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. 2024 Aug 2:15:1406150.
doi: 10.3389/fgene.2024.1406150. eCollection 2024.

Epigenetic dysregulated long non-coding RNAs in renal cell carcinoma based on multi-omics data and their influence on target drugs sensibility

Jiawei Wang  1 , Pingnan Dou  1   2 , Yunwen Sun  1   3 , Jie Zheng  1 , Guanwei Wu  1 , Heqian Liu  1 , Lingsong Tao  1
Affiliations

Epigenetic dysregulated long non-coding RNAs in renal cell carcinoma based on multi-omics data and their influence on target drugs sensibility

Jiawei Wang et al. Front Genet. .

Abstract

Purpose: Epigenetic modifications play a crucial role in cancer development, and our study utilized public data to analyze which leads to the discovery of significant epigenetic abnormalities in lncRNAs, offering valuable insights into prognosis and treatment strategies for renal carcinoma.

Methods: Public data were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) database. The analysis of the online public data was all completed in R software.

Results: We discovered a great number of epigenetic abnormalities of lncRNA in renal cancer, which is achieved by comparing the following modification and methylation of histone region changes on the promoter and enhancer of lncRNA: H3K27ac, H3K4me1, H3K4me3. As a result, 12 specific epigenetic disorders of lncRNA genes in renal cancer were identified. Finally, based on this lncRNA, we investigated the prognosis of renal cancer samples, among which 8 lncRNA can be seen as markers of prognosis in renal cancer, which had great prediction ability for ccRCC prognosis. Meanwhile, high risk score may pose response better to axitinib and nilotinib, but not sorafenib or sunitinib. Beyond, we observed an elevated level of risk score in immunotherapy non-responders. Further, biological enrichment and immuno-infiltration analysis was conducted to investigate the fundamental differences between patients categorized as high or low risk.

Conclusion: Our research improves the understanding in the function of epigenetic dysregulated long non-coding RNAs in renal carcinoma.

Keywords: ccRCC; epigenetic; lncRNAs; prognosis; sensitivity.

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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.

Figures

FIGURE 1
FIGURE 1
Identification of the epi-lncRNAs in ccRCC. Notes: (A) The proportion of epi-lncRNAs and epi-PCGs to total lncRNAs and PCGs in the genome; (B–E) Genomic characteristics comparison between epigenetic dysregulated lncRNA/PCGs and non-epigenetic dysregulated lncRNA/PCGs.
FIGURE 2
FIGURE 2
Genomic landscape of epi-lncRNAs. Notes: (A) Differential methylation regions and histone modifications lead to a distinct epi-lncRNA genomic landscape; (B) Type distribution in epi-lncRNAs with several apparent disorders.
FIGURE 3
FIGURE 3
Functional analysis of epi-lncRNA. Notes: (A) Differences between cancer and adjacent cancers in six types of apparent dysregulated lncRNAs; (B) Relationship between different histone modifications of six epi-lncRNAs and overall survival; (C) Six kinds of apparent dysregulated lncRNAs are most related to KEGG pathway.
FIGURE 4
FIGURE 4
Epi-lncRNAs in ccRCC. Notes: (A) Correlation between m6a, m5c, and m1A genes and enrichment scores of six epi-lncRNAs; (B) Intersection of disease-related lncRNA and renal cell carcinoma epi-lncRNA; (C) Specific epi-lncRNA expression associated with renal cell carcinoma is different between normal samples and tumors.
FIGURE 5
FIGURE 5
Epigenetic modification profiles of identified epi-lncRNAs. Notes: (A) Epigenetic modification profiles of PVT1, HOTAIR, LINC00160, HOTTIP, H19, LINC00461, HCP5 and XIST, (B) quantitative real-time PCR validation.
FIGURE 6
FIGURE 6
Prognosis value of identified epi-lncRNAs. Notes: (A) Univariate analysis of 12 kidney cancer-associated epi-lncRNA genes; (B–M) Kaplan-Meier survival curve for samples with high and low expression of 12 renal cell carcinoma-related EPI lncRNA genes.
FIGURE 7
FIGURE 7
Prognosis model based on the epi-lncRNAs. Notes: (A) Overview of our model in TCGA cohort; (B) Kaplan-Meier survival curve of high and low risk patients in TCGA cohort; (C) ROC curves of our model in TCGA cohort; (D) Overview of our model in ICGC cohort; (E) Kaplan-Meier survival curve for ICGC cohort patients at high and low risk; (F) ROC curves of our model in ICGC cohort.
FIGURE 8
FIGURE 8
Drug sensitivity analysis. Notes: (A–L) Drug sensitivity differences between people at high and low risk for specific target drugs.
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