A Gleason score-related outcome model for human prostate cancer: a comprehensive study based on weighted gene co-expression network analysis

Establishment of a prognostic risk model for prostate cancer based on Gleason grading and cuprotosis related genes

INTRODUCTION

Prostate cancer (PCa) is common in aging males, diagnosed via the Gleason grading system. The study explores the unexamined prognostic value of cuprotosis, a distinct cell death type, alongside Gleason grades in PCa.

METHODS

We explored Cuprotosis-related genes (CRGs) in prostate cancer (PCa), using NMF on TCGA-PRAD data for patient classification and WGCNA to link genes with Gleason scores and prognosis. A risk model was crafted via LASSO Cox regression. STX3 knockdown in PC-3 cells, analyzed for effects on cell behaviors and tumor growth in mice, highlighted its potential therapeutic impact.

RESULTS

We identified five genes crucial for a prognostic risk model, with higher risk scores indicating worse prognosis. Survival analysis and ROC curves confirmed the model's predictive accuracy in TCGA-PRAD and GSE70769 datasets. STX3 was a key adverse prognostic factor, with its knockdown significantly reducing mRNA and protein levels, impairing PC-3 cell functions. In vivo, STX3 knockdown in PC-3 cells led to significantly smaller tumors in nude mice, underscoring its potential therapeutic value.

CONCLUSION

Our prognostic model, using five genes linked to Gleason scores, effectively predicts prostate cancer outcomes, offering a novel treatment strategy angle.

The upregulation and transcriptional regulatory mechanisms of Extra spindle pole bodies like 1 in bladder cancer: An immunohistochemistry and high-throughput screening Evaluation

Background

This study aimed to explore the expression level and transcriptional regulation mechanism of Extra Spindle Pole Bodies Like 1 (ESPL1) in bladder cancer (BC).

Methods

A multicentre database of samples (n = 1391) was assayed for ESPL1 mRNA expression in BC and validated at the protein level by immunohistochemical (IHC) staining of in-house samples (n = 202). Single-cell sequencing (scRNA-seq) analysis and enrichment analysis explored ESPL1 distribution and their accompanying molecular mechanisms. ATAC-seq, ChIP-seq and Hi-C data from multiple platforms were used to investigate ESPL1 upstream transcription factors (TFs) and potential epigenetic regulatory mechanisms. Immune-related analysis, drug sensitivity and molecular docking of ESPL1 were also calculated. Furthermore, upstream microRNAs and the binding sites of ESPL1 were predicted. The expression level and early screening efficacy of miR-299-5p in blood (n = 6625) and tissues (n = 537) were examined.

Results

ESPL1 was significantly overexpressed at the mRNA level (p < 0.05, SMD = 0.75; 95 % CI = 0.09, 1.40), and IHC staining of in-house samples verified this finding (p < 0.0001). ESPL1 was predominantly distributed in BC epithelial cells. Coexpressed genes of ESPL1 were enriched in cell cycle-related signalling pathways, and ESPL1 might be involved in the communication between epithelial and residual cells in the Hippo, ErbB, PI3K-Akt and Ras signalling pathways. Three TFs (H2AZ, IRF5 and HIF1A) were detected upstream of ESPL1 and presence of promoter-super enhancer and promoter-typical enhancer loops. ESPL1 expression was correlated with various immune cell infiltration levels. ESPL1 expression might promote BC growth and affect the sensitivity and therapeutic efficacy of paclitaxel and gemcitabine in BC patients. As an upstream regulator of ESPL1, miR-299-5p expression was downregulated in both the blood and tissues, possessing great potential for early screening.

Conclusions

ESPL1 expression was upregulated in BC and was mainly distributed in epithelial cells. Elevated ESPL1 expression was associated with TFs at the upstream transcription start site (TSS) and distant chromatin loops of regulatory elements. ESPL1 might be an immune-related predictive and diagnostic marker for BC, and the overexpression of ESPL1 played a cancer-promoting role and affected BC patients' sensitivity to drug therapy. miR-299-5p was downregulated in BC blood and tissues and was also expected to be a novel marker for early screening.

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.

Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study

Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression differences (p-value = 1.3×10-4). To conclude, we identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predicted their prognoses. These models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.

