Construction and validation of an autophagy‐related long noncoding RNA signature for prognosis prediction in kidney renal clear cell carcinoma patients

Yu, Junjie; Mao, Weipu; Xu, Bin; Chen, Ming

doi:10.1002/cam4.3820

Cited by 17 publications

(13 citation statements)

References 34 publications

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“…The data has already contributed to advance in our competence to better understand cancer. [12][13][14][15] To interrogate the underlying metastatic mechanism of melanoma and identify potential therapeutic targets for melanoma, here we analyzed gene expressional profile of melanoma from the TCGA database, and validated the results with qRT-PCR in human melanoma tissues. Furthermore, we utilized the CYBERSORTx, 16 a digital cytometry that enables estimation of cell type abundances and cell-type-specific gene expression profiles from bulk tissue transcriptomes, to determine the crucial immune cell type and its gene expression profiles in advanced melanoma microenvironment.…”

Section: Introductionmentioning

confidence: 99%

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Pivotal factors associated with the immunosuppressive tumor microenvironment and melanoma metastasis

et al. 2021

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Section: Introductionmentioning

confidence: 99%

“…The data has already contributed to advance in our competence to better understand cancer. 12 , 13 , 14 , 15 …”

Section: Introductionmentioning

confidence: 99%

Pivotal factors associated with the immunosuppressive tumor microenvironment and melanoma metastasis

et al. 2021

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“…ccRCC is not sensitive to radiotherapy and chemotherapy [ 5 ]; therefore, more effective targeted therapy is needed. Other studies have explored the possible molecular markers or therapeutic targets of ccRCC from different perspectives, such as autophagy associated long noncoding RNAs (lncRNAs) [ 6 ], methylation modification of m6A [ 7 ], DNA methylation [ 8 ], and immune invasion [ 9 ]. We set out to find new potential targets from a metabolic perspective.…”

Section: Introductionmentioning

confidence: 99%

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

Chen

Liang

Chen

et al. 2021

Journal of Oncology

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Background. Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients. Methods. The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics. Results. According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage. Conclusion. We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

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“…Survival probability predicted by our angiogenesis prognostic signature was superior to the m6A-related lncRNA signature constructed by Yu et al (25) and the seven-MDEG signature constructed by Hu et al (26). For the patients with KIRC whose survival time was 1-4-years, our prognostic signature also showed a higher predictive accuracy compared with the autophagy-related long non-coding RNA signature constructed by Yu et al (24). Additionally, for the patients whose survival time was 4-8-years, our prognostic signature showed similar predictive accuracy compared with the autophagy-related long non-coding RNA signature (24) (Supplementary Figure 3).…”

Section: Construction Of a Risk Model Using The Lasso-cox Regression Analysismentioning

confidence: 64%

“…A receiver operating characteristic (ROC) curve was generated using the "survival-ROC" package in R to obtain the area under the ROC curve. To highlight the superiority and accuracy of our model, we carefully compared our prognostic model with another three prognostic signatures (an autophagyrelated long non-coding RNA signature constructed by Yu et al, an m6A-related lncRNA signature constructed by Yu et al, and a seven-MDEG signature constructed by Hu et al) (24)(25)(26). All the genes used for the construction of prognostic signatures of Yu and HU were obtained.…”

Section: Construction Of a Risk Model Using The Least Absolute Shrinkage And Selection Operator (Lasso)-cox Regression Analysismentioning

confidence: 99%

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

Che

et al. 2021

Front. Med.

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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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Construction and validation of an autophagy‐related long noncoding RNA signature for prognosis prediction in kidney renal clear cell carcinoma patients

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 17 publications

References 34 publications

Pivotal factors associated with the immunosuppressive tumor microenvironment and melanoma metastasis

Pivotal factors associated with the immunosuppressive tumor microenvironment and melanoma metastasis

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

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

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