Molecular Signature to Risk-Stratify Prostate Cancer of Intermediate Risk

Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer

PURPOSE

To established an AI system to make the pathological diagnosis of prostate cancer.

METHODS

Prostate histopathological whole mount (WM) sections from patients underwent robot-assisted laparoscopic prostatectomy were prepared. All the prostate WM pathological sections were converted to digital image data and marked with different colors on the basis of the ISUP Gleason grade group. The image was then fed into a segmentation algorithm. We chose modified U-Net as our fundamental network architecture.

RESULTS

172 patients were involved in this study. 896 pieces of prostate WM pathological sections from 160 patients, in which 826 pieces of WM sections from 148 patients were assigned to the training set randomly. After image segmentation there were totally 2,138,895 patches, of which 1,646,535 patches were valid for training. The other WM section was arranged for testing. Based on the whole image testing, AI and pathologists presented the same answers among 21 of 22 pieces of sections. To evaluate the diagnostic results at the pixel level, we anticipated correct cancer or non-cancer diagnose from this AI system. The area under the ROC curve as 96.8%. The value of pixel accuracy of three methods (binary analysis, clinically oriented analysis and analysis for different ISUP Gleason grade) were 96.93%, 95.43% and 93.88%, respectively. The value of frequency weighted IoU were 94.32%, 92.13% and 90.21%, respectively.

CONCLUSIONS

This AI system is able to assist pathologists to make a final diagnosis, indicating the great potential and a wide-range of applications of AI in the medical field.

A Novel Signature Based on Anoikis Associated with BCR-Free Survival for Prostate Cancer

This study aimed to elucidate the role of anoikis in the progression of prostate cancer (PCa) and to develop a prognostic signature based on anoikis-related genes (ARGs). To achieve this, PCa cases were subjected to nonnegative matrix factorization (NMF) analysis, which allowed for the identification of distinct patterns of anoikis modification. Additionally, immune infiltration was evaluated using single-sample gene-set enrichment analysis (ssGSEA). Survival analysis was performed using the Kaplan-Meier method, and a risk score was generated based on the expression levels of ARGs to quantitatively assess the modification of anoikis in PCa. Using the Least Absolute Shrinkage and Selection Operator (LASSO) method, four hub-genes were identified, and patients were classified into different risk groups based on their individual scores. Importantly, the low-risk subtype was characterized by a significantly improved biochemical recurrence-free survival, underscoring the clinical relevance of the ARG-based prognostic signature. To further improve the prognostic accuracy of the signature, patient age, pathological T stage, Gleason score, and prostate-specific antigen level were incorporated into the analysis, yielding a comprehensive prognostic signature. The clinical relevance of this signature was illustrated through a nomogram, providing a visual representation of the prognostic implications of the ARG-based signature. Taken together, these findings highlight the potential of ARGs in predicting the clinical outcomes of PCa patients and provide a novel and clinically relevant prognostic signature based on the modification of anoikis in PCa.

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

Background

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

Methods

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

Results

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

Conclusions

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

Comprehensive analysis of aerobic glycolysis-related genes for prognosis, immune features and drug treatment strategy in prostate cancer

Background

The dysregulated expression of aerobic glycolysis-related genes is closely related to prostate cancer progression and metastasis. However, reliable prognostic signatures based on aerobic glycolysis have not been well established.

Methods

We screened aerobic glycolysis-related gene modules by weighted gene co-expression network analysis (WGCNA) and established the aerobic glycolysis-related prognostic risk score (AGRS) by univariate Cox and lasso-Cox. In addition, enriched pathways, genomic mutation, and tumor-infiltrating immune cells were analyzed in AGRS subgroups and compared to each other. We also assessed chemotherapeutic drug sensitivity and immunotherapy response among two subgroups.

Results

An aerobic glycolysis-related 14-gene prognostic model has been established. This model has good predictive prognostic performance both in the training dataset and in two independent validation datasets. Higher AGRS group patients had better immunotherapy response. Different AGRS patients were also associated with sensitivity of multiple prostate cancer chemotherapeutic drugs. We also predicted eight aerobic glycolysis-related small-molecule drugs by differentially expressed genes.

