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. 2022 Jul 3;149(7):2805–2822. doi: 10.1007/s00432-022-04150-7

m6A regulators are differently expressed and correlated with immune response of pancreatic adenocarcinoma

Tao Zhang 1,#, Ping Sheng 2,#, Yuting Jiang 3,✉
PMCID: PMC11796531  PMID: 35780396

Abstract

Background

N6 methyladenosine (m6A) RNA methylation regulators play a key role in the occurrence and development of many tumors. However, the function of N6 methyladenosine (m6A) RNA methylation regulators in pancreatic adenocarcinoma (PAAD) has not been fully clarified.

Methods

We used data set from GEPIA 2, UALCAN, TIMER, TISIDB, CBioPortal database to analyze the gene expression of 20 major m6A RNA methylation regulators.

Results

Our study revealed that the irregularity of m6A regulators were associated with poor prognosis in PAAD. Meantime, 13 m6A regulators showed high expression in PAAD samples (ALKBH5, ELAVL1, FTO, HNRNPC, IGF2BP2, METTL14, METTL16 (METT10D), RBM15, VIRMA (KIAA1429), YTHDF1, YTHDF2, YTHDF3 and ZC3H13). In these regulators, we evaluated HNRNPC and IGF2BP2 were significantly correlated with worse outcomes and ALKBH5, IGF2BP2, METTL16 (METT10D), RBM15 were significantly correlated with PAAD in advanced stage. Moreover, we showed m6A regulators is correlated with Immuno-regulators' (Immunoinhibitors, Immunostimulators and MHC molecules) expression and levels of immune infiltration in PAAD. Bioinformatics further demonstrate m6A regulators were participated in revising in RNA processing.

Conclusions

Our study investigated that the m6A regulatory factors may serve as a biomarker and a potential target of immunotherapy for PAAD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04150-7.

Keywords: m6A regulators, RNA methylation, Pancreatic adenocarcinoma, Immune infiltration, Clinical prognosis

Introduction

Pancreatic adenocarcinoma (PAAD) is one of malignant tumor of the digestive system. The overall 5-year survival rate of PAAD was approximately 5% and PAAD is notorious for its difficult to diagnose, treatment and poor prognosis (Siegel et al. 2018; Wang et al. 2020a, b). Moreover, PAAD did not respond well to conventional chemoradiotherapy. Therefore, it was an urgent need to find a new strategy for comprehensive treatment of PAAD by understanding the biological and molecular mechanisms.

RNA's m6A modification is a reversible and dynamic process that has been associated with a lot of diseases (including cancer) (Yue et al. 2015; He et al. 2019; Lu et al. 2020; Zhang et al. 2020). In recent years, several investigations have been declared that m6A regulators played an essential role in the regulation of RNA splicing, stability and translation (Wang et al. 2014; Zhang et al. 2019a, b). Moreover, new evidences have indicated that m6A modification associated with development of cancer, such as proliferation, migration, and invasion (Hou et al. 2020a, b). For instance, Huang et al. have illustrated that m6A RNA methylation was significantly higher in circulating tumor cells compared with whole blood cells, which indicated its role in tumor metastasis (Huang et al. 2016a, b). In addition, a great number of studies have explored the dual characteristics of m6A modifications and the related regulators in cancers, such as leukemia, lung cancer, glioblastoma, gastric cancer, colorectal cancer and hepatoma (Cui et al. 2017; Li et al. 2017a, b; Pan et al. 2018; Yue et al. 2019; Yang et al. 2020). For example, METTL3 served as a crucial EMT and metastasis promoter in gastric cancer (Yue et al. 2019). METTL14 could suppress proliferation and invasion of colorectal cancer (Yang et al. 2020). Although the m6A modification could play great effect in a great number of cancers, m6A's mechanism in PAAD is still unclear.

The data in the Tumor Genome Atlas (TCGA) database were used to analyze the expression of m6A methylation regulator expression in PAAD and its relationship with clinicopathological characteristics and also bioinformatics methods were used to predict these m6A regulators' potential functions. The values of prognostic and immune infiltrates correlation of m6A regulators in PAAD were also fully explored. In this study, our analysis indicated that m6A regulators could serve as targets and biomarkers for immunotherapy in PAAD.

