Prediction and Analysis of Hub Genes in Renal Cell Carcinoma based on CFS Gene Selection Method Combined with Adaboost Algorithm

2020 ◽  
Vol 16 (5) ◽  
pp. 654-663 ◽  
Author(s):  
Yina Wang ◽  
Benrong Zheng ◽  
Manbin Xu ◽  
Shaoping Cai ◽  
Jeong Younseo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Expression Profiles ◽  
Classification Model ◽  
Feature Subset ◽  
Hub Genes ◽  
Adaboost Algorithm ◽  
Key Genes

Background: Renal cell carcinoma (RCC) is the most common malignant tumor of the adult kidney. Objective: The aim of this study was to identify key genes signatures during RCC and uncover their potential mechanisms. Methods: Firstly, the gene expression profiles of GSE53757 which contained 144 samples, including 72 kidney cancer samples and 72 controls, were downloaded from the GEO database. And then differentially expressed genes (DEGs) between the kidney cancer samples and the controls were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key genes of DEGs. In addition, the classification model between the kidney cancer samples and the controls was built by Adaboost based on the selected key genes. Results: 213 DEGs including 80 up-regulated and 133 down-regulated genes were selected as the feature genes to build the classification model between the kidney cancer samples and the controls by CFS method. The accuracy of the classification model by using 5-folds cross-validation test and independent set test is 84.4% and 83.3%, respectively. Besides, TYROBP, CD4163, CAV1, CXCL9, CXCL11 and CXCL13 also can be found in the top 20 hub genes screened by proteinprotein interaction (PPI) network. Conclusion: It indicated that CFS is a useful tool to identify key genes in kidney cancer. Besides, we also predicted genes such as TYROBP, CD4163, CAV1, CXCL9, CXCL11 and CXCL13 that might target genes to diagnose the kidney cancer.

Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Chun Qiu ◽  
Sai Li ◽  
Shenghui Yang ◽  
Lin Wang ◽  
Aihui Zeng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Expression Profiles ◽  
Independent Set ◽  
Classification Model ◽  
Support Vector ◽  
Feature Subset ◽  
Hub Genes ◽  
Effective Prevention ◽  
Key Genes ◽  
Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

Screening and identification of novel promoter signatures in clear cell renal cell carcinoma metastasis.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 737-737
Author(s):  
Yuan-Yuan Qu ◽  
Xi Tian ◽  
Wenhao Xu ◽  
Aihemutaijiang Anwaier ◽  
Dingwei Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

737 Background: Clear cell renal cell carcinoma (ccRCC) patient usually face aggressive progression when metastasis occurs. Therefore, in-depth investigation is needed to elucidate underlying mechanisms behind the metastasis of ccRCC to promote therapeutic benefits.This study aims to explore and investigate prognostic gene expression profiles based on multi-cohorts. Methods: Three microarray datasets were obtained from the Gene Expression Omnibus (GEO) database to screen and identify differentially expressed genes (DEGs) according to normalization annotation information. A total of 112 DEGs with functional enrichment were identified as candidate prognostic biomarkers. A protein–protein interaction network (PPI) of DEGs was developed, and the modules were analyzed using STRING and Cytoscape. Results: LASSO Cox regression suggested 31 significant involved genes, and 10 hub genes were identified as independent oncogenes in ccRCC patients. Distinct integrated scores of the hub genes mRNA expression showed statistical significance in predicting disease-free survival (DFS; p<0.001) and overall survival (OS; p<0.001) in TCGA and real-world cohorts. Meanwhile, ROC curves were constructed to validate specificity and sensitivity of the Cox regression penal to predict prognosis. The AUC index for the integrated genes scores was 0.758 for OS and 0.772 for DFS. Conclusions: In conclusion,the present study identifies DEGs and hub genes that may be involved in earlier recurrence and poor prognosis of ccRCC. The expression levels of ADAMTS9, C1S, DPYSL3, H2AFX, MINA, PLOD2, RUNX1, SLC19A1, TPX2 and TRIB3 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ccRCC.

scholarly journals Current advances in kidney cancer immunotherapy

2020 ◽  
Vol 15 (4) ◽  
pp. 30-38 ◽  
Author(s):  
A. A. Korotaeva ◽  
N. V. Apanovich ◽  
E. A. Braga ◽  
V. B. Matveev ◽  
A. V. Karpukhin
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Expression Profiles ◽  
Control Point ◽  
Malignant Neoplasms ◽  
Immune Control ◽  
Check Points ◽  
Immune Check Points