The Role οf Ion Channels in the Development and Progression of Prostate Cancer

Ion channels have major regulatory functions in living cells. Apart from their role in ion transport, they are responsible for cellular electrogenesis and excitability, and may also regulate tissue homeostasis. Although cancer is not officially classified as a channelopathy, it has been increasingly recognized that ion channel aberrations play an important role in virtually all cancer types. Ion channels can exert pro-tumorigenic activities due to genetic or epigenetic alterations, or as a response to molecular signals, such as growth factors, hormones, etc. Increasing evidence suggests that ion channels and pumps play a critical role in the regulation of prostate cancer cell proliferation, apoptosis evasion, migration, epithelial-to-mesenchymal transition, and angiogenesis. There is also evidence suggesting that ion channels might play a role in treatment failure in patients with prostate cancer. Hence, they represent promising targets for diagnosis, staging, and treatment, and their effects may be of particular significance for specific patient populations, including those undergoing anesthesia and surgery. In this article, the role of major types of ion channels involved in the development and progression of prostate cancer are reviewed. Identifying the underlying molecular mechanisms of the pro-tumorigenic effects of ion channels may potentially inform the development of novel therapeutic strategies to counter this malignancy.

Prognostic Significance of Amino Acid Metabolism-Related Genes in Prostate Cancer Retrieved by Machine Learning

Prostate cancer is among the leading cancers according to both incidence and mortality. Due to the high molecular, morphological and clinical heterogeneity, the course of prostate cancer ranges from slow growth that usually does not require immediate therapeutic intervention to aggressive and fatal disease that spreads quickly. However, currently available biomarkers cannot precisely predict the course of a disease, and novel strategies are needed to guide prostate cancer management. Amino acids serve numerous roles in cancers, among which are energy production, building block reservoirs, maintenance of redox homeostasis, epigenetic regulation, immune system modulation and resistance to therapy. In this article, by using The Cancer Genome Atlas (TCGA) data, we found that the expression of amino acid metabolism-related genes is highly aberrant in prostate cancer, which holds potential to be exploited in biomarker design or in treatment strategies. This change in expression is especially evident for catabolism genes and transporters from the solute carrier family. Furthermore, by using recursive partitioning, we confirmed that the Gleason score is strongly prognostic for progression-free survival. However, the expression of the genes SERINC3 (phosphatidylserine and sphingolipids generation) and CSAD (hypotaurine generation) can refine prognosis for high and low Gleason scores, respectively. Therefore, our results hold potential for novel prostate cancer progression biomarkers.

Novel biomarkers predict prognosis and drug-induced neuroendocrine differentiation in patients with prostate cancer

Background

A huge focus is being placed on the development of novel signatures in the form of new combinatorial regimens to distinguish the neuroendocrine (NE) characteristics from castration resistant prostate cancer (CRPC) timely and accurately, as well as predict the disease-free survival (DFS) and progression-free survival (PFS) of prostate cancer (PCa) patients.

Methods

Single cell data of 4 normal samples, 3 CRPC samples and 3 CRPC-NE samples were obtained from GEO database, and CellChatDB was used for potential intercellular communication, Secondly, using the "limma" package (v3.52.0), we obtained the differential expressed genes between CRPC and CRPC-NE both in single-cell RNA seq and bulk RNA seq samples, and discovered 12 differential genes characterized by CRPC-NE. Then, on the one hand, the diagnosis model of CRPC-NE is developed by random forest algorithm and artificial neural network (ANN) through Cbioportal database; On the other hand, using the data in Cbioportal and GEO database, the DFS and PFS prognostic model of PCa was established and verified through univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox regression in R software. Finally, somatic mutation and immune infiltration were also discussed.

Results

Our research shows that there exists specific intercellular communication in classified clusters. Secondly, a CRPC-NE diagnostic model of six genes (HMGN2, MLLT11, SOX4, PCSK1N, RGS16 and PTMA) has been established and verified, the area under the ROC curve (AUC) is as high as 0.952 (95% CI: 0.882-0.994). The mutation landscape shows that these six genes are rarely mutated in the CRPC and NEPC samples. In addition, NE-DFS signature (STMN1 and PCSK1N) and NE-PFS signature (STMN1, UBE2S and HMGN2) are good predictors of DFS and PFS in PCa patients and better than other clinical features. Lastly, the infiltration levels of plasma cells, T cells CD4 naive, Eosinophils and Monocytes were significantly different between the CRPC and NEPC groups.

