmed M Khalaf; Mohab M Elmohr; Justin Yu; Ahmed O Kaseb; Manal Hassan; Armeen Mahvash; Zhihui Wang; John D Hazle; David Fuentes
Journal:  Radiol Artif Intell       Date:  2019-09-25

Review 3.  Research Progress on Leucine-Rich Alpha-2 Glycoprotein 1: A Review.

Authors:  Yonghui Zou; Yi Xu; Xiaofeng Chen; Yaoqi Wu; Longsheng Fu; Yanni Lv
Journal:  Front Pharmacol       Date:  2022-01-05       Impact factor: 5.810

4.  An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification.

Authors:  Shan Lu; Ziqi Meng; Yingying Tan; Chao Wu; Zhihong Huang; Jiaqi Huang; Changgeng Fu; Antony Stalin; Siyu Guo; Xinkui Liu; Leiming You; Xiaojiaoyang Li; Jingyuan Zhang; Wei Zhou; Xiaomeng Zhang; Miaomiao Wang; Jiarui Wu
Journal:  BMC Complement Med Ther       Date:  2022-03-02

5.  Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode.

Authors:  Zhou Jiawei; Mu Min; Xing Yingru; Zhang Xin; Li Danting; Liu Yafeng; Xie Jun; Hu Wangfa; Zhang Lijun; Wu Jing; Hu Dong
Journal:  Front Mol Biosci       Date:  2020-10-27

Review 6.  LRG1: an emerging player in disease pathogenesis.

Authors:  Carlotta Camilli; Alexandra E Hoeh; Giulia De Rossi; Stephen E Moss; John Greenwood
Journal:  J Biomed Sci       Date:  2022-01-21       Impact factor: 12.771

  6 in total

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