scholarly journals Establishment of a Prognostic Prediction and Drug Selection Model for Patients with Clear Cell Renal Cell Carcinoma by Multiomics Data Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-30
Author(s):  
Aimin Jiang ◽  
Yewei Bao ◽  
Anbang Wang ◽  
Wenliang Gong ◽  
Xinxin Gan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Renal Cancer ◽  
Renal Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Cell Cancer ◽  
Drug Selection ◽  
Cell Renal Cell Carcinoma ◽  
Wide Range ◽  
Prognostic Prediction

Rationale. Patients with clear cell renal cell cancer (ccRCC) may have completely different treatment choices and prognoses due to the wide range of heterogeneity of the disease. However, there is a lack of effective models for risk stratification, treatment decision-making, and prognostic prediction of renal cancer patients. The aim of the present study was to establish a model to stratify ccRCC patients in terms of prognostic prediction and drug selection based on multiomics data analysis. Methods. This study was based on the multiomics data (including mRNA, lncRNA, miRNA, methylation, and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multiomics clustering and conducted pseudotiming analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multiomics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression. Results. A prognosis predicting model of ccRCC was established by dividing patients into the high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups ( p < 0.01 ). The area under the OS time-dependent ROC curve for 1, 3, 5, and 10 years in the training set was 0.75, 0.72, 0.71, and 0.68, respectively. Conclusion. The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.

scholarly journals Establishment of a Prognostic Prediction and Drug Selection Model for Patients with Clear Cell Renal Cell Carcinoma by Multi-Omics Data Analysis

2021 ◽  
Author(s):  
Aimin Jiang ◽  
Yewei Bao ◽  
Anbang Wang ◽  
Xinxin Gan ◽  
Jie Wang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Renal Cancer ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Cancer ◽  
Omics Data ◽  
Drug Selection ◽  
Wide Range ◽  
Prognostic Prediction ◽  
Omics Data Analysis

Rationale: Patients with clear cell renal cell cancer (ccRCC) may have completely different treatment choices and prognoses due to the wide range of heterogeneity of the disease. However, there is a lack of effective models for risk stratification, treatment decision making and prognostic prediction of renal cancer patients. The aim of the present study was to establish a model to stratify ccRCC patients in terms of prognostic prediction and drug selection based on multi-omics data analysis. Methods: This study was based on the multi-omics data (including mRNA, lncRNA, miRNA, methylation and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multi-omics clustering, and conducted pseudo-timing analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multi-omics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression. Results: A prognosis predicting model of ccRCC was established by dividing patients into high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups (p<0.01). The area under the OS time dependent ROC curve for 1, 3, 5 and 10 years in the training set was 0.75, 0.72, 0.71 and 0.68 respectively. Conclusion: The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.

scholarly journals Identification of DNA methylation signatures associated with poor outcome in lower-risk Stage, Size, Grade and Necrosis (SSIGN) score clear cell renal cell cancer

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Louis Y. El Khoury ◽  
Shuang Fu ◽  
Ryan A. Hlady ◽  
Ryan T. Wagner ◽  
Liguo Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Renal Cell ◽  
Lower Risk ◽  
Clear Cell ◽  
Cell Cancer ◽  
Low Risk ◽  
Cell Renal Cell Carcinoma ◽  
A Genome ◽  
Size Grade ◽  
Prognostic Power

