Identification and validation of a six immune-related gene signature for prediction of biochemical recurrence in localized prostate cancer following radical prostatectomy

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

Background

Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.

Methods

We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.

Results

Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.

Conclusion

Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Tumor immune contexture predicts recurrence after prostatectomy and efficacy of androgen deprivation and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with notable interpatient heterogeneity. Implications of the immune microenvironment in predicting the biochemical recurrence-free survival (BCRFS) after radical prostatectomy and the efficacy of systemic therapies in prostate cancer remain ambiguous.

METHODS

The tumor immune contexture score (TICS) involving eight immune contexture-related signatures was developed using seven cohorts of 1120 patients treated with radical prostatectomy (training: GSE46602, GSE54460, GSE70769, and GSE94767; validation: GSE70768, DKFZ2018, and TCGA). The association between the TICS and treatment efficacy was investigated in GSE111177 (androgen deprivation therapy [ADT]) and EGAS00001004050 (ipilimumab).

RESULTS

A high TICS was associated with prolonged BCRFS after radical prostatectomy in the training (HR = 0.32, 95% CI 0.24-0.45, P < 0.001) and the validation cohorts (HR = 0.45, 95% CI 0.32-0.62, P < 0.001). The TICS showed stable prognostic power independent of tumor stage, surgical margin, pre-treatment prostatic specific antigen (PSA), and Gleason score (multivariable HR = 0.50, 95% CI 0.39-0.63, P < 0.001). Adding the TICS into the prognostic model constructed using clinicopathological features significantly improved its 1/2/3/4/5-year area under curve (P < 0.05). A low TICS was associated with high homologous recombination deficiency scores, abnormally activated pathways concerning DNA replication, cell cycle, steroid hormone biosynthesis, and drug metabolism, and fewer tumor-infiltrating immune cells (P < 0.05). The patients with a high TICS had favorable BCRFS with ADT (HR = 0.25, 95% CI 0.06-0.99, P = 0.034) or ipilimumab monotherapy (HR = 0.23, 95% CI 0.06-0.81, P = 0.012).

CONCLUSIONS

Our study delineates the associations of tumor immune contexture with molecular features, recurrence after radical prostatectomy, and the efficacy of ADT and immunotherapy. The TICS may improve the existing risk stratification systems and serve as a patient-selection tool for ADT and immunotherapy in prostate cancer.

Identification and Validation of FGF-Related Prognostic Signatures in Prostate Cancer

Background

FGF signaling is critical to controlling various cancers. Nevertheless, the functions of FGF-related genes in PCa are still unknown.

Objective

The objective of this study is to build a FGF-related signature that was capable of accurately predicting PCa survival and prognosis for BCR.

Methods

The univariate and multivariate Cox regression, infiltrating immune cells, LASSO, and GSEA analyses were carried out to build a prognostic model.

Results

A FGF-related signature that consists of PIK3CA and SOS1 was developed for the purpose of predicting PCa prognosis, and all patients were categorized into low- and high-risk groups. In comparison to the low-risk group, high-risk score patients had poorer BCR survival. This signature's predictive power has been investigated utilizing the AUC of the ROC curves. The risk score has been shown to be an independent prognostic factor by multivariate analysis. The four enriched pathways of the high-risk group were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of PCa, including focal adhesion, TGF-β signaling pathway, adherens junction, and ECM receptor interaction. The high-risk groups had considerably higher levels of immune status and tumor immune cell infiltration, suggesting a more favorable response to immune checkpoint inhibitors. IHC found that the expression of the two FGF-related genes in the predictive signature was extremely different in PCa tissues.

Conclusion

To summarize, our FGF-related risk signature may effectively predict and diagnose PCa, indicating that in PCa patients, they are potential therapeutic targets and promising prognostic biomarkers.

Metabolic-related gene signature model forecasts biochemical relapse in primary prostate cancer

Metabolism plays an important role in the pathogenesis of prostate cancer (PCa). Hence, we explored candidate metabolic-related genes attributed to biochemical relapse (BCR) of PCa. Gene expression profile and clinical parameters were downloaded from GSE70769 as a "training set". Using univariate Cox and LASSO-COX regression models, risk scores (RSs) were constructed. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (t-ROC) curves were employed. Univariate and multivariate Cox models were utilized to validate prognostic factors for biochemical relapse-free survival (BCRFS). Nomogram was plotted to facilitate clinical application. The dataset obtained from GSE70768 served as "validation set". RSs were constructed by using 7 metabolic-related genes. RSs could significantly predict 1, 3, 5-year BCRFS (AUCs for training set: 0.810-0.836; AUC for validation set: 0.673-0.827). Nomograms could effectively predicted BCRFS (training set: C-index=0.831; validation set: C-index=0.737). RSs model is an independent prognostic factor for BCR, holding greater predictive value than traditional clinicopathological parameters. Clinical Relevance- We built the prognostic nomogram based on metabolic-related gene signatures and clinicopathological features. The nomogram might further optimize biochemical relapse risk stratification for prostate cancer patients with crucial accuracy.

