Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma

Liao, Xiwen; Yu, Tingdong; Yang, Chengkun; Huang, Ketuan; Wang, Xiangkun; Han, Chuangye; Huang, Rui; Liu, Xiaoguang; Liu, Yu; Zhu, Guangzhi; Su, Hao; Jun, Deng; Zeng, Xianmin; Han, Bowen; Han, Quanfa; Liu, Zheng-Qian; Zhou, Xin; Liu, Junqi; Gong, Yizhen; Liu, Zhengtao; Huang, Jianlv; Lü, Lei; Ye, Xinping; Peng, Tao

doi:10.7150/jca.31287

Cited by 13 publications

(7 citation statements)

References 72 publications

(82 reference statements)

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“…A significantly higher risk score estimated by this model was also observed in the HBV-infected group than in the non-infected group. Besides, the prognostic model based on lncRNAs developed by Xiwen Liao et al showed a good performance in HBV-related HCC [52], which was comparable with our model in terms of AUC values. Our findings implied their potential association with HBV infection.…”

Section: Discussionsupporting

confidence: 83%

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Liu

Zhu

Guo

et al. 2022

Journal of Oncology

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Background. Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC. Methods. Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs. Results. Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients ( P < 0.001 ). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I–II) and late (III–IV) HCC detection were 0.88 and 0.93, respectively. Conclusions. The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.

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Section: Discussionsupporting

confidence: 83%

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Liu

Zhu

Guo

et al. 2022

Journal of Oncology

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“…The majority of these genes are associated with HCC. For example, TOP2A displaying the most significant upregulation has been identified as a biomarker for HBV-related HCC ( Liao et al, 2019 ) and APOF with the most significant downregulation is considered a tumor suppressor in HCC ( Wang Y. B. et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…DNA topoisomerase II alpha (TOP2A) is abundantly expressed in testis, lymph node tissues, and a variety of tumor tissues, including liver cancer. Several bioinformatics analyses have validated TOP2A as a biomarker for HCC, in particular, HBV-related HCC ( Liao et al, 2019 ). Panvichian et al (2015) reported overexpression of TOP2A in 72.5% of tumor tissues and its significant association with the hepatitis B surface antigen (HBsAg) in serum.…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Song

Ding

et al. 2020

Front. Genet.

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Background Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority. Methods We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model. Results Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset. Conclusion Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

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“…Hepatocellular carcinoma (HCC) ranks fourth in cancer-related deaths worldwide 1 . Risk factors for HCC mainly consist of chronic viral hepatitis, alcoholism, and metabolic disorders 2 – 4 . HCC treatments have improved in recent years, with liver resection, transplantation, ablation, transarterial embolization, and systemic pharmacological treatment showing some efficacy in improving overall survival 5 – 7 .…”

Section: Introductionmentioning

confidence: 99%

Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

Shi

Xue

Yuan

et al. 2020

Sci Rep

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Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. N1-methyladenosine (m1A), a methylation modification on RNA, is gaining attention for its role across diverse biological processes. However, m1A-related regulatory genes expression, its relationship with clinical prognosis, and its role in HCC remain unclear. In this study, we utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database to investigate alterations within 10 m1A-related regulatory genes and observed a high mutation frequency (23/363). Cox regression analysis and least absolute shrinkage and selection operator were used to explore the association between m1A-related regulatory genes expression and HCC patient survival and identified four regulators that were remarkably associated with HCC patient prognosis. Additionally, an independent cohort from International Cancer Genome Consortium was studied to validate our discoveries and found to be consistent with those in the TCGA dataset. In terms of mechanism, gene set enrichment analysis linked these four genes with various physiological roles in cell division, the MYC pathway, protein metabolism, and mitosis. Kyoto Encyclopedia of Genes and Genomes analysis revealed that PI3K/Akt signaling pathway had potential relevance to m1A-related regulatory genes in HCC. These findings indicate that m1A-related regulatory genes may play crucial roles in regulating HCC progression and be exploited for diagnostic and prognostic purposes.

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Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma

Cited by 13 publications

References 72 publications

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

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