Analysis and experimental validation of the innate immune gene PSMD1 in liver hepatocellular carcinoma and pan-cancer

Genetic mutation and immune infiltration in embryonal tumor with multilayered rosettes

PURPOSE

Genetic mutations stand as pivotal factors leading to the occurrence of embryonal tumor with multilayered rosettes (ETMR). This study aims to identify improved treatment approaches by unraveling the genetic drivers and immune infiltration in ETMR.

METHODS

Two siblings with ETMR, treated at the General Hospital of Ningxia Medical University, were enrolled. Diagnosis involved MRI, Hematoxylin and Eosin (HE), and immunohistochemical (IHC) staining. Differentially expressed genes (DEGs) in ETMR were identified using GSE122077 and GSE14296 datasets. GO and KEGG analyses were used to determine ETMR-related pathways. Whole exome sequencing (WES) was utilized to annotate genetic variations in ETMR. Core genes, identified by protein-protein interaction (PPI), formed a diagnostic model evaluated by Logistic Regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) assessed immune infiltration in ETMR, examining correlations between immune cells and core genes.

RESULTS

Two siblings were diagnosed with ETMR. In ETMR, 135 DEGs were identified, of which 25 genes were annotated with 28 mutation sites. Moreover, ETMR-related pathways included cell cycle, synaptic functions, and neurodegeneration. Three ETMR-related core genes (ALB, PSMD1, and PAK2) were screened by protein-protein interaction (PPI). The diagnostic model constructed using these genes demonstrated an AUC value of 0.901 (95% CI: 0.811-0.991) in the training set, indicating accurate predictions in ETMR. Enhanced ssGSEA scores for 16 immune cells in ETMR tissues suggested a strong immune response.

CONCLUSION

This study identifies diagnostic models associated with three core variant genes (ALB, PSMD1, PAK2) and enhanced immune cell activity in ETMR. It reveals crucial genetic features and significant immune responses in ETMR.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.

Results

SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.

Conclusions

The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Depletion of proteasome subunit PSMD1 induces cancer cell death via protein ubiquitination and DNA damage, irrespective of p53 status

Hepatocellular carcinoma (HCC) is characterized by high incidence and fatality rates worldwide. In our exploration of prognostic factors in HCC, the 26s proteasome subunit, non-ATPase 1 (PSMD1) protein emerged as a significant contributor, demonstrating its potential as a therapeutic target in this aggressive cancer. PSMD1 is a subunit of the 19S regulatory particle in the 26S proteasome complex; the 19S particle controls the deubiquitination of ubiquitinated proteins, which are then degraded by the proteolytic activity of the complex. Proteasome-targeting in cancer therapy has received significant attention because of its practical application as an established anticancer agent. We investigated whether PSMD1 plays a critical role in cancer owing to its prognostic significance. PSMD1 depletion induced cell cycle arrest in G2/M phase, DNA damage and apoptosis of cancer cells, irrespective of the p53 status. PSMD1 depletion-mediated cell death was accompanied by an increase in overall protein ubiquitination. These phenotypes occurred exclusively in cancer cells, with no effects observed in normal cells. These findings indicate that PSMD1 depletion-mediated ubiquitination of cellular proteins induces cell cycle arrest and eventual death in cancer cells, emphasizing PSMD1 as a potential therapeutic target in HCC.