&lt;p&gt;Screening and Identification of Potential Biomarkers for Hepatocellular Carcinoma: An Analysis of TCGA Database and Clinical Validation&lt;/p&gt;

Wei, Xianli; Ke, Junzi; Huang, Haonan; Zhou, Shikun; Wang, Kun; Zhan, Yujuan; Mai, Cong; Ao, Weizhen; Xie, Fuda; Luo, Rongping; Xiao, Jianru; Wei, Hang; Chen, Bonan

doi:10.2147/cmar.s239795

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Recently, Xian et al reported that FLVCR1 showed the intracellular overproduction in HCC cell lines and that FLVCR1 expression was significantly associated with survival in HCC patients, which is consistent with our study [37].…”

Section: Discussionsupporting

confidence: 93%

Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

Wang

Embaye

Yang

et al. 2020

Preprint

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Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

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Section: Discussionsupporting

confidence: 93%

Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

Wang

Embaye

Yang

et al. 2020

Preprint

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“…Conversely, the competitive binding of some CDK inhibitory proteins and cdk4 plays a key role in inhibiting tumor cell growth (Yang et al, 2016). Compared with cct5, cdk4, cenpa, and flvcr1 directly regulate the progress of HCC less reported (Wei et al, 2020;Yao et al, 2019;Zhang et al, 2020b). Relevant studies on the impact of dtnbp1 on HCC progression and prognosis have not been reported.…”

Section: Discussionmentioning

confidence: 99%

WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

Fang¹,

Guo²,

Wang³

et al. 2022

Biocell

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Hepatocellular carcinoma (HCC) is a common immunogenic malignant tumor. Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC, the 5-year survival rate of patients is still very low. The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies. We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes. Weighted gene coexpression network analysis (WGCNA), univariate and multivariate COX regression and the LASSO COX algorithm are employed to identify prognostic-related genes and construct multi-gene signature-based risk model and nomogram for survival prediction. Support vector machine (SVM) based Cibersort's deconvolution algorithm and gene set enrichment analysis (GSEA) is used to evaluate the changes in tumor immune microenvironment and pathway differences. The study found the favorable prognostic performance of miR-93-3p and identified 389 prognostic-related target genes. The risk model based on a novel 5-gene signature (cct5, cdk4, cenpa, dtnbp1 and flvcr1) was developed and has prominent prognostic significance in the training cohort (P < 0.0001) and validation cohort (P = 0.0016). The nomogram constructed by combining the gene signature and the AJCC stage further improves the survival prediction ability of the gene signature. The infiltration level of multiple immune cells (especially T cells, B cells and macrophages) were positively correlated with the expression of prognostic signature. In addition, we found that gene markers of T cells and B cells is monitored and regulated by prognostic signature.Meanwhile, several GSEA pathways related to the immune system are enriched in the high-risk group. In general, we integrated the WGCNA, LASSO COX and SVM algorithms to develop and verify 5-gene signatures and nomograms related to immune infiltration to improve the survival prediction of patients.

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“… 38 , 39 Recently, Wei et al reported that FLVCR1 showed the intracellular overproduction in HCC cell lines and that FLVCR1 expression was significantly associated with survival in HCC patients, which was consistent with our study. 40 …”

Section: Discussionmentioning

confidence: 99%

A Novel Metabolism-Related Signature as a Candidate Prognostic Biomarker for Hepatocellular Carcinoma

Wang¹,

Embaye²,

Yang³

et al. 2021

JHC

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Purpose Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes (MRGs) for the diagnosis and treatment of hepatocellular carcinoma (HCC). Methods In total, 2752 metabolism-related gene sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). One hundred and seventy-eight the differentially expressed MRGs were identified from the ICGC cohort and TCGA cohort. Then, univariate Cox regression analysis was performed to identify these genes that were related to overall survival (OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses in the ICGC dataset. The Broad Institute’s Connectivity Map (CMap) was used in predicting which compounds on the basis of the prognostic MRGs. Furthermore, the signature was validated in the TCGA dataset. Finally, the expression levels of hub genes were validated in HCC cell lines by Western blotting (WB) and quantitative real-time PCR (qRT-PCR). Results We found that 17 MRGs were most significantly associated with OS in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this particular signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes. Candidate drugs that aimed at hub ERGs were identified. Finally, hub genes were chosen for validation and the protein, mRNA expression of FLVCR1, SLC5A11, and RRM2 were significantly increased in human HCC cell lines compared to normal human hepatic cell lines, which were in agreement with the results of differential expression analysis. Conclusion Our data provided evidence that the metabolism-related signature could serve as a reliable prognostic and predictive tool for OS in patients with HCC.

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<p>Screening and Identification of Potential Biomarkers for Hepatocellular Carcinoma: An Analysis of TCGA Database and Clinical Validation</p>

Cited by 5 publications

References 31 publications

Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

A Novel Metabolism-Related Signature as a Candidate Prognostic Biomarker for Hepatocellular Carcinoma

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