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. 2021 Dec;12(1):2432-2448.
doi: 10.1080/21655979.2021.1933868.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Qihang Yuan  1   2 , Jie Ren  3 , Lunxu Li  1   2 , Shuang Li  1   2 , Kailai Xiang  1   2 , Dong Shang  1   2
Affiliations

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Qihang Yuan et al. Bioengineered. 2021 Dec.

Abstract

Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6A-related lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high- and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high- and low-risk subgroups were investigated by functional and single-sample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

Keywords: N6-methyladenosine; Pancreatic adenocarcinoma; bioinformatics; long non-coding RNAs; prognostic signature.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

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Graphical abstract
Figure 1.
Figure 1.
Identification of the differentially expressed m6A-related lncRNAs with prognostic value in the cohort from TCGA. (a) Identification of m6A-related lncRNAs (b) Venn diagram to identify differentially expressed m6A-related lncRNAs between PAAD and normal pancreas tissues associated with prognosis. (c) Heatmap to explore mRNA levels of 57 differentially expressed m6A-related lncRNAs with prognostic values. (d) Univariate Cox regression analysis of 57 differentially expressed m6A-related lncRNAs with prognostic values
Figure 2.
Figure 2.
Prognostic performance of the m6A-LPS in the cohort from TCGA. (a) The distribution and median value of the risk scores in the cohort from TCGA. (b) PCA plot of the cohort from TCGA. (c) Distributions of OS status, OS and risk score in the cohort from TCGA. (d) Differential expression of five lncRNAs used for constructing m6A-LPS between high- and low-risk subgroups. (e) Kaplan-Meier curves for the OS of patients in the high- and low-risk groups in the cohort from TCGA. (f) AUC of the time-dependent ROC curve validated the prognostic value of the risk score in the cohort from TCGA
Figure 3.
Figure 3.
Prognostic performance of m6A-LPS in the cohort from ICGC. (a) The distribution and median value of the risk scores in the cohort from ICGC. (b) PCA plot of the cohort from ICGC. (c) Distributions of OS status, OS and risk score in the cohort from ICGC. (d) Differential expression of five lncRNAs used for constructing m6A-LPS between high- and low-risk subgroups. (e) Kaplan-Meier curves for the OS of patients in the high- and low-risk groups in the cohort from ICGC. (f) AUC of the time-dependent ROC curve validated the prognostic value of the risk score in the cohort from ICGC
Figure 4.
Figure 4.
Discrepancy in risk scores between different subgroups: (a) Age, (b) Sex, (c) Race, (d) Stage, (e) Histological grade, (f) Neoplasm location, (g) Maximum tumor dimension, (h) Surgery type, (i) Resection margins, (j) Radiation therapy, (k) Cancer status, (l) Chronic pancreatitis history, (m) Diabetes history, (n) Drinking frequency, (o) Smoking type, (p) Family history of cancer
Figure 5.
Figure 5.
Co-expression status of m6A genes and their related lncRNAs and clinical significance. (a) Sankey plot to identify a one-to-one match between m6A genes and their related lncRNAs. (b) Circle plot for the correlation between m6A genes and their related lncRNAs. (c) Differential expression of m6A genes and their-related lncRNAs between stage I/II/III/IV patients. (d) Differential expression of m6A genes and their related lncRNAs between grade I/II/III/IV patients
Figure 6.
Figure 6.
Independent prognostic analysis of the risk score acquired from m6A-LPS in the cohort from TCGA. (a) Univariate and (b) multivariate Cox regression analyses of the association between risk score, clinicopathological parameters and overall survival of patients in the cohort from TCGA. (c) Nomogram composed of grade, cancer status and risk score for the prediction of 0.5-, 1-, and 2-year OS probability. (d) Calibration plot for the evaluation of the nomogram in predicting 0.5-year, 1-year, and 2-year OS probability
Figure 7.
Figure 7.
Investigation of cellular processes, signaling pathways, and immune status affected by m6A-LPS. (a) Volcano plot of DEGs between high- and low-risk groups. (b) GO enrichment and (c) KEGG pathway analysis of DEGs between high- and low-risk groups. Comparison of ssGSEA scores between different risk subgroups. The scores for (d) 16 immune cells and (e) 13 immune-related functions are displayed in boxplots
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This work was supported by the National Key Research and Development Program of China (No. 2018YFE0195200), National Natural Science Foundation of China (No. 81873156), Key Project of Foreign training Program of Colleges and Universities in Liaoning Province (2020GJWZD004), Leading Talent Team of Support Program for High-Level Talent’s Innovation of Dalian in 2019 (2019RD11); Key Project of Foreign training Program of Colleges and Universities in Liaoning Province [2020GJWZD004].