Construction of a protein-based model for prognosis prediction of kidney renal clear cell carcinoma: an investigation based on functional proteomics data

Abstract Background Several studies have shown prognostic value of gene-based models at mRNA level in kidney renal clear cell carcinoma (KIRC). However, protein-based models for prognosis prediction of KIRC are rarely reported, then we conduct this study. Methods Proteomics and clinical data of KIRC were acquired from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA), respectively. Prognosis-associated proteins were screened by univariate Cox regression analysis and log-rank test. Patients were grouped into training set and testing set. The protein-based prognostic model was constructed by lasso Cox regression analysis in training set and validated in test set and whole set. Results A five-protein model (ACC1, IGFBP2, MIG6, PEA15 and RAD51) was constructed for KIRC prognosis prediction. It could well classify KIRC patients into high-risk and low-risk group with significantly different survival. The results was validated in the testing set and whole set. Age, AJCC stage and risk score based on the five-protein model were identified as independent prognostic parameters and they were used to construct a nomogram. The calibration plot showed the nomogram had good agreement between predicted and actual outcomes. Time-dependent ROC curves revealed the nomogram performed best in predicting the 1-year, 3-year and 5-year overall survival (OS) compared with other independent prognostic parameters. DCA demonstrated the nomogram had obviously clinical net benefit. Furthermore, several cancer-related biological signaling pathways were enriched in the functional enrichment analysis. Conclusion Our study developed an effective protein-based model to predict the OS of KIRC, which may help clinicians to offer individual treatment.

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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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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 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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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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Expression of BAG1 is associated with prognosis in kidney renal clear cell carcinoma based on bioinformatics

BMC Cancer ◽

10.1186/s12885-021-07874-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hongrong Wu ◽

Minjing Liu ◽

Yuejun He ◽

Guozhao Meng ◽

Wanbei Guo ◽

...

Keyword(s):

Cell Carcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Neoadjuvant Treatment ◽

Renal Clear Cell Carcinoma ◽

Cox Regression Analysis ◽

Low Expression

Abstract Background BCL2 associated Athano-Gene 1 (BAG1) has been described to be involved in the development and progression of cancer. But the role of BAG1 in kidney renal clear cell carcinoma (KIRC) has remained largely unknown. Methods We performed bioinformatic analysis of data from TCGA and GEO dataset. The role of BAG1 in KIRC was explored by Logistic and Cox regression model. The molecular mechanisms of BAG1 was revealed by GSEA. Results The current study found that the KIRC tumor samples have a low level of BAG1 mRNA expression compared to the matched normal tissues based on TCGA data and GEO databases. Low expression of BAG1 in KIRC was significantly associated with Sex, clinical pathological stage, tumor-node-metastasis (TNM) stage, hemoglobin levels, cancer status and history of neoadjuvant treatment. Kaplan-Meier survival analysis indicated that KIRC patients with BAG1 high expression have a longer survival time than those with BAG1 low expression (p < 0.000). Cox regression analysis showed that BAG1 remained independently associated with overall survival, with a hazard ratio (HR) of 1.75(CI:1.05–2.90; p = 0.029). GSEA indicated that the signaling pathways including fatty acid metabolism and oxidative phosphorylation were differentially enriched in high BAG1 expression phenotype. Conclusions These findings suggested that BAG1 expression may act as a potential favorable prognostic marker and challenging therapeutic target.

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A Five-Gene Signature Predicts Prognosis in Patients with Kidney Renal Clear Cell Carcinoma

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/842784 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 11

Author(s):

Yueping Zhan ◽

Wenna Guo ◽

Ying Zhang ◽

Qiang Wang ◽

Xin-jian Xu ◽

...

Keyword(s):

Cell Carcinoma ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Clinical Information ◽

Gene Signature ◽

Renal Clear Cell Carcinoma ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Tcga Dataset

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.

