Prognostic signatures associated with high infiltration of Tregs in bone metastatic prostate cancer

Cellular senescence in metastatic prostate cancer: A therapeutic opportunity or challenge (Review)

The treatment of patients with metastatic prostate cancer (PCa) is considered to be a long‑standing challenge. Conventional treatments for metastatic PCa, such as radical prostatectomy, radiotherapy and androgen receptor‑targeted therapy, induce senescence of PCa cells to a certain extent. While senescent cells can impede tumor growth through the restriction of cell proliferation and increasing immune clearance, the senescent microenvironment may concurrently stimulate the secretion of a senescence‑associated secretory phenotype and diminish immune cell function, which promotes PCa recurrence and metastasis. Resistance to established therapies is the primary obstacle in treating metastatic PCa as it can lead to progression towards an incurable state of disease. Therefore, understanding the molecular mechanisms that underly the progression of PCa is crucial for the development of novel therapeutic approaches. The present study reviews the phenomenon of treatment‑induced senescence in PCa, the dual role of senescence in PCa treatments and the mechanisms through which senescence promotes PCa metastasis. Furthermore, the present review discusses potential therapeutic strategies to target the aforementioned processes with the aim of providing insights into the evolving therapeutic landscape for the treatment of metastatic PCa.

Investigating the Role of SNAI1 and ZEB1 Expression in Prostate Cancer Progression and Immune Modulation of the Tumor Microenvironment

Prostate cancer (PCa) is an immunologically cold tumor and the molecular processes that underlie this behavior are poorly understood. In this study, we investigated a primary cohort of intermediate-risk PCa (n = 51) using two NanoString profiling panels designed to study cancer progression and immune response. We identified differentially expressed genes (DEGs) and pathways associated with biochemical recurrence (BCR) and clinical risk. Confirmatory analysis was performed using the TCGA-PRAD cohort. Noteworthy DEGs included collagens such as COL1A1, COL1A2, and COL3A1. Changes in the distribution of collagens may influence the immune activity in the tumor microenvironment (TME). In addition, immune-related DEGs such as THY1, IRF5, and HLA-DRA were also identified. Enrichment analysis highlighted pathways such as those associated with angiogenesis, TGF-beta, UV response, and EMT. Among the 39 significant DEGs, 11 (28%) were identified as EMT target genes for ZEB1 using the Harmonizome database. Elevated ZEB1 expression correlated with reduced BCR risk. Immune landscape analysis revealed that ZEB1 was associated with increased immunosuppressive cell types in the TME, such as naïve B cells and M2 macrophages. Increased expression of both ZEB1 and SNAI1 was associated with elevated immune checkpoint expression. In the future, modulation of EMT could be beneficial for overcoming immunotherapy resistance in a cold tumor, such as PCa.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma

INTRODUCTION

Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC).

METHODS

Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm.

RESULTS

Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment.

CONCLUSIONS

TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

Targeting the prostate tumor microenvironment by plant-derived natural products

Prostate cancer is among the most common malignancies for men, with limited therapy options for last stages of the tumor. There are some different options for treatment and control of prostate tumor growth. However, targeting some specific molecules and cells within tumors has been attracted interests in recent years. The tumor microenvironment (TME) has an important role in the initiation of various malignancies, which can also expand the progression of tumor and facilitate invasion of malignant cells. By regulating immune responses and distinct changes in the metabolism of cells in the tumor, TME has substantial effects in the resistance of cancer cells to therapy. TME in various solid cancers like prostate cancer includes various cells, including cancer cells, supportive stromal cells, immunosuppressive cells, and anticancer inflammatory cells. Natural products including herbal-derived agents and also other natural compounds have been well studied for their anti-tumor potentials. These compounds may modulate various signaling pathways involved in TME, such as immune responses, the metabolism of cells, epigenetics, angiogenesis, and extracellular matrix (ECM). This paper provides a review of the current knowledge of prostate TME and complex interactions in this environment. Additionally, the potential use of natural products and also nanoparticles loaded with natural products as therapeutic adjuvants on different cells and therapeutic targets within prostate TME will be discussed.

The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery

Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.

