scholarly journals PVT1 Expression Is a Predictor for Poor Survival of Prostate Cancer Patients

2021 ◽  
Vol 20 ◽  
pp. 153303382097161
Author(s):  
Jianhua Liu ◽  
Yanqing Li ◽  
Qiqi Zhang ◽  
Chaoxiang Lv ◽  
Mingwei Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Gleason Score ◽  
Roc Curve ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Molecular Therapy ◽  
Vital Status ◽  
Cox Regression Analysis

Objective: Dysregulation of long noncoding RNA is associated with a variety of cancers and LncRNA has anticancer or carcinogenic activities. PVT1, as a long noncoding RNA, plays an important role in the development of cancer. Methods: We use R to download and analyze the data in TCGA database. ROC curve is generated to evaluate the significance of PVT1 expression for the diagnosis of prostate cancer. Chi-square test is used to test correlation between PVT1 expression and clinical pathological features. Survival curve and univariate and multivariate cox regression analysis is performed to compare differences in the effect on the survival rate between PVT1 high expression and low expression. Results: The expression of PTV1 in tumor tissues was significantly higher than that in normal tissues(P<2.2e-16). The difference of PTV1 expression was observed according to vital status (P = 0.0051) and Gleason score (P = 0.0012). The expression of PTV1 is significantly associated with T classification (P < 0.0001), N classification (P = 0.0499), PSA (P = 0.0001), Gleason Score (P < 0.0001), targeted molecular therapy (P = 0.0264) and vital status(P = 0.0036). The area under the ROC curve (AUC) was 0.860, which revealed PTV1 expression has excellent diagnostic value in prostate cancer. Patients with high PVT1 expression had a worse prognosis. Conclusions: PVT1 expression may be a biomarker for the diagnosis and prognosis of prostate cancer.

Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

2021 ◽  
Vol 15 (3) ◽  
pp. 167-180
Author(s):  
Na Li ◽  
Zubin Li ◽  
Xin Li ◽  
Bingjie Chen ◽  
Huibo Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

YB-1 variant and androgen receptor axis-targeted agents in metastatic castration-resistant prostate cancer patients

2020 ◽  
Vol 21 (13) ◽  
pp. 919-928
Author(s):  
Ana Afonso ◽  
Jani Silva ◽  
Ana Rita Lopes ◽  
Sara Coelho ◽  
Ana Sofia Patrão ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Androgen Receptor ◽  
Cancer Patients ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Castration Resistant Prostate Cancer ◽  
Allelic Discrimination ◽  
Cox Regression Analysis ◽  
Castration Resistant ◽  
Increased Risk

Aim: To evaluate the influence of YB-1 rs10493112 variant as a genetic marker for response to second-generation androgen receptor axis-target agents. Methods: A hospital-based cohort study of 78 patients with metastatic castration-resistant prostate cancer was conducted. Genotyping was performed by TaqMan® allelic discrimination technology. Main results: In abiraterone-treated and high-risk patients, YB-1 rs10493112 AA genotype carriers showed lower progression-free survival than C allele genotype patients (4 vs 17 months; p = 0.009). For carriers of AA genotype, multivariate Cox regression analysis revealed a fivefold increased risk of progression (p = 0.035). Conclusion: The study findings suggest that, for metastatic and castration-resistant prostate cancer patients, this polymorphism might be a putative marker for the clinical outcome.

The Combined Percentage of Gleason Patterns 4 and 5 Is the Best Predictor of Cancer Progression After Radical Prostatectomy

2005 ◽  
Vol 23 (13) ◽  
pp. 2911-2917 ◽  
Author(s):  
Liang Cheng ◽  
Michael O. Koch ◽  
Beth E. Juliar ◽  
Joanne K. Daggy ◽  
Richard S. Foster ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Cancer Patients ◽  
Gleason Score ◽  
Cancer Progression ◽  
Tumor Volume ◽  
High Grade ◽  
Cox Regression Analysis ◽  
Total Tumor Volume ◽  
Increased Risk

