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

The Impact of Prostate-Specific Antigen and Gleason Scores on Cardiovascular Death in Prostate Cancer Patients after Radiotherapy or Chemotherapy: A Population-Based Study

Background

Tumor characteristics are associated with the risk of cardiovascular death (CVD) in cancer patients. However, the influence of tumor characteristics on CVD risk among prostate cancer (PC) patients who have received radiotherapy (RT) or chemotherapy (CT) is often overlooked. This study explored the association between PC tumor characteristics and CVD risk in PC patients who had received RT or CT.

Methods

Fine-gray competitive risk analysis was employed to identify CVD risk factors. Sensitivity analyses were conducted to adjust for confounding factors. The predicted prostate-specific antigen (PSA) and Gleason score values were visualized using a nomogram, which was subsequently validated through calibration curves and concordance indexes (C-indexes).

Results

A total of 120,908 patients were enrolled in the study, with a mean follow-up time of 80 months. PSA values between 10 and 20 ng/mL (adjusted hazard ratio (HR): 1.28, 95% confidence interval (CI): 1.20-1.36, p < 0.001) and >20 ng/mL (adjusted HR: 1.27, 95% CI: 1.21-1.35, p < 0.001), and a Gleason score >7 (adjusted HR: 1.23, 95% CI: 1.07-1.41, p = 0.004) were identified as risk factors of CVD for PC patients after RT or CT. The C-index of the training cohort was 0.66 (95% CI: 0.66-0.67), and the C-index of the validation cohort was 0.67 (95% CI: 0.65-0.68). Consistency was observed between the actual observations and the nomogram. Risk stratification was also significant (p < 0.001).

Conclusions

PSA values ≥10 ng/mL and Gleason scores >7 may be associated with an increased risk of CVD in PC patients after RT or CT. These patients may require more long-term follow-up and monitoring of CVD risk.

LIM domain only 7: a novel driver of immune evasion through regulatory T cell differentiation and chemotaxis in pancreatic ductal adenocarcinoma

With advancements in genomics and immunology, immunotherapy has emerged as a revolutionary strategy for tumor treatment. However, pancreatic ductal adenocarcinoma (PDAC), an immunologically "cold" tumor, exhibits limited responsiveness to immunotherapy. This study aimed to address the urgent need to uncover PDAC's immune microenvironment heterogeneity and identify the molecular mechanisms driving immune evasion. Using single-cell RNA sequencing datasets and spatial proteomics, we discovered LIM domain only 7 (LMO7) in PDAC cells as a previously unrecognized driver of immune evasion through Treg cell enrichment. LMO7 was positively correlated with infiltrating regulatory T cells (Tregs) and dysfunctional CD8+ T cells. A series of in vitro and in vivo experiments demonstrated LMO7's significant role in promoting Treg cell differentiation and chemotaxis while inhibiting CD8+ T cells and natural killer cell cytotoxicity. Mechanistically, LMO7, through its LIM domain, directly bound and promoted the ubiquitination and degradation of Foxp1. Foxp1 negatively regulated transforming growth factor-beta (TGF-β) and C-C motif chemokine ligand 5 (CCL5) expression by binding to sites 2 and I/III, respectively. Elevated TGF-β and CCL5 levels contribute to Treg cell enrichment, inducing immune evasion in PDAC. Combined treatment with TGF-β/CCL5 antibodies, along with LMO7 inhibition, effectively reversed immune evasion in PDAC, activated the immune response, and prolonged mouse survival. Therefore, this study identified LMO7 as a novel facilitator in driving immune evasion by promoting Treg cell enrichment and inhibiting cytotoxic effector functions. Targeting the LMO7-Foxp1-TGF-β/CCL5 axis holds promise as a therapeutic strategy for PDAC. Graphical abstract revealing LMO7 as a novel facilitator in driving immune evasion by promoting Tregs differentiation and chemotaxis, inducing CD8+ T/natural killer cells inhibition.

Role of LMO7 in cancer (Review)

Cancer constitutes a multifaceted ailment characterized by the dysregulation of numerous genes and pathways. Among these, LIM domain only 7 (LMO7) has emerged as a significant player in various cancer types, garnering substantial attention for its involvement in tumorigenesis and cancer progression. This review endeavors to furnish a comprehensive discourse on the functional intricacies and mechanisms of LMO7 in cancer, with a particular emphasis on its potential as both a therapeutic target and prognostic indicator. It delves into the molecular attributes of LMO7, its implications in cancer etiology and the underlying mechanisms propelling its oncogenic properties. Furthermore, it underscores the extant challenges and forthcoming prospects in targeting LMO7 for combating cancer.

