Comprehensive analysis of ubiquitin-proteasome system genes related to prognosis and immunosuppression in head and neck squamous cell carcinoma

Single-cell integrated transcriptomics reveals the role of keratinocytes in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignant tumor with significant morbidity and mortality. Understanding the molecular mechanisms of HNSCC and identifying prognostic markers and therapeutic targets are crucial for improving patient outcomes. In this study, we utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to comprehensively analyze HNSCC at the cellular level. We identified keratinocytes as the predominant cell type in tumor samples, suggesting their potential role in HNSCC development. Through hdWGCNA co-expression network analysis, we identified gene modules associated with HNSCC progression. Furthermore, we constructed a prognostic model based on specific genes and demonstrated its robust predictive performance in multiple datasets. The model exhibited strong correlations with immune cell infiltration patterns and signaling pathways related to tumor progression. Additionally, drug sensitivity analysis revealed potential chemotherapeutic targets for HNSCC treatment. Our findings provide valuable insights into the molecular characteristics and immune microenvironment of HNSCC, offering new perspectives for prognosis prediction and therapeutic interventions in clinical practice. Further research is warranted to validate and expand upon these findings, ultimately improving patient outcomes in HNSCC.

Development of an ubiquitin-proteasome system signature for predicting prognosis and providing therapeutic guidance for patients with triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a pathological subtype with a high mortality, and the development of inhibitors in the ubiquitin-proteasome system (UPS) component could be a novel therapeutic tool.

METHODS

Triple-negative breast cancer data were obtained from The Cancer Genome Atlas (TCGA), and subtype analysis was performed by consistent clustering analysis to identify molecular subtypes of TNBC according to UPS characteristics. Differential analysis, COX and least absolute shrinkage and selection operator (LASSO) COX regression analyses were performed to select genes associated with overall survival in TNBC. The final prognostic model (UPS score) was determined using the LASSO COX model. The model performance was assessed using receiver operating characteristic (ROC) curves and survival curves. In addition, the results of the UPS score on analyzing the abundance of immune cell infiltration and immunotherapy were explored. Finally, we developed a nomogram for TNBC survival prediction.

RESULTS

Two UPS subtypes (UPSMS1 and UPSMS2) showing significant survival differences were classified. COX regression analysis on differentially expressed genes in UPSMS1 and UPSMS2 filtered five genes that affected overall survival. Based on the regression coefficients and expression data of the five genes, we built a prognostic assessment system (UPS score). The UPS score showed consistent prognostic and therapeutic guidance values. Finally, the ROC curve of the nomogram and UPS score showed the highest predictive efficacy compared with traditional clinical prognostic indicators.

CONCLUSION

The UPS score represented a promising prognostic tool to predict overall survival and immune status and guide personalized treatment selection in TNBC patients, and this study may provide a more practical alternative for clinical monitoring and management of TNBC.

A ubiquitin-proteasome system-related signature to predict prognosis, immune infiltration, and therapy efficacy for breast cancer

The ubiquitin-proteasome system (UPS) is an essential regulatory system for maintaining homeostasis, and its dysfunction may cause various diseases. The activity of proteasome and ubiquitin-conjugating enzymes has been found to be greatly increased in breast cancer (BC), indicating that the heterogeneity of UPS may be related to the progression of BC. Gene data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and performed in multiple algorithms to construct a UPS-related signature for BC. Patients in the UPS low-risk group had greater overall and recurrence-free survival probability than those in the UPS high-risk group. This signature was closely associated with functional enrichment. Some high metabolism-related pathways were more active in the UPS high-risk group. The UPS low-risk group had more abundant anti-tumor immune cells, while in the UPS high-risk group, immunosuppressive cells were dominant. More importantly, we found that the UPS low-risk group was more sensitive to immunotherapy, while the UPS high-risk group responded better to radiotherapy. Drug sensitivity analysis identified more effective chemotherapy drugs in different UPS-related risk groups. This UPS-related signature may serve as a novel biomarker and independent prognostic factor for BC. It can effectively predict prognosis, immune infiltration, and therapy efficacy, providing new strategies for individualized treatment.

Development and validation of a ubiquitin-proteasome system gene signature for prognostic prediction and immune microenvironment evaluation in hepatocellular carcinoma

BACKGROUND

The ubiquitin proteasome has a major role in the development of many tumors. However, the prognostic importance of ubiquitin proteasome-system genes (UPSGs) in hepatocellular carcinoma (HCC) is not fully defined.

METHODS

The TCGA and ICGC datasets were utilized to obtain transcriptional profiling data as well as clinicopathological information about HCC. The 3-UPSGs signature for the TCGA cohort was developed via univariate and LASSO Cox regression analyses. Differential expression of genes was demonstrated by qRT-PCR and immunohistochemistry (IHC). Biological pathways were studied using GSVA and GSEA. Six algorithms were used to compare immune infiltration between the two risk groups. Furthermore, drug sensitivity was measured using the "pRRophetic" R package. The predictive capacity of the 3-UPSGs signature for sensitivity to immunotherapy was also explored. Moreover, we performed a pan-cancer analysis of the 3-UPSGs signature.