Purpose Clinical outcome is variable in prostate cancer patients treated with radical prostatectomy. The Gleason histologic grade of prostatic adenocarcinoma is one of the strongest predictors of biologic aggressiveness of prostate cancer. We evaluated the significance of the relative proportion of high-grade cancer (Gleason patterns 4 and/or 5) in predicting cancer progression in prostate cancer patients treated with radical prostatectomy. Patients and Methods Radical prostatectomy specimens from 364 consecutive prostate cancer patients were totally embedded and whole mounted. Various clinical and pathologic characteristics were analyzed. All pathologic data, including Gleason grading variables, were collected prospectively. Results A multiple-factor analysis was performed that included the combined percentage of Gleason patterns 4 and 5, Gleason score, tumor stage, surgical margin status, preoperative prostate-specific antigen (PSA), extraprostatic extension, and total tumor volume. Using Cox regression analysis with bootstrap resampling for predictor selection, we identified the combined percentage of Gleason patterns 4 and 5 (P < .0001) and total tumor volume (P = .009) as significant predictors of PSA recurrence. Conclusion The combined percentage of Gleason patterns 4 and 5 is one of the most powerful predictors of patient outcome, and appears superior to conventional Gleason score in identifying patients at increased risk of disease progression. On the basis of our results, we recommend that the combined percentage of Gleason patterns 4 and 5 be evaluated in radical prostatectomy specimens. The amount of high-grade cancer in a prostatectomy specimen should be taken into account in therapeutic decision making and assessment of patient prognosis.

scholarly journals An Independent Prognostic Model Based on Ten Autophagy-Related Genes in Pancreatic Cancer

2021 ◽  
Author(s):  
Jiahui Tian ◽  
yi wu ◽  
Xuan Zeng ◽  
Xiaoxiao Fang ◽  
Chunyan Fu
Keyword(s):  
Pancreatic Cancer ◽  
Roc Curve ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Target Therapy ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Gene Sets

Abstract Purpose Pancreatic cancer(PC) is a common cancer with high lethality and low survival rate. Autophagy is involved in the biological process of PC. Thus, we intended to explore the function of autophagy-related long noncoding RNA signature for survival assessment in PC. Methods Based on 10 autophagy-related lncRNAs, the prognostic model was attained through univariate and multivariate Cox regression analysis. Subsequently, the relationship network of 10 lncRNAs was crystallized in co-expression network and Sankey diagram. Survival analysis and ROC curve were used to evaluate the signature. GSEA was utilized to screen enriched gene sets. Result The OS has significant deference in low-risk group and high-risk group(P < 0.001). The ROC curve further proved the potential utility of the signature(AUC = 0.815). GSEA was significantly enriched in cancer-related gene sets. Conclusion The signature has potential to evaluate clinical prognosis in PC. The 10 autophagy-related lncRNAs may achieve great development for PC in target therapy field.

scholarly journals Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahui Pan ◽  
Xinyue Zhang ◽  
Xuedong Fang ◽  
Zhuoyuan Xin
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals Active Surveillance in Younger Men With Prostate Cancer

2017 ◽  
Vol 35 (17) ◽  
pp. 1898-1904 ◽  
Author(s):  
Michael S. Leapman ◽  
Janet E. Cowan ◽  
Hao G. Nguyen ◽  
Katsuto K. Shinohara ◽  
Nannette Perez ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Gleason Score ◽  
Cox Regression ◽  
Young Patients ◽  
Definitive Treatment ◽  
Primary Study ◽  
Study Objective ◽  
Cox Regression Analysis ◽  
Younger Age

Purpose The suitability of younger patients with prostate cancer (PCa) for initial active surveillance (AS) has been questioned on the basis of eventual treatment necessity and concerns of safety; however, the role of age on surveillance outcomes has not been well defined. Patients and Methods We identified men managed with AS at our institution with a minimum follow-up of 6 months. The primary study objective was to examine the association of age with risk of biopsy-based Gleason score upgrade during AS. We also examined the association of age with related end points, including overall biopsy-determined progression, definitive treatment, and pathologic and biochemical outcomes after delayed radical prostatectomy (RP), using descriptive statistics, the Kaplan-Meier method, and multivariable Cox proportional hazards regression. Results A total of 1,433 patients were followed for a median of 49 months; 74% underwent initial biopsy at a referring institution. Median age at diagnosis was 63 years, including 599 patients (42%) ≤ 60 years old and 834 (58%) > 60 years old. The 3- and 5-year biopsy-based Gleason score upgrade-free rates were 73% and 55%, respectively, for men ≤ 60 years old compared with 64% and 48%, respectively, for men older than 60 years ( P < .01). On Cox regression analysis, younger age was independently associated with lower risk of biopsy-based Gleason score upgrade (hazard ratio per 1-year decrease, 0.969 [95% CI, 0.956 to 0.983]; P < .01), and persisted upon restriction to men meeting strict AS inclusion criteria. There was no significant association between younger age and risk of definitive treatment or risk of biochemical recurrence after delayed RP. Conclusion Younger patient age was associated with decreased risk of biopsy-based Gleason score upgrade during AS but not with risk of definitive treatment in the intermediate term. AS represents a strategy to mitigate overtreatment in young patients with low-risk PCa in the early term.

