The Construction and Exploration of the ceRNA Network and Patterns of Tumor-Infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma

Abstract Background: Kidney renal clear cell carcinoma is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. In view of its limited diagnostic strategies and poor prognosis, bioinformatics analysis has been used to explore the possible mechanisms of renal clear cell carcinoma and effective prognostic-related biomarkers.Method: The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of kidney renal clear cell carcinoma tumor tissues and 72 cases of normal tissues is obtained from the TCGA database. Heat map and volcano map of differentially expressed genes were drawn through R language; The CeRNA network was visualized by Cytoscape software (version 3.7.2). Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of kidney renal clear cell carcinoma. Construction of a prognostic model based on tumor-infiltrating immune cells, The R "corrplot" software package was used for co-expression analysis based on the CeRNA network and tumor-infiltrating immune cells model.Results: There are 3074 differentially expressed mRNAs (1055 upregulated and 2019 downregulated), and 359 differentially expressed lncRNAs (71 upregulated and 280 downregulated) and 132 differentially expressed miRNAs (70 upregulated and 62 downregulated) that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network-based prognostic model construction. 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model based on tumor-infiltrating immune cells. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P<0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R=0.23, P<0.001).Conclusion: Based on CeRNA network and tumor-infiltrating immune cells, we explored the possible mechanism of kidney renal clear cell carcinoma and obtained effective biomarkers for predicting prognosis by Bioinformatics analysis in this study.

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Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network

PLoS ONE ◽

10.1371/journal.pone.0252452 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252452

Author(s):

Ke Gong ◽

Ting Xie ◽

Yong Luo ◽

Hui Guo ◽

Jinlan Chen ◽

...

Keyword(s):

Regression Analysis ◽

Cell Carcinoma ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cerna Network

Introduction Kidney renal clear cell carcinoma (KIRC) has a high incidence globally, and its pathogenesis remains unclear. Long non-coding RNA (lncRNA), as a molecular sponge, participates in the regulation of competitive endogenous RNA (ceRNA). We aimed to construct a ceRNA network and screened out possible lncRNAs to predict KIRC prognosis. Material and methods All KIRC data were downloaded from the TCGA database and screened to find the possible target lncRNA; a ceRNA network was designed. Next, GO functional enrichment and KEGG pathway of differentially expressed mRNA related to lncRNA were performed. We used Kaplan-Meier curve analysis to predict the survival of these RNAs. We used Cox regression analysis to construct a model to predict KIRC prognosis. Results In the KIRC datasets, 1457 lncRNA, 54 miRNA and 2307 mRNA were screened out. The constructed ceRNA network contained 81 lncRNAs, nine miRNAs, and 17 mRNAs differentially expressed in KIRC. Survival analysis of all differentially expressed RNAs showed that 21 lncRNAs, four miRNAs, and two mRNAs were related to the overall survival rate. Cox regression analysis was performed again, and we found that eight lncRNAs were related to prognosis and used to construct predictive models. Three lnRNAs from independent samples were meaningful. Conclusion The construction of ceRNA network was involved in the process and transfer of KIRC, and three lncRNAs may be potential targets for predicting KIRC prognosis.

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Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

10.21203/rs.2.23095/v1 ◽

2020 ◽

Author(s):

Di Zhang ◽

Xiaopeng Hu ◽

Song Zeng

Keyword(s):

Cell Carcinoma ◽

Noncoding Rna ◽

Prognostic Model ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Predictive Ability ◽

