A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data

2019 ◽  
Vol 411 (24) ◽  
pp. 6377-6386 ◽  
Author(s):  
Benzhe Su ◽  
Ping Luo ◽  
Zhao Yang ◽  
Pei Yu ◽  
Zaifang Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Large Scale ◽  
Analysis Method ◽  
Metabolomics Data ◽  
Biomarker Identification ◽  
Potential Biomarkers

scholarly journals A Large‐scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma

Hepatology ◽  
2018 ◽  
Vol 67 (2) ◽  
pp. 662-675 ◽  
Author(s):  
Ping Luo ◽  
Peiyuan Yin ◽  
Rui Hua ◽  
Yexiong Tan ◽  
Zaifang Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Early Detection ◽  
Large Scale ◽  
Biomarker Identification

scholarly journals Large-scale plasma-metabolome analysis identifies potential biomarkers of psoriasis and its clinical subtypes

2021 ◽  
Author(s):  
Toshihiro Kishikawa ◽  
Noriko Arase ◽  
Shigeyoshi Tsuji ◽  
Yuichi Maeda ◽  
Takuro Nii ◽  
...  
Keyword(s):  
Large Scale ◽  
Metabolome Analysis ◽  
Plasma Metabolome ◽  
Potential Biomarkers

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

scholarly journals EZ-ALBI Score for Predicting Hepatocellular Carcinoma Prognosis

Liver Cancer ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 734-743
Author(s):  
Kazuya Kariyama ◽  
Kazuhiro Nouso ◽  
Atsushi Hiraoka ◽  
Akiko Wakuta ◽  
Ayano Oonishi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Function ◽  
Large Scale ◽  
Regression Coefficient ◽  
Information Criterion ◽  
Proportional Hazard ◽  
Cox Proportional Hazard ◽  
Highly Correlated ◽  
Survival Risk ◽  
Good Agreement

<b><i>Introduction:</i></b> The ALBI score is acknowledged as the gold standard for the assessment of liver function in patients with hepatocellular carcinoma (HCC). Unlike the Child-Pugh score, the ALBI score uses only objective parameters, albumin (Alb) and total bilirubin (T.Bil), enabling a better evaluation. However, the complex calculation of the ALBI score limits its applicability. Therefore, we developed a simplified ALBI score, based on data from a large-scale HCC database.We used the data of 5,249 naïve HCC cases registered in eight collaborating hospitals. <b><i>Methods:</i></b> We developed a new score, the EZ (Easy)-ALBI score, based on regression coefficients of Alb and T.Bil for survival risk in a multivariate Cox proportional hazard model. We also developed the EZ-ALBI grade and EZ-ALBI-T grade as alternative options for the ALBI grade and ALBI-T grade and evaluated their stratifying ability. <b><i>Results:</i></b> The equation used to calculate the EZ-ALBI score was simple {[T.Bil (mg/dL)] – [9 × Alb (g/dL)]}; this value highly correlated with the ALBI score (correlation coefficient, 0.981; <i>p</i> &#x3c; 0.0001). The correlation was preserved across different Barcelona clinic liver cancer grade scores (regression coefficient, 0.93–0.98) and across different hospitals (regression coefficient, 0.98–0.99), indicating good generalizability. Although a good agreement was observed between ALBI and EZ-ALBI, discrepancies were observed in patients with poor liver function (T.Bil, ≥3 mg/dL; regression coefficient, 0.877). The stratifying ability of EZ-ALBI grade and EZ-ALBI-T grade were good and their Akaike’s information criterion values (35,897 and 34,812, respectively) were comparable with those of ALBI grade and ALBI-T grade (35,914 and 34,816, respectively). <b><i>Conclusions:</i></b> The EZ-ALBI score, EZ-ALBI grade, and EZ-ALBI-T grade are useful, simple scores, which might replace the conventional ALBI score in the future.

N‐glycan fingerprint predicts alpha‐fetoprotein negative hepatocellular carcinoma: a large‐scale multicenter study

2021 ◽  
Author(s):  
Chenjun Huang ◽  
Meng Fang ◽  
Huijuan Feng ◽  
Lijuan Liu ◽  
Ya Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Multicenter Study ◽  
Large Scale ◽  
Alpha Fetoprotein

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

scholarly journals Prognostic Biomarker Identification Through Integrating the Gene Signatures of Hepatocellular Carcinoma Properties

EBioMedicine ◽  
2017 ◽  
Vol 19 ◽  
pp. 18-30 ◽  
Author(s):  
Jialin Cai ◽  
Bin Li ◽  
Yan Zhu ◽  
Xuqian Fang ◽  
Mingyu Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Biomarker ◽  
Biomarker Identification ◽  
Gene Signatures
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