Conclusion

In summary, the aerobic glycolysis-derived signatures are promising biomarkers to predict clinical outcomes and therapeutic responses in prostate cancer.

Targeting the MALAT1 gene with the CRISPR/Cas9 technique in prostate cancer

BACKGROUND

The MALAT1 lncRNA acts as an oncogene in Prostate cancer (PC); thus, it can be severe as a cancer biomarker.

METHODS

Using bioinformatics datasets including (HTSeq-Counts, GDC, and TCGA) 5501 gene expression profiling specimens were gathered. Then, expression profiles and sample survival of lncRNA were investigated using COX regression analyses, ROC curve analysis. The Database for Annotation, Visualization, and Integrated Discovery was used to conduct GO and KEGG studies on the lncRNA-related PCGs. After MALAT1 Knockout via CRISPR/Cas9 technique, the MALAT1 expression was assessed in DU-145 cells. The deletion of the target fragment was examined by polymerase chain reaction (PCR). Also, the expression of apoptosis genes was investigated by qRT-PCR. The viability and cell proliferation were measured using the MTT assay. Cell migration capability was determined using the cell scratch assay. The results of qRT-PCR were assessed by the ΔΔCt method, and finally, statistical analysis was performed in SPSS software.

RESULTS

A maximum of 451 lncRNAs were discovered to reflect different expressions between PC and non-carcinoma tissue samples, with 307 being upregulated and 144 being down-regulated. Thirty-six lncRNAs related to OS were carefully selected, which were then subjected to stepwise multivariate Cox regression analysis, with 2 lncRNAs (MALAT1, HOXB-AS3). MALAT1 is highly expressed in PC cells. MALAT1 Knockout in DU-145 cells increases apoptosis and prevents proliferation and migration, and DU-145 transfected cells were unable to migrate based on the scratch recovery test. Overall, data suggest that MALAT1 overexpression in PC helps metastasis and tumorigenesis. Also, MALAT1 knockout can be considered a therapeutic and diagnostic target in PC.

CONCLUSION

Targeting MALAT1 by CRISPR/Cas9 technique inhibit the cell proliferation and migration, and in addition induce apoptosis. Thus, MALAT1 can act as a tumor biomarker and therapeutic target.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Background

Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims

To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results

Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions

Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

Background and objective

Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.

Methods

Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature’s prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs.

Results

Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.

Conclusion

Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

Identification of seven long noncoding RNAs signature for prediction of biochemical recurrence in prostate cancer

Accumulating evidence suggested that long noncoding RNAs (lncRNAs) possess a potential role in prostate cancer (PCa) diagnosis and prognosis. Rapid biochemical recurrence (BCR) is considered as a sign for clinical recurrence metastasis and PCa-specific mortality. Hence, the aim of the present study was to identify a lncRNA signature that can predict BCR of PCa accurately. Bioinformatics analysis, Kaplan–Meier analyses, Cox regression analyses, and Gene Set Enrichment Analysis (GSEA) were performed in a publicly available database with 499 PCa tissues and 52 matched normal tissues. A signature was identified. All these lncRNAs were differentially expressed between tumor and normal tissues and differentially expressed between high Gleason score and low Gleason score tissues. Furthermore, we developed a seven lncRNAs signature that can predict PCa BCR. Patients classified into low-risk group showed better BCR survival significantly than the patients in the high-risk group (hazard ratio = 0.32, 95% CI: 0.20–0.52, concordance index = 0.63). The area under the curve was 0.68 for BCR. The signature also had good discrimination for BCR in men with Gleason 7 PCa. In conclusion, our results suggest that the seven lncRNAs signature is a new biomarker of BCR and high risk in PCa. In addition, the individual lncRNA warrants further study to uncover the associated mechanisms of PCa progression and the signature could be used to design direct clinical trials for adjuvant therapy.