Materials and methods

Cell lines

The human pancreatic cancer cell lines BxPC-3 and PANC-1 and human pancreatic duct epithelial cells (HPDE6-C7) were purchased from the American type culture collection, and CFPAC-1 and SW1990 were obtained from the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). BxPC-3 and PANC-1 cell lines were routinely cultured in RPMI 1640 medium (HyClone, USA), and CFPAC-1, SW1990 and HPDE6-C7 cell lines was grown in Dulbecco’s modified Eagle’s medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA), penicillin (100 U/ml) and streptomycin (100 mg/ml). All cells were cultured at 37 °C with 5% CO2.

Expression analysis

We used GEPIA 2 to evaluate the expression of m6A regulators (http://gepia.cancer-pku.cn/index.html) (Tang et al. 2017). UALCAN was used to established the relationship between m6A regulators and clinical parameters in PAAD (http://ualcan.path.uab.edu/index.html) (Chandrashekar et al. 2017).

RNA isolation, reverse-transcription and quantitative real-time polymerase chain reaction (qRT-PCR)

RNA isolation and the PCR amplification conditions were followed as previously described (Zhang et al. 2021a, b). The qRT-PCR assay (SRBY Green) was performed on Applied Biosystem 7500. The relative expression levels of IGF2BP2 and METTL16 were calculated and quantified using the 2−ΔΔCT method. GAPDH served as the endogenous control, respectively. The primer sequences were designed by Primer 5.0 and are listed in Supplementary Table S1.

The human protein atlas

The Human Protein Atlas consists of cell, tissue and pathology Atlas and it offers a broad amount of proteomic and transcriptome information of distinct human samples (https://www.proteinatlas.org/). Until now, the online database provides cell-specific location information for 44 different normal tissues and 20 typical categories of cancer. Moreover, protein immunohistochemistry in normal human tissues and tumor tissues can also be obtained from this online website (Mou et al. 2021).

Immune response prediction

In our research, we discovered the correlations between m6A regulators and immune cell infiltration levels (including B cell, CD4+T cell,CD8+T cell, Myeloid dendritic cell, Macrophage and Neutrophil) in PAAD with help of the TIMER database (https://cistrome.shinyapps.io/timer/) (Li et al. 2017a, b). The correlations of the expression of m6A regulators with Immuno regulators (including Immunoinhibitors, Immunostimulator, and MHC molecules) were computed using TISIDB database (http://cis.hku.hk/TISIDB/) (Ru et al. 2019).

Genetic alteration of m6A regulators in PAAD

CBioPortal was an open-access website (https://www.cbioportal.org/). The website could explore, visualize, and analyze multidimensional cancer genomics data. In our study, it was used to analyze m6A regulators' genetic alterations in PAAD.

Survival analysis

The cBioPortal database was used to explored the correlation between survival time and m6A regulators aberrations in human cancers (Unberath et al. 2019; Buechner et al. 2020). KM diagrams were utilized to measure the correlation between m6A regulators expression and overalls survival time (http://kmplot.com/analysis/) (Gu et al. 2020).

GO analysis

Using The Database for Annotation, Visualization, and Integrated Discovery (DAVID, http://davidd.ncifcrf.gov/), Gene ontology (GO) analysis was performed based on the co-expression genes (Shi et al. 2018a, b). In addition, differences were considered significant when P < 0.05.

Statistical data

For the above analyses, P value < 0.05 was considered as statistically significant except those specifically mentioned.

Results

The abnormality of m6A regulators were associated with prognosis in cancers

The m6A regulators' functional importance were evaluated in human cancers, the correlations between survival time in human cancers and m6A regulators aberrations were analyzed using cBioPortal database. We observed the aberrations of m6A regulators were extremely correlated to poor prognosis in patients with ACC (Fig. 1A), KIRC (Fig. 1B), LIHC (Fig. 1C), LUAD (Fig. 1D) and PAAD (Fig. 1E). However, it was extremely correlated with longer overall survival (OS) time in PRAD (Fig. 1F), BLCA (Fig. 1G) and LUSC (Fig. 1H) patients. In addition, meaningfully association between survival time and m6A regulators genomic aberration in BRCA (Fig. 1I), COAD (Fig. 1J), HNSC (Fig. 1K), OV (Fig. 1L), CESC (Supplementary Fig. S1A), ESCA (Supplementary Fig. S1B), GBM (Supplementary Fig. S1C), LGG (Supplementary Fig. S1D), STAD (Supplementary Fig. S1E), and UCEC (Supplementary Fig. S1F) were not found.

