Abstract

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Highlights

  • MATERIALS AND METHODSLiver cancer is one of the leading causes of global disease burden worldwide, with 42,810 new cases and 30,160 deaths in 2020 (Yu and Schwabe, 2017; Siegel et al, 2020)

  • It can be clearly seen that the correlations between Hepatocellular carcinoma (HCC) samples can be divided into two groups (Figure 2A), and the intragroup correlation was high, which showed that these samples had some heterogeneity

  • Gu et al performed Weighted gene co-expression network analysis (WGCNA) based on the The Cancer Genome Atlas (TCGA) database and found two hub modules and 13 hub genes (SNRPD2, PRR11, SKA3, etc.) that have a high correlation with progression and prognosis in HCC

Read more

Summary

Introduction

MATERIALS AND METHODSLiver cancer is one of the leading causes of global disease burden worldwide, with 42,810 new cases and 30,160 deaths in 2020 (Yu and Schwabe, 2017; Siegel et al, 2020). Hepatocellular carcinoma (HCC) is the most frequent and common type of primary liver cancer and is attributed mainly to the progression of chronic liver disease. Two modules and 10 hub genes identified by Zhang et al (2018) were related to the tumorigenesis of oral squamous cell carcinoma. Two cervical squamous cell carcinoma-related hub modules and 116 hub genes were identified by the WGCNA method (Liu et al, 2019). Ribosomal protein L19 (RPL19) as a hub gene was identified in HCC by WGCNA in this study. The diagnostic, prognostic and therapeutic value of RPL19 in HCC has not been investigated. Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call