Energy Metabolism-Related Gene Prognostic Index Predicts Biochemical Recurrence for Patients With Prostate Cancer Undergoing Radical Prostatectomy

Background

We aimed to construct and validate an energy metabolism-related gene prognostic index (EMRGPI) to predict biochemical recurrence (BCR) in patients undergoing radical prostatectomy.

Methods

We used Lasso and COX regression analysis to orchestrate the EMRGPI in the TCGA database, and the prognostic value of EMRGPI was further validated externally using the GSE46602. All analyses were conducted with R version 3.6.3 and its suitable packages.

Results

SDC1 and ADH1B were finally used to construct the risk formula. We classified the 430 tumor patients in the TCGA database into two groups, and patients in the high-risk group had a higher risk of BCR than those in the low-risk group (HR: 1.98, 95%CI: 1.18-3.32, p=0.01). Moreover, in the GSE46602, we confirmed that the BCR risk in the high-risk group was 3.86 times higher than that in the low-risk group (95%CI: 1.61-9.24, p=0.001). We found that patients in the high-risk group had significantly higher proportions of residual tumor, older age, and T stage. SDC1 and ADH1B were significantly expressed low in the normal tissues when compared to the tumor tissues, which were opposite at the protein level. The spearman analysis showed that EMRGPI was significantly associated with B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score, and estimate score. In addition, the EMRGPI was positively associated with the 54 immune checkpoints, among which CD80, ADORA2A, CD160, and TNFRSF25 were significantly related to the BCR-free survival of PCa patients undergoing RP.

Conclusions

The EMRGPI established in this study might serve as an independent risk factor for PCa patients undergoing radical prostatectomy.

Characterization of a Pyroptosis-Related Signature for Prognosis Prediction and Immune Microenvironment Infiltration in Prostate Cancer

This study was aimed at constructing a pyroptosis-related signature for prostate cancer (PCa) and elucidating the prognosis and immune landscape and the sensitivity of immune checkpoint blockade (ICB) therapy in signature-define subgroups of PCa. We identified 22 differentially expressed pyroptosis-related genes in PCa from The Cancer Genome Atlas (TCGA) database. The pyroptosis-related genes could divide PCa patients into two clusters with differences in survival. Seven genes were determined to construct a signature that was confirmed by qRT-PCR to be closely associated with the biological characteristics of malignant PCa. The signature could effectively and independently predict the biochemical recurrence (BCR) of PCa, which was validated in the GSE116918 and GSE21034. We found that patients in the high-risk group were more prone to BCR and closely associated with high-grade and advanced-stage disease progression. Outperforming clinical characteristics and nine published articles, our signature demonstrated excellent predictive performance. The patients in the low-risk group were strongly related to the high infiltration of various immune cells including CD8+ T cells and plasma B cells. Furthermore, the high-risk group with higher TMB levels and expression of immune checkpoints was more likely to benefit from immune checkpoint therapy such as PD-1 and CTLA-4 inhibitors. The sensitivity to chemotherapy, endocrine, and targeted therapy showed significant differences in the two risk groups. Our signature was a novel therapeutic strategy to distinguish the prognosis and guide treatment strategies.

A Gene Prognostic Index Associated With Epithelial-Mesenchymal Transition Predicting Biochemical Recurrence and Tumor Chemoresistance for Prostate Cancer

BACKGROUND

We aimed to establish a novel epithelial-mesenchymal transition (EMT)-related gene prognostic index (EMTGPI) associated with biochemical recurrence (BCR) and drug resistance for prostate cancer (PCa).

METHODS

We used Lasso and Cox regression analysis to establish the EMTGPI. All analyses were conducted with R version 3.6.3 and its suitable packages.

RESULTS

We established the EMTGPI based on SFRP4 and SPP1. Patients in high-risk group had 2.23 times of BCR risk than those in low-risk group (p = 0.003), as well as 2.36 times of metastasis risk (p = 0.053). In external validation, we detected similar diagnostic efficacy and prognostic value in terms of BCR free survival. For drug resistance, we observe moderately diagnostic accuracy of EMTGPI score (AUC: 0.804). We found that PDCD1LG2 (p = 0.04) and CD96 (p = 0.01) expressed higher in BCR patients compared with their counterpart. For TME analysis, we detected that CD8+ T cells and M1 macrophages expressed higher in BCR group. Moreover, stromal score (p = 0.003), immune score (p = 0.01), and estimate score (p = 0.003) were higher in BCR patients. We found that EMTGPI was significantly related to HAVCR2 (r: 0.34), CD96 (r: 0.26), CD47 (r: 0.22), KIR3DL1 (r: -0.21), KLRD1 (r: -0.21), and CD2 (r: 0.21). In addition, we observed that EMTGPI was significantly associated with M1 macrophages (r: 0.6), M2 macrophages (r: -0.33), monocytes (r: -0.18), neutrophils (r: -0.43), CD8+ T cells (r: 0.13), and dendritic cells (r: 0.37). PHA-793887 was the common drug sensitive to SPP1 and SFRP4, and PC3 and DU145 were the common PCa-related cell lines of SPP1, SFRP4, and PHA-793887.

CONCLUSIONS

We concluded that the EMTGPI score based on SFRP4 and SPP1 could be used to predict BCR for PCa patients. We confirmed the impact of immune evasion on the BCR process of PCa.