Fig. 1.

Fig. 1

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Aberrations of m6A regulators were correlated with prognosis in human cancers. A–L Correlation between overall survival time and m6A regulators aberrations in patient with adrenocortical carcinoma, kidney renal clear cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, prostate adenocarcinoma, bladder urothelial carcinoma, lung squamous cell carcinoma, breast invasive carcinoma, colon adenocarcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma. *p < 0.05; **p < 0.01; ***p < 0.001

Amplification, deletion, and mutation of m6A regulators in PAAD

Our present research concentrated on investigating the roles of m6A regulators in PAAD. cBioPortal database was used to detect genetic variations of m6A regulators and 925 cases was analyzed (Fig. 2). Among 20 m6A regulators, varying degrees of genetic changes were found. Including ALKBH5, FTO, HNRNPC, IGF2BP2, VIRMA (KIAA1429), METTL14, METTL16 (METT10D), METTL3, RBM15, WTAP, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, ZC3H13, ELAVL1 LRPPRC FMR1 EIF3A. As revealed in Fig. 2, most of m6A regulators were amplified, deleted, mutated in PAAD and the highest incidence rate was displayed by VIRMA (KIAA1429) (2.4%).

Fig. 2.

Fig. 2

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Amplification, deletion, and mutation of m6A regulators in PAAD. The cBioPortal database was used to detect genetic variations of m6A regulators in 925 cases

Expression levels of m6A regulators in PAAD

Figure 3 was presented the level of m6A regulators in TCGA. Among them, 13 m6A regulators showed high expression in PAAD samples, including ALKBH5 (Fig. 3A), ELAVL1 (Fig. 3B), FTO (Fig. 3C), HNRNPC (Fig. 3D), IGF2BP2 (Fig. 3E), METTL14 (Fig. 3F), METTL16 (METT10D) (Fig. 3G), RBM15 (Fig. 3H), VIRMA (KIAA1429) (Fig. 3I), YTHDF1 (Fig. 3J), YTHDF2 (Fig. 3K), YTHDF3 (Fig. 3L), and ZC3H13 (Fig. 3M). However, we found EIF3A (Supplementary Fig. S2A), FMR1 (Supplementary Fig. S2B), LRPPRC (Supplementary Fig. S2C), METTL3 (Supplementary Fig. S2D), WTAP (Supplementary Fig. S2E), YTHDC1 (Supplementary Fig. S2F), and YTHDC2 (Supplementary Fig. S2G) were not differently expressed between normal and tumor samples. In addition, the m6A regulator of IGF2BP2 and METTL16 showed high expression in four pancreatic cancer cell lines (SW1990, CFPAC-1, PANC-1 and BxPC-3) compared with normal human pancreatic ductal epithelium cell line (HPDE6-C7) (Fig. 3N, O).

Fig. 3.

Fig. 3

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Expression levels of m6A regulators in PAAD. A–M Expression levels of m6A regulators revealed increased expression in PAAD samples, including ALKBH5 (A), ELAVL1 (B), FTO (C), HNRNPC (D), IGF2BP2 (E), METTL14 (F), METTL16 (G), RBM15 (H), VIRMA (I), YTHDF1 (J), YTHDF2 (K), YTHDF3 (L), and ZC3H13 (M).*P < 0.05, compared with normal tissues. N Expression levels of IGF2BP2 in normal human pancreatic ductal epithelium cell line (HPDE6-C7) and pancreatic cancer cell lines (SW1990, CFPAC-1, PANC-1 and BxPC-3). O Expression levels of METTL16 (METT10D) in normal human pancreatic ductal epithelium cell line (HPDE6-C7) and pancreatic cancer cell lines (SW1990, CFPAC-1, PANC-1 and BxPC-3).*p < 0.05, **p < 0.01

