Identifying a Ferroptosis-Related Gene Signature for Predicting Biochemical Recurrence of Prostate Cancer

Analysis and functional validations of multiple cell death patterns for prognosis in prostate cancer

Prostate cancer (PCa) has garnered significant attention due to its rising incidence, variable therapeutic outcomes, and the absence of reliable prognostic markers. The significance of different cell death patterns in tumor development underscores their potential as predictors of PCa prognosis. This study utilized The Cancer Genome Atlas (TCGA) datasets to evaluate the prognostic capabilities of 15 cell death patterns and established a Cell Death Index (CDI) signature based on necrosis and cuproptosis-related genes. The predictive efficacy of the CDI signature was validated in our PCa cohort and in two public datasets: Deutsches Krebsforschungszentrum (DKFZ) and Memorial Sloan-Kettering Cancer Center (MSKCC) PCa cohorts. Our comprehensive analysis examined the relationship between CDI signature and clinical characteristics, published prognostic signatures, gene mutations, immune cell infiltration, enrichment pathways, and drug sensitivity in PCa. In vitro and in vivo studies assessed the impact of EDA2R and LOXL2 on PCa progression. The CDI signature exhibited robust predictive performance across three independent validation sets, with 1-, 2-, 3-, 4-, and 5-year area under the curve (AUC) values in the TCGA cohort of 0.866, 0.77, 0.836, 0.776, and 0.787, respectively. Higher CDI scores were correlated with advanced T and N stages, elevated Gleason scores, increased immune cell infiltration, gene mutations, and drug sensitivity. EDA2R inhibited PCa cell proliferation and migration, related to tumor necrosis, while LOXL2 promoted these processes and was associated with cuproptosis. In summary, our study identified a novel CDI signature as an effective indicator for diagnosis, personalized treatment, and prognostic assessment in PCa.

Identification and characterization of novel ferroptosis-related genes in acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a leading cause of death and morbidity worldwide. Ferroptosis, a form of regulated cell death, plays a critical role in modulating immune functions during AMI. This study aimed to identify ferroptosis-related hub genes that could serve as potential therapeutic targets in the progression of AMI.

METHODS

Bioinformatics was used to identify overlapping genes associated with ferroptosis and the infiltration of 22 immune cells by Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) analysis. The expression of ferroptosis-related genes in AMI was validated across independent datasets, clinical samples, and in vitro cellular experiments. The predictive value for heart failure was evaluated in the first dimension of principal component analysis (PCA) using receiver operating characteristic (ROC) analysis.

RESULTS

The study identified 11 key ferroptosis-related genes significantly correlated with immune cell abundance. CIBERSORT analysis highlighted immune dysregulation in AMI. JDP2, DUSP1, TLR4, NFS1, and SLC1A5 were identified as potential biomarkers for AMI progression. Additionally, JDP2, DUSP1, and DDIT4 demonstrated strong predictive value for long-term heart failure.

CONCLUSION

This study highlights the potential association of ferroptosis-related genes with the pathogenesis of AMI, suggesting a role in the molecular mechanisms that may underlie acute coronary events.

NFS1 inhibits ferroptosis in gastric cancer by regulating the STAT3 pathway

Cysteine desulfurase (NFS1) is highly expressed in a variety of tumors, which is closely related to ferroptosis of tumor cells and affects prognosis. The relationship between NFS1 and the development of gastric cancer (GC) remains unknown. Here we showed that NFS1 expression was significantly higher in GC tissues compared to adjacent normal tissues. Patients with high expression of NFS1 in GC tissues had a lower overall survival rate than those with low expression. NFS1 was highly expressed in cultured GC cells compared to normal gastric cells. Knockdown of NFS1 expression reduced the viability, migration and invasion of GC cells. In cultured GC cells, NFS1 deficiency promoted ferroptosis. Mechanistically, NFS1 inhibited ferroptosis by upregulating the signal transduction and activator of transcription 3 (STAT3) signaling pathway in cultured GC cells. NFS1 knockdown using siRNA inhibited the STAT3 pathway, reduced the expression of glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11), and elevated intracellular levels of reactive oxygen species (ROS), ferrous ion (Fe2+), and malondialdehyde (MDA) in cultured GC cells. A specific STAT3 activator significantly reversed the inhibitory effect of NFS1 deficiency on ferroptosis in cultured GC cells. These in vitro results were further confirmed by experiments in vivo using a mouse xenograft tumor model. Collectively, THESE RESULTS INDICATE THAT NFS1 is overexpressed in human GC tissues and correlated with prognosis. NFS1 inhibits ferroptosis by activating the STAT3 pathway in GC cells. These results suggest that NFS1 may be a potential prognostic biomarker and therapeutic target to treat GC.

