Comparison of T2N0M0 and T3aN0M0 in Predicting the Prognosis of Patients With Renal Cell Carcinoma
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ABSTRACT:
SUBMITTER: He X
PROVIDER: S-EPMC7539120 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial.MethodsWe selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model.ResultsA total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively.ConclusionsOur nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.
| S-EPMC7997741 | biostudies-literature
Project description:ObjectivesTo investigate the role of DNA methylation regulators in the prognosis of clear cell renal cell carcinoma (ccRCC) and to construct a DNA methylation regulator-based signature for predicting patient outcome.MethodsData from the TCGA dataset were downloaded and analyzed to identify differentially expressed DNA methylation regulators and their interaction as well as correlation. Consensus clustering was used to establish groups of ccRCC with distinct clinical outcomes. A prognostic signature based on two sets of DNA methylation regulators was established and validated in an independent cohort.ResultsOur analysis revealed that the expression levels of DNMT3B, MBD1, SMUG1, DNMT1, DNMT3A, TDG, TET3, MBD2, UHRF2, MBD3, UHRF1, and TET2 were significantly upregulated in ccRCC samples, while UNG, ZBTB4, TET1, ZBTB38, and MECP2 were markedly downregulated. UHRF1 was identified as a hub gene in the DNA methylation regulator interaction network. Significant differences were found regarding overall survival, gender, tumor status, and grade between ccRCC patients in the two risk groups. The prognostic signature, based on two sets of DNA methylation regulators, was an independent prognostic indicator, and these findings were validated in an external, independent cohort.ConclusionsThe study provides evidence that DNA methylation regulators play a significant role in the prognosis of ccRCC and the developed DNA methylation regulator-based signature could effectively predict patient outcome.
| S-EPMC10182479 | biostudies-literature
Project description:We observed associations of IQGAP1 downregulation with poor overall survival (OS) in clear cell renal cell carcinoma (ccRCC). Differentially expressed genes (DEGs, n = 611) were derived from ccRCCs with (n = 111) and without IQGAP1 (n = 397) reduction using the TCGA PanCancer Atlas ccRCC dataset. These DEGs exhibit downregulations of immune response and upregulations of DNA damage repair pathways. Through randomization of the TCGA dataset into a training and testing subpopulation, a 9-gene panel (SigIQGAP1NW) was derived; it predicts poor OS in training, testing, and the full population at a hazard ratio (HR) 2.718, p < 2 × 10-16, p = 1.08 × 10-5, and p < 2 × 10-16, respectively. SigIQGAP1NW independently associates with poor OS (HR 1.80, p = 2.85 × 10-6) after adjusting for a set of clinical features, and it discriminates ccRCC mortality at time-dependent AUC values of 70% at 13.8 months, 69%/31M, 69%/49M, and 75.3%/71M. All nine component genes of SigIQGAP1NW are novel to ccRCC. The inclusion of RECQL4 (a DNA helicase) in SigIQGAP1NW agrees with IQGAP1 DEGs enhancing DNA repair. THSD7A affects kidney function; its presence in SigIQGAP1NW is consistent with our observed THSD7A downregulation in ccRCC (n = 523) compared to non-tumor kidney tissues (n = 100). Collectively, we report a novel multigene panel that robustly predicts poor OS in ccRCC.
| S-EPMC7700485 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most common cancers worldwide. Despite intense efforts to elucidate its pathogenesis, the molecular mechanisms and genetic characteristics of this cancer remain unknown. In this study, three expression profile data sets (GSE15641, GSE16441 and GSE66270) were integrated to identify candidate genes that could elucidate functional pathways in ccRCC. Expression data from 63 ccRCC tumors and 54 normal samples were pooled and analyzed. The GSE profiles shared 379 differentially expressed genes (DEGs), including 249 upregulated genes, and 130 downregulated genes. A protein-protein interaction network (PPI) was constructed and analyzed using STRING and Cytoscape. Functional and signaling pathways of the shared DEGs with significant p values were identified. Kaplan-Meier plots of integrated expression scores were used to analyze survival outcomes. These suggested that FN1, ICAM1, CXCR4, TYROBP, EGF, CAV1, CCND1 and PECAM1/CD31 were independent prognostic factors in ccRCC. Finally, to investigate early events in renal cancer, we screened for the hub genes CCND1 and PECAM1/CD31. In summary, integrated bioinformatics analysis identified candidate DEGs and pathways in ccRCC that could improve our understanding of the causes and underlying molecular events of ccRCC. These candidate genes and pathways could be therapeutic targets for ccRCC.
