Prostate cancer progression modeling provides insight into dynamic molecular changes associated with progressive disease states.

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multi-dimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from the TCGA study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks.

BACKGROUND: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. METHODS: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. RESULTS: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

KREH1 RNA helicase activity promotes utilization of initiator gRNAs across multiple mRNAs in trypanosome RNA editing.

Mitochondrial U-indel RNA editing in kinetoplastid protozoa is directed by trans-acting gRNAs and mediated by a holoenzyme with associated factors. Here, we examine the function of the holoenzyme-associated KREH1 RNA helicase in U-indel editing. We show that KREH1 knockout (KO) impairs editing of a small subset of mRNAs. Overexpression of helicase-dead mutants results in expanded impairment of editing across multiple transcripts, suggesting the existence of enzymes that can compensate for KREH1 in KO cells. In depth analysis of editing defects using quantitative RT-PCR and high-throughput sequencing reveals compromised editing initiation and progression in both KREH1-KO and mutant-expressing cells. In addition, these cells exhibit a distinct defect in the earliest stages of editing in which the initiator gRNA is bypassed, and a small number of editing events takes place just outside this region. Wild type KREH1 and a helicase-dead KREH1 mutant interact similarly with RNA and holoenzyme, and overexpression of both similarly disorders holoenzyme homeostasis. Thus, our data support a model in which KREH1 RNA helicase activity facilitates remodeling of initiator gRNA-mRNA duplexes to permit accurate utilization of initiating gRNAs on multiple transcripts.

Association of SNPs in the PAI1 Gene with Disease Recurrence and Clinical Outcome in Bladder Cancer.

PURPOSE: Bladder cancer (BCa) is one of the most common cancer types worldwide and is characterized by a high rate of recurrence. In previous studies, we and others have described the functional influence of plasminogen activator inhibitor-1 (PAI1) in bladder cancer development. While polymorphisms in PAI1 have been associated with increased risk and worsened prognosis in some cancers, the mutational status of PAI1 in human bladder tumors has not been well defined. METHODS: In this study, we evaluated the mutational status of PAI1 in a series of independent cohorts, comprised of a total of 660 subjects. RESULTS: Sequencing analyses identified two clinically relevant 3' untranslated region (UTR) single nucleotide polymorphisms (SNPs) in PAI1 (rs7242; rs1050813). Somatic SNP rs7242 was present in human BCa cohorts (overall incidence of 72%; 62% in Caucasians and 72% in Asians). In contrast, the overall incidence of germline SNP rs1050813 was 18% (39% in Caucasians and 6% in Asians). Furthermore, Caucasian patients with at least one of the described SNPs had worse recurrence-free survival and overall survival (p = 0.03 and p = 0.03, respectively). In vitro functional studies demonstrated that SNP rs7242 increased the anti-apoptotic effect of PAI1, and SNP rs1050813 was related to a loss of contact inhibition associated with cellular proliferation when compared to wild type. CONCLUSION: Further investigation of the prevalence and potential downstream influence of these SNPs in bladder cancer is warranted.

Comparison of induction chemotherapy combined with concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in Lymph-Node-Stage III nasopharyngeal carcinoma based on propensity score-matching.

PURPOSE: To explore the role of induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) versus CCRT alone in patients diagnosed with N3 nasopharyngeal carcinoma (NPC). PATIENTS AND METHODS: A total of 787 patients with newly diagnosed N3 NPC treated with IC + CCRT or CCRT alone were included. Progression-free survival (PFS) was the primary endpoint. We balanced variables using propensity score matching (PSM). Kaplan-Meier curves with log-rank tests were applied to evaluate the survival condition of each group. Independent prognostic factors were identified using the Cox regression analysis. RESULTS: PSM assigned 228 patients to IC + CCRT and CCRT alone groups. Survival analysis for the matched data set showed that IC + CCRT achieved better survival outcomes compared with CCRT alone, and significant difference was observed in 5-year PFS [74.8% (95%CI 69.2 âˆ¼ 80.9%) vs 65.4% (95%CI 59.4 âˆ¼ 72.0%), P = 0.008], 5-year OS [(77.4%(95%CI 71.9 âˆ¼ 83.3%) vs66.3%(95%CI 60.3 âˆ¼ 72.9%), P = 0.005)] and 5-year distant metastasis-free survival (DMFS)[(81.8%(95%CI 76.7 âˆ¼ 87.2%) vs72.4%(95%CI 66.7 âˆ¼ 78.7%), P = 0.007)] between the two treatment groups. In multivariate analysis, IC + CCRT remained an independent protective factor for PFS (adjusted HR, 0.603; 95% CI, 0.433-0.841; P = 0.003), OS (adjusted HR, 0.568; 95% CI, 0.406-0.793; P < 0.001), and DMFS (adjusted HR, 0.541; 95% CI, 0.364-0.805; P = 0.002). CONCLUSION: More chemotherapy should be considered in patients with N3 NPC because of its ability to improve survival time. This could be from the use of IC or adjuvant metronomic chemotherapy.

