ARNTL2 is a Prognostic Biomarker and Correlates with Immune Cell Infiltration in Triple-Negative Breast Cancer

Comprehensive analysis of the relationship between ubiquitin-specific protease 21 (USP21) and prognosis, tumor microenvironment infiltration, and therapy response in colorectal cancer

BACKGROUND

Ubiquitin-specific proteases family is crucial to host immunity against pathogens. However, the correlations between USP21 and immunosurveillance and immunotherapy for colorectal cancer (CRC) have not been reported.

METHODS

The differential expression of USP21 between CRC tissues and normal tissues was analyzed using multiple public databases. Validation was carried out in clinical samples through qRT-PCR and IHC. The correlation between USP21 and the prognosis, as well as clinical pathological characteristics of CRC patients, was investigated. Moreover, cell models were established to assess the influence of USP21 on CRC growth and progression, employing CCK-8 assays, colony formation assays, and wound-healing assays. Subsequently, gene set variation analysis (GSVA) was used to explore the potential biological functions of USP21 in CRC. The study also examined the impact of USP21 on cytokine levels and immune cell infiltration in the tumor microenvironment (TME). Finally, the effect of USP21 on the response to immunotherapy and chemotherapy in CRC was analyzed.

RESULTS

The expression of USP21 was significantly upregulated in CRC. High USP21 is correlated with poor prognosis in CRC patients and facilitates the proliferation and migration capacities of CRC cells. GSVA indicated an association between low USP21 and immune activation. Moreover, low USP21 was linked to an immune-activated TME, characterized by high immune cell infiltration. Importantly, CRC with low USP21 exhibited higher tumor mutational burden, high PD-L1 expression, and better responsiveness to immunotherapy and chemotherapeutic drugs.

CONCLUSION

This study revealed the role of USP21 in TME, response to therapy, and clinical prognosis in CRC, which provided novel insights for the therapeutic application in CRC.

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.

Circadian disruption: from mouse models to molecular mechanisms and cancer therapeutic targets

The circadian clock is a timekeeping system for numerous biological rhythms that contribute to the regulation of numerous homeostatic processes in humans. Disruption of circadian rhythms influences physiology and behavior and is associated with adverse health outcomes, especially cancer. However, the underlying molecular mechanisms of circadian disruption-associated cancer initiation and development remain unclear. It is essential to construct good circadian disruption models to uncover and validate the detailed molecular clock framework of circadian disruption in cancer development and progression. Mouse models are the most widely used in circadian studies due to their relatively small size, fast reproduction cycle, easy genome manipulation, and economic practicality. Here, we reviewed the current mouse models of circadian disruption, including suprachiasmatic nuclei destruction, genetic engineering, light disruption, sleep deprivation, and other lifestyle factors in our understanding of the crosstalk between circadian rhythms and oncogenic signaling, as well as the molecular mechanisms of circadian disruption that promotes cancer growth. We focused on the discoveries made with the nocturnal mouse, diurnal human being, and cell culture and provided several circadian rhythm-based cancer therapeutic strategies.

A circadian rhythm-related gene signature for prognosis, invasion and immune microenvironment of breast cancer

Background: Circadian dysregulation is linked to the onset and progression of cancer, but current knowledge of the role of circadian rhythm-related genes (CRRGs) in breast cancer (BC) is limited and incomplete. The purpose of this study was to investigate the potential role and immune-related prognostic significance of CRRGs in BC. Methods: The Cancer Genome Atlas breast cancer (TCGA-BRCA) genetic data were combined with 1369 CRRGs to create a model of BC prognosis-related CRRGs. To validate the model's predictive power in TCGA and other external datasets, the Kaplan-Meier survival curve and receptor operation characteristic curve were plotted. The relationship between CRRGs model and gene enrichment pathways, immune cell infiltration, and differences in patient response to immune checkpoint inhibitors (ICIs) therapy was then discussed. Results: A CRRG-based eighteen-gene model was developed that accurately predicted the survival time of BC patients. Based on this model, BC patients can be classified as high or low risk. The high-risk group has negative immune cell infiltration (such as macrophages M0 and M2) and a poor therapeutic response to ICIs due to lower immune checkpoint gene expression. Furthermore, TCF7 and IFNG were found to be strongly associated with immune checkpoints in CRRGs model. Conclusion: The 18 CRRGs may be useful in assessing the prognosis of BC patients, studying immune infiltration, and developing more effective immunotherapy strategies.

