Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2022

Open Access 01-12-2022 | Pancreatic Cancer | Research

Comprehensive analysis of the prognosis and immune infiltration landscape of RNA methylation-related subtypes in pancreatic cancer

Authors: Siyuan Lu, Jie Hua, Jiang Liu, Miaoyan Wei, Chen Liang, Qingcai Meng, Bo Zhang, Xianjun Yu, Wei Wang, Jin Xu

Published in: BMC Cancer | Issue 1/2022

Login to get access

Abstract

Background

RNA methylation refers to a form of methyl modification in RNA that modulates various epigenetic alterations. Mounting studies have focused on its potential mechanisms in cancer initiation and progression. However, the prognostic value and potential role of RNA methylation in the immune microenvironment of pancreatic cancer remain unclear.

Methods

Comprehensive bioinformatics analysis was performed to illuminate the expression profiles of RNA methylation modulators. In addition, the ConsensusClusterPlus algorithm was utilized to identify two remarkably different subtypes, and a feasible risk stratification method was established to accurately estimate prognosis. In addition, we validated our signature at the cytology and histology levels and conducted functional experiments to explore the biological functions of our key genes.

Results

Two subtypes with remarkable survival differences were identified by the consensus clustering algorithm. Cluster 2 tended to have higher expression levels of RNA methylation regulators and to be the high RNA methylation group. In addition, cluster 1 exhibited a significantly higher abundance of almost all immune cells and increased immune checkpoint expression compared to cluster 2. Chemotherapeutic sensitivity analysis indicated that there were significant differences in the sensitivity of four of the six drugs between different subgroups. Mutation investigation revealed a higher mutation burden and a higher number of mutations in cluster 2. An accurate and feasible risk stratification method was established based on the expression of key genes of each subtype. Patients with low risk scores exhibited longer survival times in one training (TCGA) and two validation cohorts (ICGC, GSE57495), with p values of 0.001, 0.0081, and 0.0042, respectively. In addition, our signature was further validated in a cohort from Fudan University Shanghai Cancer Center. The low-risk group exhibited higher immune cell abundance and immune checkpoint levels than the high-risk group. The characteristics of the low-risk group were consistent with those of cluster 1: higher stromal score, estimate score, and immune score and lower tumor purity. Additionally, cell function investigations suggested that knockdown of CDKN3 remarkably inhibited the proliferation and migration of pancreatic cancer cells.

