Research Paper Volume 12, Issue 20 pp 20587—20610

Multiomics integrative analysis for gene signatures and prognostic values of m6A regulators in pancreatic adenocarcinoma: a retrospective study in The Cancer Genome Atlas project

Wenzhe Gao1, , Liuyang Cheng2, , Shuhan He2, , Wei Li2, , Chengyu Zhou2, , Bixia Zhou2, , Jiamiao Liu2, , Jiahao Xu3, , Xiao Yu1, , Hongwei Zhu1, ,

  • 1 Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
  • 2 Medical College of Xiangya, Central South University, Changsha 410013, Hunan Province, China
  • 3 Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China

Received: February 27, 2020       Accepted: August 3, 2020       Published: October 20, 2020
How to Cite

Copyright: © 2020 Gao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


N6-methyladenosine(m6A) is the most abundant post-transcriptional RNA modification in eukaryotes. However, little is known about its role in pancreatic adenocarcinoma (PAAD). The aim of our study was to identify gene signatures and prognostic values of m6A regulators in PAAD. Patients from 3 different datasets with complete genomic and transcriptomic sequencing data were enrolled. Survival analysis for different gene alterations was performed using log-rank tests and Cox regression model. The association between alteration of m6A regulators and clinicopathological characteristics was examined using chi-square test. Results showed a high frequency of copy number alterations (CNAs) of m6A regulatory genes in PAAD patients, but somatic mutations were rarely happened. CNAs and mutations of m6A regulatory genes was associated with patient’s gender, pathologic stage and resected tumor size. Patients with “gain of function” for m6A “reader” genes combined with copy number loss of “writers” or “erasers” had worse overall survival (OS) compared with other patterns. Moreover, copy number gain of m6A “reader” gene insulin growth factor 2 binding protein 2 (IGF2BP2) was an independent risk factor for OS (HR = 2.392, 95%CI: 1.392-4.112, p<0.001) and disease-free survival (DFS) (HR = 2.400, 95%CI: 1.236-4.659, p=0.010). Gene Set Enrichment Analysis (GSEA) indicated that IGF2BP2 was correlated with multiple biological processes associated with cancer, of which the most significant processes were relevant to cancer cell cycle, cell immortalization and tumor immunity. To sum up, a significant relationship was found between m6A genomic alterations and worse clinical outcomes. These innovative findings are expected to guide further research on the mechanism of m6A in PAAD.


Pancreatic cancer (PC) is now the 9th common malignancy worldwide and the most lethal cancer among all kinds of malignant diseases, the 5-year survival rate of which is only 9% in the United States. It is estimated that 57,600 people will develop PC in 2020 and 47,050 will die from it in America [1]. Pancreatic adenocarcinoma (PAAD) represents more than 95% of patients of all PCs [2], its unknown cause, difficulty of early diagnosis and rapid progress make it an extremely hard nut to crack for oncology researchers around the world. Resection is currently the only way to cure PAAD. However, clinical experience has shown that tumor invasion or distant metastasis occurred in most patients by the time of consultation and indication for surgery had been lost [3]. For these advanced patients, current adjuvant and neoadjuvant chemotherapy and radiotherapy could not show ideal improvement for prognosis. Thus, exploring the molecular mechanism underlying the pathogenesis and finding targets with prognostic and therapeutic potential became the most critical work to break through the diagnosis and treatment of PAAD. Encouragingly, the past 5 years have witnessed the rapid development of molecular subtyping of PAAD. Bailey P and his colleagues firstly identified 32 recurrently mutated genes and 10 aggregated pathways to define 4 molecular subtypes for PAAD: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) [4]. This was the first research establishing an association between molecular features and histopathological subtype of PAAD. The continuous addition and development of molecular taxonomies since then have accelerated the development of individualized therapeutic methods and accurate prediction of patients’ survival [5]. However, until today, new and enormous molecular features are still needed to further understand the relationship between molecular pathological alterations and clinical manifestations of patients.