In Russia, among tumors of the genitourinary system, renal cell carcinoma takes the 2nd place after prostate cancer. In 25 % of patients at the time of diagnosis, metastases are detected. Treatment of advanced stages of renal cell carcinoma is often not effective enough. The introduction into clinical practice of modern immunotherapeutic drugs based on inhibition of immune check points has changed the prognosis of the disease for many patients with various malignant neoplasms, including kidney cancer. In this article, we described the results of recent clinical trials on the use of immunotherapy in the treatment of kidney cancer. The most effective is combination of drugs that inhibit different immune check points, and a combination of a check point inhibitor with a targeted drug. This approach is likely to be a major one in the treatment of renal cell carcinoma in the short term. Combinations of control point inhibitors with radiation therapy and immunomodulatory drugs, the role of miRNAs in the regulation of expression of immune control points, the significance and characteristics of the microbiome in connection with the success of immunotherapy for kidney cancer, gene expression profiles as biomarkers of the immune response, and other biomarkers are considered. A better understanding of the mechanisms that limit the effectiveness of immune control point inhibitors will improve future treatment.

scholarly journals Integrated analysis of a competing endogenous RNA network in renal cell carcinoma using bioinformatics tools

2019 ◽  
Vol 39 (7) ◽  
Author(s):  
Wei-dong Jiang ◽  
Zhi-hua Ye
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Cerna Network

Abstract Background: Circular RNAs (circRNAs) are known to be closely involved in tumorigenesis and cancer progression. Nevertheless, their function and underlying mechanisms in renal cell carcinoma (RCC) remain largely unknown. The aim of the present study was to explore their expression, functions, and molecular mechanisms in RCC. Methods: We downloaded the circRNA expression profiles from Gene Expression Omnibus (GEO) database, and RNA expression profiles from The Cancer Genome Atlas (TCGA) database. A ceRNA network was constructed based on circRNA–miRNA pairs and miRNA–mRNA pairs. Interactions between proteins were analyzed using the STRING database, and hub genes were identified using the cytoHubba app. We also constructed a circRNA–miRNA–hub gene regulatory module. Functional and pathway enrichment analyses were conducted using “DAVID 6.8” and R package “clusterProfiler”. Results: About 6 DEcircRNAs, 17 DEmiRNAs, and 134 DEmRNAs were selected for the construction of ceRNA network of RCC. Protein–protein interaction network and module analysis identified 8 hub genes. A circRNA–miRNA–hub gene sub-network was constructed based on 3 DEcircRNAs, 4 DEmiRNAs, and 8 DEmRNAs. GO and KEGG pathway analysis indicated the possible association of DEmRNAs with RCC onset and progression. Conclusions: These findings together provide a deeper understanding of the pathogenesis of RCC and suggest potential therapeutic targets.

scholarly journals Bioinformatics analysis of C3 and CXCR4 demonstrates their potential as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Quan ◽  
Yuchen Bai ◽  
Yunbei Yang ◽  
Er Lei Han ◽  
Hong Bai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Therapeutic Targets ◽  
Disease Free Survival ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Upregulated Genes

Abstract Background The molecular prognostic biomarkers of clear cell renal cell carcinoma (ccRCC) are still unknown. We aimed at researching the candidate biomarkers and potential therapeutic targets of ccRCC. Methods Three ccRCC expression microarray datasets (include GSE14762, GSE66270 and GSE53757) were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) between ccRCC and normal tissues were explored. The potential functions of identified DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). And then the protein - protein interaction network (PPI) was established to screen the hub genes. After that, the expressions of hub genes were identified by the oncomine database. The hub genes’ prognostic values of patients with ccRCC were analyzed by GEPIA database. Results A total of 137 DEGs were identified by utilizing the limma package and RRA method, including 63 upregulated genes and 74 downregulated genes. It is found that 137 DEGs were mainly enriched in 82 functional terms and 24 pathways in accordance with the research results. Thirteen highest-scoring genes were screened as hub genes (include 10 upregulated genes and 3 downregulated candidate genes) by utilizing the PPI network and module analysis. Through integrating the oncoming database and GEPIA database, the author found that C3 and CXCR4 are not only overexpressed in ccRCC, but also associated with the prognosis of ccRCC. Further results could reveal that patients with high C3 expression had a poor overall survival (OS), while patients with high CTSS and TLR3 expressions had a good OS; patients with high C3 and CXCR4 expressions had a poor disease-free survival (DFS), while ccRCC patients with high TLR3 expression had a good DFS. Conclusion These findings suggested that C3 and CXCR4 were the candidate biomarkers and potential therapeutic targets of ccRCC patients.