Conserved and transcript-specific functions of the RESC factors, RESC13 and RESC14, in kinetoplastid RNA editing.

Uridine insertion/deletion RNA editing is an extensive post-transcriptional modification of mitochondrial mRNAs in kinetoplastid organisms, including Trypanosoma brucei This process is carried out using trans-acting gRNAs and complex protein machinery. The essential RNA editing substrate binding complex (RESC) serves as the scaffold that modulates protein and RNA interactions during editing, and contains the guide RNA binding complex (GRBC), the RNA editing mediator complexes (REMCs), and organizer proteins. Despite the importance of RESC in editing, the functions of each protein comprising this complex are not completely understood. Here, we further define the roles of a REMC protein, RESC13, and a RESC organizer, RESC14, using high-throughput sequencing on two large pan-edited mRNAs, A6 and COIII. When comparing our analyses to that of a previously published small pan-edited mRNA, RPS12, we find that RESC13 has conserved functions across the three transcripts with regard to editing initiation, gRNA utilization, gRNA exchange, and restricting the formation of long misedited junctions that likely arise from its ability to modulate RNA structure. However, RESC13 does have transcript-specific effects on the types of long junctions whose formation it restricts. RESC14 has a conserved effect on gRNA utilization across the three transcripts analyzed, but has transcript-specific effects on editing initiation, gRNA exchange, and junction formation. Our data suggest that transcript-specific effects of both proteins are due to differences in transcript length and sequences as well as transcript-specific protein interactions. These findings highlight the importance of studying multiple transcripts to determine the function of editing factors.

A Diagnostic Gene Expression Signature for Bladder Cancer Can Stratify Cases into Prescribed Molecular Subtypes and Predict Outcome.

Bladder cancer is a biologically heterogeneous disease with variable clinical presentations, outcomes and responses to therapy. Thus, the clinical utility of single biomarkers for the detection and prediction of biological behavior of bladder cancer is limited. We have previously identified and validated a bladder cancer diagnostic signature composed of 10 biomarkers, which has been incorporated into a multiplex immunoassay bladder cancer test, Oncuria™. In this study, we evaluate whether these 10 biomarkers can assist in the prediction of bladder cancer clinical outcomes. Tumor gene expression and patient survival data from bladder cancer cases from The Cancer Genome Atlas (TCGA) were analyzed. Alignment between the mRNA expression of 10 biomarkers and the TCGA 2017 subtype classification was assessed. Kaplan-Meier analysis of multiple gene expression datasets indicated that high expression of the combined 10 biomarkers correlated with a significant reduction in overall survival. The analysis of three independent, publicly available gene expression datasets confirmed that multiplex prognostic models outperformed single biomarkers. In total, 8 of the 10 biomarkers from the Oncuria™ test were significantly associated with either luminal or basal molecular subtypes, and thus, the test has the potential to assist in the prediction of clinical outcome.

PAI-1 is a potential transcriptional silencer that supports bladder cancer cell activity.