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Identification and validation of a two‐gene metabolic signature for survival prediction in patients with kidney renal clear cell carcinoma

10.21203/rs.3.rs-51839/v1 ◽

2020 ◽

Author(s):

Xudong Guo ◽

Zhuolun Sun ◽

Shaobo Jiang ◽

Xunbo Jin ◽

Hanbo Wang

Keyword(s):

Target Genes ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Survival Prediction ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Metabolic Signature ◽

Multiple Datasets

Abstract Background: Kidney renal clear cell carcinoma (KIRC) is one of the most common malignant tumors worldwide. Deregulated tumor cell metabolism is emerging as a common feature of tumorigenesis. The expression pattern and clinical significance of metabolism-related genes (MRGs) in KIRC remains unclear.Methods: We downloaded the RNA sequencing data and corresponding clinical information for KIRC from the Cancer Genome Atlas (TCGA) database and identified the differently expressed MRGs between tumors and normal tissues. According to the Cox regression analysis and least absolute shrinkage and selection operator (LASSO), we identified target genes for prognostic signature construction. We also analyzed the correlations of the signature risk score with clinicopathological features. The robustness of the signature was further examined by stratified survival analysis. A predictive nomogram was built for the optimal strategy to predict the survival possibility of KIRC patients. The expression levels of target genes were validated in multiple datasets. Gene set enrichment analyses (GSEA) were performed to unveil several significantly enriched pathways.Results: A total of 123 differentially expressed MRGs were identified, including 60 up-regulated genes and 63 down-regulated genes. Next, RRM2 and ALDH6A1 were identified as prognosis-related genes and used to construct a prognostic signature. The signature was proved to be an independent prognostic factor for KIRC survival by multivariable Cox regression analysis. Subgroup analysis indicated that this signature could serve as a classifier for the evaluation of low- and high-risk groups. Up regulation of RRM2 and down regulation of ALDH6A1 were associated with unfavorable prognosis in patients suffering from KIRC. Nomogram including the signature suggested some clinical net benefit for overall survival prediction. In addition, the calibration curves indicated the nomogram performed well in predicting 3‐ and 5-year OS compared with the ideal model. The expression level of RRM2 was significantly up-expressed, while ALDH6A1 were significantly down-expressed in KIRC samples compared with the normal samples in multiple datasets. Furthermore, RRM2 and ALDH6A1were significantly enriched in different pathways.Conclusion: Our study identified a two‐gene metabolic signature that had important clinical implications in KIRC prognosis prediction, which might provide potential biomarkers and targets of metabolic therapeutic relevance.

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Identification and Validation of PIK3CA as a Marker Associated with Prognosis and Immune Infiltration in Renal Clear Cell Carcinoma

Journal of Oncology ◽

10.1155/2021/3632576 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Ya Li ◽

Chong Wang ◽

Yang Gao ◽

Liang Zhou

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Disease Free Survival ◽

Renal Clear Cell Carcinoma ◽

Free Survival ◽

Cox Regression Analysis ◽

Potential Biomarker

Background. Kidney renal clear cell carcinoma (KIRC) is the most prevalent renal malignancy. The therapeutic strategies for advanced KIRC are very few, with only sunitinib being widely approved. Mutations in the PIK3CA gene can affect tumor cell proliferation, metastasis, and patients’ survival. Methods. Bioinformatics analysis was performed to explore the expression and clinical significance of PIK3CA in KIRC. Moreover, qRT-PCR was conducted to verify the result. Results. Subgroup analyses of KIRC tissue based on gender, tumor grade, and cancer stage indicated downregulation of PIK3CA mRNA expression. The KIRC patients with high PIK3CA expression indicated a better overall survival, progression-free survival, and disease-free survival. A predictive nomogram was constructed and demonstrated that the calibration plots for the 3-year and 5-year OS rates were predicted relatively well compared with an ideal model in the TCGA KIRC cohort. The validation study revealed that downregulation of PIK3CA in KIRC tissues and low PIK3CA expression had a poor overall survival with an AUC of 0.775 in the ROC curve. Moreover, Cox regression analysis revealed that PIK3CA expression and clinical stage were independent factors affecting the prognosis of KIRC patients. PIK3CA expression was found to be significantly associated with the abundance of immune cells and immune biomarker sets. PIK3CA and associated genes were found to be mainly associated with immune response and the JAK-STAT signaling pathway. Conclusion. We identified PIK3CA as a potential biomarker for prognosis correlated with immune infiltrates in KIRC. Further studies should focus on the functions of PIK3CA in KIRC carcinogenesis.

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