A novel FoxM1-PSMB4 axis contributes to proliferation and progression of cervical cancer

FoxM1 promotes the proliferation of cervical adenocarcinoma cells through transcriptional activation of FAM83D

Cervical adenocarcinoma exhibits a steadily increasing global incidence with notable demographic shifts toward younger populations. Despite the absence of distinct clinical guidelines differentiating its management from squamous cell carcinoma, treatment strategies remain non-specific, contributing to suboptimal patient outcomes. To address this therapeutic gap, we systematically investigated molecular disparities between adenocarcinoma and squamous cell carcinoma through integrated ChIP-seq and RNA-seq analyses. Our multi-omics approach identified FAM83D as a novel transcriptional target directly regulated by the FoxM1 oncoprotein, demonstrating adenocarcinoma-specific expression in HeLa cells. This regulatory relationship was experimentally validated using quantitative PCR and luciferase reporter assays. Mechanistically, we delineated that FoxM1 governs cell cycle progression and proliferation via FAM83D-dependent pathways. Intriguingly, co-immunoprecipitation studies revealed a physical interaction between FoxM1 and karyopherin α2 (KPNA2), another adenocarcinoma-enriched protein, with their expression levels showing significant positive correlation in clinical specimens. This study not only elucidates the oncogenic axis of FoxM1-FAM83D but also reveals the dual regulatory role of FoxM1 as both a transcriptional activator and protein interaction hub in cervical adenocarcinoma pathogenesis. These findings expand the molecular landscape of this malignancy and identify potential therapeutic entry points for targeted adenocarcinoma interventions.

APOC1, transcriptionally regulated by FOXM1, promotes M2 macrophage polarization and cervical cancer progression

BACKGROUND

Cervical cancer (CC) is a common malignant tumor in women. M2 macrophages are associated with tumor growth, metastasis, and immunosuppression. Apolipoprotein C1 (APOC1) has been confirmed as an oncogene in CC. However, the role and mechanism of APOC1 in CC progression and M2 macrophages remain to be elucidated.

METHODS

The effects of APOC1 on CC cell malignant phenotypes were examined by CCK-8, colony formation, wound healing, and transwell assays in vitro and mice transplant tumor model in vivo. M2 macrophage polarization was assessed by qRT-PCR and flow cytometry assays. The interaction between APOC1 and forkhead box M1 (FOXM1) was determined using chromatin immunoprecipitation (ChIP) and luciferase reporter assays.

RESULTS

The expression of APOC1 and FOXM1 was upregulated in CC tissues and cells. Knockdown of APOC1 or FOXM1 resulted in the inhibition of cell proliferation, migration, invasion, and EMT. Moreover, the polarization of M2 macrophages was attenuated when APOC1 or FOXM1 was silenced. Mechanistically, FOXM1 transcriptionally activated APOC1 by binding to its promoter. Furthermore, overexpression of APOC1 reversed the inhibitory effects of FOXM1 knockdown on cell proliferation, metastasis, and M2 macrophage polarization. Additionally, the knockdown of APOC1 reduced tumor growth and M2 macrophage polarization in mice.

CONCLUSION

FOXM1/APOC1 axis is involved in the progression of CC and the regulation of M2 macrophages polarization, bringing new hope to the treatment of CC.

A novel oxidative stress-related genes signature associated with clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Oxidative stress plays a significant role in the tumorigenesis and progression of tumors. We aimed to develop a prognostic signature using oxidative stress-related genes (ORGs) to predict clinical outcome and provide light on the immunotherapy responses of clear cell renal cell carcinoma (ccRCC).

Methods

The information of ccRCC patients were collected from the TCGA and the E-MTAB-1980 datasets. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) were conducted to screen out overall survival (OS)-related genes. Then, an ORGs risk signature was built by multivariate Cox regression analyses. The performance of the risk signature was evaluated with Kaplan-Meier (K-M) survival. The ssGSEA and CIBERSORT algorithms were performed to evaluate immune infiltration status. Finally, immunotherapy responses was analyzed based on expression of several immune checkpoints.