Moreover, to evaluate m6A regulators expression at the protein level, IHC results were provided by the HPA database. As shown in Fig. 4A–G, normal pancreatic tissue samples had weak FMR1 (Fig. 4A), IGF2BP2 (Fig. 4B), METTL14 (Fig. 4C), RBM15 (Fig. 4D), YTHDC2 (Fig. 4E), ZC3H13 (Fig. 4F) staining, yet tumor tissues had moderate/strong staining. Normal pancreatic tissue had moderate HNRNPC IHC staining, while tumor tissue had strong staining (Fig. 4G).

Fig. 4.

Fig. 4

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Analysis of the m6A regulators expression in the human Protein atlas. The level of FMR1 (A), IGF2BP2 (B), METTL14 (C), RBM15 (D), YTHDC2 (E), ZC3H13 (F), and HNRNPC (G) protein in PAAD tissue was higher than that in normal tissue in the Human Protein Atlas

The m6A regulators' dysregulation were correlated with shorter OS time in PAAD

Kaplan–Meier plot was then used to detect the m6A regulators' prognostic value in PAAD. The results demonstrated that higher expression levels of HNRNPC (Fig. 5A) and IGF2BP2 (Fig. 5B) were significantly associated with worse outcomes in PAAD. However, we found that lower levels of METTL3 (Fig. 5C) and METTL16 (METT10D) (Fig. 5D) were associated with worse outcomes in PAAD. These results indicated these m6A regulators played key roles in PAAD. In addition, the expression of ALKBH5 (Supplementary Fig. S3A), EIF3A (Supplementary Fig. S3B), FMR1 (Supplementary Fig. S3C), FTO (Supplementary Fig. S3D), VIRMA (KIAA1429) (Supplementary Fig. S3E), LRPPRC (Supplementary Fig. S3F), METTL14 (Supplementary Fig. S3G), RBM15 (Supplementary Fig. S3H), WTAP (Supplementary Fig. S2I), YTHDC1 (Supplementary Fig. S3J), YTHDC2 (Supplementary Fig. S3K), YTHDF1 (Supplementary Fig. S3L), YTHDF2 (Supplementary Fig. S3M), YTHDF3 (Supplementary Fig. S3N), ZC3H13 (Supplementary Fig. S3O), and ELAVL1 (Supplementary Fig. S3P) were not corrected with worse outcomes in PAAD.

Fig. 5.

Fig. 5

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m6A regulators dysregulation were correlated with shorter OS time in PAAD. A, B Increased levels of HNRNPC (A), IGF2BP2 (B) were significantly associated with worse outcomes in PAAD. C, D Lower levels of METTL3 (C), METTL16 (METT10D) (D) were significantly associated with worse outcomes in PAAD. *p < 0.05; **p < 0.01; ***p < 0.001

Relationship between m6A regulators and the clinicopathological parameters of PAAD patients

We also studied the relationship between m6A regulators and tumor stage in PAAD patients using the GEPIA data set. The results revealed that the expression levels of ALKBH5 (Fig. 6A), IGF2BP2 (Fig. 6B), METTL16 (METT10D) (Fig. 6C), and RBM15 (Fig. 6D) were significantly correlated with the tumor stage of PAAD patients. However, the expression levels of ELAVL1 (Supplementary Fig. S4A), EIF3A (Supplementary Fig. S4B), FMR1 (Supplementary Fig. S4C), FTO (Supplementary Fig. S4D), HNRNPC (Supplementary Fig. S4E), LRPPRC (Supplementary Fig. S4F), METTL3 (Supplementary Fig. S4G), METTL14 (Supplementary Fig. S4H), WTAP (Supplementary Fig. S4I), YTHDC1 (Supplementary Fig. S4J), YTHDC2 (Supplementary Fig. S4K), YTHDF1 (Supplementary Fig. S4L), YTHDF2 (Supplementary Fig. S4M), YTHDF3 (Supplementary Fig. S4N), ZC3H13 (Supplementary Fig. S4O) and VIRMA (KIAA1429) (Supplementary Fig. S4P) were not correlated with the tumor stage of PAAD patients.

