The first competing risk survival nomogram in patients with papillary renal cell carcinoma.
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ABSTRACT:
SUBMITTER: Su X
PROVIDER: S-EPMC8178392 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPapillary renal cell carcinoma (pRCC) is the largest histologic subtype of non-clear-cell RCC. To date, there is no reliable nomogram to predict the prognosis of patients with pRCC after nephrectomy. We aimed to first establish an effective nomogram to predict the overall survival (OS) of patients with pRCC after nephrectomy.MethodsA total of 3,528 eligible patients with pRCC after nephrectomy were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The patients were randomized into the training cohort (n = 2,472) and the validation cohort (n = 1,056) at a 7:3 ratio. In total, 122 real-world samples from our institute (titled the AHMU-pRCC cohort) were used as the external validation cohort. Univariate and subsequent multivariate Cox regression analyses were conducted to identify OS-related prognostic factors, which were further used to establish a prognostic nomogram for predicting 1-, 3-, and 5-year OS probabilities. The performance of the nomogram was evaluated by using the concordance index (C-index), receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA).ResultsMultivariate Cox analysis showed that age, race, marital status, TNM stage, tumor size, and surgery were significant OS-related prognostic factors. A prognostic model consisting of these clinical parameters was developed and virtualized by a nomogram. High C-index and area under the ROC curve (AUC) values of the nomogram at 1, 3, and 5 years were found in the training, validation, and AHMU-pRCC cohorts. The calibration plot and DCA also showed that the nomogram had a satisfactory clinical application value. A risk classification system was established to risk-stratify patients with pRCC.ConclusionBased on a large cohort from the public SEER database, a reliable nomogram predicting the OS of patients with pRCC after nephrectomy was constructed, which could optimize the survival assessment and clinical treatment.
| S-EPMC9581403 | biostudies-literature
Project description:BackgroundClear-cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts in understanding the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) was involved in the development of varied tumors. However, a comprehensive analysis of the prognostic model based on lncRNA-miRNA-mRNA ceRNA regulatory network of ccRCC with large-scale sample size and RNA-sequencing expression data is still limited.MethodsRNA-sequencing expression data were taken out from GTEx database and TCGA database, a total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC-specific genes were obtained by WGCNA and differential expression analysis. Following, the communication of mRNAs and lncRNAs with targeted miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was further constructed by univariate Cox regression, Lasso methods, and multivariate Cox regression analysis.ResultsA total of 2191 mRNAs and 1377 lncRNAs was identified, and a dysregulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature including eight genes based on this ceRNA was determined followed by the development of a nomogram predicting 1-, 3-, and 5-year survival probability for ccRCC.ConclusionIt could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.
| S-EPMC8366097 | biostudies-literature
Project description:Background and aim: Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of nonsmall cell lung cancer with a poor prognosis. This study aimed to analyze the clinicopathological characteristics and survival outcomes among patients with PSC, lung squamous cell cancer (SCC), and lung adenocarcinoma (LAC), and to construct a competing risk nomogram for patients with PSC. Method: Data of 3 groups of patients diagnosed with PSC, SCC, or LAC from the surveillance, epidemiology, and end results (SEER) database between 1988 and 2015 were retrospectively reviewed. A 1:1 propensity score matching (PSM) analysis was used to balance the baseline data of patients. Independent risk factors associated with survival outcomes were screened by the least absolute shrinkage and selection operator and further determined by univariate and multivariate Cox proportional risk regression analyses. The overall survival (OS) of patients was evaluated by Kaplan-Meier analysis and compared with a log-rank test. The cumulative incidence function was used to estimate the 5-year probabilities of the cancer-specific mortality of PSC. A nomogram was constructed to illustrate the competing risk model to predict the 3- and 5-year OS, and corresponding concordance indexes (C-indexes) and calibration curves were used to assess and validate the competing risk nomogram. Results: A total of 2285 patients with PSC were included in this study. Compared with SCC and LAC patients, the Kaplan-Meier analysis showed that patients with PSC had a worse prognosis, with a median survival of 5 months (95% confidence interval [CI]: 5-6 months) and a 5-year OS rate of 15.3% (95% CI: 13.9%-16.9%). Similar outcomes were demonstrated after 1:1 PSM. Moreover, the competing risk model showed that age, T stage, M stage, tumor size, lymph node ratio (LNR), surgery, and chemotherapy were associated with PSC-specific mortality. The 5-year C-index of the nomogram was 0.718. Calibration curves illustrated that the nomogram was well-validated and had great accuracy. Conclusions: Patients with PSC had a worse survival outcome compared with SCC or LAC patients. Age, T stage, M stage, tumor size, LNR, surgery, and chemotherapy were associated with PSC-specific mortality. The competing risk nomogram displayed excellent discrimination in predicting PSC-specific mortality.
