Small molecule metabolite biomarkers for hepatocellular carcinoma with bile duct tumor thrombus diagnosis

Tan, Weifeng; He, Jingquan; Deng, Junliang; Yang, Xinwei; Cui, Longjiu; Ran, Rong-zheng; Du, Guangwei; Jiang, Xiaoqing

doi:10.1038/s41598-018-21595-4

Cited by 18 publications

(15 citation statements)

References 22 publications

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“…VIP values produced in the OPLS-DA model were applied to find differential metabolites and variables VIP > 1 were further processed for Student’s t-test. Variables with VIP > 1 and p < 0.05 were considered to be significant differential metabolites [ 37 ]. Identification of the potential metabolite biomarkers was preliminarily carried out by their accurate mass spectra, retention time and retention index, and further by searching online databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) ( ), HMDB ( ) and METLIN ( ).…”

Section: Methodsmentioning

confidence: 99%

Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats

Song

Zhu

Zhang

et al. 2019

IJMS

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Diffuse axonal injury (DAI) is a prevalent and serious brain injury with significant morbidity and disability. However, the underlying pathogenesis of DAI remains largely unclear, and there are still no objective laboratory-based tests available for clinicians to make an early diagnosis of DAI. An integrated analysis of metabolomic data and proteomic data may be useful to identify all of the molecular mechanisms of DAI and novel potential biomarkers. Therefore, we established a rat model of DAI, and applied an integrated UPLC-Q-TOF/MS-based metabolomics and isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis to obtain unbiased profiling data. Differential analysis identified 34 metabolites and 43 proteins in rat plasma of the injury group. Two metabolites (acetone and 4-Hydroxybenzaldehyde) and two proteins (Alpha-1-antiproteinase and Alpha-1-acid glycoprotein) were identified as potential biomarkers for DAI, and all may play important roles in the pathogenesis of DAI. Our study demonstrated the feasibility of integrated metabolomics and proteomics method to uncover the underlying molecular mechanisms of DAI, and may help provide clinicians with some novel diagnostic biomarkers and therapeutic targets.

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Section: Methodsmentioning

confidence: 99%

Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats

Song

Zhu

Zhang

et al. 2019

IJMS

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“…These can be used to distinguish HCC with BDTT from those without BDTT. However, the features are also observed in other biliary tract diseases, such as hepatic insu ciency, extrahepatic cholangiocarcinoma, choledochal cyst and common bile duct stone [15].…”

Section: Discussionmentioning

confidence: 93%

“…Due to advances in imaging an increased understanding of this entity, more and more HCC patients with macroscopic BDTT were con rmed preoperatively. However, the rate of misdiagnosis of HCC with BDTT before surgery is still very high [15].…”

mentioning

confidence: 99%

“…Particularly, many HCC patients with micro-BDTT could not be accurately diagnosed preoperatively before surgery. In order to resolve this problem, some new markers including some stem cell markers and small molecule metabolite biomarkers were used for HCC with diagnosis [15,16]. However, it is still urgently needed to nd a new diagnosis method which was more accurate and less destructive.…”

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confidence: 99%

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A nomogram based on combining systemic and hepatic inflammation markers for predicting microscopic bile duct tumor thrombus in hepatocellular carcinoma

Sun

et al. 2020

Preprint

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Background Bile duct invasion is a relatively rare event and is not well characterized in hepatocellular carcinoma (HCC). There are still very difficult to diagnose hepatocellular carcinoma (HCC) with bile duct tumor thrombus (BDTT) before surgery. Increasing evidences had revealed that inflammation had a critical role in tumorigenesis. The study aimed to develop the nomograms based on systemic and hepatic inflammation markers to predict microscopic bile duct tumor thrombus (micro-BDTT) before surgery in hepatocellular carcinoma (HCC). Methods A total of 418 cases with HCC who underwent hepatectomy as initial therapy between January 2012 and June 2020 were included in the study. Receiver operating characteristic (ROC) analysis was used to detect the optimal cut-off value of inflammation markers. Logistic regression was used to identify the independent risk factors of micro-BDTT. The Nomograms was constructed using the significant predictors including α-fetoprotein (AFP), alkaline phosphatase (ALP), direct bilirubin (DB), prognostic nutritional index (PNI), γ-glutamyl transferase (γ-GT)/alanine aminotransferase (ALT). The prediction accuracies of the nomograms were evaluated using the area under the receiver operating characteristic (ROC) curve. Results AFP, ALP, DB, PNI and γ-GT/ALT were the independent risk factors for predicting micro-BDTT (P = 0.043, P = 0.028, P = 0.012, P = 0.045 and P = 0.007, respectively), which were assembled into the nomograms. The area under the ROC curve of the nomograms combining PNI and γ-GT/ALT for predicting micro-BDTT were 0.809 (95% confidence intervals (CI): 0.747–0.871). The sensitivity and specificity value when used in predicting micro-BDTT before surgery were 0.778 (95% confidence intervals (CI): 0.656–0.899) and 0.724 (95% confidence intervals (CI): 0.678–0.769), respectively. Conclusion The nomogram based on combining systemic and hepatic inflammation markers is suitable for predicting micro-BDTT before surgery in hepatocellular carcinoma patients, leading to a rational therapeutic choice for HCC.

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“…Small molecule inhibitors and hormone-like drugs have been used in the treatment of cancer 36 . Small molecule metabolites can be used as potential biomarkers of liver cancer and better distinguish liver cancer from other bile duct diseases with bile duct tumor thrombosis 37 . The activation of effector CD8 T cells can initiate an immune response that is positively associated with breast cancer survival and be used as a marker for personalized immunotherapy 38 .…”

Section: Discussionmentioning

confidence: 99%

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer

Tian

Wang

2021

Cell Transplant

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About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite different, even in patients with kidney cancer at the same clinical stage. Therefore, there is an urgent need to screen for kidney cancer prognosis biomarkers. Differentially expressed genes (DEGs) were identified using kidney cancer RNA sequencing data from the Gene Expression Omnibus (GEO) database. Biomarkers were screened using random forest (RF) and support vector machine (SVM) models, and a multigene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. Univariate and multivariate Cox regression analyses were performed to explore the relationships between clinical features and prognosis. Finally, the reliability and clinical applicability of the model were validated, and relationships with biological pathways were identified. Western blots were also performed to evaluate gene expression. A total of 50 DEGs were obtained by intersecting the RF and SVM models. A seven-gene signature (RNASET2, EZH2, FXYD5, KIF18A, NAT8, CDCA7, and WNT7B) was constructed by LASSO regression. Univariate and multivariate Cox regression analyses showed that the seven-gene signature was an independent prognostic factor for kidney cancer. Finally, a predictive nomogram was established in The Cancer Genome Atlas (TCGA) cohort and validated internally. In tumor tissue, RNASET2 and FXYD5 were highly expressed and NAT8 was lowly expressed at the protein and transcription levels. This model could complement the clinicopathological characteristics of kidney cancer and promote the personalized management of patients with kidney cancer.

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Small molecule metabolite biomarkers for hepatocellular carcinoma with bile duct tumor thrombus diagnosis

Cited by 18 publications

References 22 publications

Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats

Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats

A nomogram based on combining systemic and hepatic inflammation markers for predicting microscopic bile duct tumor thrombus in hepatocellular carcinoma

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer

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