Fig. 6.

Fig. 6

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m6A regulators dysregulation were correlated with clinically advanced stage in PAAD. A–D Expression of ALKBH5, IGF2BP2, METTL16 (METT10D), RBM15 were detected in different PAAD stage. *p < 0.05; **p < 0.01; ***p < 0.001

Immune infiltration levels in PAAD was correlated with m6A regulators expression

The correlation of immune cell infiltration levels in PAAD with m6A regulators were detected in the TIMER database. The results revealed that FTO (Fig. 7A) was significantly associated with B cell, CD8+T cell and Macrophage. IGF2BP2 (Fig. 7B) was significantly associated with CD8+T cell, Myeloid dendritic cell, Macrophage and Neutrophil. RBM15 (Fig. 7C) was conspicuously associated with B cell, CD8+T cell, Macrophage and Neutrophil. WTAP (Fig. 7D) was remarkably associated with B cell, CD8+T cell and Macrophage. METTL16 (METT10D) (Fig. 7E) was significantly associated with B cell, CD8+T cell, Myeloid dendritic cell, and Macrophage M2. ALKBH5 (Supplementary Fig. S5A) was revealingly associated with B cell, CD8+T cell, Myeloid dendritic cell and Macrophage M2. EIF3A (Supplementary Fig. S5B) was significantly associated with B cell, Macrophage and Neutrophil. ELAVL1 (Supplementary Fig. S5C) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil. FMR1 (Supplementary Fig. S5D) was significantly associated with Myeloid dendritic cell, Macrophage and Neutrophil. VIRMA (KIAA1429) (Supplementary Fig. S5E) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil. METTL3 (Supplementary Fig. S5F) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil. METTL14 (Supplementary Fig. S5G) was significantly associated with B cell, CD8+T cell AND Macrophage. YTHDC1 (Supplementary Fig. S5H) was significantly associated with B cell, CD8+T cell, Macrophage M2 and Neutrophil. YTHDC2 (Supplementary Fig. S5I) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil. YTHDF1 (Supplementary Fig. S5J) was significantly associated with Neutrophil. YTHDF2 (Supplementary Fig. S5K) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil. YTHDF3 (Supplementary Fig. S5L) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil. ZC3H13 (Supplementary Fig. S5M) was significantly associated with B cell, CD8+T cell, Macrophage and Neutrophil.

Fig. 7.

Fig. 7

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Immune infiltration levels was associated with m6A regulators expression in PAAD. A–F According to the TIMER database, we investigated the correlation of IGF2BP2 (A), METTL16 (METT10D) (B), FTO (C), HNRNPC (D), RBM15 (E), WTAP (F) with levels of B cell, CD8+ T cell, CD4+ T cell, Macrophage, Neutrophil and Dendritic cell

Next, Kaplan–Meier plots were drawn to visualize the survival differences between immune infiltrates and m6A regulators. The results showed that under the immune cell infiltration, higher levels of IGF2BP2 (Fig. 8A), HNRNPC (Fig. 8B) and LRPPRC (Fig. 8C) were significantly associated with worse outcomes in PAAD. In contrast, lower levels of METTL3 (Fig. 8D) and METTL16 (METT10D) (Fig. 8E) were significantly associated with worse outcomes under the immune cell infiltration in PAAD. The other m6A regulators have no statistical significance associated with OS under immune cell infiltration (Supplementary Fig. S6A–O).

Fig. 8.

Fig. 8

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Dysregulation of m6A regulators under the condition of immune cells infiltration were correlated with OS time in PAAD. A–C Higher levels of IGF2BP2 (A), HNRNPC (B) and LRPPRC (C) were significantly associated with worse outcomes in PAAD. D–E Lower levels of METTL3 (C), METTL16 (METT10D) (D) were significantly associated with worse outcomes in PAAD. *p < 0.05; **p < 0.01

m6A regulators expression was associated with the expression of immune-regulators in PAAD

To further explore m6A regulators' effects on tumor immune response, we calculated the correlations between m6A regulators' expression and Immuno regulators. The results shown that IGF2BP2 and YTHDF1were adversely correlated to Immunoinhibitors (Fig. 9A), Immunostimulators (Fig. 9B), and MHC molecules (Fig. 9C). However, FTO, METTL16 (METT10D), RBM15 and WTAP were positively associated with Immunoinhibitors (Fig. 9A), Immunostimulators (Fig. 9B), and MHC molecules (Fig. 9C) in PAAD.