Study on the mechanism of S100A4-mediated cancer oncogenesis in uveal melanoma cells through the integration of bioinformatics and in vitro experiments

BACKGROUND

The elevated metastasis rate of uveal melanoma (UM) is intricately correlated with patient prognosis, significantly affecting the quality of life. S100 calcium-binding protein A4 (S100A4) has tumorigenic properties; therefore, the present study investigated the impact of S100A4 on UM cell proliferation, apoptosis, migration, and invasion using bioinformatics and in vitro experiments.

METHODS

Bioinformatic analysis was used to screen S100A4 as a hub gene and predict its possible mechanism in UM cells, and the S100A4 silencing cell line was constructed. The impact of S100A4 silencing on the proliferative ability of UM cells was detected using the Cell Counting Kit-8 and colony formation assays. Annexin V-FITC/PI double fluorescence and Hoechst 33342 staining were used to observe the effects of apoptosis on UM cells. The effect of S100A4 silencing on the migratory and invasive capabilities of UM cells was assessed using wound healing and Transwell assays. Western blotting was used to detect the expression of related proteins.

RESULTS

The present study found that S100A4 is a biomarker of UM, and its high expression is related to poor prognosis. After constructing the S100A4 silencing cell line, cell viability, clone number, proliferating cell nuclear antigen, X-linked inhibitor of apoptosis protein, and survivin expression were decreased in UM cells. The cell apoptosis rate and relative fluorescence intensity increased, accompanied by increased levels of Bax and caspase-3 and decreased levels of Bcl-2. Additionally, a decrease in the cell migration index and relative invasion rate was observed with increased E-cadherin expression and decreased N-cadherin and vimentin protein expression.

CONCLUSION

S100A4 silencing can inhibit the proliferation, migration, and invasion and synchronously induces apoptosis in UM cells.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Identification of potential biomarkers for progression and prognosis of renal clear cell carcinoma by comprehensive bioinformatics analysis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of renal cell carcinoma (RCC), and effective biomarkers will improve diagnosis and treatment.

OBJECTIVE

This study investigated NPEPL1 expression in ccRCC through public databases and clinical samples and assessed its correlation with clinicopathological features and patient prognosis.

METHOD

Data from The Cancer Genome Atlas and clinical specimens were gathered, NPEPL1 expression levels were analyzed; a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of NPEPL1; and clinicopathological data was used to study the correlations between expression and clinical parameters. NPEPL1's prognostic value was appraised using a Kaplan-Meier (K-M) survival curve, Cox regression analysis, and a nomogram model; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differently expressed genes between tissues with high and low NPEPL1 expression were used to estimate the underlying mechanisms involved.

RESULTS

NPEPL1 was significantly higher-expressed in ccRCC tissue. ROC analysis showed that NPEPL1 had noteworthy diagnostic efficacy. NPEPL1 expression was closely related to clinicopathological parameters, such as T and M stage. K-M analysis showed that overall survival was significantly shortened with high NPEPL1 expression. Cox regression analysis showed that NPEPL1 expression was an independent risk factor predicting overall survival. The nomogram showed a significantly high clinical value in predicting the 1-, 3-, and 5-year survival probabilities in ccRCC. GO and KEGG enrichment analysis suggested that NPEPL1 may promote the occurrence and development of ccRCC via the Ras signaling and other pathways.

CONCLUSION

NPEPL1 expression in ccRCC was higher than that in normal kidney tissues and was significantly associated with advanced clinical stage and poor prognosis. Therefore, NPEPL1 is a promising prognostic biomarker.

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.

Dissecting the effects of METTL3 on alternative splicing in prostate cancer

Although the role of METTL3 has been extensively studied in many cancers, its role in isoform switching in prostate cancer (PCa) has been poorly explored. To investigate its role, we applied standard RNA-sequencing and long-read direct RNA-sequencing from Oxford Nanopore to examine how METTL3 affects alternative splicing (AS) in two PCa cell lines. By dissecting genome-wide METTL3-regulated AS events, we noted that two PCa cell lines (representing two different PCa subtypes, androgen-sensitive or resistant) behave differently in exon skipping and intron retention events following METTL3 depletion, suggesting AS heterogeneity in PCa. Moreover, we revealed that METTL3-regulated AS is dependent on N6-methyladenosine (m6A) and distinct splicing factors. Analysis of the AS landscape also revealed cell type specific AS signatures for some genes (e.g., MKNK2) involved in key functions in PCa tumorigenesis. Finally, we also validated the clinical relevance of MKNK2 AS events in PCa patients and pointed to the possible regulatory mechanism related to m6A in the exon14a/b region and SRSF1. Overall, we characterize the role of METTL3 in regulating PCa-associated AS programs, expand the role of METTL3 in tumorigenesis, and suggest that MKNK2 AS events may serve as a new potential prognostic biomarker.