Conclusions

RNA methylation has a close correlation with prognosis, immune infiltration and therapy in pancreatic cancer. Our subtypes and risk stratification method can accurately predict prognosis and the efficacy of immune therapy and chemotherapy.
Appendix
Available only for authorised users
Literature
1.
Strobel O, Neoptolemos J, Jäger D, Büchler MW. Optimizing the outcomes of pancreatic cancer surgery. Nat Rev Clin Oncol. 2019;16(1):11–26.PubMedCrossRef
2.
3.
Hoos WA, James PM, Rahib L, Talley AW, Fleshman JM, Matrisian LM. Pancreatic cancer clinical trials and accrual in the United States. J Clin Oncol. 2013;31(27):3432–8.PubMedCrossRef
4.
Dolladille C, Ederhy S, Sassier M, Cautela J, Thuny F, Cohen AA, et al. Immune checkpoint inhibitor Rechallenge after immune-related adverse events in patients with Cancer. JAMA Oncol. 2020;6(6):865–71.PubMedCrossRef
5.
6.
7.
8.
9.
Perry RP, Kelley DE, LaTorre J. Synthesis and turnover of nuclear and cytoplasmic polyadenylic acid in mouse L cells. J Mol Biol. 1974;82(3):315–31.PubMedCrossRef
10.
Cui X, Liang Z, Shen L, Zhang Q, Bao S, Geng Y, et al. 5-Methylcytosine RNA methylation in Arabidopsis Thaliana. Mol Plant. 2017;10(11):1387–99.PubMedCrossRef
11.
Motorin Y, Lyko F, Helm M. 5-methylcytosine in RNA: detection, enzymatic formation and biological functions. Nucleic Acids Res. 2010;38(5):1415–30.PubMedCrossRef
12.
David R, Burgess A, Parker B, Li J, Pulsford K, Sibbritt T, et al. Transcriptome-wide mapping of RNA 5-Methylcytosine in Arabidopsis mRNAs and noncoding RNAs. Plant Cell. 2017;29(3):445–60.PubMedPubMedCentralCrossRef
13.
Huang W, Lan MD, Qi CB, Zheng SJ, Wei SZ, Yuan BF, et al. Formation and determination of the oxidation products of 5-methylcytosine in RNA. Chem Sci. 2016;7(8):5495–502.PubMedPubMedCentralCrossRef
14.
Pandolfini L, Barbieri I, Bannister AJ, Hendrick A, Andrews B, Webster N, et al. METTL1 promotes let-7 MicroRNA processing via m7G methylation. Mol Cell. 2019;74(6):1278–1290.e1279.PubMedPubMedCentralCrossRef
15.
Guy MP, Phizicky EM. Two-subunit enzymes involved in eukaryotic post-transcriptional tRNA modification. RNA Biol. 2014;11(12):1608–18.PubMedCrossRef
16.
17.
Mauer J, Luo X, Blanjoie A, Jiao X, Grozhik AV, Patil DP, et al. Reversible methylation of m(6)a(m) in the 5′ cap controls mRNA stability. Nature. 2017;541(7637):371–5.PubMedCrossRef
18.
Wang X, Zhao BS, Roundtree IA, Lu Z, Han D, Ma H, et al. N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell. 2015;161(6):1388–99.PubMedPubMedCentralCrossRef
19.
Xiao W, Adhikari S, Dahal U, Chen YS, Hao YJ, Sun BF, et al. Nuclear m(6) a reader YTHDC1 regulates mRNA splicing. Mol Cell. 2016;61(4):507–19.PubMedCrossRef
20.
Hsu PJ, Zhu Y, Ma H, Guo Y, Shi X, Liu Y, et al. Ythdc2 is an N(6)-methyladenosine binding protein that regulates mammalian spermatogenesis. Cell Res. 2017;27(9):1115–27.PubMedPubMedCentralCrossRef
21.
Jin D, Guo J, Wu Y, Du J, Yang L, Wang X, et al. M(6) a mRNA methylation initiated by METTL3 directly promotes YAP translation and increases YAP activity by regulating the MALAT1-miR-1914-3p-YAP axis to induce NSCLC drug resistance and metastasis. J Hematol Oncol. 2019;12(1):135.PubMedPubMedCentralCrossRef
22.
Dahal U, Le K, Gupta M. RNA m6A methyltransferase METTL3 regulates invasiveness of melanoma cells by matrix metallopeptidase 2. Melanoma Res. 2019;29(4):382–9.PubMedCrossRef
23.
Wang Q, Chen C, Ding Q, Zhao Y, Wang Z, Chen J, et al. METTL3-mediated m(6) a modification of HDGF mRNA promotes gastric cancer progression and has prognostic significance. Gut. 2020;69(7):1193–205.PubMedCrossRef