| S-EPMC6949065 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is the most frequent type of renal cell carcinoma (RCC). The present study aimed to examine prognostic markers and construct a prognostic prediction system for ccRCC. The mRNA sequencing data of ccRCC was downloaded from The Cancer Genome Atlas (TCGA) database, and the GSE40435 dataset was obtained from the Gene Expression Omnibus database. Using the Limma package, the differentially expressed genes (DEGs) in the TCGA dataset and GSE40435 dataset were obtained, respectively, and the overlapped DEGs were selected. Subsequently, Cox regression analysis was applied for screening prognosis?associated genes. Following visualization of the co?expression network using Cytoscape software, the network modules were examined using the GraphWeb tool. Functional annotation for genes in the network was performed using the clusterProfiler package. Finally, a prognostic prediction system was constructed through Bayes discriminant analysis and confirmed with the GSE29609 validation dataset. The results revealed a total of 263 overlapped DEGs and 161 prognosis?associated genes. Following construction of the co?expression network, 16 functional terms and three pathways were obtained for genes in the network. In addition, red, yellow (involving chemokine ligand 10 (CXCL10), CD27 molecule (CD27) and runt?related transcription factor 3 (RUNX3)], green (involving angiopoietin?like 4 (ANGPTL4), stanniocalcin 2 (STC2), and sperm associated antigen 4 (SPAG4)], and cyan modules were extracted from the co?expression network. Additionally, the prognostic prediction system involving 44 signature genes, including ANGPTL4, STC2, CXCL10, SPAG4, CD27, matrix metalloproteinase (MMP9) and RUNX3, was identified and confirmed. In conclusion, the 44?gene prognostic prediction system involving ANGPTL4, STC2, CXCL10, SPAG4, CD27, MMP9 and RUNX3 may be utilized for predicting the prognosis of patients with ccRCC.
| S-EPMC6202093 | biostudies-literature
Project description:Despite advances in its treatment, patients diagnosed with clear cell renal cell carcinoma (ccRCC) have a poor prognosis. The mechanism of cuproptosis has been found to differ from other mechanisms that regulate cell death, including apoptosis, iron poisoning, pyrophosphate poisoning, and necrosis. Cuproptosis is an essential component in the regulation of a wide variety of biological processes, such as cell wall remodeling and oxidative stress responses. However, cuproptosis-related genes' expression in ccRCC patients and their association with the patient's prognosis remain ambiguous. Evaluation of The Cancer Genome Atlas (TCGA) identified 11 genes associated with cuproptosis that were differently expressed in ccRCC and nearby nontumor tissue. To construct a multigene prognostic model, the prognostic value of 11 genes was assessed and quantified. A signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and this signature was used to separate ccRCC patients into different risk clusters, with low-risk patients having a much better prognosis. This five-gene signature, when combined with patients' clinical characteristics, might serve as one independent predictor of overall survival (OS) in ccRCC patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that cuproptosis-related genes were enriched in patients with ccRCC. Then, quantitative real-time PCR (qPCR) was employed to verify these genes' expression. Generally, research has indicated that cuproptosis-related genes are important in tumor immunity and can predict OS of ccRCC patients.