Chromogranine A and neuron-specific enolase as the main predictors of survival for hormone-refractory prostate cancer (HRPC) patients

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 15594-15594
Author(s):  
A. Banu ◽  
E. Banu ◽  
D. Dionysopoulos ◽  
J. Medioni ◽  
F. Scotte ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Gleason Score ◽  
Cox Regression ◽  
Neuron Specific Enolase ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Metastatic Sites ◽  
Ecog Ps ◽  
The Impact

15594 Background: Clinical studies suggested that the extent of neuro-endocrine differentiation in prostate cancer increases with tumor progression and the development of androgen refractory status. Chromogranine (CgA) and neuron-specific enolase (NSE) are currently explored as surrogate markers. Methods: Eligible chemonaive HRPC patients (pts) were required to have an ECOG performance status (PS) ≤ 2. Before chemotherapy initiation, we quantified NSE, CgA and PSA in the venous blood using commercial kits. We evaluated the impact of baseline NSE, CgA and PSA on overall survival (OS) using multivariate Cox regression analysis, stratified by chemotherapy regimen. Secondary, we studied the correlation between NSE, CgA, PSA and other important variables as age, Gleason score, hemoglobin, number of metastatic sites and ECOG PS. Results: Data of 39 consecutive HRPC pts treated between December 01–06 in a single French center were analyzed. Chemotherapy was docetaxel-based in 92% of pts. Median age was 71 years (range 51–86) and 79% of pts had bone metastases. Elevated NSE, CgA and PSA were observed in 6, 9 and 30% of pts and median levels were 10.8, 67 and 23.3 ng/mL, respectively. Gleason 8–10 was present in 49% of pts. Significant correlations were observed between NSE and the number of metastatic sites and between CgA and age, hemoglobin and ECOG PS. The baseline PSA was only correlated with Gleason score. Median OS for the entire cohort was 24.4 months (95% CI, 18.8–29.9). Two-year OS was 15% and only 19% of patients are dead. Univariate Cox regression analysis showed only a significant relationship between OS and baseline NSE: hazard ratio= 1.09 (95% CI, 1.03–1.16), P=0.006. No other known prognostic factors are related to outcome. A multivariate model including baseline NSE, CgA, ECOG PS and Gleason score showed a 15% rise of the risk of death related to NSE (borderline P value). Conclusions: NSE was the most powerful predictor of survival for HRPC pts. Our results emphasize the theory that cells secreting NSE are chemoresistant, with a negative impact on OS. No significant financial relationships to disclose.

scholarly journals Loss of miR-378 in prostate cancer, a common regulator of KLK2 and KLK4, correlates with aggressive disease phenotype and predicts the short-term relapse of the patients

2014 ◽  
Vol 395 (9) ◽  
pp. 1095-1104 ◽  
Author(s):  
Margaritis Avgeris ◽  
Konstantinos Stravodimos ◽  
Andreas Scorilas
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Cox Regression ◽  
Elevated Serum ◽  
Recurrence Risk ◽  
Tumor Stage ◽  
Short Term ◽  
Prostate Hyperplasia ◽  
Cox Regression Analysis ◽  
Improve Risk Stratification

Abstract A large number of prostate cancer (PCa) patients receive treatment without significant benefits, strengthening the need for accurate prognosis, which can be supported by the study of miRNAs. In silico specificity analysis was performed for the identification of miRNAs able to regulate KLK2 and KLK4 expression. Total RNA was extracted from prostate tissues obtained from PCa and benign prostate hyperplasia patients. Thereafter, RNA was polyadenylated and reverse transcribed to cDNA, which was used for qPCR analysis. miR-378 was predicted to target both KLK2 and KLK4 and downregulated levels detected in PCa patients (p=0.050). The reduction of miR-378 was correlated with higher Gleason score (p=0.018), larger diameter tumors (p=0.034), and elevated serum PSA (p=0.006). Regarding prognosis, miR-378 was able to improve risk stratification according to Gleason score or tumor stage, while higher risk to recur highlighted for the patients expressing lower miR-378 levels. Finally, the loss of miR-378 was able to predict the short-term relapse of ‘high’- and ‘very high’-recurrence-risk patients, independent of Gleason score, tumor stage, PSA, and age as indicated by Kaplan-Meier survival curves (p=0.030) and multivariate Cox regression analysis (p=0.018). In conclusion, loss of miR-378 expression increases the risk for PCa progression and relapse, despite active treatment.

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