Identification of novel molecular subtypes and a signature to predict prognosis and therapeutic response based on cuproptosis-related genes in prostate cancer

Background

Prostate cancer (PCa) is the most common malignant tumor of the male urinary system. Cuproptosis, as a novel regulated cell death, remains unclear in PCa. This study aimed to investigate the role of cuproptosis-related genes (CRGs) in molecular stratification, prognostic prediction, and clinical decision-making in PCa.

Methods

Cuproptosis-related molecular subtypes were identified by consensus clustering analysis. A prognostic signature was constructed with LASSO cox regression analyses with 10-fold cross-validation. It was further validated in the internal validation cohort and eight external validation cohorts. The tumor microenvironment between the two risk groups was compared using the ssGSEA and ESTIMATE algorithms. Finally, qRT-PCR was used to explore the expression and regulation of these model genes at the cellular level. Furthermore, 4D Label-Free LC-MS/MS and RNAseq were used to investigate the changes in CRGs at protein and RNA levels after the knockdown of the key model gene B4GALNT4.

Results

Two cuproptosis-related molecular subtypes with significant differences in prognoses, clinical features, and the immune microenvironment were identified. Immunosuppressive microenvironments were associated with poor prognosis. A prognostic signature comprised of five genes (B4GALNT4, FAM83D, COL1A, CHRM3, and MYBPC1) was constructed. The performance and generalizability of the signature were validated in eight completely independent datasets from multiple centers. Patients in the high-risk group had a poorer prognosis, more immune cell infiltration, more active immune-related functions, higher expression of human leukocyte antigen and immune checkpoint molecules, and higher immune scores. In addition, anti-PDL-1 immunotherapy prediction, somatic mutation, chemotherapy response prediction, and potential drug prediction were also analyzed based on the risk signature. The validation of five model genes' expression and regulation in qPCR was consistent with the results of bioinformatics analysis. Transcriptomics and proteomics analyses revealed that the key model gene B4GALNT4 might regulate CRGs through protein modification after transcription.

Conclusion

The cuproptosis-related molecular subtypes and the prognostic signature identified in this study could be used to predict the prognosis and contribute to the clinical decision-making of PCa. Furthermore, we identified a potential cuproptosis-related oncogene B4GALNT4 in PCa, which could be used as a target to treat PCa in combination with cuproptosis.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusion

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

Comprehensive Analysis of Transcriptomic Profiles Identified the Prediction of Prognosis and Drug Sensitivity of Aminopeptidase-Like 1 (NPEPL1) for Clear Cell Renal Cell Carcinoma

Aminopeptidase-like 1 (NPEPL1) is a member of the aminopeptidase group that plays a role in the development and progression of various diseases. Expression of NPEPL1 has been reported to be involved in prostate, breast, and colorectal cancers. However, the role and mechanism of NPEPL1 in clear cell renal cell carcinoma (ccRCC) are unclear. The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases were used to predict the relationship between clinicopathological features and NPEPL1 expression. Changes in immune status and drug sensitivity with NPEPL1 expression were analyzed by the "CIBERSORT" function in R software. The results found that NPEPL1 expression was upregulated in ccRCC tissues, with expression progressively increasing with ccRCC stage and grade. Patients with high NPEPL1 expression presented with a poor prognosis across different clinicopathological features. Univariate and multivariate Cox regression analyses indicated that aberrant NPEPL1 expression was an independent risk factor for ccRCC. The nomogram showed that NPEPL1 expression improved the accuracy of predicting the prognosis of ccRCC patients. The Gene Ontology (GO) term enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that NPEPL1 may be involved in the development of ccRCC through the voltage-gated calcium channel complex, channel activity, cAMP signaling pathway, and oxytocin signaling pathway. The coexpression analysis found that NPEPL1 altered tumor characteristics by interacting with related genes. The "CIBERSORT" analysis showed that elevated NPEPL1 expression was followed by an enrichment of regulatory T cells and follicular helper T cells in the microenvironment. The drug sensitivity analysis found patients with high NPEPL1 expression had a higher benefit from axitinib, cisplatin, and GSK429286A. In conclusion, upregulation of NPEPL1 expression was involved in ccRCC prognosis and treatment. NPEPL1 could be used as a therapeutic target to guide clinical dosing.

