A novel immune-related gene-based prognostic signature to predict biochemical recurrence in patients with prostate cancer after radical prostatectomy

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

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.

Role of interferon regulatory factor 5 (IRF5) in tumor progression: Prognostic and therapeutic potential

Canonically, the transcription factor interferon regulatory factor 5 (IRF5) is a key mediator of innate and adaptive immunity downstream of pathogen recognition receptors such as Toll-like receptors (TLRs). Hence, dysregulation of IRF5 function has been widely implicated in inflammatory and autoimmune diseases. Over the last few decades, dysregulation of IRF5 expression has been also reported in hematologic malignancies and solid cancers that support a role for IRF5 in malignant transformation, tumor immune regulation, clinical prognosis, and treatment response. This review will provide an in-depth overview of the current literature regarding the mechanisms by which IRF5 functions as either a tumor suppressor or oncogene, its role in metastasis, regulation of the tumor-immune microenvironment, utility as a prognostic indicator of disease, and new developments in IRF5 therapeutics that may be used to remodel tumor immunity.

A tumor-associated macrophages related model for predicting biochemical recurrence and tumor immune environment in prostate cancer

OBJECTIVE

To identify tumor-associated macrophages (TAMs) related molecular subtypes and develop a TAMs related prognostic model for prostate cancer (PCa).

METHODS

Consensus clustering analysis was used to identify TAMs related molecular clusters. A TAMs related prognostic model was developed using univariate and multivariate Cox analysis.

RESULTS

Three TAMs related molecular clusters were identified and were confirmed to be associated with prognosis, clinicopathological characteristics, PD-L1 expression levels and tumor microenvironment. A TAMs related prognostic model was constructed. Patients in low-risk group all showed a more appreciable biochemical recurrence-free survival (BCRFS) than patients in high-risk group in train cohort, test cohort, entire TCGA cohort and validation cohort. SLC26A3 attenuated progression of PCa and prevented macrophage polarizing to TAMs phenotype, which was initially verified.

CONCLUSIONS

We successfully identified molecular clusters related to TAMs. Additionally, we developed a prognostic model involving TAMs that exhibits excellent predictive performance for biochemical recurrence-free survival in PCa.

Clinical significance of immune checkpoint proteins in HPV-infected cervical cancer

OBJECTIVE

To investigate T cell immunoreceptor with Ig and ITIM domain (TIGIT), V-domain Ig suppressor of T cell activation (VISTA), and lymphocyte-activation gene-3 (LAG-3) expression in pathological tissue of human papillomavirus (HPV)-infected cervical cancer (CC) patients and their relationship with patient prognosis.

METHODS

Clinical data of 175 patients with HPV-infected CC were collected retrospectively. Tumor tissue sections were stained immunohistochemically for TIGIT, VISTA, and LAG-3. The Kaplan-Meier method calculated patient survival. Univariate and multivariate Cox proportional hazards models analyzed all potential risk factors for survival.

RESULTS

When combined positive score (CPS)= 1 was used as the cut-off value, the Kaplan-Meier survival curve showed that the progression-free survival (PFS) and overall survival (OS) of patients with positive expression of TIGIT and VISTA are shorter (both p < 0.05). Univariate COX regression analysis showed that the positive expression of TIGIT and VISTA are related to patient PFS and OS (both HR>1.0 and p < 0.05). Multivariate COX regression analysis showed that TIGIT-positive patients had shorter OS and VISTA-positive patients had shorter PFS (both HR>1.0 and p < 0.05). There is no significant correlation between LAG-3 expression and PFS or OS. When CPS= 10 was used as the cut-off value, Kaplan-Meier survival curve showed that TIGIT-positive patients had shorter OS (p = 0.019). Univariate COX regression analysis showed that TIGIT-positive expression was associated with the OS of patients (HR=2.209, CI: 1.118-4.365, p = 0.023). However, multivariate COX regression analysis showed that TIGIT expression was not associated significantly with OS. There was no significant correlation between VISTA and LAG-3 expression and PFS or OS.

CONCLUSION

TIGIT and VISTA are associated closely with HPV-infected CC prognosis and are effective biomarkers.

Comprehensive analysis regarding the prognostic significance of downregulated ferroptosis-related gene AKR1C2 in gastric cancer and its underlying roles in immune response

Ferroptosis is a cell death form that has been reported to be involved in the progression of gastric cancer (GC). However, the underlying mechanism of ferroptosis in GC still needs to be further explored. This study conducted a survey regarding the biological functions of ferroptosis-related gene AKR1C2 in GC. Multiple bioinformatic platforms were applied to indicate that the expression level of AKR1C2 was downregulated in GC tissues, which displayed good prognostic value. Clinical statistics proved that AKR1C2 expression was correlated with several tumor characteristics of GC patients, such as characteristics of N-stage tumor or residual tumor. Additionally, LinkedOmics was employed to explore the co-expression network and molecular pathways of AKR1C2 in GC. Eventually, AKR1C2 was found to be involved in several immune-related signatures, such as immunostimulators, immunoinhibitors, chemokines and chemokine receptors. To sum up, these results may provide a novel insight into the significance and biological functions of ferroptosis-related gene AKR1C2 in GC tumorigenesis.