Renal Clear Cell Carcinoma ◽

Differentially Expressed ◽

Clinical Cohort ◽

Cerna Network

Abstract Background: Lone noncoding RNA (lncRNA) is generally identified as competing endogenous RNA (ceRNA) that plays a vital role in the pathogenesis of kidney renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma with poor prognosis and unclear pathogenesis. This study established a novel ceRNA network and thus identified a three-lncRNA prognostic model in KIRC patients. Methods: Differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) database. The lncATLAS was applied to determine the differentially expressed lncRNAs (DElncRNAs) located in the cytoplasm. The miRcode, miRDB, miRTarBase, and TargetScan databases were utilized to predict the interactions of DElncRNAs, DEmiRNAs, and DEmRNAs. Cytoscape was used to construct the ceRNA network. Then, a lncRNA prognostic model (LPM) was constructed based on ceRNA-related lncRNA that was significantly related to overall survival (OS), and its predictive ability was evaluated. Moreover, an LPM-based nomogram model was constructed. The significantly different expression of genes in the LPM was validated in an independent clinical cohort (N=21) by quantitative RT-PCR. Results: A novel ceRNA regulatory network, including 73 lncRNAs, 8 miRNAs, and 21 mRNAs was constructed. Functional enrichment analysis indicated that integral components of membrane and PI3K-Akt signaling pathway represented the most significant GO terms and pathway, respectively. The LPM established based on three lncRNAs (MIAT, LINC00460, and LINC00443) of great prognostic value from the ceRNA network was proven to be independent of conventional clinical parameters to differentiate patients with low or high risk of poor survival, with the AUC of 1-, 5- and 10-year OS were 0.723, 0.714 and 0.826 respectively. Furthermore, the nomogram showed a better predictive value in KIRC patients than individual prognostic parameters. The expression of MIAT and LINC00460 was significantly upregulated in the KIRC samples, while the expression of LINC00443 was significantly downregulated compared with the adjacent normal samples in the clinical cohort, TCGA, and GTEx. Conclusion: This LPM based on three-lncRNA could serve as an independent prognostic factor with a tremendous predictive ability for KIRC patients, and the identified novel ceRNA network may provide insight into the prognostic biomarkers and therapeutic targets of KIRC.

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MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal clear cell carcinoma

BMC Medical Genomics ◽

10.1186/s12920-021-00932-z ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Minjiang Huang ◽

Ti Zhang ◽

Zhi-Yong Yao ◽

Chaoqung Xing ◽

Qingyi Wu ◽

...

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Prognostic Model ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Critical Role ◽

Renal Clear Cell Carcinoma ◽

Pathway Enrichment Analysis ◽

Survival Status ◽

Model Based

Abstract Background Kidney renal clear cell carcinoma (KIRC) is the most common type of kidney cell carcinoma which has the worst overall survival rate. Almost 30% of patients with localized cancers eventually develop to metastases despite of early surgical treatment carried out. MicroRNAs (miRNAs) play a critical role in human cancer initiation, progression, and prognosis. The aim of our study was to identify potential prognosis biomarkers to predict overall survival of KIRC. Methods All data were downloaded from an open access database The Cancer Genome Atlas. DESeq2 package in R was used to screening the differential expression miRNAs (DEMs) and genes (DEGs). RegParallel and Survival packages in R was used to analysis their relationships with the KIRC patients. David version 6.8 and STRING version 11 were used to take the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Results We found 2 DEGs (TIMP3 and HMGCS1) and 3 DEMs (hsa-miR-21-5p, hsa-miR-223-3p, and hsa-miR-365a-3p) could be prognosis biomarkers for the prediction of KIRC patients. The constructed prognostic model based on those 2 DEGs could effectively predict the survival status of KIRC. And the constructed prognostic model based on those 3 DEMs could effectively predict the survival status of KIRC in 3-year and 5-year. Conclusion The current study provided novel insights into the miRNA related mRNA network in KIRC and those 2 DEGs biomarkers and 3 DEMs biomarkers may be independent prognostic signatures in predicting the survival of KIRC patients.

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Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

Journal of Oncology ◽

10.1155/2020/6657013 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Guangzhen Wu ◽

Yingkun Xu ◽

Chenglin Han ◽

Zilong Wang ◽

Jiayi Li ◽

...

Keyword(s):

Immune Response ◽

Cell Carcinoma ◽

Sensitivity And Specificity ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Lasso Regression ◽

Regression Curve ◽

Cox Regression Analysis

Purpose. To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. Materials and Methods. KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis P < 0.05 . The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. Results. Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues P < 0.05 . Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). Conclusion. We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.