Fig. 9.

Fig. 9

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Correlations between the expression of Immuno regulators and m6A regulators expression in PAAD. The associations between the expression levels of IGF2BP2, FTO, METTL16 (METT10D), RBM15, WTAP, YTHDF1 and Immunoinhibitors (A), Immunostimulator (B), and MHC molecules (C) were investigated based on TISIDB database

GO enrichments

To explore the downstream pathways of m6A regulators in PAAD in depth, Gene Ontology analysis was performed using 6 m6A regulators' co-expression genes. The results indicated that FTO was interconnected to mRNA methylation, RNA destabilization, RNA catabolic and metabolic process, RNA stability, RNA modification, translation, cellular amide metabolic process, nucleobase-containing compound catabolic process, heterocycle catabolic process, cellular nitrogen compound catabolic process, aromatic compound catabolic process, organic cyclic compound catabolic process and peptide biosynthetic process (Fig. 10A). IGF2BP2 was related to RNA stability, RNA catabolic process, RNA metabolic, translation, cellular amide metabolic process, RNA transport, nucleic acid transport, RNA localization, processing of Capped Intron-Containing Pre-mRNA, nucleobase-containing compound transport, translation, peptide biosynthetic process (Fig. 10B). RBM15 was related to mRNA methylation, dosage compensation, RNA methylation, alternative mRNA splicing via spliceosome, RNA modification, translation, dosage compensation by inactivation of X chromosome, mRNA metabolic process, RNA splicing, macromolecule methylation, RNA splicing, mRNA processing, Spliceosome, methylation, Notch signaling pathway, regulation of gene expression, epigenetic (Fig. 10C). WTAP was related to RNA methylation, CEN complex, alternative mRNA splicing, via spliceosome, RNA modification, RNA splicing, macromolecule methylation, mRNA processing, Spliceosome, Processing of Capped Intron-Containing Pre-mRNA, methylation, Metabolism of RNA (Fig. 10D). METTL16 was related to RNA destabilization, mRNA and RNA methylation, mRNA and RNA metabolic process, mRNA and RNA catabolic process, mRNA and RNA modification, mRNA and RNA stabilization, RNA splicing, translation, mRNA and RNA processing, cellular amide metabolic process, macromolecule methylation, peptide biosynthetic process and ribonucleoprotein complex biogenesis (Fig. 10E). YTHDF1 was related to RNA destabilization, RNA stability, mRNA catabolic process, translation, regulation of mRNA metabolic process, catabolic process, peptide biosynthetic process (Fig. 10F).

Fig. 10.

Fig. 10

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GO enrichments. A–F Bioinformatics analysis of FTO, IGF2BP2, RBM15, WTAP, METTL16 (METT10D) and YTHDF1 in PAAD

Discussion

The roles of m6A regulators in PAAD had been investigated in many studies during the past decades. For instance, ALKBH5 serves as a PAAD suppressor by regulating the posttranscriptional activation of PER1 through m6A abolishment (Guo et al. 2020). Cigarette smoke-induced miR-25-3p excessive maturation via m6A modification promotes the development and progression of pancreatic cancer (Zhang et al. 2019a, b). DANCR is a novel target for IGF2BP2 through m6A modification, and IGF2BP2 and DANCR work together to promote cancer stemness-like properties and pancreatic cancer pathogenesis (Hu et al. 2020). The m6A regulatory factors could be participated in the advanced progression of PC, and constructing risk markers based on m6A regulatory factors may be a promising prognostic biomarker, which can help guide the individualized treatment of PAAD patients (Hou et al. 2020a, b). In our research, we found the irregularity of m6A regulators in genome were significantly associated with poor prognosis in patients with adrenocortical carcinoma, kidney renal clear cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma and PAAD. In this study, using multiple data sets and comprehensively analysis of the association between cancer progression and m6A regulators, a remarkable prognostic value of m6A regulators was observed in PAAD. In addition, several previous studies have revealed the relationship between m6A methylation and tumor immune infiltration. Liu et al. analyzed the N6-methyladenosine (m6A) modification patterns of immune cells infiltrating the tumor microenvironment of breast cancer (BC) to provide a new perspective for the early diagnosis and treatment of BC (Liu et al. 2021). Geng et al. revealed the molecular characteristics of m6A modification, its prognostic value, and the infiltration of immune cell populations in head and neck squamous cell carcinoma (HNSCC) (Geng et al. 2021). We also investigated the relevance between m6A modulators and immune response of anti-tumor. This study proved for that the m6A regulatory factors may serve as biomarkers and a potential target of immunotherapy for PAAD.