Loss of AR-regulated AFF3 contributes to prostate cancer progression and reduces ferroptosis sensitivity by downregulating ACSL4 based on single-cell sequencing analysis

Prostate cancer (PCa) is one of the most common cancers affecting the health of men worldwide. Castration-resistant prostate cancer (CRPC), the advanced and refractory phase of prostate cancer, has multiple mechanisms of resistance to androgen deprivation therapy (ADT) such as AR mutations, aberrant androgen synthase, and abnormal expression of AR-related genes. Based on the research of the AR pathway, new drugs for the treatment of CRPC have been developed in clinical practice, such as Abiraterone and enzalutamide. However, many areas in this pathway are still worth exploring. In this study, single-cell sequencing analysis was utilized to scrutinize significant genes in the androgen receptor (AR) pathway related to CRPC. Our analysis of single-cell sequencing combined with bulk-cell sequencing revealed a substantial downregulation of AR-regulated AFF3 in CRPC. Overexpression of AFF3 restricted the proliferation and migration of prostate cancer cells whilst also increasing their sensitivity towards enzalutamide, while knockdown of AFF3 had the opposite effect. To elucidate the mechanism of tumor inhibition by AFF3, we applied GSVA and GSEA to investigate the metabolic pathways related to AFF3 and revealed that AFF3 had an impact on fatty acids metabolism and ferroptosis through the regulation of ACSL4 protein expression. Based on correlation analysis and flow cytometry, we can speculate that AFF3 can impact the sensitivity of the CRPC cell lines to the ferroptosis inducer (RSL3) by regulating ACSL4. Therefore, our findings may provide new insights into the mechanisms of drug resistance in CRPC, and AFF3 may serve as a novel prognostic biomarker in prostate cancer.

Unlocking ferroptosis in prostate cancer - the road to novel therapies and imaging markers

Ferroptosis is a distinct form of regulated cell death that is predominantly driven by the build-up of intracellular iron and lipid peroxides. Ferroptosis suppression is widely accepted to contribute to the pathogenesis of several tumours including prostate cancer. Results from some studies reported that prostate cancer cells can be highly susceptible to ferroptosis inducers, providing potential for an interesting new avenue of therapeutic intervention for advanced prostate cancer. In this Perspective, we describe novel molecular underpinnings and metabolic drivers of ferroptosis, analyse the functions and mechanisms of ferroptosis in tumours, and highlight prostate cancer-specific susceptibilities to ferroptosis by connecting ferroptosis pathways to the distinctive metabolic reprogramming of prostate cancer cells. Leveraging these novel mechanistic insights could provide innovative therapeutic opportunities in which ferroptosis induction augments the efficacy of currently available prostate cancer treatment regimens, pending the elimination of major bottlenecks for the clinical translation of these treatment combinations, such as the development of clinical-grade inhibitors of the anti-ferroptotic enzymes as well as non-invasive biomarkers of ferroptosis. These biomarkers could be exploited for diagnostic imaging and treatment decision-making.

FSP1: a key regulator of ferroptosis

Ferroptosis suppressor protein 1 (FSP1) is one of the main regulatory molecules of ferroptosis. FSP1 functions through the FSP1-coenzyme Q10 (CoQ10)-NAD(P)H axis and the vitamin K redox cycle. FSP1 is regulated by upstream factors, including transcription factors and noncoding RNA (ncRNA), and is subject to epigenetic modifications, which affect the progress of FSP1-related diseases. FSP1 is closely associated with the poor prognosis of malignant tumors and plays an important role in disease treatment. This review aims to provide a comprehensive understanding of the role of FSP1 in ferroptosis regulation by summarizing regulatory pathways, possible mechanisms involving FSP1, and the relationship between FSP1 and disease prognosis and treatment.

An anoikis-related gene signature for prediction of the prognosis in prostate cancer

Purpose

This study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs).