Identification of Immune Infiltration in Odontogenic Keratocyst by Integrated Bioinformatics Analysis

BACKGROUND

Odontogenic keratocyst (OKC) is a relatively common odontogenic lesion characterized by local invasion in the maxillary and mandibular bones. In the pathological tissue slices of OKC, immune cell infiltrations are frequently observed. However, the immune cell profile and the molecular mechanism for immune cell infiltration of OKC are still unclear. We aimed to explore the immune cell profile of OKC and to explore the potential pathogenesis for immune cell infiltration in OKC.

METHODS

The microarray dataset GSE38494 including OKC and oral mucosa (OM) samples were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in OKC were analyzed by R software. The hub genes of OKC were performed by protein-protein interaction (PPI) network. The differential immune cell infiltration and the potential relationship between immune cell infiltration and the hub genes were performed by single-sample gene set enrichment analysis (ssGSEA). The expression of COL1A1 and COL1A3 were confirmed by immunofluorescence and immunohistochemistry in 17 OKC and 8 OM samples.

RESULTS

We detected a total of 402 differentially expressed genes (DEGs), of which 247 were upregulated and 155 were downregulated. DEGs were mainly involved in collagen-containing extracellular matrix pathways, external encapsulating structure organization, and extracellular structure organization. We identified ten hub genes, namely FN1, COL1A1, COL3A1, COL1A2, BGN, POSTN, SPARC, FBN1, COL5A1, and COL5A2. A significant difference was observed in the abundances of eight types of infiltrating immune cells between the OM and OKC groups. Both COL1A1 and COL3A1 exhibited a significant positive correlation with natural killer T cells and memory B cells. Simultaneously, they demonstrated a significant negative correlation with CD56dim natural killer cells, neutrophils, immature dendritic cells, and activated dendritic cells. Immunohistochemistry analysis showed that COL1A1 (P = 0.0131) and COL1A3 (P < 0.001) were significantly elevated in OKC compared with OM.

CONCLUSIONS

Our findings provide insights into the pathogenesis of OKC and illuminate the immune microenvironment within these lesions. The key genes, including COL1A1 and COL1A3, may significantly impact the biological processes associated with OKC.

Overcoming Immune Resistance With Radiation Therapy in Prostate Cancer

Prostate cancer is the second most common cancer in men and represents a significant healthcare burden worldwide. Therapeutic options in the metastatic castration-resistant setting remain limited, despite advances in androgen deprivation therapy, precision medicine and targeted therapies. In this review, we summarize the role of immunotherapy in prostate cancer and offer perspectives on opportunities for future development, based on current knowledge of the immunosuppressive tumor microenvironment. Furthermore, we discuss the potential for synergistic therapeutic strategies with modern radiotherapy, through modulation of the tumor microenvironment. Emerging clinical and pre-clinical data suggest that radiation can convert immune desert tumors into an inflamed immunological hub, potentially sensitive to immunotherapy.

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

Background

Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.

Methods

High-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.

Results

A total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.

Conclusion

This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

Syndecan Family Gene and Protein Expression and Their Prognostic Values for Prostate Cancer

Prostate cancer (PCa) is the leading cause of cancer-associated mortality in men, and new biomarkers are still needed. The expression pattern and protein tissue localization of proteoglycans of the syndecan family (SDC 1-4) and syntenin-1 (SDCBP) were determined in normal and prostatic tumor tissue from two genetically engineered mouse models and human prostate tumors. Studies were validated using SDC 1-4 and SDCBP mRNA levels and patient survival data from The Cancer Genome Atlas and CamCAP databases. RNAseq showed increased expression of Sdc1 in Pb-Cre4/Pten^f/f mouse Pca and upregulation of Sdc3 expression and downregulation of Sdc2 and Sdc4 when compared to the normal prostatic tissue in Pb-Cre4/Trp53^f/f-;Rb1^f/f mouse tumors. These changes were confirmed by immunohistochemistry. In human PCa, SDC 1-4 and SDCBP immunostaining showed variable localization. Furthermore, Kaplan-Meier analysis showed that patients expressing SDC3 had shorter prostate-specific survival than those without SDC3 expression (log-rank test, p = 0.0047). Analysis of the MSKCC-derived expression showed that SDC1 and SDC3 overexpression is predictive of decreased biochemical recurrence-free survival (p = 0.0099 and p = 0.045, respectively), and SDC4 overexpression is predictive of increased biochemical recurrence-free survival (p = 0.035). SDC4 overexpression was associated with a better prognosis, while SDC1 and SDC3 were associated with more aggressive tumors and a worse prognosis.