In our research, the aberrations of m6A regulators in genome were found to correlate with poor prognosis in PAAD. In our study, we indicated most regulators of m6A were amplified, deleted, mutated in PAAD, and the highest incidence rate was displayed by VIRMA (KIAA1429) (2.4%). VIRMA played a key role in mediating mRNA m6A methylation in 3′UTR and near stop codon. Some studies has shown that VIRMA (KIAA1429) play a great significance in cancer cell growth and metastasis. For instance, KIAA1429 promoted liver cancer progression through GATA3 N6-methyladenosine-dependent post-transcriptional modification (Lan et al. 2019). Meanwhile, 13 m6A regulators revealed higher expression in PAAD samples, including ALKBH5, ELAVL1, FTO, HNRNPC, IGF2BP2, METTL14, METTL16 (METT10D), RBM15, VIRMA, YTHDF1, YTHDF2, YTHDF3, ZC3H13. Among these regulators, HNRNPC, IGF2BP2, and METTL16 (METT10D) were found significantly correlated to poor outcomes and advanced tumor in PAAD, revealing that these m6A regulators associated with advanced stage in PAAD and play a significant role to the worse prognosis of patients. In the above genes, IGF2BP2 served as an RNA binding protein, was associated with m6A modification regulation, mRNA localization, stability and translation. Many previous studies have reported this protein was contributed to cancer cell growth and metastasis. For example, high-regulation of IGF2BP2 in pancreatic cancer promotes cancer cell proliferation by activating the PI3K/Akt signaling pathway, enhances liver cancer growth through an m6AFEN1 dependent mechanism (Xu et al. 2019). HNRNPC has been identified as m6A readers, which could identify those m6A modified RNA and played a role in RNA processing, nuclear export, translation and decay. Wu et al. have demonstrated that breast cancer cell proliferation and tumor growth were inhibited by repressing the expression of HNRNPC (Wu et al. 2018). In addition, HNRNPC has been determined as a marker of prognostic and therapeutic in gastric cancer (Huang et al. 2016a, b). Wang et al. confirmed that METTL16 (METT10D) was downregulated in hepatocellular carcinoma and decreased METT16 expression was association with multiple metabolic pathways activation and poor OS in hepatocellular carcinoma (Wang et al. 2020a, b).

Previous studies have reported that immune infiltration could affect the prognosis of patients (Jin et al. 2020; Zhang et al. 2021a, b). These findings together suggested that these m6A regulators may have an impact on the changes in the tumor immune microenvironment and the development of PAAD. In our research, the correlation between the immune cell infiltration level and m6A regulators in PAAD were evaluated from TISIDB. Our analysis indicated that there was a moderate to strong differences between m6A regulators expression level and infiltration level of CD8+T cells, macrophages, neutrophils and DCs. Meanwhile, Kaplan–Meier plots was drawn to visualize the survival differences between immune infiltrates and m6A regulators in using TIMER. It is worth noting that under the condition of immune cells infiltration, the higher expression of IGF2BP2, HNRNPC and LRPPRC were negative related to cumulative survival. However, higher expression of METTL16 (METT10D) and METTL3 were positive related to cumulative survival.