An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD. Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively. Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in "ECM Receptor Interaction," "WNT Signaling Pathway" and "Colorectal Cancer." Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888. Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.

TGF-β Pathways Stratify Colorectal Cancer into Two Subtypes with Distinct Cartilage Oligomeric Matrix Protein (COMP) Expression-Related Characteristics

BACKGROUND

Colorectal cancers (CRCs) continue to be the leading cause of cancer-related deaths worldwide. The exact landscape of the molecular features of TGF-β pathway-inducing CRCs remains uncharacterized.

METHODS

Unsupervised hierarchical clustering was performed to stratify samples into two clusters based on the differences in TGF-β pathways. Weighted gene co-expression network analysis was applied to identify the key gene modules mediating the different characteristics between two subtypes. An algorithm integrating the least absolute shrinkage and selection operator (LASSO), XGBoost, and random forest regression was performed to narrow down the candidate genes. Further bioinformatic analyses were performed focusing on COMP-related immune infiltration and functions.

RESULTS

The integrated machine learning algorithm identified COMP as the hub gene, which exhibited a significant predictive value for two subtypes with an area under the curve (AUC) value equaling 0.91. Further bioinformatic analysis revealed that COMP was significantly upregulated in various cancers, especially in advanced CRCs, and regulated the immune infiltration, especially M2 macrophages and cancer-associated fibroblasts in CRCs.

CONCLUSIONS

Comprehensive immune analysis and experimental validation demonstrate that COMP is a reliable signature for subtype prediction. Our results could provide a new point for TGFβ-targeted anticancer drugs and contribute to guiding clinical decision making for CRC patients.

A prognostic signature consisting of metabolism-related genes and SLC17A4 serves as a potential biomarker of immunotherapeutic prediction in prostate cancer

Background

Prostate cancer (PCa), a prevalent malignant cancer in males worldwide, screening for patients might benefit more from immuno-/chemo-therapy remained inadequate and challenging due to the heterogeneity of PCa patients. Thus, the study aimed to explore the metabolic (Meta) characteristics and develop a metabolism-based signature to predict the prognosis and immuno-/chemo-therapy response for PCa patients.

Methods

Differentially expressed genes were screened among 2577 metabolism-associated genes. Univariate Cox analysis and random forest algorithms was used for features screening. Multivariate Cox regression analysis was conducted to construct a prognostic Meta-model based on all combinations of metabolism-related features. Then the correlation between MetaScore and tumor was deeply explored from prognostic, genomic variant, functional and immunological perspectives, and chemo-/immuno-therapy response. Multiple algorithms were applied to estimate the immunotherapeutic responses of two MeteScore groups. Further in vitro functional experiments were performed using PCa cells to validate the association between the expression of hub gene SLC17A4 which is one of the model component genes and tumor progression. GDSC database was employed to determine the sensitivity of chemotherapy drugs.

Results

Two metabolism-related clusters presented different features in overall survival (OS). A metabolic model was developed weighted by the estimated regression coefficients in the multivariate Cox regression analysis (0.5154*GAS2 + 0.395*SLC17A4 - 0.1211*NTM + 0.2939*GC). This Meta-scoring system highlights the relationship between the metabolic profiles and genomic alterations, gene pathways, functional annotation, and tumor microenvironment including stromal, immune cells, and immune checkpoint in PCa. Low MetaScore is correlated with increased mutation burden and microsatellite instability, indicating a superior response to immunotherapy. Several medications that might improve patients` prognosis in the MetaScore group were identified. Additionally, our cellular experiments suggested knock-down of SLC17A4 contributes to inhibiting invasion, colony formation, and proliferation in PCa cells in vitro.

Conclusions

Our study supports the metabolism-based four-gene signature as a novel and robust model for predicting prognosis, and chemo-/immuno-therapy response in PCa patients. The potential mechanisms for metabolism-associated genes in PCa oncogenesis and progression were further determined.

Combination Blockade of the IL6R/STAT-3 Axis with TIGIT and Its Impact on the Functional Activity of NK Cells against Prostate Cancer Cells