ARNTL2 is an indicator of poor prognosis, promotes epithelial-to-mesenchymal transition and inhibits ferroptosis in lung adenocarcinoma

OBJECTIVES

ARNTL2, as a circadian transcription factor, has been recently proposed to play an important role in a variety of tumors. however, the role of ARNTL2 in lung carcinogenesis and progression remains unclear. The purpose of this study was to investigate the effect of ARNTL2 on the clinical characteristics and prognosis of lung adenocarcinoma and to explore the relationship between ARNTL2 and EMT, ferroptosis in lung adenocarcinoma.

METHODS

The Cancer Genome Atlas (TCGA) database's multi-omics data were downloaded using the Xena browser. Based on the expression levels of ARNTL2, patients with lung adenocarcinoma from TCGA were divided into two groups: those with high ARNTL2 expression and those with low ARNTL2 expression. ARNTL2 was studied for its effects on lung adenocarcinoma's clinicopathological, genomic, and immunological characteristics. Furthermore, in vivo and in vitro assays were used to confirm the impact of ARNLT2 knockdown on lung adenocarcinoma cells.

RESULTS

We found ARNTL2 is highly expressed in lung adenocarcinoma and was an independent predictor of a poor prognosis in patients with lung adenocarcinoma. In addition, we demonstrated that knockdown of ARNTL2 promoted ferroptosis, inhibited EMT, cell proliferation, migration and invasion in lung adenocarcinoma. In contrast, overexpressing ARNTL2 yielded the opposite results.

CONCLUSIONS

ARNTL2 is an independent unfavorable prognostic factor for lung adenocarcinoma. It plays a facilitating role in the development of lung adenocarcinoma, especially in promoting EMT and inhibiting ferroptosis, revealing that ARNTL2 may be a potential biomarker for lung adenocarcinoma.

Construction of a Novel Prognostic Model in Lung Adenocarcinoma Based on 7-Methylguanosine-Related Gene Signatures

Increasing evidence has implicated the modification of 7-methylguanosine (m7G), a type of RNA modification, in tumor progression. However, no comprehensive analysis to date has summarized the predicted role of m7G-related gene signatures in lung adenocarcinoma (LUAD). Herein, we aimed to develop a novel prognostic model in LUAD based on m7G-related gene signatures. The LUAD transcriptome profiling data and corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus datasets. After screening, we first obtained 29 m7G-related genes, most of which were upregulated in tumor tissues and negatively associated with overall survival (OS). According to the expression similarity of m7G-related genes, the combined samples from the TCGA-LUAD and GSE68465 datasets were further classified as two clusters that exhibit distinct OS rates and genetic heterogeneity. Then, we constructed a novel prognostic model involving four genes by using 130 differentially expressed genes among the two clusters. The combined samples were randomly divided into a training cohort and an internal validation cohort in a 1:1 ratio, and the GSE72094 dataset was used as an external validation cohort. The samples were divided into high- and low-risk groups. We demonstrated that a higher risk score was an independent negative prognostic factor and predicted poor OS. A nomogram was further constructed to better predict the survival of LUAD patients. Functional enrichment analyses indicated that cell cycle and DNA replication-related biological processes and pathways were enriched in the high-risk group. More importantly, the low-risk group had greater infiltration and enrichment of most immune cells, as well as higher ESTIMATE, immune, and stromal scores. In addition, the high-risk group had a lower TIDE score and higher expressions of most immune checkpoint-related genes. We finally noticed that patients in the high-risk group were more sensitive to chemotherapeutic agents commonly used in LUAD. In conclusion, we herein summarized for the first time the alterations and prognostic role of m7G-related genes in LUAD and then constructed a prognostic model based on m7G-related gene signatures that could accurately and stably predict survival and guide individualized treatment decision-making in LUAD patients.