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Analysis of N6-methyladenosine-related lncRNAs in the tumor immune microenvironment and their prognostic role in pancreatic cancer

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Background

Pancreatic cancer (PC) is a rare solid malignancy with a poor prognosis. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play essential roles in tumorigenesis and progression. However, little is known about the role of m6A-related lncRNAs in PC.

Methods

m6A-related lncRNAs were extracted by Pearson analysis, and then prognosis-related lncRNAs were filtered from the m6A-related lncRNAs by univariate Cox regression analysis. Based on the expression patterns of the prognosis-related lncRNAs, samples were classified into distinct clusters. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a m6A-lncRNA-related prognostic signature for PC patients. Receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) values were used to evaluate the prognostic ability of the model.

Results

A total of 178 tumor and 4 normal samples were extracted from The Cancer Genome Atlas (TCGA) database in our study. Based on the expression of 12 filtered prognosis-related lncRNAs, two distinct clusters were eventually identified; these clusters were characterized by differences in the tumor immune microenvironment (TIME) and prognosis. A risk model comprising ten m6A-related lncRNAs was identified as an independent predictor of prognosis. ROC analysis revealed that this model had an acceptable prognostic value for PC patients. The prognostic signature was related to the TIME and the expression of critical immune checkpoint molecules.

Conclusion

This study comprehensively assessed the expression pattern and prognostic value of m6A-related lncRNAs in PC. The different clusters correlated with distinct TIMEs and prognoses. The study also constructed a ten-gene signature prognostic model based on m6A-related lncRNAs, which showed good accuracy in predicting overall survival.

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Data availability

The datasets generated for this study are available on request to the corresponding author.

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Acknowledgements

The authors thank American Journal Experts for language editing.

Funding

This work was supported by the National Natural Science Foundation of China under Grant (No. 81373172, 81770646).

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Authors and Affiliations

  1. Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong Province, China

    Yong Liu, Junjie Kong & Jun Liu

  2. Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong Province, China

    Tao Wang, Junjie Kong & Jun Liu

  3. Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong Province, China

    Ziqi Fang

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  1. Yong Liu
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  2. Tao Wang
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  4. Junjie Kong
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  5. Jun Liu
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Contributions

YL and JL contributed to the conception and design of the study. YL and TW extracted the data. YL and ZQF analyzed the data. YL, JJK and TW drafted the manuscript. WT, JJK and ZQF contributed to a critical revision of the manuscript. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Jun Liu.

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Conflict of interest

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.

Ethics approval and consent to participate

All human PC tissue samples used in this study were obtained from patients who underwent surgery in the Shandong Provincial Hospital Affiliated to Shandong First Medical University. This project was approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University and was performed in accordance with the Declaration of Helsinki. Each participant provided written informed consent.

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Supplementary Information

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Figure S1 Boxplot of the analysis of differences in immune cell infiltration in distinct clusters.

(A) Memory B cells. (B) Naive B cells. (C) Activated dendritic cells. (D) Resting dendritic cells. (E) Eosinophils. (F) M0 macrophages. (G) M1 macrophages. (H) M2 macrophages. (I) Activated mast cells. (J) Resting mast cells. (K) Monocytes. (L) Neutrophils. (M) Activated NK cells. (N) Resting NK cells. (O) Plasma cells. (P) Activated memory CD4+ T cells. (Q) Resting memory CD4+ T cells. (R) Naive CD4+ T cells. (S) CD8+ T cells. (T) Helper follicular T cells. (U) Delta gamma T cells. (V) Regulatory T cells (Tregs). (TIF 3078 kb)

Figure S2 Analysis of model validation in patients with different clinicopathological characteristics. (A) Patients with age>65.

(B) Patients with age<=65. (C) Patients with female. (D) Patients with male. (E) Patients with G1-2. (F) Patients with G3-4. (G) Patients with N0. (H) Patients with N1-3. (I) Patients with stage I-II disease. (J) Patients with stage III-IV disease. (K) Patients with T1-2. (L) Patients with T3-4. (TIF 2292 kb)

Figure S3 Boxplot of the analysis of the correlations between risk score and clinical features.

(A) The correlations between risk score and age. (B) The correlations between risk score and cluster. (C) The correlations between risk score and sex. (D) The correlations between risk score and grade. (E) The correlations between risk score and immune score. (F) The correlations between risk score and M stage. (G) The correlations between risk score and N stage. (H) The correlations between risk score and tumor stage. (I) The correlations between risk score and T stage. (TIF 2803 kb)

Figure S4 Scatterplots of the analysis of the correlations between immune cells and the risk score

. (A) The correlations between risk score and memory B cells. (B) The correlations between risk score and naive B cells. (C) The correlations between risk score and M0 macrophages. (D) The correlations between risk score and monocytes. (E) The correlations between risk score and plasma cells. (F) The correlations between risk score and activated memory CD4+ T cells. (G) The correlations between risk score and CD8+ T cells. (TIF 3670 kb)

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Liu, Y., Wang, T., Fang, Z. et al. Analysis of N6-methyladenosine-related lncRNAs in the tumor immune microenvironment and their prognostic role in pancreatic cancer. J Cancer Res Clin Oncol 148, 1613–1626 (2022). https://doi.org/10.1007/s00432-022-03985-4

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