Conclusions

This study revealed the heterogeneity between CRPC and CRPC-NE from different perspectives, and developed a reliable diagnostic model of CRPC-NE and robust prognostic models for PCa.

An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence

Background

The most common site of prostate cancer metastasis is bone tissue with many recent studies having conducted genomic and clinical research regarding bone metastatic prostate cancer. However, further work is needed to better define those patients that are at an elevated risk of such metastasis.

Methods

SEER and TCGA databases were searched to develop a nomogram for predicting prostate cancer bone metastasis.

Results

Herein, we leveraged the Surveillance, Epidemiology, and End Results (SEER) database to construct a predictive nomogram capable of readily and accurately predicted the odds of bone metastasis in prostate cancer patients. This nomogram was utilized to assign patients with prostate cancer included in The Cancer Genome Atlas (TCGA) to cohorts at a high or low risk of bone metastasis (HRBM and LRBM, respectively). Comparisons of these LRBM and HRBM cohorts revealed marked differences in mutational landscapes between these patient cohorts, with increased frequencies of gene fusions, somatic copy number variations (CNVs), and single nucleotide variations (SNVs), particularly in the P53 gene, being evident in the HRBM cohort. We additionally identified lncRNAs, miRNAs, and mRNAs that were differentially expressed between these two patient cohorts and used them to construct a competing endogenous RNA (ceRNA) network. Moreover, three weighted gene co-expression network analysis (WGCNA) modules were constructed from the results of these analyses, with KIF14, MYH7, and COL10A1 being identified as hub genes within these modules. We further found immune response activity levels in the HRBM cohort to be elevated relative to that in the LRBM cohort, with single sample gene enrichment analysis (ssGSEA) scores for the immune checkpoint signature being increased in HRBM patient samples relative to those from LRBM patients.

Conclusion

We successfully developed a nomogram capable of readily detecting patients with prostate cancer at an elevated risk of bone metastasis.

The Prognostic Value of Liquid Biopsies for Benefit of Salvage Radiotherapy in Relapsed Oligometastatic Prostate Cancer

Simple Summary

Around 30% of patients with oligometastatic prostate cancer relapse will benefit from local PET/CT-guided ablative radiotherapy (RT) with improved progression-free and ADT (Androgene Deprivation Therapy)-free survivals. Therefore, there is an urgent need for predictive testing for therapeutic benefits prior to initiation. Various tests have already been established on tumor specimens for the prediction of prostate cancer’s behavior or therapy outcome. However, in imaging-proven relapse tumor tissue from the local recurrence or metastases is often not available. Hence, there is a need for a liquid biopsy-based testing. We aimed to assess the prognostic value of CTCs- associated mRNA and blood-derived RNA for the benefit of PSMA PET-guided salvage RT in oligometastatic prostate cancer relapses. Significant correlations were found between the relative transcript levels of several investigated genes and clinicopathological parameters. Furthermore, distinct “transcriptional signatures” were found in patients with temporary and long-term benefits from RT.

Abstract

To assess the prognostic value of “liquid biopsies” for the benefit of salvage RT in oligometastatic prostate cancer relapse, we enrolled 44 patients in the study between the years 2016 and 2020. All the patients were diagnosed as having an oligometastatic prostate cancer relapse on prostate-specific membrane antigen (PSMA)-targeted PET-CT and underwent irradiation at the Department of Radiotherapy at the Hannover Medical School. Tumor cells and total RNA, enriched from the liquid biopsies of patients, were processed for the subsequent quantification analysis of relative transcript levels in real-time PCR. In total, 54 gene transcripts known or suggested to be associated with prostate cancer or treatment outcome were prioritized for analysis. We found significant correlations between the relative transcript levels of several investigated genes and the Gleason score, PSA (prostate-specific antigen) value, or UICC stage (tumor node metastasis -TNM classification of malignant tumors from Union for International Cancer Control). Furthermore, a significant association of MTCO2, FOXM1, SREBF1, HOXB7, FDXR, and MTRNR transcript profiles was found with a temporary and/or long-term benefit from RT. Further studies on larger patients cohorts are necessary to prove our preliminary findings for establishing liquid biopsy tests as a predictive examination method prior to salvage RT.

Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.

Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.

Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.

Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.

Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

Chemokine CCL17 Affects Local Immune Infiltration Characteristics and Early Prognosis Value of Lung Adenocarcinoma

CCL17 is an important chemokine that plays a vital immunomodulatory role in the tumor microenvironment (TME). Analysis of lung adenocarcinoma (LUAD) data in Kaplan–Meier plotter databases found that the overall survival of patients in the CCL17 high-expression group was higher than that of the low-expression group, especially for patients with early (stages I and II) LUAD, which has a more positive prognostic value. Expression of CCL17 in LUAD was positively correlated with the proportion of tumor-infiltrating lymphocytes, immunostimulators, and major histocompatibility complexes using the TISIDB databases. Based on the RNA-seq and clinical data of 491 LUAD patients obtained from the TCGA database, 1,455 differential genes were found between the CCL17 high- and low-expression groups. Using WGCNA analysis confirmed that the expression of differential genes in the blue module is negatively correlated with poor survival and clinical stages of LUAD patients, and CCL17 and CCR4 genes belong to the hub genes in the blue module. Further analysis by the ESTIMATE and CIBERSORT algorithm found that the naive B cells and CD8⁺ T cells in the CCL17 high-expression group have a higher distribution ratio in the early LUAD patients, and the high immune score has a positive relationship with the overall survival rate. Using somatic mutation data of TCGA-LUAD, we found that 1) the tumor mutation burden values of the CCL17 high-expression group were significantly lower than those of the CCL17 low-expression group and 2) the expression levels of CCL17 and the tumor mutation burden values were negatively correlated. Transwell chemotaxis and cytotoxicity assays confirmed that CCL17 contributes to the migration of CCR4-positive lymphocytes into the H1993 LUAD TME and enhances the specific lysis of LUAD cells. In summary, high expression of CCL17 in the LUAD TME promotes local immune cell infiltration and antitumor immune response, which may contribute to the better survival and prognosis of patients with early LUAD.

RNA N6-methyladenosine reader IGF2BP2 promotes lymphatic metastasis and epithelial-mesenchymal transition of head and neck squamous carcinoma cells via stabilizing slug mRNA in an m6A-dependent manner

Background

Lymph node metastasis is the main cause of poor prognosis of head and neck squamous carcinoma (HNSCC) patients. N6-methyladenosine (m6A) RNA modification is an emerging epigenetic regulatory mechanism for gene expression, and as a novel m6A reader protein, IGF2BP2 has been implicated in tumor progression and metastasis. However, not much is currently known about the functional roles of IGF2BP2 in HNSCC, and whether IGF2BP2 regulates lymphatic metastasis through m6A modification in HNSCC remains to be determined.

Methods

The expression and overall survival (OS) probability of m6A-related regulators in HNSCC were analyzed with The Cancer Genome Atlas (TCGA) dataset and GEPIA website tool, respectively. The expression levels of IGF2BP2 were measured in HNSCC tissues and normal adjacent tissues. To study the effects of IGF2BP2 on HNSCC cell metastasis in vitro and in vivo, gain- and loss- of function methods were employed. RIP, MeRIP, luciferase reporter and mRNA stability assays were performed to explore the epigenetic mechanism of IGF2BP2 in HNSCC.

Results

We investigated 20 m6A-related regulators in HNSCC and discovered that only the overexpression of IGF2BP2 was associated with a poor OS probability and an independent prognostic factor for HNSCC patients. Additionally, we demonstrated that IGF2BP2 was overexpressed in HNSCC tissues, and significantly correlated to lymphatic metastasis and poor prognosis. Functional studies have shown that IGF2BP2 promotes both HNSCC cell migration as well as invasion via the epithelial-mesenchymal transition (EMT) process in vitro, and IGF2BP2 knockdown significantly inhibited lymphatic metastasis and lymphangiogenesis in vivo. Mechanistic investigations revealed that Slug, a key EMT-related transcriptional factor, is the direct target of IGF2BP2, and essential for IGF2BP2-regulated EMT and metastasis in HNSCC. Furthermore, we demonstrated that IGF2BP2 recognizes and binds the m6A site in the coding sequence (CDS) region of Slug and promotes its mRNA stability.

Conclusions

Collectively, our study uncovers the oncogenic role and potential mechanism of IGF2BP2, which serves as a m6A reader, in controlling lymphatic metastasis and EMT in HNSCC, suggesting that IGF2BP2 may act as a therapeutic target and prognostic biomarker for HNSCC patients with metastasis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02212-1.

