Identification of key genes and pathways in hepatocellular carcinoma

Wu, Min; Liu, Zhaobo; Zhang, Aiying; Li, Ning

doi:10.1097/md.0000000000014287

Cited by 30 publications

(30 citation statements)

References 42 publications

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“…CCNB1, a regulatory protein that plays an essential role in controlling mitosis, was upregulated in HCC patients with poor outcome [62,63]. Liu et al reported that CCNB1 was closely correlated with GTSE1: after GTSE1 knockdown, CCNB1 and FoxM1 were significantly downregulated, while p53 expression was significantly increased in BCa.…”

Section: The Hub Genes Potentially Associated With the Clinical Prognmentioning

confidence: 99%

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

2020

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Bladder cancer (BCa) is one of the most common malignancies and has a relatively poor outcome worldwide. However, the molecular mechanisms and processes of BCa development and progression remain poorly understood. Therefore, the present study aimed to identify candidate genes in the carcinogenesis and progression of BCa. Five GEO datasets and TCGA-BLCA datasets were analyzed by statistical software R, FUNRICH, Cytoscape, and online instruments to identify differentially expressed genes (DEGs), to construct protein‒protein interaction networks (PPIs) and perform functional enrichment analysis and survival analyses. In total, we found 418 DEGs. We found 14 hub genes, and gene ontology (GO) analysis revealed DEG enrichment in networks and pathways related to cell cycle and proliferation, but also in cell movement, receptor signaling, and viral carcinogenesis. Compared with noncancerous tissues, TPM1, CRYAB, and CASQ2 were significantly downregulated in BCa, and the other hub genes were significant upregulated. Furthermore, MAD2L1 and CASQ2 potentially play a pivotal role in lymph nodal metastasis. CRYAB and CASQ2 were both significantly correlated with overall survival (OS) and disease-free survival (DFS). The present study highlights an up to now unrecognized possible role of CASQ2 in cancer (BCa). Furthermore, CRYAB has never been described in BCa, but our study suggests that it may also be a candidate biomarker in BCa.

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Section: The Hub Genes Potentially Associated With the Clinical Prognmentioning

confidence: 99%

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

2020

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“…In previous studies, bioinformatics analysis of microarray and RNA-seq gene data profiles identified key onco-genes, such as cell division cycle 20 and cell division cycle associated 5 involved in the prognosis of HCC (7,8). In a preliminary bioinformatics study, Wu et al (9) identified 12 genes, including non-SMC condensin I complex subunit G and TTK protein kinase that were associated with the progression of HCC. In addition, Pan et al (10) revealed that micro(mi) RNA-15b-5p serves an oncogenic role in HCC.…”

Section: Identification Of 40s Ribosomal Protein S8 As a Novel Biomarmentioning

confidence: 99%

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

Sun²,

Lei³

et al. 2020

Oncol Rep

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Alcohol-associated hepatocellular carcinoma (HCC) is a subtype of HCC with poor prognosis. The present study aimed to identify key biomarkers for alcohol-associated HCC. The gene data profiles and corresponding clinical traits of patients with alcohol-associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Firstly, good genes and good samples were identified, which were subsequently used to conduct weighted gene co-expression network analysis (WGCNA). Hub genes in the significant modules were selected following Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and from constructing a protein-protein interaction (PPI) network. Real hub genes among hub genes were determined following progression, survival analysis and gene set enrichment analysis (GSEA), as well as reverse transcription-quantitative PCR and immunohistochemical staining of non-alcohol-and alcohol-associated HCC samples. In total, 64 good samples of alcohol-associated HCC with height score <160 were selected, from which 15,195 good genes were identified and used to conduct WGCNA; 8 gene co-expressed modules were identified using WGCNA, while 3 modules (including pink, magenta and turquoise) were significantly associated with Child-Pugh score, T-stage and body weight. Following GO and KEGG analysis and construction of the PPI network, a total of 30 hub genes were identified in the aforementioned 3 gene co-expressed modules, while 16 hub genes (including AURKB,

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“…In the past, studies focusing on analysis of HCC-related genes usually included patients with HCC caused by various factors such as chronic hepatitis B or C virus infection and alcoholic liver disease [18,19,34]. But there is limited research that only discusses HCV-related liver cancer on the background of HCV-cirrhosis, so we re-analyzed the data in GSE6764.…”

Section: Disscusionmentioning

confidence: 99%

“…Now, there are rich public repositories of RNA-seq and microarray datasets, such as Gene Expression Omnibus (GEO) database [16] and The Cancer Genome Atlas (TCGA) [17]. Further analysis of those datasets combined with computational approach such as prediction model can provide the opportunity to establish a novel biomarker for disease diagnosis with high accuracy [18,19].…”

Section: Introductionmentioning

confidence: 99%

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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Identification of key genes and pathways in hepatocellular carcinoma

Cited by 30 publications

References 42 publications

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

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

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