Background/Aims. Prostate cancer (PCa) is one of the neoplasms with the highest incidence and mortality rate in men worldwide. Advanced stages of the disease are usually very aggressive, and most are treated with chemotherapeutic drugs that generally cause side effects in these patients. However, additional therapeutic targets such as the IL6R/STAT-3 axis and TIGIT have been proposed, mainly due to their relevance in the development of PCa and regulation of NK cell-mediated cytotoxicity. Here, we evaluate the effect of inhibitors directed against these therapeutic targets primarily via an analysis of NK cell function versus prostate cancer cells. Methods. We analyzed the secretion of cytokines, chemokines, and growth factors in 22Rv1, LNCaP, and DU145 cells. In these cells, we also evaluated the expression of NK ligands, IL6R, STAT-3, and phosporylated STAT-3. In NK-92 cells, we evaluated the effects of Stattic (Stt) and tocilizumab (Tcz) on NK receptors. In addition, we assessed if the disruption of the IL6R/STAT-3 pathway and blockade of TIGIT potentiated the cytotoxicity of NK-92 cells versus DU145 cells. Results. DU145 abundantly secretes M-CSF, VEGF, IL-6, CXCL8, and TGF-β. Furthermore, the expression of CD155 was found to increase in accordance with aggressiveness and metastatic status in the prostate cancer cells. Stt and Tcz induce a decrease in STAT-3 phosphorylation in the DU145 cells and, in turn, induce an increase of NKp46 and a decrease of TIGIT expression in NK-92 cells. Finally, the disruption of the IL6R/STAT-3 axis in prostate cancer cells and the blocking of TIGIT on NK-92 were observed to increase the cytotoxicity of NK-92 cells against DU145 cells through an increase in sFasL, granzyme A, granzyme B, and granulysin. Conclusions. Our results reveal that the combined use of inhibitors directed against the IL6R/STAT-3 axis and TIGIT enhances the functional activity of NK cells against castration-resistant prostate cancer cells.

Developing an immune-related gene prognostic index associated with progression and providing new insights into the tumor immune microenvironment of prostate cancer

We developed an immune-related gene prognostic index (IGPI) associated with progression and provided new insights into the tumor immune microenvironment (TIME) for prostate cancer (PCA) patients undergoing radical prostatectomy. All analyses were conducted with R software (version 3.6.3) and its suitable packages. Meta analysis was performed through STATA 16.0. TUBB3, WDR62, and PPARGC1A were finally identified to establish the IGPI score. IGPI score increased with the augment of Gleason score and T stage, as well as biochemical recurrence (BCR) and prostate specific antigen (PSA). Patients with higher IGPI score were at higher risk of progress (HR: 2.88; 95%CI: 95%CI: 1.80-4.61). Gene set enrichment analysis indicated that patients in high-risk group was positively associated with mismatch repair, cell cycle, DNA replication, base excision repair, nucleotide excision repair, homologous recombination, and pyrimidine metabolism. We observed that patients in the high-risk group had significantly higher tumor mutation burden score and microsatellite instability score than those in the low-risk group. For analysis of immune checkpoint, ADORA2A, CD80, TNFRSF4, TNFRSF18, and TNFRSF25 were differentially expressed between no progress and progress groups, and were significantly associated with progress free survival. We observed positive correlations between IGPI score, and lymphoid immune cells, macrophages M2 and immune score, while negative association between IGPI score, and dendritic cells, fibroblasts, stromal score, and microenvironment score. In conclusion, the IGPI score constructed in this study might serve as an independent risk factor associated with PCA progression. ADORA2A, CD80, TNFRSF4, TNFRSF18, and TNFRSF25 might be the potential targets in the treatment of PCA.

A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer

Background

Pyroptosis can not only inhibit the occurrence and development of tumors but also develop a microenvironment conducive to cancer growth. However, pyroptosis research in prostate cancer (PCa) has rarely been reported.

Methods

The expression profile and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were divided into different clusters using consensus clustering analysis, and differential genes were obtained. We developed and validated a prognostic biomarker for biochemical recurrence (BCR) of PCa using univariate Cox analysis, Lasso-Cox analysis, Kaplan–Meier (K–M) survival analysis, and time-dependent receiver operating characteristics (ROC) curves.

Results

The expression levels of most pyroptosis-related genes (PRGs) are different not only between normal and tumor tissues but also between different clusters. Cluster 2 patients have a better prognosis than cluster 1 patients, and there are significant differences in immune cell content and biological pathway between them. Based on the classification of different clusters, we constructed an eight genes signature that can independently predict the progression-free survival (PFS) rate of a patient, and this signature was validated using a GEO data set (GSE70769). Finally, we established a nomogram model with good accuracy.

Conclusions

In this study, PRGs were used as the starting point and based on the expression profile and clinical data, a prognostic signature with a high predictive value for biochemical recurrence (BCR) following radical prostatectomy (RP) was finally constructed, and the relationship between pyroptosis, immune microenvironment, and PCa was explored, providing important clues for future research on pyroptosis and immunity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01172-5.

The tumor innate immune microenvironment in prostate cancer: an overview of soluble factors and cellular effectors

Prostate cancer (PCa) accounts as the most common non-cutaneous disease affecting males, and as the first cancer, for incidence, in male. With the introduction of the concept of immunoscore, PCa has been classified as a cold tumor, thus driving the attention in the development of strategies aimed at blocking the infiltration/activation of immunosuppressive cells, while favoring the infiltration/activation of anti-tumor immune cells. Even if immunotherapy has revolutionized the approaches to cancer therapy, there is still a window failure, due to the immune cell plasticity within PCa, that can acquire pro-tumor features, subsequent to the tumor microenvironment (TME) capability to polarize them. This review discussed selected relevant soluble factors [transforming growth factor-beta (TGFβ), interleukin-6 (IL-6), IL-10, IL-23] and cellular components of the innate immunity, as drivers of tumor progression, immunosuppression, and angiogenesis within the PCa-TME.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).

Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.

Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.

Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.