Abstract Background Despite using prognostic algorithms and standard surveillance guidelines, 17% of patients initially diagnosed with low risk clear cell renal cell carcinoma (ccRCC) ultimately relapse and die of recurrent disease, indicating additional molecular parameters are needed for improved prognosis. Results To address the gap in ccRCC prognostication in the lower risk population, we performed a genome-wide analysis for methylation signatures capable of distinguishing recurrent and non-recurrent ccRCCs within the subgroup classified as ‘low risk’ by the Mayo Clinic Stage, Size, Grade, and Necrosis score (SSIGN 0–3). This approach revealed that recurrent patients have globally hypermethylated tumors and differ in methylation significantly at 5929 CpGs. Differentially methylated CpGs (DMCpGs) were enriched in regulatory regions and genes modulating cell growth and invasion. A subset of DMCpGs stratified low SSIGN groups into high and low risk of recurrence in independent data sets, indicating that DNA methylation enhances the prognostic power of the SSIGN score. Conclusions This study reports a global DNA hypermethylation in tumors of recurrent ccRCC patients. Furthermore, DMCpGs were capable of discriminating between aggressive and less aggressive tumors, in addition to SSIGN score. Therefore, DNA methylation presents itself as a potentially strong biomarker to further improve prognostic power in patients with low risk SSIGN score (0–3).

scholarly journals Identification of a Novel Stem Cell Subtype for Clear Cell Renal Cell Carcinoma Based on Stem Cell Gene Profiling

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongzhi Wang ◽  
Hanjiang Xu ◽  
Quan Cheng ◽  
Chaozhao Liang
Keyword(s):  
Renal Cell Carcinoma ◽  
Stem Cell ◽  
Cancer Stem Cell ◽  
Renal Cancer ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Gene Sets ◽  
Cell Gene ◽  
Stem Cell Gene

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer and is characterized by high rates of metastasis. Cancer stem cell is a vital cause of renal cancer metastasis and recurrence. However, little is known regarding the change and the roles of stem cells during the development of renal cancer. To clarify this problem, we developed a novel stem cell clustering strategy. Based on The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) genomic datasets, we used 19 stem cell gene sets to classify each dataset. A machine learning method was used to perform the classification. We classified ccRCC into three subtypes—stem cell activated (SC-A), stem cell dormant (SC-D), and stem cell excluded (SC-E)—based on the expressions of stem cell-related genes. Compared with the other subtypes, C2(SC-A) had the highest degree of cancer stem cell concentration, the highest level of immune cell infiltration, a distinct mutation landscape, and the worst prognosis. Moreover, drug sensitivity analysis revealed that subgroup C2(SC-A) had the highest sensitivity to immunotherapy CTLA-4 blockade and the vascular endothelial growth factor receptor (VEGFR) inhibitor sunitinib. The identification of ccRCC subtypes based on cancer stem cell gene sets demonstrated the heterogeneity of ccRCC and provided a new strategy for its treatment.

scholarly journals Integrated bioinformatics analysis of the NEDD4 family reveals a prognostic value of NEDD4L in clear-cell renal cell cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11880
Author(s):  
Hui Zhao ◽  
Junjun Zhang ◽  
Xiaoliang Fu ◽  
Dongdong Mao ◽  
Xuesen Qi ◽  
...  
Keyword(s):  
Renal Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Cancer ◽  
Data Sets ◽  
Potential Therapeutic Target ◽  
Cell Renal Cell Carcinoma ◽  
Cellular Energy ◽  
Testing Data

The members of the Nedd4-like E3 family participate in various biological processes. However, their role in clear cell renal cell carcinoma (ccRCC) is not clear. This study systematically analyzed the Nedd4-like E3 family members in ccRCC data sets from multiple publicly available databases. NEDD4L was identified as the only NEDD4 family member differentially expressed in ccRCC compared with normal samples. Bioinformatics tools were used to characterize the function of NEDD4L in ccRCC. It indicated that NEDD4L might regulate cellular energy metabolism by co-expression analysis, and subsequent gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A prognostic model developed by the LASSO Cox regression method showed a relatively good predictive value in training and testing data sets. The result revealed that NEDD4L was associated with biosynthesis and metabolism of ccRCC. Since NEDD4L is downregulated and dysregulation of metabolism is involved in tumor progression, NEDD4L might be a potential therapeutic target in ccRCC.

