Proliferating Cell Nuclear Antigen (PCNA) Overexpression in Hepatocellular Carcinoma Predicts Poor Prognosis as Determined by Bioinformatic Analysis
Hepatocellular carcinoma (HCC) remains one of the most challenging cancers to treat, with a persistently poor overall survival (OS) rate despite advancements in early diagnosis and clinical therapy. The identification of novel prognostic biomarkers and potential therapeutic targets is crucial to improving patient outcomes. Proliferating cell nuclear antigen (PCNA), a protein with a molecular weight of 36,000, plays a critical role as a DNA sliding clamp and is essential in regulating cell proliferation. Emerging evidence suggests that high expression levels of PCNA or overexpression of p53 negatively impact tumor recurrence, tumor growth, and survival. This study aims to explore the potential of PCNA as a prognostic biomarker in HCC through comprehensive bioinformatic analysis.
The study utilized RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database, comprising 465 cases, including 407 HCC samples and 58 adjacent nontumorous samples (with 58 paired HCC samples). Clinical information from 379 HCC patients was also obtained. After removing missing values, 369 HCC patients with complete clinical characteristics were retained for analysis. Additionally, four liver cancer-related datasets (GSE54236, GSE76427, GSE14520, and GSE64041) were downloaded from the Gene Expression Omnibus (GEO) database. These datasets included 272 HCC patients and 208 controls, providing further validation for the differential expression of PCNA. The paired samples from GSE14520 (19 pairs) and GSE64041 (60 pairs) were used to validate the findings from the TCGA database.
The expression of PCNA was also tested in an HCC cell line (Hep3B) and a normal hepatocyte cell line (HL-7702). Gene set enrichment analysis (GSEA) was performed to gain insights into the biological pathways associated with PCNA in HCC. The results demonstrated that PCNA expression was significantly higher in HCC samples compared to normal samples across all datasets. In the TCGA database, PCNA expression in 407 HCC patients was higher than in 58 normal patients (P < 0.001). Similarly, in the combined GEO datasets, PCNA expression was higher in the tumor group than in the normal group (P < 0.001). The paired samples from TCGA, GSE14520, and GSE64041 also confirmed that PCNA expression was consistently higher in tumor groups compared to normal groups (all P < 0.001).
In the cell line experiments, PCNA expression in Hep3B cells was approximately 1.12-fold higher than in HL-7702 cells (P < 0.001), aligning with the findings from the TCGA and GEO databases. Furthermore, PCNA expression was significantly correlated with histologic grade, clinical stage, and tumor size (all P < 0.001). As the histologic stage and tumor size increased, so did the level of PCNA expression.
Kaplan-Meier survival analysis revealed that HCC patients with higher PCNA expression had a worse prognosis than those with lower PCNA expression (P = 0.01). Univariate Cox regression analysis identified clinical stage (hazard ratio [HR]: 1.86; 95% confidence interval [CI]: 1.46–2.39), tumor size (HR: 1.80; 95% CI: 1.43–2.27), and PCNA expression (HR: 1.013; 95% CI: 1.008–1.019) as significant predictors of poor prognosis in HCC (all P < 0.001). Multivariate Cox regression analysis confirmed that PCNA expression could serve as an independent prognostic factor for HCC, with an HR of 1.654 (95% CI: 1.234–2.218, P < 0.001).
GSEA identified significant differences in biological pathways associated with high and low PCNA expression. Pathways such as “Cell cycle,” “DNA replication,” and “P53 signaling pathway” were differentially enriched in the high PCNA expression phenotype. Additionally, tumor-related pathways including “Thyroid cancer,” “Bladder cancer,” and “Pancreatic cancer” were also enriched in the high PCNA expression group. On the other hand, pathways such as “Complement and coagulation cascades,” “Primary bile acid biosynthesis,” “Fatty acid metabolism,” “Valine leucine and isoleucine degradation,” “Retinal metabolism,” and “PPAR signaling pathway” were enriched in the low PCNA expression phenotype.
The study highlights the critical role of PCNA in HCC progression and prognosis. The overexpression of PCNA in HCC samples compared to normal samples underscores its potential as a prognostic biomarker. The correlation between PCNA expression and histologic grade, clinical stage, and tumor size further supports its relevance in predicting disease severity. The findings from the Kaplan-Meier and Cox regression analyses reinforce the prognostic value of PCNA, indicating that higher PCNA expression is associated with poorer survival outcomes.
The GSEA results provide deeper insights into the biological mechanisms underlying PCNA’s role in HCC. The enrichment of cell cycle and DNA replication pathways in the high PCNA expression phenotype aligns with PCNA’s known function in cell proliferation. The involvement of the P53 signaling pathway suggests a potential interplay between PCNA and p53 in tumorigenesis. The dysregulation of fatty acid metabolism and PPAR signaling pathways in the low PCNA expression phenotype highlights the metabolic alterations associated with HCC progression.
In conclusion, this study demonstrates that PCNA overexpression in HCC is associated with poor prognosis and can serve as an independent prognostic indicator. The findings suggest that monitoring PCNA expression could be beneficial in predicting the prognosis of HCC patients. Furthermore, the insights gained from the GSEA analysis underscore the importance of targeting cell cycle pathways and related genes in HCC treatment. Future research should focus on exploring the mechanistic roles of PCNA in HCC progression and its potential as a therapeutic target.
doi.org/10.1097/CM9.0000000000001192
Was this helpful?
0 / 0