Methods

In this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa.

Results

Based on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer.

Conclusion

This signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.

Therapeutic implications for localized prostate cancer by multiomics analyses of the ageing microenvironment landscape

Background: Numerous studies have substantiated the association between aging and the progression of malignant tumors in humans, notably prostate cancer (PCa). Nevertheless, to the best of our knowledge, no studies have comprehensively elucidated the intricate characteristics of the aging microenvironment (AME) in PCa. Methods: AME regulatory patterns were determined using the NMF algorithm. Then an ageing microenvironment index (AMI) was constructed, with excellent prognostic and immunotherapy prediction ability, and its' clinical relevance was surveyed through spatial transcriptomics. Further, the drug response was analysed using the Genomics of Drug Sensitivity in Cancer (GDSC), the Connectivity Map (CMap) and CellMiner database for patients with PCa. Finally, the AME was studied using in vitro and vivo experiments. Results: Three different AME regulatory patterns were identified across 813 PCa patients, associated with distinct clinical prognosis and physiological pathways. Based on the AMI, patients with PCa were divided into the high-score and low-score subsets. Higher AMI score was significantly infiltrated with more immune cells, higher rate of biochemical recurrence (BCR) and worse response to immunotherapy, antiandrogen therapy and chemotherapy in PCa. In addition, we found that the combination of bicalutamide and embelin was capable of suppressing tumor growth of PCa. Besides, as the main components of AMI, COL1A1 and BGLAP act as oncogenes and were verified via in vivo and in vitro experiments. Conclusions: AME regulation is significantly associated with the diversity and complexity of TME. Quantitative evaluation of the AME regulatory patterns may provide promising novel molecular markers for individualised therapy in PCa.

Novel gene signature for predicting biochemical recurrence-free survival of prostate cancer and PRAME modulates prostate cancer progression

Biochemical recurrence (BCR) is considered as an early sign of prostate cancer (PCa) progression after initial treatment, such as radical prostatectomy and radiotherapy; hence, it is important to stratify patients at risk of BCR. In this study, we established a robust 8-gene signature (APOF, Clorf64, RPE65, SEMG1, ARHGDIG, COMP, MKI67 and PRAME) based on the PCa transcriptome profiles in the Cancer Genome Atlas (TCGA) for predicting BCR-free survival of PCa, which was further validated in the MSK-IMPACT Clinical Sequencing Cohort (MSKCC) PCa cohort. Moreover, we found that one risk-related gene (PRAME) was upregulated in tumor samples, particularly in high-risk group was well as in patients metastatic tumor and was correlated with chemotherapeutic drug response. In vitro experiments showed that knocking down PRAME reduced the proliferation, migration, and invasion of PCa cells. Therefore, our study established a new 8-gene signature that could accurately predict the BCR risk of PCa. Inhibition of PRAME attenuated the proliferation, invasion, and migration of PCa cells. These findings provide a novel tool for stratifying high-risk PCa patient and shed light on the mechanism of PCa progression.

FOXM1-regulated ZIC2 promotes the malignant phenotype of renal clear cell carcinoma by activating UBE2C/mTOR signaling pathway

Background: As a transcription factor, Zic family member 2 (ZIC2) has been involved in more and more studies of tumorigenesis, which has been proved by our research team to be an effective prognostic marker for Pan-cancer. However, the prognosis, tumor promoting effect and regulatory mechanism of ZIC2 in clear cell renal cell carcinoma (ccRCC) are still unknown. Methods: The potential clinical significance of ZIC2 was evaluated by bioinformatics analysis using data from TCGA, GEO, and ArrayExpress data sets. WB and IHC were used to detect ZIC2 expression in tumors and adjacent tissues. CCK-8, EdU, colony formation, cell cycle, wound healing, transwell, subcutaneous xenograft, and lung metastasis models were used to detect the biological function of ZIC2. The regulatory mechanism of ZIC2 was confirmed by data of RNA-seq, ATAC-seq, MS-PCR, Chip-PCR, and luciferase reporter experiments. Results: ZIC2 was markedly upregulated and correlated with poor clinicopathological features in ccRCC. Knockdown of ZIC2 resulted in reduced cell proliferation, invasion, migration, induction of G2/M phase arrest, and reduced tumor formation and lung metastasis in nude mice. The opposite was observed after overexpression. Mechanistically, the high expression of ZIC2 is regulated by hypomethylation and high H3K4Me3 in the promoter region, as well as positive transcriptional regulation by FOXM1. And then, ZIC2 transcriptase-positively regulates UBE2C and activates AKT/mTOR signaling pathway to promote tumor malignant progression. Conclusion: This study reveals that FOXM1-ZIC2-UBE2C-mTOR signaling axis promotes the progression of ccRCC, which can be used as a prognostic indicator and potential therapeutic target.