A 10-gene signature as a predictor of biochemical recurrence after radical prostatectomy in patients with prostate cancer and a Gleason score ≥7

The time and speed of biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) is highly variable. Stratification methods based on TNM staging and Gleason score (GS) do not allow the identification of patients at risk of BCR following RP. Therefore, the aim of the present study was to identify molecular signatures that can predict BCR risk effectively and facilitate treatment-related decisions for patients with PCa. RNA sequencing data and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Oncomine databases. Bioinformatics analysis was performed to identify differentially expressed genes in patients with GS=6 and GS ≥7. Cox regression models were used to determine the PCa signature (PCasig) and a clinical nomogram for the prediction of BCR. The performance of nomograms was assessed using time-dependent receiver operating characteristic curves and the concordance index (C-index). A PCasig comprising 10 genes, including SEMG2, KCNJ16, TFAP2B, SYCE1, KCNU1, AFP, GUCY1B2, GRIA4, NXPH1 and SOX11, was significantly associated with BCR, which was identified in TCGA cohort [hazard ratio (HR), 5.18; 95% CI, 3.241-8.272; C-index, 0.777] and validated in the Oncomine cohort (HR, 2.78; 95% CI, 1.39-5.54; C-index, 0.66). The expression levels of SEMG2, KCNJ16 and TFAP2B were downregulated in patients with GS ≥7. The expression levels of SYCE1, KCNU1, AFP, GUCY1B2, GRIA4, NXPH1 and SOX11 were upregulated in patients with GS ≥7. The clinical nomogram was constructed based on the GS and pathologic T stage (HR, 4.15; 95% CI, 1.39-5.54; C-index, 0.713). The addition of the PCasig to the clinical nomogram significantly improved prognostic value (HR, 7.25; 95% CI, 4.54-11.56; C-index, 0.782) with an net reclassification improvement of 75.3% (95% CI, 46.8-104.6%). Furthermore, the endogenous expression of each gene in the PCasig was measured in five PCa cell lines and in normal prostate cells, and these genes exhibited different expression levels relative to one another. In conclusion, an PCasig was identified by mining TCGA and successfully validated in an Oncomine cohort. This PCasig was an independent prognostic factor with a greater prognostic value for all patients regardless of GS than traditional clinical variables, which can improve the performance of clinical nomograms in predicting BCR of patients with GS ≥7.

A novel gene signature to predict immune infiltration and outcome in patients with prostate cancer

Prostate cancer (PCa) is one of the most common malignancies in male. We aim to establish a novel gene signature for immune infiltration and outcome (biochemical recurrence (BCR) and overall survival (OS)) of patients with prostate cancer (PCa) to augment Gleason patterns for evaluating prognosis and managing patients undergoing radical prostatectomy (RP). Combined with our microarray data and the Cancer Genome Atlas Project (TCGA) database (discovery set), we identified a six-gene signature. The Gene Expression Omnibus (GEO) database served as the test set. The databases of Fudan University Shanghai Cancer Center (FUSCC) and Third Affiliated Hospital of Nantong University (TAHNU) served as an external validation set. Immunohistochemistry was used to investigate the relationship between risk groups and the immune infiltrate. We identified a six-gene signature to predict immune cell infiltration and outcome of PCa patients. The AUC values used to predict early BCR in the discovery, test, FUSCC, and TAHNU sets were 0.73, 0.76, 0.72, and 0.81, respectively. Low-risk score patients in each dataset experienced significantly longer OS (P = .01, 0.04, 0.02, respectively). The signature also predicted high regulatory T cells (Tregs) and M2-polarized macrophages infiltration in high-risk score patients with PCa. Additionally, high mutation load, related signal pathways, and sensitivity to anticancer drugs that correlated with high-risk score of cancer progression and death were also identified. The six-gene signature may improve prognostic information, serve as a prognostic tool to manage patients after RP, and advance basic studies of PCa.

Role of steroid receptor and coregulator mutations in hormone-dependent cancers

Steroid hormones mediate critical lineage-specific developmental and physiologic responses. They function by binding their cognate receptors, which are transcription factors that drive specific gene expression programs. The requirement of most prostate cancers for androgen and most breast cancers for estrogen has led to the development of endocrine therapies that block the action of these hormones in these tumors. While initial endocrine interventions are successful, resistance to therapy often arises. We will review how steroid receptor-dependent genomic signaling is affected by genetic alterations in endocrine therapy resistance. The detailed understanding of these interactions will not only provide improved treatment options to overcome resistance, but, in the future, will also be the basis for implementing precision cancer medicine approaches.