Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma

Chen, Jiang; Yuan, Xin; Li, Jiang Fen; Xie, Yu; Zhang, An Zhi; Wang, Xue Li; Yang, Lan; Liu, Chun Xia; Liang, Wei; Pang, Li; Zou, Hong; Chen, Xiao; Shen, Xi; Yan, Qi; Jin, Jianhua; Gu, Wen; Li, Feng; Hu, Jian

doi:10.1186/s12967-020-02229-8

Cited by 45 publications

(40 citation statements)

References 46 publications

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“…There are 500,000 new liver cancer cases worldwide due to hepatitis B, hepatitis C and alcoholic liver disease 3 . The mechanisms of the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically 4 - 6 . Although a huge progress has been made on the research of HCC 7 - 9 , there is only an elemental understanding of its molecular pathogenesis.…”

Section: Introductionmentioning

confidence: 99%

PRIM1 promotes the proliferation of hepatocellular carcinoma cells in vitro and in vivo

Jiang

Zhang

et al. 2020

J. Cancer

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PRIM1 plays an important role during oncogenesis, however it has never been reported in liver cancer, and thus our objective is to explore the role of PRIM1 in liver cancer. We selected RNAseq data of 50 paired liver cancer samples from the Cancer Gene Atlas (TCGA), and then bioinformatics methods and Mann-Whitney U test were used to analyze the correlation between PRIM1 and the clinical pathological stage of liver cancer. Quantitative polymerase chain reaction (QPCR) was used to detect mRNA expression of PRIM1 in BEL-7404, BCL-7402, HepG2 and SMMC-7721 cell lines. LV-PRIM1-RNAi was transfected into BEL-7404 and SMMC-7721 cells by lentivirus, and then Celigo imaging cytometer, Caspase3/7 Assay, flow cytometry and MTT assay were used to detect the proliferation and apoptosis of BEL-7404 and SMMC-7721 cells with ≥50% gene reduction rate after lentivirus transfection detected by QPCR. BEL-7404 and SMMC-7721 carrying PRIM1 gene were used for oncogenesis in vitro to observe the weight and fluorescence intensity of the tumor. Bioinformatics method was used to obtain the information about PRIM1 gene, and the correlation between PRIM1 and clinical pathological stage of liver cancer was analyzed by Mann-Whitney U test. QPCR results showed that PRIM1 was expressed in BEL-7404, BCL-7402, HepG2 and SMMC-7721 cell lines, which was highest in BCL-7404 cell line. Celigo imaging cytometer, Caspase3/7 Assay, flow cytometry and MTT assay showed that the proliferative ability of BEL-7404 and SMMC-7721 were decreased after LV-PRIM1-RNAi transfection. Furthermore, the weight and the fluorescence intensity of the tumors in vitro formed by LV-PRIM1-RNAi cells on SCID mice were decreased. So, interference of PRIM1 expression can inhibit the proliferation of BEL-7404 and SMMC-7721 cells, as well as induce the apoptosis of liver cancer cells.

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Section: Introductionmentioning

confidence: 99%

PRIM1 promotes the proliferation of hepatocellular carcinoma cells in vitro and in vivo

Jiang

Zhang

et al. 2020

J. Cancer

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“…No single biomarker has adequate sensitivity or speci city for HCC diagnosis. More and more studies are beginning to notice the use of genome-wide expression analysis to predict and diagnose diseases [35,36]. Compared with the single-gene biomarker, our multiple-genes based risk score-related prediction model got high accuracy with high sensitivity and speci city, which could be also used in other studies.…”

Section: Disscusionmentioning

confidence: 94%

Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

nie

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors worldwide. Hepatitis C virus (HCV)-induced cirrhosis (HCV-cirrhosis) is one of the leading causes of HCC occurrence. Due to the lack of effective biomarkers, most patients with HCV-induced HCC (HCV-HCC) are at advanced stages when they are first diagnosed. Our study aims to employ transcriptomic profiling dataset to identify potential biomarkers that can predict HCV-HCC at early stage on the background of HCV-cirrhosis. Methods: The dataset incorporating HCV-cirrhosis and HCV-HCC subjects at different stages from Gene Expression Omnibus was analyzed to identify gene signature-related to HCV-HCC on the background of HCV-cirrhosis. Multiple-genes based risk score-related prediction model was established to predict HCV-HCC samples at different stages, and receiver-operating characteristic (ROC) curve analysis was used to select the best cutoff value of risk scores for prediction and to evaluate the prediction model. Samples with risk scores higher than cutoff value were defined as high score group.Results: Highly clustered 20 genes were identified and were all gradually increased as HCV-HCC progressed from HCV-cirrhosis to very advanced HCC. Compared with HCV-cirrhosis, the prevalence of HCV-HCC at any of the stages in high score group was 100%. The risk score-related prediction model based on the 20 genes-biomarker got the accuracy of over 95.2 % with area under ROC (AUC) over 0.94. To implement the biomarker easily in real life and make it economical, we tried to limit the gene numbers. Interestingly, CDKN3 and FAM83D were significantly decreased in HCV-cirrhosis tissues as compared to normal tissues and then increased as HCV-HCC progressed. The results were attractive that the prediction model based on this 2 genes-biomarker indicated that prevalence of HCV-HCC in high score group was still 100% and got the predictive accuracy of over 95.2 % with AUC over 0.96, revealing even a better performance.Conclusion: Our study indicated a 2-genes-based biomarker that could identify HCV-HCC at earlier stages on the background of HCV-cirrhosis, which might be a promising biomarker for early diagnosis of HCV-HCC and potential novel treatment target.

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“…In a previous study utilizing bioinformatics-based screening, the upregulation of Cdkn3 was also identified as a marker of transformation from cirrhosis into HCC and correlated with the occurrence, invasion, and recurrence of HCC [50]. Akr7a3 [51], Cdc2a [50,52], and Cdkn3 [50] have also been found as biomarkers for early diagnosis, staging, and prognosis in human liver cancer clinically.…”

Section: Analysis Of the Time-invariant Biomarkersmentioning

confidence: 97%

“…More than 80% of HCC, the most common type of liver cancer, develops in fibrotic or cirrhotic livers, suggesting the importance of the two conditions in promoting liver cancer development [56]. Recently, many bioinformatics approaches have identified several key genes and pathways for transforming cirrhosis to HCC, and Akr7a3, Cdc2a, and Cdkn3 were identified by these studies [42,50,51]. There are some limitations to the study.…”

Section: Analysis Of the Time-invariant Biomarkersmentioning

confidence: 99%

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Huang

Lin

Tung

2020

IJERPH

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Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.

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Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma

Cited by 45 publications

References 46 publications

PRIM1 promotes the proliferation of hepatocellular carcinoma cells in vitro and in vivo

PRIM1 promotes the proliferation of hepatocellular carcinoma cells in vitro and in vivo

Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

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