Ferroptosis landscape in prostate cancer from molecular and metabolic perspective

Prostate cancer is a major disease that threatens men's health. Its rapid progression, easy metastasis, and late castration resistance have brought obstacles to treatment. It is necessary to find new effective anticancer methods. Ferroptosis is a novel iron-dependent programmed cell death that plays a role in various cancers. Understanding how ferroptosis is regulated in prostate cancer will help us to use it as a new way to kill cancer cells. In this review, we summarize the regulation and role of ferroptosis in prostate cancer and the relationship with AR from the perspective of metabolism and molecular pathways. We also discuss the feasibility of ferroptosis in prostate cancer treatment and describe current limitations and prospects, providing a reference for future research and clinical application of ferroptosis.

The role of ferroptosis in prostate cancer: a novel therapeutic strategy

The incidence of prostate cancer is the second most among male cancers after lung cancer. Prostate cancer develops rapidly and is inclined to metastasize, and castration-resistant prostate cancer (CRPC) can be formed in the later stage, which brings great challenges to the prognosis and treatment. At present, the main treatment of prostate cancer is generally divided into four methods: surgery, chemotherapy, radiotherapy and endocrine therapy. However, the efficacy of these methods fails to satisfy the demands of patient prognosis. Ferroptosis is a newly discovered iron-dependent process, characterized by lipid peroxidation. Ferroptosis is associated with many diseases, especially tumor growth. In recent years, inhibiting tumor growth and overcoming tumor drug resistance by inducing ferroptosis has become a hot research topic. Previous studies have shown that induction of ferroptosis may be a new treatment for prostate cancer. We review the research progress of ferroptosis in prostate cancer in order to provide highly effective therapies for patients with prostate cancer.

ELOVL5 and IGFBP6 genes modulate sensitivity of breast cancer cells to ferroptosis

Introduction: Relapse of breast cancer is one of the key obstacles to successful treatment. Previously we have shown that low expression of ELOVL5 and IGFBP6 genes in breast cancer tissue corresponded to poor prognosis. ELOVL5 participates directly in the elongation of polyunsaturated fatty acids (PUFAs) that are considered to play an important role in cancer cell metabolism. Thus, in this work we studied the changes in lipid metabolism in breast cancer cells with reduced expression of either ELOVL5 or IGFBP6 gene. Methods: MDA-MB-231 cells with a stable knockdown of either ELOVL5 or IGFBP6 gene were used in this study. Transcriptomic and proteomic analysis as well as RT-PCR were utilized to assess gene expression. Content of individual fatty acids in the cells was measured with HPLC-MS. HPLC was used for analysis of the kinetics of PUFAs uptake. Cell viability was measured with MTS assay. Flow cytometry was used to measure activation of apoptosis. Fluorescent microscopy was utilized to assess accumulation of ROS and formation of lipid droplets. Glutathione peroxidase activity was measured with a colorimetric assay. Results: We found that the knockdown of IGFBP6 gene led to significant changes in the profile of fatty acids in the cells and in the expression of many genes associated with lipid metabolism. As some PUFAs are known to inhibit proliferation and cause death of cancer cells, we also tested the response of the cells to single PUFAs and to combinations of docosahexaenoic acid (DHA, a n-3 PUFA) with standard chemotherapeutic drugs. Our data suggest that external PUFAs cause cell death by activation of ferroptosis, an iron-dependent mechanism of cell death with excessive lipid peroxidation. Moreover, both knockdowns increased cells' sensitivity to ferroptosis, probably due to a significant decrease in the activity of the antioxidant enzyme GPX4. Addition of DHA to commonly used chemotherapeutic drugs enhanced their effect significantly, especially for the cells with low expression of IGFBP6 gene. Discussion: The results of this study suggest that addition of PUFAs to the treatment regimen for the patients with low expression of IGFBP6 and ELOVL5 genes can be potentially beneficial and is worth testing in a clinically relevant setting.