| S-EPMC8859694 | biostudies-literature
Project description:BACKGROUND:The present study aims to investigate the gene expression changes in papillary renal cell carcinoma(pRCC) and screen several genes and associated pathways of papillary renal cell carcinoma progression. METHODS:The papillary renal cell carcinoma RNA sequencing (RNA-seq) data set was downloaded from TCGA (The Cancer Genome Atlas). We identified the differentially expressed mRNAs between cancer and normal tissues and performed annotation of differentially expressed mRNAs to figure out the functions and pathways they were enriched in. Then, we constructed a risk score that relied on the 5-mRNA. The optimal value for the patients'classification risk level was identified by ROC analysis. The relationship between mRNA expression and prognosis of papillary renal cell carcinoma was evaluated by univariate Cox regression model. The 5-mRNA based risk score was validated in both complete set and testing set. RESULT:In general, the 5-mRNA (CCNB2, IGF2BP3, KIF18A, PTTG1, and BUB1) were identified and validated, which can predict papillary renal cell carcinoma patient survival. This study revealed the 5-mRNA expression profile and the potential function of a single mRNA as a prognostic target for papillary renal cell carcinoma. CONCLUSION:In addition, these findings may have significant implications for potential treatments options and prognosis for patients with papillary renal cell carcinoma.
| S-EPMC6396926 | biostudies-literature
Project description:Background: Papillary renal cell carcinoma (PRCC) accounts for 15% of all renal cell carcinomas. The molecular mechanisms of renal papillary cell carcinoma remain unclear, and treatments for advanced disease are limited.Result: We built the computing model as follows: Risk score = 1.806 * TPX2 - 0.355 * TXNRD2 - 0.805 * SLC6A20. The 3-year AUC of overall survival was 0.917 in the training set (147 PRCC samples) and 0.760 in the test set (142 PRCC samples). Based on the robust model, M2 macrophages showed positive correlation with risk score, while M1 macrophages were the opposite. PRCC patients with low risk score showed higher tumor mutation burden. TPX2 is a risk factor, and co-expression factors were enriched in cell proliferation and cancer-related pathways. Finally, the proliferation and invasion of PRCC cell line were decreased in the TPX2 reduced group, and the differential expression was identified. TPX2 is a potential risk biomarker which involved in cell proliferation in PRCC.Conclusion: We conducted a study to develop a three gene model for predicting prognosis in patients with papillary renal cell carcinoma. Our findings may provide candidate biomarkers for prognosis that have important implications for understanding the therapeutic targets of papillary renal cell carcinoma.Method: Gene expression matrix and clinical data were obtained from TCGA (The Cancer Genome Atlas), GSE26574, GSE2048, and GSE7023. Prognostic factors were identified using "survival" and "rbsurv" packages, and a risk score was constructed using Multivariate Cox regression analysis. The co-expression networks of the factors in model were constructed using the "WGCNA" package. The co-expression genes of factors were enriched and displayed the biological process. Based on this robust risk model, immune cells infiltration proportions and tumor mutation burdens were compared between risk groups. Subsequently, using the PRCC cell line, the role of TPX2 was determined by Cell proliferation assay, 5-Ethynyl-20-deoxyuridine assay and Transwell assay.
| S-EPMC7695399 | biostudies-literature
Project description:Renal papillary cell carcinoma
| PRJNA749556 | ENA
Project description:BackgroundRenal cell carcinoma (RCC) is a common malignant tumor in the elderly, with an increasing trend in recent years. We aimed to construct a nomogram of cancer-specific survival (CSS) and overall survival (OS) in elderly patients with nonmetastatic renal cell carcinoma (nmRCC).MethodsClinicopathological information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program in elderly patients with nmRCC from 2010 to 2015. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression analyses were used to identify independent risk factors for patient outcomes in the training cohort. A nomogram was constructed based on these independent risk factors to predict the 1-, 3-, and 5-year CSS and OS in elderly patients with nmRCC. We used a range of methods to validate the accuracy and reliability of the model, including the calibration curve, consistency index (C-index), and the area under the receiver operating curve (AUC). Decision curve analysis (DCA) was used to test the clinical utility of the model.ResultsA total of 12,116 patients were enrolled in the study. Patients were randomly assigned to the training cohort (N = 8,514) and validation cohort (N = 3,602). In the training cohort, univariate and multivariate Cox regression analysis showed that age, marriage, tumor histological type, histological tumor grade, TN stage, tumor size, and surgery are independent risk factors for prognosis. A nomogram was constructed based on independent risk factors to predict CSS and OS at 1-, 3-, and 5- years in elderly patients with nmRCC. The C-index of the training and validation cohorts in CSS were 0.826 and 0.831; in OS, they were 0.733 and 0.734, respectively. The AUC results of the training and validation cohort were similar to the C-index. The calibration curve indicated that the observed value is highly consistent with the predicted value, meaning the model has good accuracy. DCA results suggest that the clinical significance of the nomogram is better than that of traditional TNM staging.ConclusionsWe built a nomogram prediction model to predict the 1-, 3- and 5-year CSS and OS of elderly nmRCC patients. This model has good accuracy and discrimination and can help doctors and patients make clinical decisions and active monitoring.