Bioinformatics analysis of m6A regulators was performed in PAAD. Our results informed that m6A regulators were involved in regulating in RNA processing. Previous studies of m6A regulators in regulating RNA processing have witnessed a substantial progress (Chen et al. 2019). For example, FTO regulates nuclear mRNA alternative splicing by binding with SRSF2 (Bartosovic et al. 2017). YTHDF1 interacts with the translation initiation factor eIF3 to promote highly efficient cap-dependent translation, and has been revealed to modulate mRNA degradation (Meyer et al. 2015; Wang et al. 2015). Therefore, m6A could regulate the occurrence and development of tumors by regulating RNA processes.

In conclusion, through bioinformatics analysis our research indicated that the dysregulation of m6A regulators was associated with poor prognosis of PAAD patients, so m6A regulators could be served as a biomarker of prognostic, meanwhile, the unique and common regulators in different PAAD samples are also shown in Fig. 11A. Moreover, we confirmed that the expression of m6A regulators was associated with immune cell infiltration levels and the Immuno regulators expression in PAAD. Our study showed that the regulators of m6A may serve as a biomarker and potential immunotherapy target in PAAD (Fig. 11B).

Fig. 11.

Fig. 11

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Venn diagram and flow chart. A Venn diagram showed the unique and common regulators in different PAAD samples. B Flow chart to summarize the analysis

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (TIF 1455 KB) (1.4MB, tif)
Supplementary file2 (TIF 1060 KB) (1MB, tif)
Supplementary file3 (TIF 4369 KB) (4.3MB, tif)
Supplementary file4 (TIF 3396 KB) (3.3MB, tif)
Supplementary file5 (TIF 7104 KB) (6.9MB, tif)
Supplementary file6 (TIF 3750 KB) (3.7MB, tif)
Supplementary file7 (PDF 123 KB) (123.3KB, pdf)

Acknowledgements

This work was supported by the Yunnan Digestive Endoscopy Clinical Medical Center Foundation for Health Commission of Yunnan Province [2021LCZXXF-XH10]and[2022LCZXKF-XH09] of [ZX2019-01-02], Yunnan Fundamental Research Projects (grant NO. 202201AU070031), and we would like to express our sincere thanks to the database of GEPIA 2, UALCAN, TIMER, TISIDB, CBioPortal for supporting our research.

Abbreviations

ACC

Adrenocortical carcinoma

BLCA

Bladder urothelial carcinoma

BRCA

Breast invasive carcinoma

CESC

Cervical squamous cell carcinoma and endocervical adenocarcinoma

COAD

Colon adenocarcinoma

ESCA

Esophageal carcinoma

GBM

Glioblastoma multiforme

HNSC

Head and neck squamous cell carcinoma

KIRC

Kidney renal clear cell carcinoma

LGG

Brain lower grade glioma

LIHC

Liver hepatocellular carcinoma

LUAD

Lung adenocarcinoma

LUSC

Lung squamous cell carcinoma

OV

Ovarian serous cystadenocarcinoma

PAAD

Pancreatic adenocarcinoma

PRAD

Prostate adenocarcinoma

STAD

Stomach adenocarcinoma

UCEC

Uterine corpus endometrial carcinoma

OS

Overall survival

Author contributions

YJ designed the study. TZ and PS performed the analyses and experiments. All authors wrote the manuscript, read and approved the manuscript.

Funding

This study was founded by the Yunnan Digestive Endoscopy Clinical Medical Center Foundation for Health Commission of Yunnan Province [2021LCZXXF-XH10]and[2022LCZXKF-XH09] of [ZX2019-01-02], Yunnan Fundamental Research Projects (grant NO. 202201AU070031).

Data availability

The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Tao Zhang and Ping Sheng are contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (TIF 1455 KB) (1.4MB, tif)
Supplementary file2 (TIF 1060 KB) (1MB, tif)
Supplementary file3 (TIF 4369 KB) (4.3MB, tif)
Supplementary file4 (TIF 3396 KB) (3.3MB, tif)
Supplementary file5 (TIF 7104 KB) (6.9MB, tif)
Supplementary file6 (TIF 3750 KB) (3.7MB, tif)
Supplementary file7 (PDF 123 KB) (123.3KB, pdf)

Data Availability Statement

The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Articles from Journal of Cancer Research and Clinical Oncology are provided here courtesy of Springer

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