scholarly journals A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma

Aging ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 14933-14948
Author(s):  
Guangzhen Wu ◽  
Qifei Wang ◽  
Yingkun Xu ◽  
Quanlin Li ◽  
Liang Cheng
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Survival Model ◽  
Cell Renal Cell Carcinoma ◽  
Model Based ◽  
Prognostic Prediction

scholarly journals Ubiquitin-specific protease 44 inhibits cell proliferation and migration via inhibition of JNK pathway in clear cell renal cell cancer

2019 ◽  
Author(s):  
Jiangqiao Zhou ◽  
Tianyu Wang ◽  
Tao Qiu ◽  
Zhongbao Chen ◽  
Xiaoxiong Ma ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Cancer ◽  
Cell Renal Cell Carcinoma ◽  
Jnk Pathway ◽  
Cell Proliferation And Migration ◽  
And Migration ◽  
Ccrcc Cell ◽  
Proliferation And Migration

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is the most common form of adult kidney cancer. USP44 has been reported to be involved in various cancers. This study aimed to investigate the function role and molecular mechanism of USP44 in ccRCC. Methods: Data obtained from TCGA data portal and GSO database were analyzed to uncover the clinical relevance of USP44 expression and tumor development. The function of USP44 in cell proliferation and migration was assessed by cellular and molecular analysis. Results: USP44 was lowly expressed in the ccRCC cancer tissues compared to the normal tissue. Further, USP44 expression was negatively correlated with tumor stage, tumor grade, and patient survival . USP44 overexpression significantly inhibited tumor cell proliferation and migration of 786-O cell as well as Caki-1 cell. In addition, USP44 overexpression also prohibited cell proliferation by up-regulating P21, down-regulating Cyclin D1 expression, and inhibited cell migration by up-regulating MMP2 and MMP9 expression. In contrast, USP44 knockdown enhances ccRCC cell proliferation and migration. Furthermore, the USP44 function in inhibiting ccRCC cell proliferation and migration is associated with the phosphorylation level of JNK. Conclusion: In summary, this study showed that USP44 may be a marker in predicting the ccRCC progression and USP44 inhibits ccRCC cell proliferation and migration dependent on the JNK pathway.

scholarly journals Metastatic Lesion of the Tibia from Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Piotr Młodożeniec ◽  
Krzysztof Balawender ◽  
Mateusz Zasadny
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell Cancer ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Cancer ◽  
Local Therapy ◽  
X Rays ◽  
Cell Renal Cell Carcinoma ◽  
Lower Limb Pain

Introduction. Renal cell carcinoma is responsible for 3% of all cancers, with the highest incidence occurring in Western countries. Additionally, in patients with osseous metastasis, only 3% occur within the tibia. Rarely, a patient presents with a primary complaint of lower limb pain in advanced metastatic renal cell carcinoma. Case Presentation. The patient arrived at the emergency department with a primary complaint of left ankle pain. Ankle X-rays demonstrated a lytic lesion involving the medial malleolus with possible metastatic disease. CT scan confirmed a tumor within the right kidney. The patient was treated with a laparoscopic radical nephrectomy with histopathologic confirmation of clear cell renal cell carcinoma. Biopsy was then performed of the tibial lesion, confirming metastatic clear cell renal cell carcinoma. The tibial lesion was treated with local radiotherapy, and because of the progression of the tibia lesion, a decision was made to amputate the leg. Additionally, the patient was enrolled to sunitinib treatment and was disease free at one year of follow-up. 13 months after diagnosis of cancer, she was suffering a major stroke of the brain that caused her to die. Conclusion. The treatment of patients with osseous metastases of renal cell cancer depends on the number of metastases, location of metastases, and overall health of the patient. We performed an overview of available literature and provided a summary regarding the use of cytoreductive nephrectomy, local therapy, target therapy, and bone-targeting agents in the treatment of metastatic renal cell cancer.