RESULTS

A risk model containing 3 UPSGs (DCAF13, CDC20 and PSMB5) was developed. IHC and qRT-PCR results showed that signature genes were significantly overexpressed in HCC tissues. The high-risk group had a worse prognosis, with a higher clinicopathological grade, higher levels of tumor mutation burden (TMB), elevated levels of immune checkpoint (IC) expression, as well as increased sensitivity to immunotherapy. The two risk groups also differ in their sensitivity to chemotherapeutic drugs. Furthermore, the three UPSGs may play crucial roles in the progression of multiple types of cancers.

CONCLUSION

We created a 3-UPSGs signature to estimate the prognosis of HCC and to assist in individualized treatment.

Gene network analyses of larvae under different egg-protecting behaviors provide novel insights into immune response mechanisms of Amphioctopus fangsiao

Amphioctopus fangsiao was a representative economic species in cephalopods, which was vulnerable to marine bacteria. Vibrio anguillarum was a highly infectious pathogen that have recently been found to infect A. fangsiao and inhibit its growth and development. There were significant differences in the immune response mechanisms between egg-protected and egg-unprotected larvae. To explore larval immunity under different egg-protecting behaviors, we infected A. fangsiao larvae with V. anguillarum for 24 h and analyzed the transcriptome data about egg-protected and egg-unprotected larvae infected with 0, 4, 12, and 24 h using weighted gene co-expression networks (WGCNA) and protein-protein interaction (PPI) networks. Network analyses revealed a series of immune response processes after infection, and identified six key modules and multiple immune-related hub genes. Meanwhile, we found that ZNF family, such as ZNF32, ZNF160, ZNF271, ZNF479, and ZNF493 might play significant roles in A. fangsiao immune response processes. We first creatively combined WGCNA and PPI network analysis to deeply explore the immune response mechanisms of A. fangsiao larvae with different egg-protecting behaviors. Our results provided further insights into the immunity of V. anguillarum infected invertebrates, and laid the foundation for exploring the immune differences among cephalopods with different egg protecting behaviors.

Ubiquitin-proteasome system-based signature to predict the prognosis and drug sensitivity of hepatocellular carcinoma

Background: Ubiquitin-proteasome system (UPS) is implicated in cancer occurrence and progression. Targeting UPS is emerging as a promising therapeutic target for cancer treatment. Nevertheless, the clinical significance of UPS in hepatocellular carcinoma (HCC) has not been entirely elucidated. Methods: Differentially expressed UPS genes (DEUPS) were screened from LIHC-TCGA datasets. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analysis were conducted to establish a UPS-based prognostic risk model. The robustness of the risk model was further validated in HCCDB18, GSE14520, and GSE76427 cohorts. Subsequently, immune features, clinicopathologic characteristics, enrichment pathways, and anti-tumor drug sensitivity of the model were further evaluated. Moreover, a nomogram was established to improve the predictive ability of the risk model. Results: Seven UPS-based signatures (ATG10, FBXL7, IPP, MEX3A, SOCS2, TRIM54, and PSMD9) were developed for the prognostic risk model. Individuals with HCC with high-risk scores presented a more dismal prognosis than those with low-risk scores. Moreover, larger tumor size, advanced TNM stage, and tumor grade were observed in the high-risk group. Additionally, cell cycle, ubiquitin-mediated proteolysis, and DNA repair pathways were intimately linked to the risk score. In addition, obvious immune cell infiltration and sensitive drug response were identified in low-risk patients. Furthermore, both nomogram and risk score showed a significant prognosis-predictive ability. Conclusion: Overall, we established a novel UPS-based prognostic risk model in HCC. Our results will facilitate a deep understanding of the functional role of UPS-based signature in HCC and provide a reliable prediction of clinical outcomes and anti-tumor drug responses for patients with HCC.

TRIM9 Interacts with ZEB1 to Suppress Esophageal Cancer by Promoting ZEB1 Protein Degradation via the UPP Pathway

Background

Esophageal cancer remains one of the most lethal malignant diseases globally. Previous studies indicated that TRIM9 (Tripartite Motif Containing 9) is a potential marker in breast cancer patients. Therefore, in the current research, we intended to clarify the regulatory network of TRIM9 and its relative role in esophageal cancer patients. We aimed to elucidate the regulatory role of TRIM9 in esophageal cancer.

Methods

Clinical tumor tissue samples combined with cancer cell line models were utilized to explore the TRIM9 expression pattern. Functional experiments including transwell assay, cell viability assay, and ubiquitination blocking experiments were performed to evaluate the role of the TRIM9/ZEB1 (zinc finger E-box binding homeobox 1) axis and UPP pathway in esophageal cancer progression and exacerbation.

Results

Both esophageal cancer samples and cell line models showed significantly suppressed levels of TRIM9. Functional experiments confirmed that TRIM9 overexpression inhibited the cell viability, invasiveness, and stem-like phenotype of cancer cells. Subsequent investigations suggested that TRIM9-ZEB1 interaction accelerated ZEB1 protein degradation through the modulation of the UPP pathway, which confirmed the protective role of TRIM9 in esophageal cancer progression and metastasis.

