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. 2022 Mar 4:9:741726.
doi: 10.3389/fsurg.2022.741726. eCollection 2022.

Identification of the Ferroptosis-Related Long Non-Coding RNAs Signature to Improve the Prognosis Prediction in Papillary Renal Cell Carcinoma

Xinfang Tang  1 , Feng Jiang  2 , Xiaoyu Wang  1 , Ying Xia  3 , Yan Mao  4 , Yan Chen  5
Affiliations

Identification of the Ferroptosis-Related Long Non-Coding RNAs Signature to Improve the Prognosis Prediction in Papillary Renal Cell Carcinoma

Xinfang Tang et al. Front Surg. .

Abstract

Papillary renal cell carcinoma (pRCC) is one of the epithelial renal cell carcinoma (RCC) histological subtypes. Ferroptosis is a new iron-dependent form of cell death that has been seen in a variety of clinical situations. Using differentially expressed ferroptosis-related long non-coding RNAs (lncRNAs) from patients with pRCC in The Cancer Genome Atlas; we built a prognostic lncRNA-based signature. We discovered seven different lncRNAs that were strongly linked to the prognosis of patients with pRCC. High-risk scores were linked to a poor prognosis for pRCC, which was confirmed by the findings of Kaplan-Meier studies. In addition, the constructed lncRNA signature has a 1-year area under the curve (AUC) of 0.908, suggesting that it has a high predictive value in pRCC. In the high-risk group, Gene set enrichment analyses (GSEA) analysis identified immunological and tumor-related pathways. Furthermore, single-sample GSEA (ssGSEA) revealed significant differences in T cell functions checkpoint, antigen presenting cell (APC) co-stimulation, inflammation promoting, and para inflammation between the two groups with different risk scores. In addition, immune checkpoints like PDCD1LG2 (PD-L2), LAG3, and IDO1 were expressed differently in the two risk groups. In summary, a novel signature based on ferroptosis-related lncRNAs could be applied in predicting the prognosis of patients with pRCC.

Keywords: ferroptosis; immune environment; lncRNA; papillary renal cell carcinoma; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer JL declared a shared affiliation, though no other collaboration, with the authors to the handling Editor.

Figures

Figure 1
Figure 1
Enrichment pathway analyses for differentially expressed genes (DEG) related to ferroptosis. (A) Gene ontology (GO) and (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed ferroptosis-related genes.
Figure 2
Figure 2
The construction of the ferroptosis-related long non-coding RNAs (lncRNAs) prognostic risk model. (A,B) Lasso Cox regression analyses for the ferroptosis-based lncRNAs according to the results of univariate Cox regression analysis. (C) Multivariate Cox regression analysis for the ferroptosis-associated lncRNAs.
Figure 3
Figure 3
Performance evaluation of the identified risk signature in papillary renal cell carcinoma (pRCC). (A) Kaplan–Meier overall survival (OS) curve for patients with pRCC divided into the high- and low-risk groups. (B) Receiver operating characteristic (ROC) curves exhibiting the predictive efficiency of the ferroptosis-related lncRNAs-based risk model. (C) Risk survival status plot in the high- and low-risk groups. (D) A heatmap showing the five identified ferroptosis-based lncRNAs expression profiles in two risk pRCC groups from The Cancer Genome Atlas (TCGA) database.
Figure 4
Figure 4
Prognostic value of the identified signature in pRCC. (A,B) Univariate and multivariate COX regression analyses for the expression levels of ferroptosis-related lncRNAs. (C) The decision curve analysis (DCA) results for the risk factors. (D) Sankey diagram showing the relationship between the identified lncRNAs and mRNAs. (E) A heatmap for the identified ferroptosis-related lncRNAs in the prognostic risk model, along with the clinicopathological features.
Figure 5
Figure 5
A nomogram for the ferroptosis-related lncRNAs with prognostic value and the clinicopathological factors of patients with pRCC. ***p < 0.001.
Figure 6
Figure 6
Gene set enrichment analyses (GSEA) analyses between the two risk pRCC groups divided by the lncRNAs related to ferroptosis in TCGA.
Figure 7
Figure 7
A heatmap of immune responses according to Tumor Immune Estimation Resource (TIMER) algorithms, CIBERSORT, QUANTISEQ, XCELL, MCP counter, and Estimating the Proportion of Immune and Cancer cells (EPIC) between the high- and low-risk pRCC groups.
Figure 8
Figure 8
Immune-related analyses for the patients with pRCC from TCGA. (A) Correlation analysis for immune cell subpopulations between the two pRCC groups. (B) The expression levels of immune checkpoint genes between the two pRCC groups. *p < 0.05; **p < 0.01; ***p < 0.001; ns = non-significant.
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References

    1. Cui Y, Miao C, Hou C, Wang Z, Liu B. Apolipoprotein C1 (APOC1): A novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma. Front Oncol. (2020) 10:1436. 10.3389/fonc.2020.01436 - DOI - PMC - PubMed
    1. Gao Z, Zhang D, Duan Y, Yan L, Fan Y, Fang Z, et al. . A five-gene signature predicts overall survival of patients with papillary renal cell carcinoma. PLoS ONE. (2019) 14:e211491. 10.1371/journal.pone.0211491 - DOI - PMC - PubMed
    1. Liu T, Zhang M, Sun D. Immune cell infiltration and identifying genes of prognostic value in the papillary renal cell carcinoma microenvironment by bioinformatics analysis. Biomed Res Int. (2020) 2020:5019746. 10.1155/2020/5019746 - DOI - PMC - PubMed
    1. Pang JS, Li ZK, Lin P, Wang XD, Chen G, Yan HB, et al. . The underlying molecular mechanism and potential drugs for treatment in papillary renal cell carcinoma: A study based on TCGA and Cmap datasets. Oncol Rep. (2019) 41:2089–102. 10.3892/or.2019.7014 - DOI - PMC - PubMed
    1. Steffens S, Janssen M, Roos FC, Becker F, Schumacher S, Seidel C, et al. . Incidence and long-term prognosis of papillary compared to clear cell renal cell carcinoma–a multicentre study. Eur J Cancer. (2012) 48:2347–52. 10.1016/j.ejca.2012.05.002 - DOI - PubMed