scholarly journals Precision Surgery and Kidney Cancer: Knowledge of Genetic Alterations Influences Surgical Management

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 261 ◽  
Author(s):  
Patrick T. Gomella ◽  
W. Linehan ◽  
Mark W. Ball
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Surgical Management ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Management Strategies ◽  
Genetic Alterations ◽  
Cancer Knowledge ◽  
Biological Potential

Renal cell carcinoma is a term that represents multiple different disease processes, each driven by different genetic alterations, with distinct histology, and biological potential which necessitates divergent management strategies. This review discusses the genetic alterations seen in several forms of hereditary kidney cancer and how that knowledge can dictate when and how to intervene with a focus on the surgical management of these tumors.

scholarly journals Down-regulation of BCL2L13 renders poor prognosis in clear cell and papillary renal cell carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Meng ◽  
Luojin Zhang ◽  
Mingjun Zhang ◽  
Kaiqin Ye ◽  
Wei Guo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
Papillary Renal Cell Carcinoma ◽  
Functional Enrichment ◽  
Cancer Tissue ◽  
Down Regulation

Abstract Background BCL2L13 belongs to the BCL2 super family, with its protein product exhibits capacity of apoptosis-mediating in diversified cell lines. Previous studies have shown that BCL2L13 has functional consequence in several tumor types, including ALL and GBM, however, its function in kidney cancer remains as yet unclearly. Methods Multiple web-based portals were employed to analyze the effect of BCL2L13 in kidney cancer using the data from TCGA database. Functional enrichment analysis and hubs of BCL2L13 co-expressed genes in clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were carried out on Cytoscape. Evaluation of BCL2L13 protein level was accomplished through immunohistochemistry on paraffin embedded renal cancer tissue sections. Western blotting and flow cytometry were implemented to further analyze the pro-apoptotic function of BCL2L13 in ccRCC cell line 786-0. Results BCL2L13 expression is significantly decreased in ccRCC and pRCC patients, however, mutations and copy number alterations are rarely observed. The poor prognosis of ccRCC that derived from down-regulated BCL2L13 is independent of patients’ gender or tumor grade. Furthermore, BCL2L13 only weakly correlates with the genes that mutated in kidney cancer or the genes that associated with inherited kidney cancer predisposing syndrome, while actively correlates with SLC25A4. As a downstream effector of BCL2L13 in its pro-apoptotic pathway, SLC25A4 is found as one of the hub genes that involved in the physiological function of BCL2L13 in kidney cancer tissues. Conclusions Down-regulation of BCL2L13 renders poor prognosis in ccRCC and pRCC. This disadvantageous factor is independent of any well-known kidney cancer related genes, so BCL2L13 can be used as an effective indicator for prognostic evaluation of renal cell carcinoma.

scholarly journals Identification of Six Potentially Long Noncoding RNAs as Biomarkers Involved Competitive Endogenous RNA in Clear Cell Renal Cell Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Kang Yang ◽  
Xiao-fan Lu ◽  
Peng-cheng Luo ◽  
Jie Zhang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cerna Network

Background. Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), usually is representative of metastatic heterogeneous neoplasm that links with poor prognosis, but the pathogenesis of ccRCC remains unclear. Currently, numerous evidences prove that long noncoding RNAs (lncRNAs) are considered as competing endogenous RNA (ceRNA) to participate in cellular processes of tumors. Therefore, to investigate the underlying mechanisms of ccRCC, the expression profiles of lncRNAs, miRNAs, and mRNAs were downloaded from the Cancer Genome Atlas (TCGA) database. A total of 1526 differentially expressed lncRNAs (DElncRNAs), 54 DEmiRNAs, and 2352 DEmRNAs were identified. To determine the connection of them, all DElncRNAs were input to the miRcode database. The results indicated that 85 DElncRNAs could connect with 9 DEmiRNAs in relation to our study. Then, databases of TargetScan and miRDB were used to search for targeted genes with reference to DEmiRNAs. The results showed that 203 out of 2352 targeted genes were identified in our TCGA set. Subsequently, ceRNA network was constructed according to Cytoscape and the targeted genes were functionally analyzed to elucidate the mechanisms of DEmRNAs. The results of survival analysis and regression analysis indicated that 6 DElncRNAs named COL18A1-AS1, WT1-AS, LINC00443, TCL6, AL356356.1, and SLC25A5-AS1 were significantly correlative with the clinical traits of ccRCC patients and could be served as predictors for ccRCC. Finally, these findings were validated by quantitative RT-PCR (qRT-PCR). Based on these discoveries, we believe that this identified ceRNA network will provide a novel perspective to elucidate ccRCC pathogenesis.

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