Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

PurposeThe present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).MethodsA total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.ResultsA total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.ConclusionsThe nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

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GALNT14 expression in renal clear cell carcinoma and its effect on prognosis

10.21203/rs.3.rs-761157/v1 ◽

2021 ◽

Author(s):

Yuqin Wei ◽

Fan Wu ◽

Shengfeng Zhang ◽

Yanlin Tan ◽

Qunying Wu ◽

...

Keyword(s):

Cell Carcinoma ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Regulatory Pathways ◽

Cox Analysis ◽

The Relationship

Abstract Background The expression of GALNT14 in kidney renal clear cell carcinoma (KIRC) and its clinical significance remains unknown. Methods The KIRC data expressed by GALNT14 was downloaded from The Cancer Genome Atlas (TCGA) database. The expression of GALNT14 was analyzed by R software, Perl software and online analysis database. The relationship between GALNT14 expression and clinicopathological features in KIRC was analyzed by univariate, multivariate Cox regression and some databases. Gene Expression Profling Interactive Analysis (GEPIA), Starbase v3.0, UALCAN, and Kaplan-Meier were used to analyze the relationship between GALNT14 expression and overall survival (OS) in KIRC. UALCAN detects the expression of GALNT14 methylation in KIRC. Linkedomics and Genemania were used to analyze the gene co-expression of GALNT14. Gene Set Enrichment Analysis (GSEA) was performed to search for potential regulatory pathways. Results We found that GALNT14 was overexpressed in KIRC (p=1.433e-25). Patients with high GALNT14 expression in KIRC had a better prognosis than patients with low GALNT14 expression (p=0.008). In addition, high GALNT14 expression in KIRC was significantly associated with low T stage and positive OS (p<0.05). Univariate Cox analysis showed that GALNT14 was positively correlated with OS (p<0.001). Multivariate Cox analysis showed that GALNT14 was associated with OS (p<0.001), age (p=0.01) and histological grade (p=0.02). GALNT14 methylation is low expressed in KIRC (p<0.001). GSEA analysis showed that GALNT14 was enriched in histidine metabolism, peroxisome, and renin-angiotensin system pathways. Conclusion GALNT14 can be used as an independent prognostic factor for renal clear cell carcinoma and a potential target for clinical diagnosis and treatment of KIRC.

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Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

Journal of Ovarian Research ◽

10.1186/s13048-020-00727-3 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-He Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

10.21203/rs.3.rs-39370/v2 ◽

2020 ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-he Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background: Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC.Methods: Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups.Results: A total of 1541 patients from SEER registries were randomly divided into a training cohort (n=1079) and a validation cohort (n=462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3‐ and 5‐year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773-0.831] and 0.802 (0.769-0.835), respectively, in the training cohort, while 0.746 (0.691-0.801) and 0.770 (0.721-0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups.Conclusions: We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

10.21203/rs.3.rs-39370/v1 ◽

2020 ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-he Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of useful prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO COX regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for prediction of 3- and 5‐year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in survival curves of different risk subgroups. Conclusions We constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma

Disease Markers ◽

10.1155/2020/8841859 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Wenkai Han ◽

Xiaoyan Xu ◽

Kai Che ◽

Guofeng Ma ◽

Danxia Li ◽

...

Keyword(s):

High Risk ◽

Prognostic Factor ◽

Cell Carcinoma ◽

Risk Model ◽

Clear Cell ◽

Risk Group ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

High Risk Group ◽

Low Risk

Background. Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC). Methods. We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed. Results. The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability. Conclusion. The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.