A Novel Four Mitochondrial Respiration-Related Signature for Predicting Biochemical Recurrence of Prostate Cancer

The biochemical recurrence (BCR) of patients with prostate cancer (PCa) after radical prostatectomy is high, and mitochondrial respiration is reported to be associated with the metabolism in PCa development. This study aimed to establish a mitochondrial respiratory gene-based risk model to predict the BCR of PCa. RNA sequencing data of PCa were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and mitochondrial respiratory-related genes (MRGs) were sourced via GeneCards. The differentially expressed mitochondrial respiratory and BCR-related genes (DE-MR-BCRGs) were acquired through overlapping BCR-related differentially expressed genes (BCR-DEGs) and differentially expressed MRGs (DE-MRGs) between PCa samples and controls. Further, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a DE-MRGs-based risk model. Then, a nomogram was established by analyzing the independent prognostic factor of five clinical features and risk scores. Moreover, Gene Set Enrichment Analysis (GSEA), tumor microenvironment, and drug susceptibility analyses were employed between high- and low-risk groups of PCa patients with BCR. Finally, qRT-PCR was utilized to validate the expression of prognostic genes. We identified 11 DE-MR-BCRGs by overlapping 132 DE-MRGs and 13 BCR-DEGs and constructed a risk model consisting of 4 genes (APOE, DNAH8, EME2, and KIF5A). Furthermore, we established an accurate nomogram, including a risk score and a Gleason score, for the BCR prediction of PCa patients. The GSEA result suggested the risk model was related to the PPAR signaling pathway, the cholesterol catabolic process, the organic hydroxy compound biosynthetic process, the small molecule catabolic process, and the steroid catabolic process. Simultaneously, we found six immune cell types relevant to the risk model: resting memory CD4+ T cells, monocytes, resting mast cells, activated memory CD4+ T cells, regulatory T cells (Tregs), and macrophages M2. Moreover, the risk model could affect the IC50 of 12 cancer drugs, including Lapatinib, Bicalutamide, and Embelin. Finally, qRT-PCR showed that APOE, EME2, and DNAH8 were highly expressed in PCa, while KIF5A was downregulated in PCa. Collectively, a mitochondrial respiratory gene-based nomogram including four genes and one clinical feature was established for BCR prediction in patients with PCa, which could provide novel strategies for further studies.

A seven-gene prognosis model to predict biochemical recurrence for prostate cancer based on the TCGA database

Background

The incidence rate of prostate cancer is increasing rapidly. This study aims to explore the gene-associated mechanism of prostate cancer biochemical recurrence (BCR) after radical prostatectomy and to construct a biochemical recurrence of prostate cancer prognostic model.

Methods

The DEseq2 R package was used for the differential expression of mRNA. The ClusterProfiler R package was used to analyze the functional enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore related mechanisms. The Survival, Survminer, and My.stepwise R packages were used to construct the prognostic model to predict the biochemical recurrence-free probability. The RMS R package was used to draw the nomogram. For evaluating the prognostic model, the timeROC R package was used to draw the time-dependent ROC curve (receiver operating characteristic curve).

Result

To investigate the association between mRNA and prostate cancer, we performed differential expression analysis on the TCGA (The Cancer Genome Atlas) database. Seven protein-coding genes (VWA5B2, ARC, SOX11, MGAM, FOXN4, PRAME, and MMP26) were picked as independent prognostic genes by regression analysis. Based on their Cox coefficient, a risk score formula was proposed. According to the risk scores, patients were divided into high- and low-risk groups based on the median score. Kaplan-Meier plot curves showed that the low-risk group had a better biochemical recurrence-free probability compared to the high-risk group. The 1-year, 3-year, and 5-year AUCs (areas under the ROC curve) of the model were 77%, 81%, and 86%, respectively. In addition, we built a nomogram based on the result of multivariate Cox regression analysis. Furthermore, we select the GSE46602 dataset as our external validation. The 1-year, 3-year, and 5-year AUCs of BCR-free probability were 83%, 82%, and 80%, respectively. Finally, the levels of seven genes showed a difference between PRAD tissues and adjacent non-tumorous tissues.

