Dataset Information

ZNF750: A Novel Prognostic Biomarker in Metastatic Prostate Cancer.

ABSTRACT: Prostate cancer is the most frequently diagnosed cancer and the fifth leading cause of cancer death among men in 2020. The clinical decision making for prostate cancer patients is based on the stratification of the patients according to both clinical and pathological parameters such as Gleason score and prostate-specific antigen levels. However, these tools still do not adequately predict patient outcome. The aim of this study was to investigate whether ZNF750 could have a role in better stratifying patients, identifying those with a higher risk of metastasis and with the poorest prognosis. The data reported here revealed that ZNF750 protein levels are reduced in human prostate cancer samples, and this reduction is even higher in metastatic samples. Interestingly, nuclear positivity is significantly reduced in patients with metastatic prostate cancer, regardless of both Gleason score and grade group. More importantly, the bioinformatics analysis indicates that ZNF750 expression is positively correlated with better prognosis. Overall, our findings suggest that nuclear expression of ZNF750 may be a reliable prognostic biomarker for metastatic prostate cancer, which lays the foundation for the development of new biological therapies.

SUBMITTER: Montanaro M

PROVIDER: S-EPMC10095592 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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ZNF750: A Novel Prognostic Biomarker in Metastatic Prostate Cancer.

Montanaro Manuela M Agostini Massimiliano M Anemona Lucia L Bonanno Elena E Servadei Francesca F Finazzi Agrò Enrico E Asimakopoulos Anastasios D AD Ganini Carlo C Cipriani Chiara C Signoretti Marta M Bove Pierluigi P Rugolo Francesco F Imperiali Benedetta B Melino Gerry G Mauriello Alessandro A Scimeca Manuel M

International journal of molecular sciences 20230330 7

Prostate cancer is the most frequently diagnosed cancer and the fifth leading cause of cancer death among men in 2020. The clinical decision making for prostate cancer patients is based on the stratification of the patients according to both clinical and pathological parameters such as Gleason score and prostate-specific antigen levels. However, these tools still do not adequately predict patient outcome. The aim of this study was to investigate whether ZNF750 could have a role in better stratif ...[more]

PMID: 37047491

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Project description:Background: UBASH3B (STS1) is an important gene that negatively regulates T-cell receptor signaling in activated T-lymphocytes that involved in cancers. However, the function of UBASH3B in prostate cancer (PCa) and the correlation between UBASH3B and tumor-infiltrating immune cells still remain unclear. Methods: Real-time PCR and immunohistochemistry were applied to detect mRNA and protein expression of UBASH3B in PCa patients and benign prostate hyperplasia patients (BPH). Clinical features of patients with PCa were recorded and Kaplan Meier curve was subsequently plotted. Based on mRNA expression of UBASH3B, patients with PCa from TCGA database were divided into low-UBASH3B-expression group and high-UBASH3B-expression group for construct lncRNA-miRNA-mRNA network and analyzing GO and KEGG pathways. Single gene analysis method was performed by using GSEA to interpret gene expression data in PCa. The PPI network was constructed using STRING and the correlation between UBASH3B and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT. Results: The mRNA and protein expression of UBASH3B were upregulated in PCa. The abundant expression of UBASH3B is associated with poor prognosis in PCa. The subnetwork of UBASH3B contains three lncRNAs (MIAT, LINC01297, MYLK-AS1) and four miRNAs (hsa-miR-200a-3p, hsa-miR-455-5p, hsa-miR-192-5p, hsamiR- 215-5P). The mRNA expression of UBASH3B was involved in 28 KEGG pathways. GSEA analysis showed that 18 hallmark gene sets were significantly enriched in high-UBASH3B-expression, whereas 1 gene set was enriched in low-UBASH3B-expression. PPI network revealed a tightly interaction between UBASH3B and LCP2 (an immune related gene). TIMER and CIBERSORT database indicated that UBASH3B was correlated with 11 types of tumor-infiltrating immune cells (naïve B cell, memory B cells, resting CD4+ memory T cell, activated CD4+ memory T cell, regulatory T cell, activated NK cell, M2 macrophages, resting dendritic cells, activated dendritic cells, resting mast cells, neutrophils). Conclusions: In conclusion, UBASH3B may be a novel potential prognostic biomarker and is associated with tumor-infiltrating immune cells in tumor microenvironment, suggesting UBASH3B as a potential target for future treatment of PCa.

Project description:PURPOSE:This study evaluates the prognostic significance of MST1R (RON) expression in breast cancer with respect to disease progression, long-term survival, subtype, and association with conventional prognostic factors. METHODS:The approach includes interrogation of survival and tumor staging with paired MST1R RNA expression from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Protein expression evaluation was performed using immunohistochemistry (IHC) staining of MST1R on breast cancer tissue samples from the Cancer Diagnosis Program Breast Cancer Progression tissue microarray and locally obtained breast tumor tissue samples analyzed with paired survival, metastasis, and subtype. RESULTS:Data from TCGA (n?=?774) show poorer relapse-free survival (RFS) in patients with high MST1R expression (P?=?0.32) and no difference in MST1R expression based on tumor stage (P?=?0.77) or nodal status (P?=?0.94). Patients in the GEO-derived Kaplan-Meier Plotter microarray dataset demonstrate the association of MST1R and poorer overall survival (n?=?1402, P?=?0.018) and RFS in patients receiving chemotherapy (n?=?798, P?=?0.041). Patients with high MST1R expression display worse overall survival (P?=?0.01) and receiver operator characteristic (ROC) analysis demonstrate the predictive capacity of increased MST1R with early death (P?=?0.0017) in IHC-stained samples. Paired IHC-stained breast tumor samples from the primary versus metastatic site show MST1R expression is associated with metastatic progression (P?=?0.032), and ROC analysis supports the predictive capacity of MST1R in metastatic progression (P?=?0.031). No associations of MST1R with estrogen receptor (ER), progesterone receptor (PR), both ER and PR, HER2 positivity, or triple-negativity were found (P?=?0.386, P?=?0.766, P?=?0.746, P?=?0.457, P?=?0.947, respectively). CONCLUSIONS:MST1R expression has prognostic value in breast cancer with respect to survival and metastatic progression. MST1R expression is not associated with tumor stage, nodal status, or subtype.

Dataset Information

ZNF750: A Novel Prognostic Biomarker in Metastatic Prostate Cancer.

Publications

ZNF750: A Novel Prognostic Biomarker in Metastatic Prostate Cancer.

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