CFHR3 is a potential novel biomarker for hepatocellular carcinoma

Liu, Jun; Li, Wenli; Zhao, Hetong

doi:10.1002/jcb.29551

Cited by 23 publications

(21 citation statements)

References 35 publications

(60 reference statements)

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“…Tissue-based approaches were con rmed to be able to characterize the tumor in ltrates in clear cell renal cell carcinoma and primary glioblastoma multiforme, according to previous studies [56,57]. A recent research showed that high-risk cohorts in HCC have higher immune, and stromal scores than that in low-risk cohorts, which was consistent with our results [58].…”

Section: Discussionsupporting

confidence: 89%

Transcriptomic analysis and Novel gene pair-based signatures for Hepatitis B-related hepatocellular carcinoma

Song

Wang

2021

Preprint

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Background: Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) misses the opportunity for surgery because it is not detected early. The molecular mechanism of hepatitis B-related liver cancer needs further understanding, and effective diagnostic and prognostic models are in urgent need. Methods: Expression profiles from the Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC), GSE121248, GSE94660 GSE76724 from Gene Expression Omnibus (GEO) database were obtained. Differentially expressed genes (DEGs) between normal and tumor HBV-related HCC samples based on GSE121248 and GSE94660. Gene pairs are generated by comparing the expression levels of every two DEGs. A diagnostic signature of pairs of DEGs was built using cross-validation Lasso and Best Subset Selection regression. Hub genes and significant modules were screened by Cytoscape, and potential drugs were predicted by DGIdb. A prognostic signature was established and xCell and ssGSEA were utilized to reveal the cell composition and cancer hallmarks to get an elucidation for the risk.Results: 457 DEGs were screened. A powerful diagnostic signature of 2 pairs of DEGs was built and validated in TCGA-LIHC and GEO datasets repeatedly with assured performance. 10 Hub genes were found and fostamatinib was predicted to have potential therapeutic effect on HBV-related HCC. A prognostic signature with good efficiency (Log-rank P value<0.05, AUC=93.1%) were established, with stromal score and several hallmarks related to the risk Conclusion: Taken together, the study provided sight into the molecular mechanism as well as a novel strategy for the early diagnosis and prognosis for HBV-related HCC.

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Section: Discussionsupporting

confidence: 89%

Transcriptomic analysis and Novel gene pair-based signatures for Hepatitis B-related hepatocellular carcinoma

Song

Wang

2021

Preprint

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“…Although the risk effect of FTL was small following adjustment in the multivariate Cox model, the potential role of changes to iron metabolism in the progression of HCC should not be ignored. CFHR3 was demonstrated to exert a protective effect in patients with HCC, and physiologically, CFHR3 is exclusively expressed in normal liver (51). CFHR3 is associated with compliment factor H, which can bind to the C3d region of C3b, regulating the function of compliment system (52).…”

Section: Discussionmentioning

confidence: 99%

Immune system‑associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma

Huang

Chen

et al. 2020

Int J Oncol

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Hepatocellular carcinoma (HCC) is one of the most malignant types of cancer, and is associated with high recurrence rates and a poor response to chemotherapy. Immune signatures in the microenvironment of HCC have not been well explored systematically. The aim of the present study was to identify prognostic immune signatures and build a nomogram for use in clinical evaluation. Using bioinformatics analysis, RNA-seq data and overall survival (OS) information on 370 HCC cases from TCGA and 232 HCC cases from ICGC were analyzed. The differential expression of select immune genes, based on previously published studies, between HCC and adjacent tissue were analyzed using the limma package in R. Enrichment of pathways and gene ontology analysis was performed using clusterProfiler. Subsequently, univariate Cox regression analysis, Lasso penalty linear regression and multivariate Cox regression models were used to construct a model for immune risk score (IRS). The R packages, survival and survivalROC, were used to plot survival and the associated receiver operating characteristic curves. Infiltration of immune cells was calculated using Tumor IMmune Estimation Resource, with significance examined using a Pearson's correlation test. P<0.05 was considered significant. Based on the analysis, expression of 200 immune genes were upregulated and 47 immune genes were downregulated immune genes. In the multivariate Cox model, 5 genes (enhancer of zest homology 2, ferritin light chain, complement factor H related 3, isthmin 2, cyclin dependent kinase 5) were used to generate the IRS. By stratifying according to the median IRS, it was shown that patients with a high IRS had poor OS rates after 1, 2, 3 and 5 years, and this result was consistent across the testing, training and independent validation cohorts. Additionally, the IRS was correlated with the abundance of infiltrating immune cells. The nomogram built using IRS and clinical characteristics, was able to predict 1, 3 and 5 year OS with area under the curve values of >0.8. These results suggest that the model developed to calculate the IRS may be used to monitor the effectiveness of treatment strategies and for prognostic prediction.

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“…SNP in PRKAG2 was responsible for advancement of colon and rectal cancer [Slattery et al 2010], but this polymorphic gene may be liable for progression of hepatoblastoma. Enriched genes such as STAB2 [Wu et al 2018], IGF1 [Shi and Teng, 2015], HRG (histidine rich glycoprotein) [Zhang et al 2015], CFHR3 [Liu et al 2020] and CD5L [Aran et al 2018] were linked with proliferation of hepatocellular carcinoma cells, but these genes may be involved in proliferation of hepatoblastoma cells. Methylation inactivation of ADHFE1 was identified with growth of colorectal cancer [Hu et al 2019], but loss this gene may be responsible for progression of hepatoblastoma.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential core genes in hepatoblastoma via bioinformatics analysis

Vastrad¹,

Vastrad²,

Kotturshetti³

2020

Preprint

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Hepatoblastoma is the childhood liver cancer. Profound efforts have been made to illuminate the pathology, but the molecular mechanisms of hepatoblastoma are still not well understood. To identify the candidate genes in the carcinogenesis and progression of hepatoblastoma, microarray dataset GSE131329 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and pathway and Gene Ontology (GO) enrichment analysis were performed. The protein-protein interaction network (PPI), module analysis, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed. A total of 996 DEGs were identified, consisting of 499 up regulated genes and 497 down regulated genes. The pathway and Gene Ontology (GO) enrichment analysis of the DEGs include proline biosynthesis, superpathway of tryptophan utilization, chromosome organization and organic acid metabolic process. Twenty-four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell cycle, chromosome organization, lipid metabolic process and oxidation-reduction process. Validation of hub genes showed that TP53, PLK1, AURKA, CDK1, ANLN, ESR1, FGB, ACAT1, GOT1 and ALAS1 may be involved in the carcinogenesis, invasion or recurrence of hepatoblastoma. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of hepatoblastoma, and provide candidate targets for diagnosis and treatment of hepatoblastoma.

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CFHR3 is a potential novel biomarker for hepatocellular carcinoma

Cited by 23 publications

References 35 publications

Transcriptomic analysis and Novel gene pair-based signatures for Hepatitis B-related hepatocellular carcinoma

Transcriptomic analysis and Novel gene pair-based signatures for Hepatitis B-related hepatocellular carcinoma

Immune system‑associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma

Identification of potential core genes in hepatoblastoma via bioinformatics analysis

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