Conclusion

This study concluded that TRIM9 was a tumor suppressor that interacted with ZEB1 and accelerated ZEB1 protein degradation via the ubiquitin-proteasome pathway (UPP). Our research emphasized TRIM9-ZEB1 interaction as a valuable target for esophageal cancer treatment in future development. More detailed studies are needed to further consolidate our findings.

A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature

BACKGROUND

Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. However, the ubiquitin-proteasome system (UPS) genes have not yet been established as a prognostic predictor for MM, despite their potential applications in other cancers.

METHODS

RNA sequencing data and corresponding clinical information were acquired from Multiple Myeloma Research Foundation (MMRF)-COMMPASS and served as a training set (n=787). Validation of the prediction signature were conducted by the Gene Expression Omnibus (GEO) databases (n=1040). To develop a prognostic signature for overall survival (OS), least absolute shrinkage and selection operator regressions, along with Cox regressions, were used.

RESULTS

A six-gene signature, including KCTD12, SIAH1, TRIM58, TRIM47, UBE2S, and UBE2T, was established. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients with high-risk conditions had a significantly worse prognosis than those with low-risk conditions. Furthermore, UPS-related signature is associated with a positive immune response. For predicting survival, a simple to use nomogram and the corresponding web-based calculator (https://jiangyanxiamm.shinyapps.io/MMprognosis/) were built based on the UPS signature and its clinical features. Analyses of calibration plots and decision curves showed clinical utility for both training and validation datasets.

CONCLUSIONS

As a result of these results, we established a genetic signature for MM based on UPS. This genetic signature could contribute to improving individualized survival prediction, thereby facilitating clinical decisions in patients with MM.

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer. Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential. Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups. Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer.

PSMD2 promotes the progression of bladder cancer and is correlated with immune infiltration

Introduction

PSMD2 plays an oncogenic role in multiple human malignancies, while it is still unclear that the potential roles and underlying mechanisms of PSMD2 in BCa.

Methods

The RNA-seq from TCGA and GTEx database was utilized to preliminarily analyze the expression of PSMD2 in BCa tissues, qRT-PCR was adopted to verify the PSMD2 expression in BCa cell lines. Cox regression analyses were applied to assess the prognostic values of PSMD2 in BCa. GSEA analysis was used to explore the underlying mechanisms of PSMD2. In vitro assays such as wound healing and colony formation assays were applied to determine the carcinogenesis of PSMD2 in BCa. xCell and ssGSEA algorithms were applied to analyze the associations of PSMD2 with TIME.

Results

The results revealed that in comparison with normal bladder tissues and cell line, PSMD2 was found to be significantly elevated in BCa tissues and cell lines. Elevated expression of PSMD2 can independently predict unfavorable OS for BCa patients. The PSMD2 expression and other clinicopathologic factors were combined to develop a nomogram, which can help to predict OS for BCa patients. GSEA analyses revealed that PSMD2 is correlated with the cell cycle, antigen processing and presentation, JAK-STAT signaling pathway, Toll like receptor signaling pathway, P53 and MAPK signaling pathway. Knockdown of PSMD2 could remarkably inhibit the wound healing and colony formation efficiency of BCa cells. xCell analysis revealed that overexpressed PSMD2 is positively related to the Th2 cells infiltrates and expression levels of immune escape markers, and negatively associated with the infiltrating levels of NK T cell and CD8+ T cell.

Discussion

In conclusion, overexpressed PSMD2 is tightly linked to the immune infiltrates and promotes the progression of BCa.

A Ubiquitin-Proteasome Gene Signature for Predicting Prognosis in Patients With Lung Adenocarcinoma

Background: Dysregulation of the ubiquitin-proteasome system (UPS) can lead to instability in the cell cycle and may act as a crucial factor in both tumorigenesis and tumor progression. However, there is no established prognostic signature based on UPS genes (UPSGs) for lung adenocarcinoma (LUAD) despite their value in other cancers.

Methods: We retrospectively evaluated a total of 703 LUAD patients through multivariate Cox and Lasso regression analyses from two datasets, the Cancer Genome Atlas (n = 477) and GSE31210 (n = 226). An independent dataset (GSE50081) containing 128 LUAD samples were used for validation.

Results: An eight-UPSG signature, including ARIH2, FBXO9, KRT8, MYLIP, PSMD2, RNF180, TRIM28, and UBE2V2, was established. Kaplan-Meier survival analysis and time-receiver operating characteristic curves for the training and validation datasets revealed that this risk signature presented with good performance in predicting overall and relapsed-free survival. Based on the signature and its associated clinical features, a nomogram and corresponding web-based calculator for predicting survival were established. Calibration plot and decision curve analyses showed that this model was clinically useful for both the training and validation datasets. Finally, a web-based calculator (https://ostool.shinyapps.io/lungcancer) was built to facilitate convenient clinical application of the signature.

Conclusion: An UPSG based model was developed and validated in this study, which may be useful as a novel prognostic predictor for LUAD.