Epigenetics refer to changes in gene expression that are stable during cell division, and sometimes stable between generations, without changing in DNA sequence. Epigenetic changes related to DNA and histone, like methylation and acetylation, have been widely studied in PAAD progression over the past decades [6]. In recent years, the attention of scientists has gradually shifted to the study of RNA epigenetics. So far, more than one hundred kinds of chemically modified nucleotides have been found in different types of RNAs, of which the earliest discovered one was N6-methyladenosine(m6A) [7]. This reversible RNA modification is very abundant in eukaryotes. So far, over 7600 mRNAs of protein coding genes and hundreds of non-coding RNAs have been confirmed to have the presence of m6A [8], with approximately 3-5 adenylate residues on average appearing in each mRNA [9]. These m6A sites are mainly located in the 3’UTR region of mRNA near the stop codons and long internal exons [10]. M6A modification is mainly driven by three kinds of regulatory proteins (enzymes): methyltransferase complex (“Writers”), demethylases (“Erasers”) and reader proteins (“Readers”). “Writers” serve to mediate the methylation modification process of adenylate residues on RNAs. In contrast, “Erasers” mediate the demethylation process of RNA, reversing chemical response catalyzed by the “Writer complex”. “Readers” are responsible for "reading" the information of RNA methylations and are involved in downstream RNA processing, including exportation of RNAs from nucleus, post-transcriptional splicing, mRNA translation, RNA decay and many other processes related to RNA stability [11].

M6A dysregulation is involved in diverse pathophysiological processes, the most significant one of which is cancer. The review published by Chen X [12] and his colleagues in 2019 summarized the relationship between m6A and the pathogenesis of various malignancies. They concluded that dysregulated m6A modification could affect tumor proliferation, differentiation, tumorigenesis, invasion and metastasis by regulating mRNAs of proto oncogenes and tumor suppressor genes in post-transcriptional level. Moreover, many m6A regulatory proteins played a double-sided role in promoting or suppressing cancer in different tumors, indicating that meticulous and complex m6A regulatory mechanisms existed in neoplastic diseases. There has been some research focusing on the relationship between m6A and PAAD. Zhang J et al [13] found that the “writer” protein METTL3 could promote PAAD by enhanced m6A modification of microRNA-25 (miR-25), leading to the excessively maturation of miR-25 and further activation of oncogenic AKT-p70S6K signaling. Another research from Tang B et al [14] demonstrated that m6A “eraser” ALKBH5 was downregulated in gemcitabine-resistant xenograft models and its overexpression sensitized PAAD cells to chemotherapy. This mechanism might be related to the activation of Wnt signaling pathway. Since little has been known about the role of other m6A regulatory genes in the pathogenesis of PAAD so far, this study aimed to explore gene signatures and prognostic values of all the currently recognized m6A regulators by analysis of genomic and transcriptomic data in The Cancer Genome Atlas (TCGA) project and other available datasets using bioinformatic methods.


Mutations and CNAs of m6A regulatory genes in TCGA_PAAD cohorts

M6A-related research has grown rapidly in recent years. To include all genes that have been experimentally confirmed to yield m6A regulating functions as much as possible, we first searched PubMed for all literatures relating to m6A. After carefully archiving, 2 reviews published in 2019 that comprehensively summarized all the m6A regulatory genes was adopted [15, 16]. Finally, twenty-four genes were included in subsequent studies by intersecting with the genome and transcriptome-sequencing gene set on TCGA, including 9 “writers”, 13 “readers” and 2 “erasers”. Protein-protein interaction (PPI) network analysis was then applied using “STRING” database ( and CytoScape software (National Resource for Network Biology, USA) to display the interaction between these m6A regulators. As shown in Figure 1, three “writer” genes, METTL3, METTL14 and KIAA1429 (also known as VIRMA) seemed to be the hub genes in this network. This result brought some preliminary hints for our next research.

Protein-protein interaction (PPI) network of the 24 included m6A regulators.

Figure 1. Protein-protein interaction (PPI) network of the 24 included m6A regulators.