Results

A prognostic 9-gene signature with ABCB1, AGER, E2F1, FOXM1, HADH, ISG15, KCNMA1, PLG, and TEK. The patients in the high risk group had apparently poor survival (TCGA: p < 0.001; E-MTAB-1980: p < 0.001). The AUC of the signature was 0.81 at 1 year, 0.76 at 3 years, and 0.78 at 5 years in the TCGA, respectively, and was 0.8 at 1 year, 0.82 at 3 years, and 0.83 at 5 years in the E-MTAB-1980, respectively. Independent prognostic analysis proved the stable clinical prognostic value of the signature (TCGA cohort: HR = 1.188, 95% CI =1.142-1.236, p < 0.001; E-MTAB-1980 cohort: HR =1.877, 95% CI= 1.377-2.588, p < 0.001). Clinical features correlation analysis proved that patients in the high risk group were more likely to have a larger range of clinical tumor progression. The ssGSEA and CIBERSORT analysis indicated that immune infiltration status were significantly different between two risk groups. Finally, we found that patients in the high risk group tended to respond more actively to immunotherapy.

Conclusion

We developed a robust prognostic signature based on ORGs, which may contribute to predict survival and guide personalize immunotherapy of individuals with ccRCC.

Identification of key biomarkers and related immune cell infiltration in cervical cancer tissue based on bioinformatics analysis

Cervical cancer (CC) is the most common gynecological malignant tumor. Immunotherapy has become a new model for the treatment of CC, especially advanced and recurrent cancer. At present, many studies are exploring the safety and efficacy of immunotherapy for advanced or recurrent CC. In this study, CIBERSORT was used to analyze the immune cell infiltration in CC patients, to evaluate the proportion of immune cell types in CC samples, to quantify the cell composition of the immune response, and to analyze its prognostic value. The expression profile datasets of CC were downloaded from the GEO. The differentially expressed genes (DEGs) between CC and normal cervical tissues were identified via R software (version 4.1.1), and their functions and pathways were enriched and analyzed. A protein-protein interaction network was constructed to screen the hub gene. Immune cell infiltration in CC was analyzed via scientific reverse convolution algorithm (CIBERSORT), and the hub gene was analyzed via survival analysis to screen the diagnostic biomarkers of CC. A total of 144 DEGs and 12 hub genes were identified. DEGs are mainly involved in molecular functions such as serine-peptidase activity, serine-hydrolase activity, and chemokine activity. The enrichment pathway is closely related to the interaction between viral proteins and cytokines and cytokine receptors, the interleukin 17 signaling pathway, and chemokine signaling pathway. The immune cell infiltration analysis showed that T cells were the main infiltrating immune cells in CC, especially T cells CD8+ and CD4+ . The survival analysis of the hub gene showed that CEP55, MCM2, RFC4, and RRM2 had high diagnostic value. CEP55, MCM2, RFC4, and RRM2 can be used as diagnostic markers for CC. CD8+ and CD4+ T cells are closely related to the occurrence and development of CC.

Cellular PSMB4 Protein Suppresses Influenza A Virus Replication through Targeting NS1 Protein

The nonstructural protein 1 (NS1) of influenza A virus (IAV) possesses multiple functions, such as the inhibition of the host antiviral immune responses, to facilitate viral infection. To search for cellular proteins interacting with the IAV NS1 protein, the yeast two-hybrid system was adopted. Proteasome family member PSMB4 (proteasome subunit beta type 4) was found to interact with the NS1 protein in this screening experiment. The binding domains of these two proteins were also determined using this system. The physical interactions between the NS1 and cellular PSMB4 proteins were further confirmed by co-immunoprecipitation assay and confocal microscopy in mammalian cells. Neither transiently nor stably expressed NS1 protein affected the PSMB4 expression in cells. In contrast, PSMB4 reduced the NS1 protein expression level, especially in the presence of MG132. As expected, the functions of the NS1 protein, such as inhibition of interferon activity and enhancement of transient gene expression, were suppressed by PSMB4. PSMB4 knockdown enhances IAV replication, while its overexpression attenuates IAV replication. Thus, the results of this study suggest that the cellular PSMB4 protein interacts with and possibly facilitates the degradation of the NS1 protein, which in turn suppresses IAV replication.

Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis

Introduction

This study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis.

Materials and methods

Gene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene's protein levels with an immunohistochemical assay to confirm the accuracy of our analysis.

Results

A total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues.

Conclusion

FOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.