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Identification of serologic biomarkers for predicting microvascular invasion in hepatocellular carcinoma.

ABSTRACT: Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a major risk factor for early recurrence and poor survival after curative surgical therapies. However, MVI can only be diagnosed by pathological examination following resection. The aim of this study is to identify serologic biomarkers for predicting MVI preoperatively to help facilitate treatment decisions. We used the sero-proteomic approach to identify antigens that induce corresponding antibody responses either specifically in the serum from MVI (+) patients or from MVI (-) patients. Six antigens were subsequently identified as HSP 70, HSP 90, alpha-enolase (Eno-1), Annexin A2, glutathione synthetase and beta-actin by mass spectrometry. The antibodies titers in sera corresponding to four of these six antigens were measured by ELISA and compared between 35 MVI (+) patients and 26 MVI (-) patients. The titers of anti-HSP 70 antibodies were significantly higher in MVI (-) patients than those in MVI (+) patients; and the titers of anti-Eno-1 antibodies were significantly lower in MVI (-) patients than those in MVI (+) patients. The results were subjected to multivariate analysis together with other clinicopathologic factors, suggesting that antibodies against HSP 70 and Eno-1 in sera are potential biomarkers for predicting MVI in HCC prior to surgical resection. These biomarkers should be further investigated as potential therapeutic targets.

SUBMITTER: Yu YQ

PROVIDER: S-EPMC4941320 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Identification of serologic biomarkers for predicting microvascular invasion in hepatocellular carcinoma.

Yu Yuan-Quan YQ Wang Liang L Jin Yun Y Zhou Jia-Le JL Geng Yan-Hua YH Jin Xing X Zhang Xiao-Xiao XX Yang Jun-Jie JJ Qian Cheng-Ming CM Zhou Dong-Er DE Liu Da-Ren DR Peng Shu-You SY Luo Yan Y Zheng Lei L Li Jiang-Tao JT

Oncotarget 20160301 13

Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a major risk factor for early recurrence and poor survival after curative surgical therapies. However, MVI can only be diagnosed by pathological examination following resection. The aim of this study is to identify serologic biomarkers for predicting MVI preoperatively to help facilitate treatment decisions. We used the sero-proteomic approach to identify antigens that induce corresponding antibody responses either specifically in ...[more]

PMID: 26918350

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Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Dataset Information

Identification of serologic biomarkers for predicting microvascular invasion in hepatocellular carcinoma.

Publications

Identification of serologic biomarkers for predicting microvascular invasion in hepatocellular carcinoma.

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