24.
Chen X, Xu M, Xu X, Zeng K, Liu X, Pan B, et al. METTL14-mediated N6-methyladenosine modification of SOX4 mRNA inhibits tumor metastasis in colorectal cancer. Mol Cancer. 2020;19(1):106.PubMedPubMedCentralCrossRef
25.
Taketo K, Konno M, Asai A, Koseki J, Toratani M, Satoh T, et al. The epitranscriptome m6A writer METTL3 promotes chemo- and radioresistance in pancreatic cancer cells. Int J Oncol. 2018;52(2):621–9.PubMed
26.
Tang B, Yang Y, Kang M, Wang Y, Wang Y, Bi Y, et al. M(6) a demethylase ALKBH5 inhibits pancreatic cancer tumorigenesis by decreasing WIF-1 RNA methylation and mediating Wnt signaling. Mol Cancer. 2020;19(1):3.PubMedPubMedCentralCrossRef
27.
Li HB, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, et al. M(6) a mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature. 2017;548(7667):338–42.PubMedPubMedCentralCrossRef
28.
Han D, Liu J, Chen C, Dong L, Liu Y, Chang R, et al. Anti-tumour immunity controlled through mRNA m(6) a methylation and YTHDF1 in dendritic cells. Nature. 2019;566(7743):270–4.PubMedPubMedCentralCrossRef
29.
Gu X, Zhang Y, Li D, Cai H, Cai L, Xu Q. N6-methyladenosine demethylase FTO promotes M1 and M2 macrophage activation. Cell Signal. 2020;69:109553.PubMedCrossRef
30.
Pan J, Huang Z, Xu Y. m5C RNA methylation regulators predict prognosis and regulate the immune microenvironment in lung squamous cell carcinoma. Front Oncol. 2021;11:657466.PubMedPubMedCentralCrossRef
31.
Squires JE, Patel HR, Nousch M, Sibbritt T, Humphreys DT, et al. Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Res. 2012;40(11):5023–33.PubMedPubMedCentralCrossRef
32.
Reid R, Greene PJ, Santi DV. Exposition of a family of RNA m(5) C methyltransferases from searching genomic and proteomic sequences. Nucleic Acids Res. 1999;27(15):3138–45.PubMedPubMedCentralCrossRef
33.
Goll MG, Kirpekar F, Maggert KA, Yoder JA, Hsieh CL, et al. Methylation of tRNAAsp by the DNA methyltransferase homolog Dnmt2. Science (New York, NY). 2006;311(5759):395–8.CrossRef
34.
Yang Y, Wang L, Han X, Yang WL, Zhang M, et al. RNA 5-Methylcytosine facilitates the maternal-to-zygotic transition by preventing maternal mRNA decay. Mol Cell. 2019;75(6):1188–1202.e1111.PubMedCrossRef
35.
36.
Liu J, Yue Y, Han D, Wang X, Fu Y, et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat Chem Biol. 2014;10(2):93–5.PubMedCrossRef
37.
Su R, Dong L, Li Y, Gao M, He PC, et al. METTL16 exerts an m(6)A-independent function to facilitate translation and tumorigenesis. Nat Cell Biol. 2022;24(2):205–16.PubMedCrossRef
38.
Ping XL, Sun BF, Wang L, Xiao W, Yang X, et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 2014;24(2):177–89.PubMedPubMedCentralCrossRef
39.
Lan T, Li H, Zhang D, Xu L, Liu H, et al. KIAA1429 contributes to liver cancer progression through N6-methyladenosine-dependent post-transcriptional modification of GATA3. Mol Cancer. 2019;18(1):186.PubMedPubMedCentralCrossRef
40.
Cai X, Chen Y, Man D, Yang B, Feng X, et al. RBM15 promotes hepatocellular carcinoma progression by regulating N6-methyladenosine modification of YES1 mRNA in an IGF2BP1-dependent manner. Cell Death Discov. 2021;7(1):315.PubMedPubMedCentralCrossRef
41.
Tan C, Xia P, Zhang H, Xu K, Liu P, et al. YY1-targeted RBM15B promotes hepatocellular carcinoma cell proliferation and Sorafenib resistance by promoting TRAM2 expression in an m6A-dependent manner. Front Oncol. 2022;12:873020.PubMedPubMedCentralCrossRef
42.