The extracellular activity of Plasminogen activator inhibitor-1 (PAI-1) is well described, acting as an inhibitor of tissue plasminogen activator and urokinase-type plasminogen activator, impacting fibrinolysis. Recent studies have revealed a pro-tumorigenic role of PAI-1 in human cancers, via the regulation of angiogenesis and tumor cell survival. In this study, immunohistochemical staining of 939 human bladder cancer specimens showed that PAI-1 expression levels correlated with tumor grade, tumor stage and overall survival. The typical subcellular localization of PAI-1 is cytoplasmic, but in approximately a quarter of the cases, PAI-1 was observed to be localized to both the tumor cell cytoplasm and the nucleus. To investigate the potential function of nuclear PAI-1 in tumor biology we applied chromatin immunoprecipitation (ChIP)-sequencing, gene expression profiling, and rapid immunoprecipitation mass spectrometry to a pair of bladder cancer cell lines. ChIP-sequencing revealed that PAI-1 can bind DNA at distal intergenic regions, suggesting a role as a transcriptional coregulator. The downregulation of PAI-1 in bladder cancer cell lines caused the upregulation of numerous genes, and the integration of ChIP-sequence and RNA-sequence data identified 57 candidate genes subject to PAI-1 regulation. Taken together, the data suggest that nuclear PAI-1 can influence gene expression programs and support malignancy.

Application of a multiplex urinalysis test for the prediction of intravesical BCG treatment response: A pilot study.

BACKGROUND: Intravesical Bacillus Calmette-Guerin (BCG), a live attenuated tuberculosis vaccine that acts as a non-specific immune system stimulant, is the most effective adjuvant treatment for patients with intermediate or high-risk non-muscle-invasive bladder cancer (NMIBC). However, to date, there are no reliable tests that are predictive of BCG treatment response. In this study, we evaluated the performance of OncuriaTM, a bladder cancer detection test, to predict response to intravesical BCG. METHODS: OncuriaTM data was evaluated in voided urine samples obtained from a prospectively collected cohort of 64 subjects with intermediate or high risk NMIBC prior to treatment with intravesical BCG. The OncuriaTM test, which measures 10 cancer-associated biomarkers was performed in an independent clinical laboratory. The ability of the test to identify those patients in whom BCG is ineffective against tumor recurrence was tested. Predictive models were derived using supervised learning and cross-validation analyses. Model performance was assessed using ROC curves. RESULTS: Pre-treatment urinary concentrations of MMP9, VEGFA, CA9, SDC1, PAI1, APOE, A1AT, ANG and MMP10 were increased in patients who developed disease recurrence. A combinatorial predictive model of treatment outcome achieved an AUROC 0.89 [95% CI: 0.80-0.99], outperforming any single biomarker, with a test sensitivity of 81.8% and a specificity of 84.9%. Hazard ratio analysis revealed that patients with higher urinary levels of ANG, CA9 and MMP10 had a significantly higher risk of disease recurrence. CONCLUSIONS: Monitoring the urinary levels of a cancer-associated biomarker panel enabled the discrimination of patients who did not respond to intravesical BCG therapy. With further study, the multiplex OncuriaTM test may be applicable for the clinical evaluation of bladder cancer patients considering intravesical BCG treatment.

Influencing Factors on the Oncuria™ Urinalysis Assay: An Experimental Model.

BACKGROUND: The Oncuria™ urine test for the detection of bladder cancer measures a multiplex protein signature. In this study, we investigated the influence of urinary cellularity, protein, and hematuria on the performance of the Oncuria™ test in an ex vivo experimental model. MATERIALS AND METHODS: Pooled urine from healthy subjects was spiked with cultured benign (UROtsa) or malignant cells (T24), cellular proteins, or whole blood. The resulting samples were analyzed using the Oncuria™ test following the manufacturer's instructions. RESULTS: Urine samples obtained from healthy subjects were negative for bladder cancer by Oncuria™ test criteria. The majority of the manipulated conditions did not result in a false-positive test. The addition of whole blood (high concentration) did result in a false-positive result, but this was abrogated by sample centrifugation prior to analysis. The addition of cellular proteins (high concentration) resulted in a positive Oncuria™ test, and this was unaffected by pre-analysis sample centrifugation. CONCLUSIONS: The Oncuria™ multiplex test performed well in the ex vivo experimental model and shows promise for clinical application. The identification of patients who require additional clinical evaluation could reduce the need to subject patients who do not have bladder cancer to frequent, uncomfortable and expensive cystoscopic examinations, thus benefiting both patients and the healthcare system.