scholarly journals Effect of Aberrant Long Noncoding RNA on the Prognosis of Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Han Wu ◽  
Haixiao Wu ◽  
Peng Sun ◽  
Desheng Zhu ◽  
Min Ma ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Noncoding Rna ◽  
Risk Model ◽  
Clear Cell ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Clear cell renal cell carcinoma (ccRCC) is a kind of lethal cancer. Although there are mature treatment methods, there is still a lack of rigorous and scientific means for cancer diagnosis. Long noncoding RNAs (lncRNAs) are a kind of noncoding RNA (ncRNA). Recent studies find that alteration of lncRNA expression is related to the occurrence of many cancers. In order to find lncRNAs which can effectively predict the prognosis of ccRCC, RNA-seq count data and clinical information were downloaded from TCGA-KIRC, and gene expression profiles from 530 patients were included. Then, K -means was used for clustering, and the number of clusters was determined to be 5. The R-package “edgeR” was used to perform differential expression analysis. Subsequently, a risk model composed of 10 lncRNA biomarkers significantly related to prognosis was identified via Cox and LASSO regression analyses. Then, patients were divided into two groups according to the model-based risk score, and then, GSEA pathway enrichment was performed. The results showed that metabolism- and mTOR-related pathways were activated while immune-related pathways were inhibited in the high-risk patients. Combined with previous studies, it is believed that these 10 lncRNAs are potential targets for the treatment of ccRCC. In addition, Cox regression analysis was used to verify the independence of the risk model, and as results revealed, the risk model can be used to independently predict the prognosis of patients. In conclusion, our study found 10 lncRNAs related to the prognosis of ccRCC and provided new ideas for clinical diagnosis and drug development.

Robustness of the 16-gene signature for prediction of recurrence-free interval in localized clear cell renal cell carcinoma.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 455-455
Author(s):  
Bernard J. Escudier ◽  
Serge Koscielny ◽  
Tara Maddala ◽  
Christer Svedman ◽  
Virginie Verkarre ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cancer ◽  
Renal Cell ◽  
Clear Cell ◽  
Gene Signature ◽  
Stage I ◽  
Rt Pcr ◽  
Cell Renal Cell Carcinoma ◽  
Risk Of Recurrence

455 Background: The Renal Cancer assay is a clinically validated RT-PCR assay developed to estimate the risk of recurrence in stage I-III clear cell renal cell carcinoma (ccRCC) patients (pts) treated with nephrectomy. The assay measures expression of 16 genes that are combined to calculate the Recurrence Score result (RS). The RS is associated with recurrence, renal cancer-specific survival and overall survival (all p<0.001) (Escudier, ASCO 2014). The performance of the RS in clinically relevant subgroups, compared to the Leibovich score, and its within-patient variability was examined. Methods: The algorithm, endpoints, methods, and analysis plan were pre-specified prior to merging clinical and molecular data. RT-PCR of RNA from fixed paraffin-embedded ccRCC tissue was performed without knowledge of clinical data. Recurrence-free internval (RFI) was analyzed using Cox regression stratified by stage with data censored at 5 years, and Kaplan-Meier methods. Multivariable models incorporating the Leibovich score were used to assess the additional contribution of the RS to prediction of recurrence. Within- and between-tumor block reproducibility was assessed in an independent study using two separate tumor blocks from 8 pts, where each block was analyzed at 3 depths. Results: RS was generated in 626/645 pts (97%): 398 stage I, 54 stage II, 174 stage III. Median follow up was 5.5 yrs. The RS was significantly associated with risk of recurrence after adjustment for the Leibovich score (HR=4.20, p<0.001). Additionally, the performance of RS was similar across age groups (<60, 60-70 or ≥70), gender, nephrectomy type, tumor size (≤4, 4-7 or >7cm), grade, and presence/absence of invasion (all interaction p>0.29). Within-patient variability in the score (std. dev. of 1.73 and 4.74 RS units for within- and between-tumor block, respectively) was lower than patient-to-patient variability (std. dev. of 15.6 in validation study). Conclusions: The 16-gene signature remains strongly associated with risk of recurrence after adjustment for the Leibovich score and performs consistently across clinically relevant subgroups. Examination of within-patient and between-patient variability indicates that the score is robust to tumor heterogeneity.

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