| S-EPMC9852727 | biostudies-literature
Project description:Clear cell papillary renal cell carcinoma (CCPRCC) is a low-grade renal neoplasm with morphological characteristics mimicking both clear cell renal cell carcinoma (CCRCC) and papillary renal cell carcinoma (PRCC). However, despite some overlapping features, their morphological, immunohistochemical, and molecular profiles are distinct. Micro-RNAs (miRNAs) are small noncoding RNAs that play a crucial role in regulating gene expression and are involved in various biological processes, including cancer development. To better understand the biology of this tumor, we aimed to analyze the miRNA expression profile of a set of CCPRCC using microarray and quantitative reverse transcription-polymerase chain reaction. A total of 15 cases diagnosed as CCPRCC were used in this study. Among the most differentially expressed miRNA in CCPRCC, we found miR-210, miR-122, miR-34a, miR-21, miR-34b*, and miR-489 to be up-regulated, whereas miR-4284, miR-1202, miR-135a, miR-1973, and miR-204 were down-regulated compared with normal renal parenchyma. To identify consensus of differentially regulated miRNA between CCPRCC, CCRCC, and PRCC, we additionally determined differential miRNA expression using 2 publically available microarray data sets from the NCBI Gene Expression Omnibus database (GSE41282 and GSE3798). This comparison revealed that the miRNA expression profile of CCPRCC shows some overlapping characteristics between CCRCC and PRCC. Moreover, CCPRCC lacks dysregulation of important miRNAs typically associated with aggressive behavior. In summary, we describe the miRNA expression profile of a relatively infrequent type of renal cancer. Our results may help in understanding the molecular underpinning of this newly recognized entity.
| S-EPMC4332813 | biostudies-literature
Project description:BackgroundLittle data is available on prognostic biomarkers and effective treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP) patients, to find potential prognostic biomarkers and new targets was an urgent mission for KIRP therapy.MethodsThe differentially expressed autophagy-related genes (DEARGs) were screened out according to the RNA sequencing data in The Cancer Genome Atlas database, then identified survival-related DEARGs to establish a prognostic model for survival predicting of KIRP patients. Then we verified the robustness and validity of the prognostic risk model through clinicopathological data. At last, we evaluate the prognostic value of genes that formed the prognostic risk model individually.ResultsWe analyzed the expression of 232 autophagy-related genes (ARGs) in 289 KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as DEARGs. The functional and pathway enrichment analysis was done and protein-protein interaction network was constructed for all DEARGs. To sift candidate DEARGs associated with KIRP patients' survival and create an autophagy-related risk prognostic model, univariate and multivariate Cox regression analysis were did separately. Eventually 3 desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were picked out and used for construct the autophagy-related risk model. The accuracy of the prognostic risk model for survival prediction was assessed by Kaplan-Meier plotter, receiver-operator characteristic curve, and clinicopathological correlational analyses. The prognostic value of above 3 genes was verified individually by survival analysis and expression analysis on mRNA and protein level.ConclusionsThe autophagy-related prognostic model is accurate and applicable, it can predict OS independently for KIRP patients. Three independent prognostic DEARGs can benefit for facilitate personalized target treatment too.
| S-EPMC8048278 | biostudies-literature
Project description:The survival benefit of metastasectomy (MSX) in patients with metastatic renal cell carcinoma (mRCC) remains unclear. A reliable model to predict an individuals' risk of cancer-specific mortality (CSM) and to identify optimal candidates for MSX is needed. We identified 2,911 mRCC patients who underwent cytoreductive nephrectomy from the Surveillance, Epidemiology, and End Results database (2010-2015). Based on the Fine and Gray competing risks analyses, we created a nomogram to predict the survival of mRCC patients. Decision tree analysis was useful for patient stratification. The impact of MSX was assessed among three different subgroups. Overall, 579 (19.9%) cases underwent MSX. In the entire patients, the 1-, 2-, and 3-year cumulative incidence of CSM were 32.8, 47.2, and 57.9%, respectively. MSX was significantly associated with improved survival (hazard ratio [HR] = 0.875, 95% confidence interval [CI] 0.773-0.991; P = 0.015). Based on risk scores, patients were divided into three risk groups using decision tree analysis. In the low-risk group, MSX was significantly associated with a 12.8% risk reduction of 3-year CSM (HR = 0.689, 95% CI 0.507-0.938; P = 0.008), while MSX was not associated with survival in intermediate- and high-risk groups. We proposed a novel nomogram and patient stratification approach to identify suitable patients for MSX. The newly identified patient subgroup with a low-risk of CSM might benefit more from aggressive surgery. These results should be further validated and improved by the prospective trials.
| S-EPMC7793951 | biostudies-literature