A Novel Nine Apoptosis-Related Genes Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma and its Associations with Immune Infiltration

Wang, Yi; Chen, Yinhao; Zhu, Bingye

doi:10.3389/fmolb.2021.567730

Cited by 16 publications

(14 citation statements)

References 46 publications

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“…40 In the IFI44 family, the interferon-induced protein 44-like (IFI44L), a momentous paralog of IFI44, is a type I interferon-stimulated gene (ISG). It has been reported that the depletion of IFI44L enhances the migration, invasion and lung metastasis of hepatocellular carcinoma by activating the Met/Src signaling pathway, 41 while in ccRCC, patients with high IFI44 expression exhibit reduced survival, 42 consistent with our findings. In addition, higher levels of M1 macrophages and M2 macrophages 43 were observed in tumor sections with high IFI44 expression.…”

Section: Discussionsupporting

confidence: 92%

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma

Liu

Fang

et al. 2021

JIR

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Background Interferon plays a crucial role in the pathogenesis and progression of tumors. Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant urinary system tumor. An effective predictive model is required to evaluate the prognosis of patients to optimize treatment. Materials and Methods RNA-sequencing data and clinicopathological data from TCGA were involved in this retrospective study. The IFN-γ response genes with significantly different gene expression were screened out. Univariate Cox regression, LASSO regression and multivariate Cox regression were used to establish a new prognostic scoring model for the training group. Survival curves and ROC curves were drawn, and nomogram was constructed. At the same time, we conducted subgroup analysis and experimental verification using our own samples. Finally, we evaluated the relatedness between the prognostic signature and immune infiltration landscapes. In addition, the sensitivity of different risk groups to six drugs and immune checkpoint inhibitors was calculated. Results The IFN-γ response-related signature included 7 genes: C1S, IFI44, ST3GAL5, NUP93, TDRD7, DDX60, and ST8SIA4. The survival curves of the training and testing groups showed the model’s effectiveness (P = 4.372e-11 and P = 1.08e-08, respectively), the ROC curves showed that the signature was stable, and subgroup analyses showed the wide applicability of the model (P<0.001). Multivariate Cox regression analysis showed that the risk model was an independent prognostic factor of ccRCC. A high-risk score may represent an immunosuppressive microenvironment, while the high-risk group exhibited poor sensitivity to drugs. Conclusion Our findings strongly indicate that the IFN-γ response-related signature can be used as an effective prognostic indicator of ccRCC.

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Section: Discussionsupporting

confidence: 92%

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma

Liu

Fang

et al. 2021

JIR

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“…For example, MALAT1 is a lncRNA that acts as a marker in various cancers [ 39 ], and miR-182-5p can reduce the proliferation of ccRCC by binding with MALAT1 [ 38 ]. C4orf3 [ 40 ] and MDK [ 41 ] were also used as markers to evaluate ccRCC. Research on the remaining genes in ccRCC is rare, although these genes play important roles in other cancers.…”

Section: Discussionmentioning

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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“…Despite early surgical treatment, 30% of patients with a localized tumor eventually develop metastases, and the five-year overall survival rate of metastatic KIRC is only 12% [2][3][4]. Although immune-checkpoint and targeted therapeutics inhibitors have changed the landscape of treatment for KIRC, most patients have never experienced significant clinical benefits [5,6]. Therefore, it is essential to reveal the underlying molecular mechanisms of KIRC and find more powerful diagnostic biomarkers or therapeutic targets.…”

Section: Introductionmentioning

confidence: 99%

TF–RBP–AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers

2021

IJMS

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Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF–RBP–AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA–KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF–RBP–AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer.

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A Novel Nine Apoptosis-Related Genes Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma and its Associations with Immune Infiltration

Cited by 16 publications

References 46 publications

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma

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

TF–RBP–AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers

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