| S-EPMC9450221 | biostudies-literature
Project description:BackgroundKidney renal clear cell carcinoma (KIRC), with low survival rate, is the most frequent subtype of renal cell carcinoma. Recently, more and more studies indicate that cuproptosis-related genes (CRGs) and long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of many types of cancers. However, the roles of cuproptosis-related lncRNAs (CRlncRNAs) in the KIRC was uncertain.ResultsIn our study, CRlncRNAs were obtained by coexpression between differentially expressed and prognostic CRGs and differentially expressed and prognostic lncRNAs, and an 8-CRlncRNAs (AC007743.1, AC022915.1, AP005136.4, APCDD1L-DT, HAGLR, LINC02027, MANCR and SMARCA5-AS1) risk model was established according to least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. This risk model could differentiate immune cell infiltration, immune function and gene mutation.ConclusionsThis 8-CRlncRNAs risk model may be promising for the clinical prediction of prognoses, tumor immune, immunotherapy response and chemotherapeutic response in KIRC patients.
| S-EPMC10860879 | biostudies-literature
Project description:Early [stage I and II (T2N0M0)] laryngeal cancer types are currently recommended to be treated with a single modality, consisting of definitive radiation therapy or larynx-preserving surgery. Although the treatment outcomes of stage I are good, the frequency of successful outcomes decreases with T2N0M0. Therefore, the present study investigated the treatment outcomes of different treatment methods in T2N0M0 laryngeal cancer. In total, 83 patients with previously untreated T2N0M0 laryngeal squamous cell carcinoma were enrolled. Patients were grouped by treatment method: Radiation therapy (RT; 27 patients); chemoradiotherapy (CRT; 46 patients) with cisplatin base; and surgery-based therapy (SBT; ten patients). The recurrence rates of the RT, CRT and SBT groups were 44.4, 19.6 and 50%, respectively. Moreover, the local control rates of the RT, CRT and SBT groups were 55.6, 87.0 and 80%, respectively. The CRT group had a significantly lower recurrence rate and higher local control rate compared with the RT group (P<0.05). In the survival analysis, overall and disease-specific survival rate did not differ significantly among the treatment groups. However, 3- and 5-year disease-free survival rates (DFS) of the RT group were both 55%, those of the SBT group were both 50% and those of the CRT group were both 80%. Furthermore, the DFS was significantly higher in CRT group compared with the other groups (P=0.02). Using multivariate analysis with Cox regression, it was found that the treatment method was the most important factor for DFS and had a significant impact in the CRT group. In addition, in patients with glottic cancer with anterior commissure and subglottic invasion, the CRT group had significantly improved DFS compared with the RT group, whereas there was no significant difference between the two groups in patients without subglottic invasion. According to National Cancer Institution Common Toxicity Criteria (version 5.0), more patients had toxicity in the CRT group compared with the RT group. However, in the RT and CRT groups, no patients demonstrated mortality due to toxicity, and treatment-related toxicities were manageable. Collectively, although definitive conclusions could not be established, due to the limitations of this retrospective study, the results suggest that CRT had a positive impact on the local control and DFS rates with manageable toxicity in patients with T2N0M0 laryngeal cancer.
| S-EPMC7285832 | biostudies-literature
Project description:Immune status affects the initiation and progression of clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma. In this study, we identified an immune-related, five-gene signature that improves survival prediction in ccRCC. Patients were classified as high- and low-risk based on the signature risk score. Survival analysis showed differential prognosis, while principal component analysis revealed distinctly different immune phenotypes between the two risk groups. High-risk patients tended to have advanced stage, higher grade disease, and poorer prognoses. Functional enrichment analysis showed that the signature genes were mainly involved in the cytokine-cytokine receptor interaction pathway. Moreover, we found that tumors from high-risk patients had higher relative abundance of T follicular helper cells, regulatory T cells, and M0 macrophages, and higher expression of PD-1, CTLA-4, LAG3, and CD47 than low-risk patients. This suggests our gene signature may not only serve as an indicator of tumor immune status, but may be a promising tool to select high-risk patients who may benefit from immune checkpoint inhibitor therapy. Multivariate Cox regression analysis showed that the signature remained an independent prognostic factor after adjusting for clinicopathological variables, while prognostic accuracy was further improved after integrating clinical parameters into the analysis.
| S-EPMC7041771 | biostudies-literature
Project description:PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.
| S-EPMC8187870 | biostudies-literature