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Prognostic and therapeutic implications of extracellular matrix associated gene signature in renal clear cell carcinoma

Scientific Reports ◽

10.1038/s41598-021-86888-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pankaj Ahluwalia ◽

Meenakshi Ahluwalia ◽

Ashis K. Mondal ◽

Nikhil Sahajpal ◽

Vamsi Kota ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Cell Carcinoma ◽

Clear Cell ◽

Risk Group ◽

Clear Cell Carcinoma ◽

Gene Signature ◽

Renal Clear Cell Carcinoma ◽

High Risk Group ◽

Elevated Expression

AbstractComplex interactions in tumor microenvironment between ECM (extra-cellular matrix) and cancer cell plays a central role in the generation of tumor supportive microenvironment. In this study, the expression of ECM-related genes was explored for prognostic and immunological implication in clear cell renal clear cell carcinoma (ccRCC). Out of 964 ECM genes, higher expression (z-score > 2) of 35 genes showed significant association with overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS). On comparison to normal tissue, 12 genes (NUDT1, SIGLEC1, LRP1, LOXL2, SERPINE1, PLOD3, ZP3, RARRES2, TGM2, COL3A1, ANXA4, and POSTN) showed elevated expression in kidney tumor (n = 523) compared to normal (n = 100). Further, Cox proportional hazard model was utilized to develop 12 genes ECM signature that showed significant association with overall survival in TCGA dataset (HR = 2.45; 95% CI [1.78–3.38]; p < 0.01). This gene signature was further validated in 3 independent datasets from GEO database. Kaplan–Meier log-rank test significantly associated patients with elevated expression of this gene signature with a higher risk of mortality. Further, differential gene expression analysis using DESeq2 and principal component analysis (PCA) identified genes with the highest fold change forming distinct clusters between ECM-rich high-risk and ECM-poor low-risk patients. Geneset enrichment analysis (GSEA) identified significant perturbations in homeostatic kidney functions in the high-risk group. Further, higher infiltration of immunosuppressive T-reg and M2 macrophages was observed in high-risk group patients. The present study has identified a prognostic signature with associated tumor-promoting immune niche with clinical utility in ccRCC. Further exploration of ECM dynamics and validation of this gene signature can assist in design and application of novel therapeutic approaches.

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Construction and Validation of a Ferroptosis-related Prognostic Signature in Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-113462/v1 ◽

2020 ◽

Author(s):

Zhuolun Sun ◽

Changying Jing ◽

Chutian Xiao ◽

Mingxiao Zhang ◽

Zhenqing Wang ◽

...

Keyword(s):

High Risk ◽

Cell Carcinoma ◽

Clear Cell ◽

Risk Group ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

High Risk Group ◽

Differentially Expressed ◽

Consensus Clustering ◽

Prognostic Signature

Abstract Background: Kidney renal clear cell carcinoma (KIRC) is the most common and lethal renal cell carcinoma (RCC) histological subtype. Ferroptosis is a newly discovered programmed cell death and serves an essential role in tumor occurrence and development. The purpose of this study is to analyze ferroptosis-related gene (FRG) expression profiles and to construct a multi-gene signature for predicting the prognosis of KIRC patients.Methods:RNA-sequencing data and clinicopathological data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed FRGs between KIRC and normal tissues were identified using ‘limma’ package in R. GO and KEGG enrichment analyses were conducted to elucidate the biological functions and pathways of differentially expressed FRGs. Consensus clustering was used to investigate the relationship between the expression of FRGs and clinical phenotypes. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to screen genes related to prognosis and construct the optimal signature. Then, a nomogram was established to predict individual survival probability by combining clinical features and prognostic signature.Results: A total of 19 differentially expressed FRGs were identified. Consensus clustering identified two clusters of KIRC patients with distinguished prognostic. Functional analysis revealed that metabolism-related pathways were enriched, especially lipid metabolism. A 7-gene ferroptosis-related prognostic signature was constructed to stratify the TCGA training cohort into high- and low-risk groups where the prognosis was significantly worse in the high-risk group. The signature was identified as an independent prognostic indicator for KIRC. These findings were validated in the testing cohort, the entire cohort, and the International Cancer Genome Consortium (ICGC) cohort. We further demonstrated that the signature-based risk score was highly associated with the KIRC progression. Further stratified survival analysis showed that the high-risk group had a significantly lower overall survival (OS) rate than those in the low-risk group. Moreover, we constructed a nomogram that had a strong ability to forecast the OS of the KIRC patients.Conclusion: We constructed a ferroptosis-related prognostic signature, which might provide a reliable prognosis assessment tool for clinician to guide clinical decision-making and outcomes research.