Wen J, Lv R, Ma H, Shen H, He C, et al. Zc3h13 regulates nuclear RNA m(6) a methylation and mouse embryonic stem cell self-renewal. Mol Cell. 2018;69(6):1028–1038.e1026.PubMedPubMedCentralCrossRef
43.
Zhang Y, Hua X, Shi H, Zhang L, Xiao H, et al. Systematic analyses of the role of prognostic and immunological EIF3A, a reader protein, in clear cell renal cell carcinoma. Cancer Cell Int. 2021;21(1):680.PubMedPubMedCentralCrossRef
44.
Huang H, Weng H, Sun W, Qin X, Shi H, et al. Recognition of RNA N(6)-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat Cell Biol. 2018;20(3):285–95.PubMedPubMedCentralCrossRef
45.
Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature. 2012;485(7397):201–6.PubMedCrossRef
46.
Yu J, Chai P, Xie M, Ge S, Ruan J, et al. Histone lactylation drives oncogenesis by facilitating m(6) a reader protein YTHDF2 expression in ocular melanoma. Genome Biol. 2021;22(1):85.PubMedPubMedCentralCrossRef
47.
Chang G, Shi L, Ye Y, Shi H, Zeng L, et al. YTHDF3 induces the translation of m(6)A-enriched gene transcripts to promote breast Cancer brain metastasis. Cancer Cell. 2020;38(6):857–871.e857.PubMedPubMedCentralCrossRef
48.
Wang LC, Chen SH, Shen XL, Li DC, Liu HY, et al. M6A RNA methylation regulator HNRNPC contributes to tumorigenesis and predicts prognosis in Glioblastoma Multiforme. Front Oncol. 2020;10:536875.PubMedPubMedCentralCrossRef
49.
Jia G, Fu Y, Zhao X, Dai Q, Zheng G, et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat Chem Biol. 2011;7(12):885–7.PubMedPubMedCentralCrossRef
50.
Zheng G, Dahl JA, Niu Y, Fedorcsak P, Huang CM, et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol Cell. 2013;49(1):18–29.PubMedCrossRef
51.
Orellana EA, Liu Q, Yankova E, Pirouz M, De Braekeleer E, et al. METTL1-mediated m(7) G modification of Arg-TCT tRNA drives oncogenic transformation. Mol Cell. 2021;81(16):3323–3338.e3314.PubMedCrossRef
52.
Lin S, Liu Q, Lelyveld VS, Choe J, Szostak JW, et al. Mettl1/Wdr4-mediated m(7) G tRNA Methylome is required for Normal mRNA translation and embryonic stem cell self-renewal and differentiation. Mol Cell. 2018;71(2):244–255.e245.PubMedPubMedCentralCrossRef
53.
Qin X, Li J, Hu W, Yang J. Machine learning K-means clustering algorithm for interpolative separable density fitting to accelerate hybrid functional calculations with numerical atomic orbitals. J Phys Chem A. 2020;124(48):10066–74.PubMedCrossRef
54.
Alboukadel Kassambara, ， MK, Biecek P: survminer: Drawing Survival Curves using 'ggplot2'. R package version 0.4.9. 2021.
55.
T T: A Package for survival analysis in R_. R package ，version 3.2-7. 2020.
56.
57.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.PubMedPubMedCentralCrossRef
58.
Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One. 2014;9(9):e107468.PubMedPubMedCentralCrossRef
59.
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747–56.PubMedPubMedCentralCrossRef
60.
Kassambara A: ggpubr: 'ggplot2' Based Publication Ready Plots. R package version 0.4.0. 2020.
61.
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.CrossRef
62.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.PubMedPubMedCentralCrossRef
63.
Wei T, Simko V. R package 'corrplot': Visualization of a Correlation Matrix (Version 0.92); 2021.
64.
65.
Carlson M: org. Hs.eg.db: Genome wide annotation for Human. R package version 3.12.0. 2020.
66.
Ishwaran H, U. B K: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC), R package version 2.12.0. 2021.
67.
68.
Jr FEH: Rms: regression modeling strategies. R package version 6.2-0. 2021.
69.
Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32(30):5381–97.PubMedCrossRef
70.
Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108–12.PubMedPubMedCentralCrossRef
71.
Yoshihara K, Kim H, Verhaak RG. Estimate: estimate of stromal and immune cells in malignant tumor tissues from: Expression Data; 2016.
72.
Tang R, Zhang Y, Liang C, Xu J, Meng Q, Hua J, et al. The role of m6A-related genes in the prognosis and immune microenvironment of pancreatic adenocarcinoma. PeerJ. 2020;8:e9602.PubMedPubMedCentralCrossRef
73.
Meng Z, Yuan Q, Zhao J, Wang B, Li S, Offringa R, et al. The m(6)A-related mRNA signature predicts the prognosis of pancreatic Cancer patients. Mol Ther Oncolytics. 2020;17:460–70.PubMedPubMedCentralCrossRef
74.
Xu F, Zhang Z, Yuan M, Zhao Y, Zhou Y, Pei H, et al. M6A regulatory genes play an important role in the prognosis, progression and immune microenvironment of pancreatic adenocarcinoma. Cancer Investig. 2021;39(1):39–54.CrossRef
75.
Lei X, Lei Y, Li JK, Du WX, Li RG, Yang J, et al. Immune cells within the tumor microenvironment: biological functions and roles in cancer immunotherapy. Cancer Lett. 2020;470:126–33.PubMedCrossRef
76.
Xia T, Wu X, Cao M, Zhang P, Shi G, Zhang J, et al. The RNA m6A methyltransferase METTL3 promotes pancreatic cancer cell proliferation and invasion. Pathol Res Pract. 2019;215(11):152666.PubMedCrossRef
77.
Tian J, Zhu Y, Rao M, Cai Y, Lu Z, Zou D, et al. N(6)-methyladenosine mRNA methylation of PIK3CB regulates AKT signalling to promote PTEN-deficient pancreatic cancer progression. Gut. 2020;69(12):2180–92.PubMedCrossRef
78.
Dougan SK. The pancreatic Cancer microenvironment. Cancer J (Sudbury, Mass). 2017;23(6):321–5.CrossRef
79.
Dong L, Cao Y, Hou Y, Liu G. N(6) -methyladenosine RNA methylation: a novel regulator of the development and function of immune cells. J Cell Physiol. 2021.
80.
Ni HH, Zhang L, Huang H, Dai SQ, Li J. Connecting METTL3 and intratumoural CD33(+) MDSCs in predicting clinical outcome in cervical cancer. J Transl Med. 2020;18(1):393.PubMedPubMedCentralCrossRef
Metadata
Title
Comprehensive analysis of the prognosis and immune infiltration landscape of RNA methylation-related subtypes in pancreatic cancer
Authors
Siyuan Lu
Jie Hua
Jiang Liu
Miaoyan Wei
Chen Liang
Qingcai Meng
Bo Zhang
Xianjun Yu
Wei Wang
Jin Xu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2022
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-022-09863-z

Other articles of this Issue 1/2022

BMC Cancer 1/2022 Go to the issue

Research

Cancer risk in persons with new-onset anaemia: a population-based cohort study in Denmark

Research

Newly diagnosed multiple myeloma patients with CD56 expression benefit more from autologous stem cell transplantation

Research article

Adverse effects in hematologic malignancies treated with chimeric antigen receptor (CAR) T cell therapy: a systematic review and Meta-analysis

Research

A multicenter, dose-finding, phase 1b study of imatinib in combination with alpelisib as third-line treatment in patients with advanced gastrointestinal stromal tumor

Research

Isolated central nervous system relapses in patients with high-risk neuroblastoma -clinical presentation and prognosis: experience of the Polish Paediatric Solid Tumours Study Group

Study protocol

A protocol to clinically evaluate liquid biopsies as a tool to speed up diagnosis of children and young adults with aggressive infection-related lymphoma in East Africa “(AI-REAL)”
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits