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Hepatitis B virus pre-S2 deletion (nucleotide 1 to 54) in plasma predicts recurrence of hepatocellular carcinoma after curative surgical resection.

ABSTRACT: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Despite curative surgical resection, high recurrence of HCC after surgery results in poor patient survival. To develop prognostic markers is therefore important for better prevention and therapy of recurrent HCC to improve patient outcomes. Deletion mutations over the pre-S1 and pre-S2 gene segments of hepatitis B virus (HBV) have been closely associated with recurrence of HCC after curative surgical resection. In this study, we applied a next-generation sequencing-based approach to further evaluate the association of pre-S deletion regions with HCC recurrence. We demonstrated that the pre-S2 deletion (nucleotide 1 to 54) was the most predominant deletion regions of pre-S gene in plasma of HBV-related HCC patients. Moreover, patients with the pre-S2 deletion (nucleotide 1 to 54) exhibited a significantly higher risk of HCC recurrence after curative surgical resection than those without. The pre-S2 deletion (nucleotide 1 to 54) in plasma represented a prognostic factor that independently predicted HCC recurrence with greater performance than other clinicopathological and viral factors. Our data suggest that detection of the pre-S2 deletion (nucleotide 1 to 54) in plasma may be a promising noninvasive strategy for identifying patients at high risk for HCC recurrence after curative surgical resection.

SUBMITTER: Teng CF

PROVIDER: S-EPMC7688176 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Hepatitis B virus pre-S2 deletion (nucleotide 1 to 54) in plasma predicts recurrence of hepatocellular carcinoma after curative surgical resection.

Teng Chiao-Fang CF Li Tsai-Chung TC Huang Hsi-Yuan HY Chan Wen-Ling WL Wu Han-Chieh HC Shyu Woei-Cherng WC Su Ih-Jen IJ Jeng Long-Bin LB

PloS one 20201125 11

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Despite curative surgical resection, high recurrence of HCC after surgery results in poor patient survival. To develop prognostic markers is therefore important for better prevention and therapy of recurrent HCC to improve patient outcomes. Deletion mutations over the pre-S1 and pre-S2 gene segments of hepatitis B virus (HBV) have been closely associated with recurrence of HCC after curative surgical resection. ...[more]

PMID: 33237972

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Project description:PurposePatients with rectal cancer without distant metastases are typically treated with radical surgery. Post curative resection, several factors can affect tumor recurrence. This study aimed to analyze factors related to rectal cancer recurrence after curative resection using different machine learning techniques.MethodsConsecutive patients who underwent curative surgery for rectal cancer between 2004 and 2018 at Gil Medical Center were included. Patients with stage IV disease, colon cancer, anal cancer, other recurrent cancer, emergency surgery, or hereditary malignancies were excluded from the study. The Synthetic Minority Oversampling Technique with Tomek link (SMOTETomek) technique was used to compensate for data imbalance between recurrent and no-recurrent groups. Four machine learning methods, logistic regression (LR), support vector machine (SVM), random forest (RF), and Extreme gradient boosting (XGBoost), were used to identify significant factors. To overfit and improve the model performance, feature importance was calculated using the permutation importance technique.ResultsA total of 3320 patients were included in the study. After exclusion, the total sample size of the study was 961 patients. The median follow-up period was 60.8 months (range:1.2-192.4). The recurrence rate during follow-up was 13.2% (n = 127). After applying the SMOTETomek method, the number of patients in both groups, recurrent and non-recurrent group were equalized to 667 patients. After analyzing for 16 variables, the top eight ranked variables {pathologic Tumor stage (pT), sex, concurrent chemoradiotherapy, pathologic Node stage (pN), age, postoperative chemotherapy, pathologic Tumor-Node-Metastasis stage (pTNM), and perineural invasion} were selected based on the order of permutational importance. The highest area under the curve (AUC) was for the SVM method (0.831). The sensitivity, specificity, and accuracy were found to be 0.692, 0.814, and 0.798, respectively. The lowest AUC was obtained for the XGBoost method (0.804), with a sensitivity, specificity, and accuracy of 0.308, 0.928, and 0.845, respectively. The variable with highest importance was pT as assessed through SVM, RF, and XGBoost (0.06, 0.12, and 0.13, respectively), whereas pTNM had the highest importance when assessed by LR (0.05).ConclusionsIn the current study, SVM showed the best AUC, and the most influential factor across all machine learning methods except LR was found to be pT. The rectal cancer patients who have a high pT stage during postoperative follow-up are need to be more close surveillance.

Dataset Information

Hepatitis B virus pre-S2 deletion (nucleotide 1 to 54) in plasma predicts recurrence of hepatocellular carcinoma after curative surgical resection.

Publications

Hepatitis B virus pre-S2 deletion (nucleotide 1 to 54) in plasma predicts recurrence of hepatocellular carcinoma after curative surgical resection.

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