Conclusions

This study shows that establishing a biochemical recurrence prediction prognostic model comprising seven protein-coding genes is an effective and precise method for predicting the progression of prostate cancer.

Second generation androgen receptor antagonists and challenges in prostate cancer treatment

Prostate cancer is a hormone-dependent malignancy, whose onset and progression are closely related to the activity of the androgen receptor (AR) signaling pathway. Due to this critical role of AR signaling in driving prostate cancer, therapy targeting the AR pathway has been the mainstay strategy for metastatic prostate cancer treatment. The utility of these agents has expanded with the emergence of second-generation AR antagonists, which began with the approval of enzalutamide in 2012 by the United States Food and Drug Administration (FDA). Together with apalutamide and darolutamide, which were approved in 2018 and 2019, respectively, these agents have improved the survival of patients with prostate cancer, with applications for both androgen-dependent and castration-resistant disease. While patients receiving these drugs receive a benefit in the form of prolonged survival, they are not cured and ultimately progress to lethal neuroendocrine prostate cancer (NEPC). Here we summarize the current state of AR antagonist development and highlight the emerging challenges of their clinical application and the potential resistance mechanisms, which might be addressed by combination therapies or the development of novel AR-targeted therapies.

Prediction of Prognosis in Patients With Endometrial Carcinoma and Immune Microenvironment Estimation Based on Ferroptosis-Related Genes

Background: Ferroptosis, a form of non-apoptotic cell death, has aroused worldwide interest in cancer researchers. However, the current study about the correlation between ferroptosis-related genes (FRGs) and endometrial cancer (EC) remains limited.

Methods: First, the transcriptome profiling and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) program as the training group and testing group, respectively. FRGs were acquired through literature mining. Then, we used R 4.1.1 software to screen the differently expressed FRGs from TCGA, which was also connected with the prognosis of EC patients. Subsequently, the risk score of each tumor sample was identified by LASSO regression analysis, and we classified these samples into the high- and low-risk groups in the light of the median risk score. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier analysis were performed to assess the accuracy of this signature. Significantly, the data from CPTAC was used to validate the prediction model externally. Furthermore, we evaluated the immune microenvironment in this model via single-sample gene set enrichment analysis (ssGSEA).

Results: Among the 150 FRGs, 6 differentially expressed genes (DEGs) based on TCGA had a relationship with the prognosis of EC patients, namely, TP53, AIFM2, ATG7, TLR4, PANX1 and MDM2. The survival curve indicated a higher survival probability in the low-risk group. Moreover, the FRGs-based signature acted well in the prediction of overall survival (OS). The results of external verification confirmed the prediction model we established. Finally, ssGSEA revealed significant differences in the abundance of 16 immune cells infiltration and the activity of 13 immune functions between different risk groups.

Conclusion: We identified a novel ferroptosis-related gene signature which could concisely predict the prognosis and immunotherapy in EC patients.

A Novel Angiogenesis-Related Gene Signature to Predict Biochemical Recurrence of Patients with Prostate Cancer following Radical Therapy

Background

Prostate cancer (PCa) is one of the most common malignancies in males; we aim to establish a novel angiogenesis-related gene signature for biochemical recurrence (BCR) prediction in PCa patients following radical therapy.

Methods

Gene expression profiles and corresponding clinicopathological data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We quantified the levels of various cancer hallmarks and identified angiogenesis as the primary risk factor for BCR. Then machine learning methods combined with Cox regression analysis were used to screen prognostic genes and construct an angiogenesis-related gene signature, which was further verified in external cohorts. Furthermore, estimation of immune cell abundance and prediction of drug responses were also conducted to detect potential mechanisms.

Results

Angiogenesis was regarded as the leading risk factor for BCR in PCa patients (HR = 1.58, 95% CI: 1.38–1.81), and a novel prognostic signature based on three genes (NRP1, JAG2, and VCAN) was developed in the training cohort and successfully validated in another three independent cohorts. In all datasets, this signature was identified as a prognostic factor with promising and robust predictive values. Moreover, it also predicted higher infiltration of regulatory T cells and M2-polarized macrophages and increased drug sensitivity of angiogenesis inhibitors in high-risk patients.

Conclusions

The angiogenesis-related three-gene signature may serve as an independent prognostic factor for BCR, which would contribute to risk stratification and personalized management of PCa patients after radical therapy in clinical practice.

Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

BACKGROUND

Biochemical recurrence (BCR) after initial treatment, such as radical prostatectomy, is the most frequently adopted prognostic factor for patients who suffer from prostate cancer (PCa). In this study, we aimed to construct a prognostic model consisting of gene expression profiles to predict BCR-free survival.

METHODS

We analyzed 70 metabolic pathways in 152 normal prostate samples and 494 PCa samples from the UCSC Xena dataset (training set) via gene set enrichment analysis (GSEA) to select BCR-related genes and constructed a BCR-related gene risk score (RS) model. We tested the power of our model using Kaplan-Meier (K-M) plots and receiver operator characteristic (ROC) curves. We performed univariate and multivariate analyses of RS using other clinicopathological features and established a nomogram model, which has stronger prediction ability. We used GSE70770 and DFKZ 2018 datasets to validate the results. Finally, we performed differential expression and quantitative real-time polymerase chain reaction analyses of the UCSC data for further verification of the findings.

RESULTS

A total of 194 core enriched genes were obtained through GSEA, among which 16 BCR-related genes were selected and a three-gene RS model based on the expression levels of CA14, LRAT, and MGAT5B was constructed. The outcomes of the K-M plots and ROC curves verified the accuracy of the RS model. We identified the Gleason score, pathologic T stage, and RS model as independent predictors through univariate and multivariate Cox analyses and constructed a nomogram model that presented better predictability than the RS model. The outcomes of the validation set were consistent with those of the training set. Finally, the results of differential expression analyses support the effectiveness of our model.

CONCLUSION

We constructed an RS model based on metabolic genes that could predict the prognosis of PCa patients. The model can be easily used in clinical applications and provide important insights into future research on the underlying mechanism of PCa.

A Ferroptosis-Related Gene Prognostic Index Associated With Biochemical Recurrence and Radiation Resistance for Patients With Prostate Cancer Undergoing Radical Radiotherapy

Background: Ferroptosis is a new type of programmed cell death which has been reported to be involved in the development of various cancers. In this study, we attempted to explore the possible links between ferroptosis and prostate cancer (PCa), and a novel ferroptosis-related gene prognostic index (FGPI) was constructed to predict biochemical recurrence (BCR) and radiation resistance for PCa patients undergoing radical radiotherapy (RRT). Moreover, the tumor immune microenvironment (TME) of PCa was analyzed.

Methods: We merged four GEO datasets by removing batch effects. All analyses were conducted with R version 3.6.3 and its suitable packages. Cytoscape 3.8.2 was used to establish a network of transcriptional factor and competing endogenous RNA.

Results: We established the FGPI based on ACSL3 and EPAS1. We observed that FGPI was an independent risk factor of BCR for PCa patients (HR: 3.03; 95% CI: 1.68–5.48), consistent with the result of internal validation (HR: 3.44; 95% CI: 1.68–7.05). Furthermore, FGPI showed high ability to identify radiation resistance (AUC: 0.963; 95% CI: 0.882–1.00). LncRNA PART1 was significantly associated with BCR and might modulate the mRNA expression of EPAS1 and ACSL3 through interactions with 60 miRNAs. Gene set enrichment analysis indicated that FGPI was enriched in epithelial–mesenchymal transition, allograft rejection, TGF beta signaling pathway, and ECM receptor interaction. Immune checkpoint and m6A analyses showed that PD-L2, CD96, and METTL14 were differentially expressed between BCR and no BCR groups, among which CD96 was significantly associated with BCR-free survival (HR: 1.79; 95% CI: 1.06–3.03). We observed that cancer-related fibroblasts (CAFs), macrophages, stromal score, immune score, estimate score, and tumor purity were differentially expressed between BCR and no BCR groups and closely related to BCR-free survival (HRs were 2.17, 1.79, 2.20, 1.93, 1.92, and 0.52 for cancer-related fibroblasts, macrophages, stromal score, immune score, estimate score, and tumor purity, respectively). Moreover, cancer-related fibroblasts (coefficient: 0.20), stromal score (coefficient: 0.14), immune score (coefficient: 0.14), estimate score (coefficient: 0.15), and tumor purity (coefficient: −0.15) were significantly related to FGPI, among which higher positive correlation between cancer-related fibroblasts and FGPI was observed.

Conclusion: We found that FGPI based on ACSL3 and EPAS1 might be used to predict BCR and radiation resistance for PCa patients. CD96 and PD-L2 might be a possible target for drug action. Besides, we highlighted the importance of immune evasion in the process of BCR.

Classification of pyroptosis patterns and construction of a novel prognostic model for prostate cancer based on bulk and single-cell RNA sequencing

BACKGROUND

Emerging evidence suggests an important role for pyroptosis in tumorigenesis and recurrence, but it remains to be elucidated in prostate cancer (PCa). Considering the low accuracy of common clinical predictors of PCa recurrence, we aimed to develop a novel pyroptosis-related signature to predict the prognosis of PCa patients based on integrative analyses of bulk and single-cell RNA sequencing (RNA-seq) profiling.

METHODS

The RNA-seq data of PCa patients was downloaded from several online databases. PCa patients were stratified into two Classes by unsupervised clustering. A novel signature was constructed by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The Kaplan-Meier curve was employed to evaluate the prognostic value of this signature and the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analysis tumor-infiltrating immune cells. At single-cell level, we also classified the malignant cells into two Classes and constructed cell developmental trajectories and cell-cell interaction networks. Furthermore, RT-qPCR and immunofluorescence were used to validate the expression of core pyroptosis-related genes.

RESULTS

Twelve prognostic pyroptosis-related genes were identified and used to classify PCa patients into two prognostic Classes. We constructed a signature that identified PCa patients with different risks of recurrence and the risk score was proven to be an independent predictor of the recurrence free survival (RFS). Patients in the high-risk group had a significantly lower RFS (P<0.001). The expression of various immune cells differed between the two Classes. At the single-cell level, we classified the malignant cells into two Classes and described the heterogeneity. In addition, we observed that malignant cells may shift from Class1 to Class2 and thus have a worse prognosis.

CONCLUSION

We have constructed a robust pyroptosis-related signature to predict the RFS of PCa patients and described the heterogeneity of prostate cancer cells in terms of pyroptosis.

A novel ferroptosis-related gene prognostic index for prognosis and response to immunotherapy in patients with prostate cancer

BACKGROUND

Prostate cancer (PCa) is among the leading causes of cancer death worldwide. Ferroptosis refers to an iron-dependent form of regulated cell death and is involved in prostate tumorigenesis. A few ferroptosis-related gene signatures have been developed to predict the prognosis for PCa patients. However, previous signatures were typically established based on biochemical recurrence-free survival, which has proven not to be a good surrogate for overall survival (OS). This study aimed to construct a novel ferroptosis-related gene prognostic index (FRGPI) to predict disease-free survival (DFS) and response to immunotherapy for PCa patients after radical prostatectomy.

METHODS

Gene expression and clinicopathological data on PCa patients were obtained from the TCGA database. Ferroptosis-related hub genes associated with DFS of PCa patients were identified by an in-depth bioinformatics analysis using a novel and comprehensive algorithm based on functional enrichment, consensus clustering, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. The FRGPI was established on the basis of the genes selected using multivariate cox regression analysis and further validated in two additional PCa cohorts. Next, the clinicopathological, molecular, and immune profiles were characterized and compared between FRGPI-high and FRGPI-low subgroups. Finally, the predictive role of the FRGPI in response to immunotherapy was estimated using a metastatic urothelial cancer cohort treated with an anti-PD-L1 agent.

RESULTS

The FRGPI was constructed based on four genes (E2F1, CDC20, TYMS, and NUP85), and FRGPI-high patients had worse DFS than FRGPI-low patients. Multivariate cox regression analysis revealed that FRGPI could act as an independent prognostic factor for PCa patients after radical prostatectomy. A prognostic nomogram comprising the FRGPI and other clinicopathological parameters was established to predict the DFS for PCa patients quantitatively. In addition, comprehensive results demonstrated that high FRGPI scores showed a significantly positive correlation with worse clinicopathological features, higher mutation counts, increased frequency of copy number variations (CNVs), higher homologous recombination deficiency (HRD) and immune scores, higher mRNAsi, and more importantly, enhanced sensitivity to immunotherapy.

CONCLUSIONS

FRGPI is not only a promising and robust prognostic biomarker, but also a potential indicator of immunotherapeutic outcomes for PCa patients after radical prostatectomy.