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. 2022 Jul 14:20:3839-3850.
doi: 10.1016/j.csbj.2022.07.020. eCollection 2022.

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

Li Guo  1 , Yihao Kang  1 , Yiqi Xiong  1 , Lin Jia  2 , Xiaoqiang Yan  1 , Daoliang Xia  1 , Jiafeng Yu  3 , Jun Wang  1 , Tingming Liang  2
Affiliations

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

Li Guo et al. Comput Struct Biotechnol J. .

Abstract

As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine.

Keywords: Cross-talk; Prognostic biomarker; Prostate adenocarcinoma (PRAD); Weighted gene co-expression analysis (WGCNA); ceRNA network.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

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Graphical abstract
Fig. 1
Fig. 1
Integrative analysis of abnormally expressed genes and WGCNA analysis. A. Expression distributions of the top 100 up-regulated (n = 50) and down-regulated (n = 50) genes. B. The distributions of soft thresholding powers. The left picture indicates the distribution of R2 (scale free topology fitting index R2), and the right picture indicates the distribution of mean connectivity. C. WGCNA analysis indicates several gene modules. D. Correlations of genes associated with cancer and genes in green module. E. Functional enrichment analysis for screened genes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
In-depth analysis for screened 8 genes associated with cancer. A. Expression distributions for screened genes in tumor and normal samples. B. Paired gene correlations among the 8 genes based on their expression patterns. C. A heatmap shows expression patterns in high-risk group and low-risk group for the 8 genes based on multivariate COX proportional hazard model. All patients are divided into high-risk group and low-risk group, respectively. D. Distributions of risk scores and survival time of patient. A total of 10 dead patients are divided in high-risk group. E. Survival analysis and ROC curve of the 8 screened genes based on data in TCGA. F. Survival analysis and ROC curve of the 8 screened genes based on validation data from GEO (GSE116918).
Fig. 3
Fig. 3
Interaction networks among diverse RNAs. A. Interaction networks among screened mRNAs, related miRNAs and lncRNAs. B. The screened ceRNA network according to interactions of diverse RNAs. C. Survival analysis of TK1 in PRAD. D. Pan-cancer expression analysis of TK1 in different cancer types, and most of cancers are found with up-regulated TK1 expression patterns. The log2FC value is presented, and ** indicates p < 0.001, *** indicates p < 0.0001.
Fig. 4
Fig. 4
The potential prognostic values and expression distributions of screened RNAs. A. Survival analysis of miR-222-3p and SNHG3 also indicate the potential prognostic values in PRAD. B. The detailed expression patterns for homologous miRNA genes loci (miR-221-3p and miR-222-3p) based on the screened dominantly expressed 5 isomiRs. The circled isomiRs are the abundant isomiR species in the specific miRNA locus. The location on chromosome X: 184–203 indicates hg38:chrX:45746184–45746203:-, and 32–57 indicates hg38:chrX:45747032–45747057:-.C. IsomiR expression patterns in miR-222-3p locus across diverse cancer types. * indicates the significantly deregulated isomiRs (log2FC > 1.2 or < -1.2 and padj < 0.05). The expression distributions of multiple isomiRs in PRAD are circled. D. Pan-cancer analysis of SNHG3. The significantly up-regulated gene is presented the detailed log2FC value, and *** indicates p < 0.0001. E. The correlation analysis of paired RNAs (TK1 and miR-222-3p, miR-222-3p and SNHG3, and TK1 and SNHG3).
Fig. 5
Fig. 5
In-depth analysis of screened RNAs. A. Correlations of TK1 expression and infiltration levels in different immune cells B. Mutation frequencies of TK1 in different cancer types. C. Significant negative correlations of expression and methylation in TK1 and SNHG3. D. Methylation distributions of TK1 between tumor and normal samples. E. The protein levels of TK1 based on Human Protein Altas. F. The synthetical lethal interactions of TK1 with other mRNAs and their expression patterns in PRAD. G. The subcellular localization of lncRNA SNHG3.
Fig. 6
Fig. 6
Potential biological roles of screened ncRNAs in diverse cancer types. A. The potential biological roles of SNHG3 in several cancer types , , , , , . Yellow circle shows lncRNA, purple circle shows miRNA, and blue circle shows mRNA. B. The potential biological roles of miR-222-3p in several cancer types , , , , , . (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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