FCGR3A: A new biomarker with potential prognostic value for prostate cancer

Zha, Zeyu; Hong, Yuan; Tang, Zhenfeng; Du, Qizhen; Wang, Yan; Yang, Sheng-Bang; Wu, Yunxiang; Tan, Huijing; Jiang, Funneng; Zhong, Weide

doi:10.3389/fonc.2022.1014888

Cited by 3 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It encodes a transmembrane receptor that allows the immune cells to recognize and kill targeted cells [47]. Recently, researchers have found that FCGR3A is highly expressed in pan-cancer, including PCa, and it could be an independent biomarker for PCa patients [39,48]. In this study, FCGR3A was identified as a hub gene and prognosis-related gene via PPI and Cox regression analyses.…”

Section: Discussionmentioning

confidence: 84%

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Guo

Fan

et al. 2023

Aging

View full text Add to dashboard Cite

Immunotherapy has become a revolutionary treatment for cancer and brought new vitality to tumor immunity. Bone metastases are the most prevalent metastatic site for advanced prostate cancer (PCa). Therefore, finding new immunotherapy targets in PCa patients with bone metastasis is urgently needed. We conducted an elaborative bioinformatics study of immune-related genes (IRGs) and tumor-infiltrating immune cells (TIICs) in PCa bone metastases. Databases were integrated to obtain RNA-sequencing data and clinical prognostic information. Univariate and multivariate Cox regression analyses were conducted to construct an overall survival (OS) prediction model. GSE32269 was analyzed to acquire differentially expressed IRGs. The OS prediction model was established by employing six IRGs (MAVS, HSP90AA1, FCGR3A, CTSB, FCER1G, and CD4). The CIBERSORT algorithm was adopted to assess the proportion of TIICs in each group. Furthermore, Transwell, MTT, and wound healing assays were employed to determine the effect of MAVS on PCa cells. High-risk patients had worse OS compared to the low-risk patients in the training and validation cohorts. Meanwhile, clinically practical nomograms were generated using these identified IRGs to predict the 3-and 5-year survival rates of patients. The infiltration percentages of some TIICs were closely linked to the risk score of the OS prediction model. Some tumor-infiltrating immune cells were related to the OS. FCGR3A was closely correlated with some TIICs. In vitro experiments verified that up-regulation of MAVS suppressed the proliferation and metastatic abilities of PCa cells. Our work presented a thorough interpretation of TIICs and IRGs for illustrating and discovering new potential immune checkpoints in bone metastases of PCa.

show abstract

Section: Discussionmentioning

confidence: 84%

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Guo

Fan

et al. 2023

Aging

View full text Add to dashboard Cite

show abstract

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Guo

Wan

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Immunotherapy has become a revolutionary treatment for cancer and brought new vitality to tumor immunity. Still, using either immunotherapy alone is unlikely to significantly change the outcome of prostate cancer (PCa), especially metastatic PCa. Bone metastases are the most prevalent metastatic site for advanced PCa. Therefore, finding new immunotherapy targets in PCa patients with bone metastasis is urgently needed. Methods We conducted an elaborative bioinformatics study of immune related genes (IRGs) and tumor-infiltrating immune cells (TIICs) in PCa bone metastases. The TCGA-PRAD and cBioPortal databases were integrated to obtain RNA-sequencing data and clinical prognostic information. Univariate and multivariate Cox regression analyses were conducted to construct an overall survival (OS) prediction model. GSE32269 in the GEO database was analyzed to acquire differentially expressed IRGs. A total of 209 differentially expressed IRGs were identified, of which 159 were down-regulated and 50 were up-regulated. Subsequently, the PPI network was established by Cytoscape for identifying hub genes and biological network. The OS prediction model was established by employing six IRGs (MAVS, HSP90AA1, FCGR3A, CTSB, FCER1G, and CD4). The CIBERSORT algorithm was adopted to assess the proportion of TIICs in each group. Furthermore, Transwell, MTT, and wound healing assays were employed to determine the effect of MAVS on PCa cells. Results High-risk patients had worse OS compared to the low-risk patients in the training and validation cohorts. Meanwhile, clinically practical nomograms were generated using these identified IRGs to predict the 3- and 5-year survival rates of patients. The infiltration percentages of some TIICs were closely linked to the risk score of the OS prediction model. Naïve B cells, M1 and M2 macrophages, and CD4 memory resting T cells were related to the OS. FCGR3A was closely correlated with some TIICs. In vitro experiments verified that up-regulation of MAVS suppressed the proliferation and metastatic abilities of PCa cells. Conclusions Our work presented a thorough interpretation of TIICs and IRGs for illustrating and discovering new potential immune checkpoints in bone metastases of PCa. Additionally, we developed a trustworthy OS risk score model that may serve as a prognostic biomarker and potential immune checkpoints for immunotherapy.

show abstract

Two Different Immune Profiles Are Identified in Sentinel Lymph Nodes of Early-Stage Breast Cancer

Ribeiro,

Mendes,

Gante

et al. 2024

Cancers

View full text Add to dashboard Cite

In the management of early-stage breast cancer (BC), lymph nodes (LNs) are typically characterised using the One-Step Nucleic Acid Amplification (OSNA) assay, a standard procedure for assessing subclinical metastasis in sentinel LNs (SLNs). The pivotal role of LNs in coordinating the immune response against BC is often overlooked. Our aim was to improve prognostic information provided by the OSNA assay and explore immune-related gene signatures in SLNs. The expression of an immune gene panel was analysed in SLNs from 32 patients with Luminal A early-stage BC (cT1-T2 N0). Using an unsupervised approach based on these expression values, this study identified two clusters, regardless of the SLN invasion: one evidencing an adaptive anti-tumoral immune response, characterised by an increase in naive B cells, follicular T helper cells, and activated NK cells; and another with a more undifferentiated response, with an increase in the activated-to-resting dendritic cells (DCs) ratio. Through a protein—protein interaction (PPI) network, we identified seven immunoregulatory hub genes: CD80, CD40, TNF, FCGR3A, CD163, FCGR3B, and CCR2. This study shows that, in Luminal A early-stage BC, SLNs gene expression studies enable the identification of distinct immune profiles that may influence prognosis stratification and highlight key genes that could serve as potential targets for immunotherapy.

show abstract

FCGR3A: A new biomarker with potential prognostic value for prostate cancer

Cited by 3 publications

References 43 publications

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Two Different Immune Profiles Are Identified in Sentinel Lymph Nodes of Early-Stage Breast Cancer

Contact Info

Product

Resources

About