Prediction of Biochemical Recurrence-Free Survival of Prostate Cancer Patients Leveraging Multiple Gene Expression Profiles in Tumor Microenvironment

Tumor-adjacent normal (TAN) tissues, which constitute tumor microenvironment and are different from healthy tissues, provide critical information at molecular levels that can be used to differentiate aggressive tumors from indolent tumors. In this study, we analyzed 52 TAN samples from the Cancer Genome Atlas (TCGA) prostate cancer patients and developed a 10-gene prognostic model that can accurately predict biochemical recurrence-free survival based on the profiles of these genes in TAN tissues. The predictive ability was validated using TAN samples from an independent cohort. These 10 prognostic genes in tumor microenvironment are different from the prognostic genes detected in tumor tissues, indicating distinct progression-related mechanisms in two tissue types. Bioinformatics analysis showed that the prognostic genes in tumor microenvironment were significantly enriched by p53 signaling pathway, which may represent the crosstalk tunnels between tumor and its microenvironment and pathways involving cell-to-cell contact and paracrine/endocrine signaling. The insight acquired by this study has advanced our knowledge of the potential role of tumor microenvironment in prostate cancer progression.

Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database

Prostate cancer (PC) is a polygenic disease with multiple gene interactions. Therefore, a detailed analysis of its epidemiology and evaluation of risk factors can help to identify more accurate predictors of aggressive disease. We used the transcriptome data from a cohort of 243 patients from the Cancer Genome Atlas (TCGA) database. Key regulatory genes involved in proliferation activity, in the regulation of stress, and in the regulation of inflammation processes of the tumor microenvironment were selected to test a priori multi-dimensional scaling (MDS) models and create a combined score to better predict the patients' survival and disease-free intervals. Survival was positively correlated with cortisol expression and negatively with Mini-Chromosome Maintenance 7 (MCM7) and Breast-Related Cancer Antigen2 (BRCA2) expression. The disease-free interval was negatively related to the expression of enhancer of zeste homolog 2 (EZH2), MCM7, BRCA2, and programmed cell death 1 ligand 1 (PD-L1). MDS suggested two separate pathways of activation in PC. Within these two dimensions three separate clusters emerged: (1) cortisol and brain-derived neurotrophic factor BDNF, (2) PD-L1 and cytotoxic-T-lymphocyte-associated protein 4 (CTL4); (3) and finally EZH2, MCM7, BRCA2, and c-Myc. We entered the three clusters of association shown in the MDS in several Kaplan-Meier analyses. It was found that only Cluster 3 was significantly related to the interval-disease free, indicating that patients with an overall higher activity of regulatory genes of proliferation and DNA repair had a lower probability to have a longer disease-free time. In conclusion, our data study provided initial evidence that selecting patients with a high grade of proliferation and DNA repair activity could lead to an early identification of an aggressive PC with a potentials for metastatic development.

A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer

Background

Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa.

Methods

Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples.

Results

The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and calibration both in the SMOTE-balanced training (AUC: 0.946) and validation (AUC: 0.910) sets. The dysregulated expression of hub genes between PCa and LNI samples was also validated.

Conclusion

The proposed nomogram combining the 37-gene-based SVM model with the Gleason grade had the potential to preoperatively predict LNI in PCa. Some of the hub genes should be prioritized for functional studies and mechanistic analyses.

Novel Gene Signatures Predictive of Patient Recurrence-Free Survival and Castration Resistance in Prostate Cancer

Simple Summary

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. This study aims to identify castration-resistant PCa (CRPC)-associated genes and develop robust RFS and CRPC signatures. Among 287 genes differentially expressed between localized CRPC and hormone-sensitive PCa (HSPC) samples, 6 genes constituted a signature (CRPC-derived prognosis signature, CRPCPS) that predicted RFS. Moreover, a 3-gene panel derived from the 6 CRPCPS genes was capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and maintained prognostic in patients stratified by tumor stage, Gleason score, and lymph node metastasis status. It also predicted overall survival and metastasis-free survival. Notably, the signature was validated in another six independent cohorts. These findings suggest that these two signatures could be robust tools for predicting RFS and CRPC in clinical practice.

