An Analysis Regarding the Association Between Proteasome (PSM) and Hepatocellular Carcinoma (HCC)

Background

The Proteasome (PSM) is a large multi-catalytic protease complex consisting of a 20S core particle and a 19S regulatory particle whose main function is to accept and degrade ubiquitinated substrates, are now considered as one of the potential regulators of tumor proliferation, and stemness maintenance. However, to date, studies on the relationship between PSM and hepatocellular carcinoma (HCC) are limited.

Methods

This study used a bioinformatics approach combining validation experiments to investigate the biological mechanisms that may be related with PSM. A series of experiments in vivo and in vitro were performed to explore the function of the 26S proteasome non-ATPase regulatory subunit 13 (PSMD13) in HCC.

Results

HCC patients can be divided into two clusters. Cluster 1 (C1) patients having a significantly worse prognosis than Cluster (C2). Two subtypes had significant differences in proliferation-related signaling. In particular, the frequency of TP53 mutation was significantly higher in C1 than in C2. In addition, PSM-associated genes were highly consistent with the expression of DNA repair-related signatures, suggesting a potential link between PSM and genomic instability. We also found that downregulation of PSMD13 expression significantly inhibited stemness of tumor cells and impaired the Epithelial mesenchymal transition (EMT) process. Finally, the correlation between the PSMD13 and Ki67 was found to be strong.

Conclusion

PSM is a valid predictor of prognosis and therapeutic response in patients with HCC disease. Furthermore, PSMD13 may be a potential therapeutic target.

Identification and Validation in a Novel Classification of Helicase Patterns for the Prediction of Tumor Proliferation and Prognosis

Background

Helicases have been classified as a class of enzymes that determine the stability of the cellular genome. There is growing evidence that helicases can help tumor cells resist drug killing by repairing Deoxyribose Nucleic Acid (DNA) or stabilizing transcription, which may contribute to the understanding of drug resistance. Currently, identifying cancer biomarkers among helicases and stratifying patients according to helicase activity will be able to guide treatment well.

Methods

We clustered 371 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) by consensus clustering based on helicase expression profiles to identify potential molecular subtypes. The Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm was used to find core differential gene modules between different molecular subtypes, and single-cell analysis was utlized to explore the potential function of hub gene. Immunohistochemical (IHC) staining was used to verify the diagnostic value of DDX56 and its ability to reflect the proliferation efficiency of cancer cells.

Results

We identified two subtypes associated with helicase. High helicase subtype was associated with poor clinical outcome, massive M0 macrophage infiltration, and highly active cell proliferation features. In addition, we identified a new biomarker, DDX56, which has not been reported in HCC, was highly expressed in HCC tissues, associated with poor prognosis, and was also shown to be associated with high cell proliferative activity.

Conclusion

In conclusion, based on helicase expression profiles, we have developed a new classification system for HCC, which is a proliferation-related system, and has clinical significance in evaluating prognosis and treating HCC patients, including immunotherapy and chemotherapy. In addition, we identified a new biomarker, DDX 56, which is overexpressed in HCC tissues, predicts a poor prognosis and is a validated index of tumor cell proliferation.