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Construction and Validation of a Ferroptosis-Related Prognostic Signature in Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-105339/v1 ◽

2020 ◽

Author(s):

Zhuolun Sun ◽

Changying Jing ◽

Chutian Xiao ◽

Mingxiao Zhang ◽

Zhenqing Wang ◽

...

Keyword(s):

High Risk ◽

Cell Carcinoma ◽

Clear Cell ◽

Risk Group ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

High Risk Group ◽

Differentially Expressed ◽

Consensus Clustering ◽

Prognostic Signature

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Development of an Autophagy-Related Gene Prognostic Model and Nomogram for Estimating Renal Clear Cell Carcinoma Survival

Journal of Oncology ◽

10.1155/2021/8810849 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Ying Wang ◽

Yinhui Yao ◽

Jingyi Zhao ◽

Chunhua Cai ◽

Junhui Hu ◽

...

Keyword(s):

Cell Carcinoma ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Bioinformatic Analysis ◽

Renal Clear Cell Carcinoma ◽

Functional Enrichment ◽

Differentially Expressed ◽

Lasso Regression ◽

Decision Curve Analysis ◽

Cox Analysis

Background. Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma. Materials and Methods. We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness. Results. A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index = 0.75, AUC = >0.7). Decision curve analysis also showed that the nomogram was clinically useful. Conclusions. KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.

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Hypoxia-related lncRNA Signature to Predict the Survival of Patients with Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-1178273/v1 ◽

2022 ◽

Author(s):

Fu Liu ◽

Xinyuan Li ◽

Xiang Zhou ◽

Hang Tong ◽

Xin Gou

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Risk Group ◽

Clear Cell Carcinoma ◽

Risk Groups ◽

Renal Clear Cell Carcinoma ◽

Low Risk ◽

Cell Renal Cell Carcinoma

Abstract Background: Renal cell carcinoma is the most common aggressive tumor of the genitourinary system. The main pathological subtype is clear cell renal cell carcinoma (ccRCC), and its treatment options are very limited. Therefore, identifying specific markers of renal clear cell carcinoma is of great significance for diagnosis and prognosis.Methods: From the TCGA database, we obtained information on 611 patients with renal clear cell carcinoma to analyze the relationship between hypoxia-related lncRNAs and overall survival. According to the coexpression of hypoxia genes and lncRNAs, genes related to hypoxia were identified. Difference analysis and Cox regression analysis were applied to assess survival-related risk factors. According to the median risk score of hypoxia-related genes, patients were divided into high-risk and low-risk groups. According to these gene characteristics and clinical parameters, a nomogram map was built, and GSEA was used for gene function annotation. RT-qRCR, Western Blot and Flow Cytometry were used to determine the role of SNHG19 in RCC cells.Results: By analyzing the coexpression of hypoxia genes and lncRNAs, 310 hypoxia-related genes were obtained. Six sHRlncRs were significantly correlated with the clinical outcomes of patients with ccRCC. Four sHRlncRs (AC011445.2, PTOV1-AS2, AP004609.3, and SNHG19) with the highest prognostic values were included in the group to construct the HRRS model. The high-risk group had a shorter OS than the low-risk group. HR-lncRNAs were considered to be an independent prognostic factor and associated with OS. The high- and low-risk groups showed different pathways in GSEA. Experiments showed that SNHG19 plays essential roles in autophagy and apoptosis of RCC cells.Conclusion: Our research shows that we established and verified a hypoxia-related lncRNA model that accurately correlates with ccRCC patients. This study also provides novel insights into hypoxia-based mechanisms and provides new biomarkers for the poor prognosis of ccRCC patients.

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