Abstract

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa. This signature included NPEPL1, VWF, LMO7, ALDH2, NUAK1, and TPT1, and was named CRPC-derived prognosis signature (CRPCPS). Interestingly, three of these 6 genes constituted another signature capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and remained valid in patients stratified by tumor stage, Gleason score, and lymph node status. The signature also predicted overall survival and metastasis-free survival. The signature’s robustness was demonstrated by the C-index (0.55–0.74) and the calibration plot in all nine cohorts and the 3-, 5-, and 8-year area under the receiver operating characteristic curve (0.67–0.77) in three cohorts. The nomogram analyses demonstrated CRPCPS’ clinical applicability. The CRPCPS thus appears useful for RFS prediction in PCa.

Identification of a Prognostic Signature Model with Tumor Microenvironment for predicting Disease-free Survival after Radical Prostatectomy

Background: The tumor microenvironment (TME) and immune checkpoint inhibitors have been shown to promote active immune responses through different mechanisms. We attempted to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).

Methods: The gene transcriptome profiles and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database, and the immune and stromal scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of the risk score (RS) model based on univariate Cox analysis and least absolute shrinkage and selection operation (LASSO) Cox regression analysis and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 dataset was utilized for external validation. Twenty-two subsets of tumor-infiltrating immune cells were analyzed using the CIBERSORT algorithm.

Results: In this study, the patients with higher immune/stromal scores were associated with a worse DFS, higher Gleason score, and higher pathological T stage. Based on the immune and stromal scores, 515 differentially expressed genes (DEGs) were identified. The univariate Cox and LASSO Cox regression models were employed to select 18 DEGs from 515 DEGs and construct an RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUCs for the 1-year, 3-year and 5-year DFS rates in the RS model were 0.890, 0.877 and 0.841, respectively. A nomogram of DFS was established based on the RS and Gleason score, and the AUCs for the 1-year, 3-year and 5-year DFS rates in the nomogram were 0.907, 0.893, and 0.872, respectively. These results were further validated in the GSE70768 dataset. In addition, the proportion of Tregs was determined to be higher in high-RS patients (P<0.05), and the expression levels of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) were observed to be higher in high-RS patients (P<0.05).

Conclusions: Our study established and validated an 18-gene prognostic signature model associated with TME, which might serve as a prognosis stratification tool to predict DFS in PCa patients after radical prostatectomy.

Identification of HCG18 and MCM3AP-AS1 That Associate With Bone Metastasis, Poor Prognosis and Increased Abundance of M2 Macrophage Infiltration in Prostate Cancer

Background:

Bone metastasis is a leading cause of the high mortality rate of prostate cancer (PCa), but curative strategies remain lacking. Recent studies suggest long non-coding RNAs (lncRNAs) may be potential targets to develop drugs. However, PCa bone metastasis-specifically-related lncRNAs were rarely reported. This study aimed to identify crucial lncRNAs and reveal their function mechanisms.

Methods:

GSE32269 and GSE26964 microarray datasets, downloaded from the Gene Expression Omnibus database, were used to analyze differentially expressed genes (DEGs)/lncRNAs (DELs) and miRNAs (DEMs), respectively. Weighted gene co-expression network analysis was performed to screen PCa bone metastasis-associated modules. The co-expression and competing endogenous RNAs (ceRNAs) networks were constructed to identify hub lncRNAs. Univariate Cox regression analysis was conducted to determine their prognostic values. The correlation of lncRNAs with immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource. Therapeutic drugs were predicted by querying the Connectivity Map (CMap) and the Comparative Toxicogenomics Database (CTD).

Results:

A total of 18 DELs, 2,614 DEGs and 86 DEMs were screened between bone metastatic and primary PCa samples. Four modules enriched by DEGs were shown to be bone metastasis-associated. LncRNA HCG18 and MCM3AP-AS1 were identified to be important because they existed in both of the co-expression and ceRNA networks (forming the relationship pairs: HCG18/MCM3AP-AS1-KNTC1, MCM3AP-AS1-hsa-miR-508-3p-DTL and HCG18/MCM3AP-AS1-hsa-miR-127-3p-CDKN3). All the genes in these interaction pairs were significantly associated with overall survival of PCa patients. Also, HCG18, MCM3AP-AS1 and their target mRNAs were positively correlated with various tumor-infiltrated immune cells, especially increased M2 macrophages. Valproic acid and trichostatin A may be effective to treat PCa bone metastasis by targeting HCG18 and MCM3AP-AS1.

Conclusion:

HCG18 and MCM3AP-AS1 that regulate M2 macrophage infiltration may be important targets to treat PCa bone metastasis and improve prognosis.