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Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

Cancer Cell International ◽

10.1186/s12935-020-01423-4 ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Di Zhang ◽

Song Zeng ◽

Xiaopeng Hu

Keyword(s):

Cell Carcinoma ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Prognostic Model ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Competitive Endogenous Rna

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High PON1 Expression Predicts Poor Prognosis In Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-1112733/v1 ◽

2021 ◽

Author(s):

Chenxia Jiang ◽

Xinyu Zhang ◽

Xiaoyan Li ◽

Jia Li ◽

Hua Huang

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Prognostic Factor ◽

Cell Carcinoma ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Independent Prognostic Factor ◽

Cox Regression Analysis

Abstract Background: Relevant study had demonstrated that Paraoxonase-1 (PON1) had relationship with occurrence and development of tumors which suggested that PON1 was a key gene in promoting tumor progression. However, the relationship between PON1 and Kidney renal clear cell carcinoma (KIRC) is still unclear so far. Methods: We downloaded relevant data about KIRC from TCGA dataset and compared it with normal renal tissues. Immunohistochemistry (IHC) was applied to analyze the expression of PON1. Univariate cox regression analysis and multivariate cox regression analysis were also utilized to analyze independent factors associated with prognosis. Gene set enrichment analysis was conducted to find the signaling pathways of PON1 in KIRC. Finally, we also investigated whether PON1 had relationship with immunity. Results: As shown in results, PON1 expression was decreased in KIRC compared with adjacent paracancer tissues. Immunohistochemistry (IHC) was utilized to find the expression of PON1. After survival analysis, the high expression of PON1 was significantly related to overall survival (P<0.001). Univariate/Multivariate cox regression analysis both revealed that PON1 could serve as an independent prognostic factor. To analyze overall survival (OS) of patients with KIRC, nomogram was developed. GSEA revealed that PON1 was correlated with homologous recombination. Besides, PON1 had few relationships with immunity. Conclusions: Our results revealed that PON1 could serve as an independent prognostic factor for KIRC, providing a novel target for KIRC future treatments.

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Hub Long Noncoding RNAs with m6A Modification for Signatures and Prognostic Values in Kidney Renal Clear Cell Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.682471 ◽

2021 ◽

Vol 8 ◽

Author(s):

Gaoteng Lin ◽

Huadong Wang ◽

Yuqi Wu ◽

Keruo Wang ◽

Gang Li

Keyword(s):