Development of a 4-miRNA prognostic signature for endometrial cancer

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan-Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

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 Potential Prognostic Biomarkers Associated with Monocyte Infiltration in Lung Squamous Cell Carcinoma

The five-year survival rate of lung squamous cell carcinoma is significantly lower than that of other cancer types. It is therefore urgent to discover novel prognosis biomarkers and therapeutic targets and understand their correction with infiltrating immune cells to improve the prognosis of patients with lung squamous cell carcinoma. In this study, we employed robust rank aggregation algorithms to overcome the shortcomings of small sizes and potential bias in each Gene Expression Omnibus dataset of lung squamous cell carcinoma and identified 513 robust differentially expressed genes including 220 upregulated and 293 downregulated genes from six microarray datasets. Functional enrichment analysis showed that these robust differentially expressed genes were obviously involved in the extracellular matrix and structure organization, epidermis development, cell adhesion molecule binding, p53 signaling pathway, and interleukin-17 signaling pathway to affect the progress of lung squamous cell carcinoma. We further identified six hub genes from 513 robust differentially expressed genes by protein-protein interaction network and 10 topological analyses. Moreover, the results of immune cell infiltration analysis from six integrated Gene Expression Omnibus datasets and our sequencing transcriptome data demonstrated that the abundance of monocytes was significantly lower in lung squamous cell carcinoma compared to controls. Immune correlation analysis and survival analysis of hub genes suggested that three hub genes, collagen alpha-1(VII) chain, mesothelin, and chordin-like protein 1, significantly correlated with tumor-infiltrating monocytes as well as may be potential prognostic biomarkers and therapy targets in lung squamous cell carcinoma. The investigation of the correlation of hub gene markers and infiltrating monocytes can also improve to well understand the molecular mechanisms of lung squamous cell carcinoma development.

Signature for Prostate Cancer Based on Autophagy-Related Genes and a Nomogram for Quantitative Risk Stratification

Background. Prostate cancer (PCa) ranks as the most common malignancy and the second leading cause of cancer-related death among males worldwide. The essential role of autophagy in the progression of PCa and treatment resistance has been preliminarily revealed. However, comprehensive molecular elucidations of the correlation between PCa and autophagy are rare. Method. We obtained transcription information and corresponding clinicopathological profiles of PCa patients from TCGA, MSKCC, and GEO datasets. LAASO analysis was employed to select gene signatures and estimate the autophagy score for each patient. Correlations between the signature and prognosis of PCa were investigated by K-M and multivariate Cox regression analyses. A nomogram was established on the basis of the above results. Further validations relied on ROC, calibration analysis, decision curve analysis, and external cohorts. Variable activated signaling pathways were revealed using GSVA algorithms, and the genetic alteration landscape was elucidated via the oncodrive module from the “maftools” R package. In addition, we also examined the therapeutic role of the signature based on phenotype data from GDSC 2016. Result. Six autophagy-related genes were eventually selected to establish the signature, including ULK1, CAPN10, FKBP5, UBE2T, NLRC4, and BNIP3L. We used these genes and corresponding coefficients to calculate an autophagy score (AutS) for each patient in this study. A high AutS group and a low AutS group were divided on the mean AutS of the patients. Longer overall survival, higher Gleason score and PSA, and better response to ADT were observed in patients with high AutS. Meanwhile, we found that high AutS PCa was related to more proliferation-associated signaling activation and higher genetic mutation frequencies, manifesting a poor prognosis. A nomogram was constructed based on GS, T stage, PSA, and AutS as covariates. Its discriminative efficacy and clinical value were validated using robust statistical methods. Finally, we tested its prognostic value through two external cohorts and six published signatures. Conclusion. The autophagy-related gene signature is a highly discriminative model for risk stratification and drug therapy in PCa, and a nomogram incorporating AutS might be a promising tool for precision medicine.

Establishment of a Novel Combined Nomogram for Predicting the Risk of Progression Related to Castration Resistance in Patients With Prostate Cancer

Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance.

Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages “limma” and “WGCNA” in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan–Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA).

Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA.

Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability.

Patient participation in treatment decision-making of prostate cancer: a qualitative study

PURPOSE

Despite increasing development in decision-making strategies for patients with prostate cancer, little is known about patients' individual experience and perception throughout the decision-making process. The objective of this study was to explore patients' experiences and perceptions towards treatment decision-making.