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Noncoding Rnas ◽

Renal Clear Cell Carcinoma ◽

Expression Level ◽

Molecular Signatures ◽

Cox Regression Analysis ◽

Qrt Pcr ◽

Cerna Network

Background: N6-methyladenosine (m6A)–modified long noncoding RNAs (m6A-lncRNAs) have been proven to be involving in regulating tumorigenesis, invasion, and metastasis for a variety of tumors. The present study aimed to screen lncRNAs with m6A modification and investigate their biological signatures and prognostic values in kidney renal clear cell carcinoma (KIRC).Materials and Methods: lncRNA-seq, miRNA-seq, and mRNA-seq profiles of KIRC samples and the clinical characteristics of corresponding patients were downloaded from The Cancer Genome Atlas (TCGA). The R package “edgeR” was utilized to perform differentially expressed analysis on these profiles to gain DElncRNAs, DEmiRNAs, and DEmRNAs, respectively. The results of intersection of DElncRNAs and m6A-modified genes were analyzed by the weighted gene co-expression network analysis (WGCNA) to screen hub m6A-lncRNAs. Then, WGCNA was also used to construct an lncRNA-miRNA-mRNA (ceRNA) network. The Cox regression analysis was conducted on hub m6A-lncRNAs to construct the m6A-lncRNAs prognostic index (m6AlRsPI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of m6AlRsPI. The m6AlRsPI model was tested by internal and external cohorts. The molecular signatures and prognosis for hub m6A-lncRNAs and m6AlRsPI were analyzed. The expression level of hub m6A-lncRNAs in KIRC cell lines were quantified by qRT-PCR.Results: A total of 21 hub m6A-lncRNAs associated with tumor metastasis were identified in the light of WGCNA. The ceRNA network for 21 hub m6A-lncRNAs was developed. The Cox regression analysis was performed on the 21 hub m6A-lncRNAs, screening two m6A-lncRNAs regarded as independent prognostic risk factors. The m6AlRsPI was established based on the two m6A-lncRNAs as follows: (0.0006066 × expression level of LINC01820) + (0.0020769 × expression level of LINC02257). The cutoff of m6AlRsPI was 0.96. KM survival analysis for m6AlRsPI showed that the high m6AlRsPI group could contribute to higher mortality. The area under ROC curve for m6AlRsPI for predicting 3- and 5-year survival was 0.760 and 0.677, respectively, and the m6AlRsPI was also tested. The mutation and epithelial–mesenchymal transition (EMT) analysis for m6AlRsPI showed that the high m6AIRsPI group had more samples with gene mutation and had more likely caused EMT. Finally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for mRNAs interacted with the two m6A-lncRNAs, showing they were involved in the process of RNA splicing and regulation of the mRNA surveillance pathway. qRT-PCR analysis showed that the two m6A-lncRNAs were upregulated in KIRC.Conclusion: In the present study, hub m6A-lncRNAs were determined associated with metastasis in KIRC, and the ceRNA network demonstrated the potential carcinogenic regulatory pathway. Two m6A-lncRNAs associated with the overall survival were screened and m6AlRsPI was constructed and validated. Finally, the molecular signatures for m6AlRsPI and the two m6A-lncRNAs were analyzed to investigate the potential modulated processes in KIRC.

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Angiogenesis Pathway in Kidney Renal Clear Cell Carcinoma and Its Prognostic Value for Cancer Risk Prediction

Frontiers in Medicine ◽

10.3389/fmed.2021.731214 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiangyu Che ◽

Wenyan Su ◽

Xiaowei Li ◽

Nana Liu ◽

Qifei Wang ◽

...

Keyword(s):

Cell Carcinoma ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Treatment Plan ◽

Renal Clear Cell Carcinoma ◽

Targeted Drugs ◽

Cox Regression Analysis ◽

Cluster 2 ◽

Angiogenesis Pathway

Angiogenesis, a process highly regulated by pro-angiogenic and anti-angiogenic factors, is disrupted and dysregulated in cancer. Despite the increased clinical use of angiogenesis inhibitors in cancer therapy, most molecularly targeted drugs have been less effective than expected. Therefore, an in-depth exploration of the angiogenesis pathway is warranted. In this study, the expression of angiogenesis-related genes in various cancers was explored using The Cancer Genome Atlas datasets, whereupon it was found that most of them were protective genes in the patients with kidney renal clear cell carcinoma (KIRC). We divided the samples from the KIRC dataset into three clusters according to the mRNA expression levels of these genes, with the enrichment scores being in the order of Cluster 2 (upregulated expression) > Cluster 3 (normal expression) > Cluster 1 (downregulated expression). The survival curves plotted for the three clusters revealed that the patients in Cluster 2 had the highest overall survival rates. Via a sensitivity analysis of the drugs listed on the Genomics of Drug Sensitivity in Cancer database, we generated IC50 estimates for 12 commonly used molecularly targeted drugs for KIRC in the three clusters, which can provide a more personalized treatment plan for the patients according to angiogenesis-related gene expression. Subsequently, we investigated the correlation between the angiogenesis pathway and classical cancer-related genes as well as that between the angiogenesis score and immune cell infiltration. Finally, we used the least absolute shrinkage and selection operator (LASSO)–Cox regression analysis to construct a risk score model for predicting the survival of patients with KIRC. According to the areas under the receiver operating characteristic (ROC) curves, this new survival model based on the angiogenesis-related genes had high prognostic prediction value. Our results should provide new avenues for the clinical diagnosis and treatment of patients with KIRC.

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