METHODS

We conducted a qualitative interview study with 30 patients diagnosed with prostate cancer. We transcribed interviews verbatim and inductively identified codes. Thematic analysis was used to develop and refine a codebook that aided in the identification of themes.

RESULTS

Three key themes and nine subthemes emerged, which were as follows: I. less involved in treatment decision-making, (i) passive decisional control, (ii) lack of medical knowledge, and (iii) domination by family members; II. the right to be informed of the disease condition and to choose treatment options, (i) sociocultural influences, (ii) patients believe that they should know the true facts of the disease, and (iii) patient autonomy during treatment; and III. future consideration and advance care planning, (i) fewer future concerns, (ii) advance care planning is poorly understood, and (iii) acceptance of advance care planning.

CONCLUSION

The study results show that patients with prostate cancer have a diversity of needs to cultivate their ability to make treatment decisions, and healthcare professionals should empower patients, as well as provide decision aids or decision support for patients.

The Multifaceted Role of Aldehyde Dehydrogenases in Prostate Cancer Stem Cells

Cancer stem cells (CSCs) are the only tumor cells possessing self-renewal and differentiation properties, making them an engine of tumor progression and a source of tumor regrowth after treatment. Conventional therapies eliminate most non-CSCs, while CSCs often remain radiation and drug resistant, leading to tumor relapse and metastases. Thus, targeting CSCs might be a powerful tool to overcome tumor resistance and increase the efficiency of current cancer treatment strategies. The identification and isolation of the CSC population based on its high aldehyde dehydrogenase activity (ALDH) is widely accepted for prostate cancer (PCa) and many other solid tumors. In PCa, several ALDH genes contribute to the ALDH activity, which can be measured in the enzymatic assay by converting 4, 4-difluoro-4-bora-3a, 4a-diaza-s-indacene (BODIPY) aminoacetaldehyde (BAAA) into the fluorescent product BODIPY-aminoacetate (BAA). Although each ALDH isoform plays an individual role in PCa biology, their mutual functional interplay also contributes to PCa progression. Thus, ALDH proteins are markers and functional regulators of CSC properties, representing an attractive target for cancer treatment. In this review, we discuss the current state of research regarding the role of individual ALDH isoforms in PCa development and progression, their possible therapeutic targeting, and provide an outlook for the future advances in this field.

Construction of a Novel Gene-Based Model for Survival Prediction of Hepatitis B Virus Carriers With HCC Development

Despite the effectiveness of hepatitis B virus (HBV) vaccination in reducing the prevalence of chronic HBV infection as well as the incidence of acute hepatitis B, fulminant hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC), there was still a large crowd of chronically infected populations at risk of developing cirrhosis or HCC. In this study, we established a comprehensive prognostic system covering multiple signatures to elevate the predictive accuracy for overall survival (OS) of hepatitis B virus carriers with HCC development. Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and multivariate COX analysis, along with a suite of other online analyses were successfully applied to filtrate a three-gene signature model (TP53, CFL1, and UBA1). Afterward, the gene-based risk score was calculated based on the Cox coefficient of the individual gene, and the prognostic power was assessed by time-dependent receiver operating characteristic (tROC) and Kaplan–Meier (KM) survival analysis. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and TNM stage), was revealed by the calibration plot and tROC curves, which was verified in the validation set. Taken together, our study may be more effective in guiding the clinical decision-making of personalized treatment for HBV carriers.

CXCR3 Expression and Genome-Wide 3' Splice Site Selection in the TCGA Breast Cancer Cohort

CXCR3 is a chemokine receptor with two well-characterized isoforms that have unique, context-dependent roles: CXCR3-A and CXCR3-B, which are produced through alternative 3′ splice site selection (A3SS). RNA-seq data from The Cancer Genome Atlas (TCGA) were used to correlate CXCR3 expression with breast cancer progression. This analysis revealed significant CXCR3 expression patterns associated with survival and differential expression between the tumor and adjacent normal tissue. TCGA data were used to estimate abundance of immune cells in breast cancer, which demonstrated the association of CXCR3 with immune infiltration, particularly in the triple-negative subtype. Given the importance of A3SS in CXCR3, genome-wide analysis of A3SS events was performed to identify events that were differentially spliced between breast cancer tissue and adjacent normal tissue. A total of 481 splicing events in 424 genes were found to be differentially spliced. The parent genes of differentially spliced events were enriched in RNA processing and splicing functions, indicating an underappreciated role of A3SS in the integrated splicing network of breast cancer. These results further validated the role of CXCR3 in immune infiltration of tumors, while raising questions about the role of A3SS splicing.