To analyze somatic mutations of m6A regulatory genes, three cohort from different countries with complete genomic sequencing data and clinical information were included, they were TCGA_PAAD cohort, ICGC_AU_PAAD cohort and ICGC_CA_PAAD cohort, respectively (ICGC: international cancer genome consortium; TCGA: from America; AU: from Australia; CA: from Canada). All included genomic sequencing data were got from the same sequencing platform (Illumina HiSeq 2000, SAN DIEGO, USA). Among all 753 patients with complete sequencing data and clinical information, mutations of 24 m6A regulatory genes were found in 53 (7.04%) independent samples (Figure 2, Table 1, and Supplementary Table 1). On an individual level, most of these 53 cases had only one single nucleotide mutation of a certain m6A regulatory gene (Table 1 and Supplementary Table 1). The only one exception was from TCGA_PAAD cohort (ID: TCGA.IB.7651.01) who suffered a huge m6A mutation burden, with 21 non-synonymous mutations occurred in 11 m6A regulatory genes. From a genetic point of view, none of the included m6A genes had a mutation frequency of more than 1% in TCGA and ICGC database; compared with the mutation frequency of control genes KRAS and TP53. Thus, we had enough reason to believe that mutations of m6A regulatory genes were not a common event in PAAD patients (Figure 2). More specifically, a total of 6 patients had 6 mutation events in WTAP, made it the gene with the highest incidence of mutations in all 753 included patients; another 4 and 3 patients had 10 mutations in IGF2BP1 and YTHDC1, respectively. IGF2BP1 and YTHDC1 became the genes with the highest number of mutation events.

Landscape of somatic mutation status of m6A regulators in PAAD. (A) The mutation frequency of 24 m6A regulators in 177 patients from TCGA

Figure 2. Landscape of somatic mutation status of m6A regulators in PAAD. (A) The mutation frequency of 24 m6A regulators in 177 patients from TCGA_PAAD cohort. Each column represented individual patients. The upper barplot showed tumor mutation burden (TMB). (B) The mutation frequency of 24 m6A regulators in 616 patients from ICGC_AU_PAAD and ICGC_CA_PAAD cohort. The upper barplot showed mutation events happeded in per 1 million bases. Due to the difference in the definition and naming of somatic mutations, it is unable to integrate (A) and (B) into the same waterfall chart.

Table 1. Mutations of m6A regulatory genes in 149 TCGA_PAAD patients.

TCGA.IB.7651.01R327HH204YR454H,A516DR473WG175D, P182S, A519T, Q563HA191VR717*, A185VK565N, X374_splice, E224K, Q168HG15V, V681IH391YE505G, Y377C
TCGA.F2.A44G.01R298H, X23_splice

Given that no copy number alteration (CNA) related data was provided in two datasets from ICGC database, TCGA_PAAD cohort became our only remaining data source for the next CNA analysis. In this cohort, 125 of these 149 cases (83.89%) had CNA events of m6A regulatory genes, which meant that CNAs occurred very frequently in PAAD patients. The “writer” gene METTL16 had the highest frequency of CNA events (57.05%, 85/149), followed by WTAP and ALKBH5, which were “writer” and “eraser” genes, respectively (Figure 3A).

Landscape of CNA events for m6A regulatory genes in PAAD. (A) Percentage of PAAD samples with CNAs of the m6A regulators based on the data from TCGA. (B) Events of copy number gain or loss of m6A regulatory genes in ccRCC samples. (C) Proportion of four kinds of CNA vents.

Figure 3. Landscape of CNA events for m6A regulatory genes in PAAD. (A) Percentage of PAAD samples with CNAs of the m6A regulators based on the data from TCGA. (B) Events of copy number gain or loss of m6A regulatory genes in ccRCC samples. (C) Proportion of four kinds of CNA vents.

Then we evaluated CNA patterns in TCGA_PAAD cohort. The total number of CNA events was 1084 and most events led to the copy number loss of m6A genes (60.15%, 652/1084) (Figure 3B, 3C), which consisted of homozygous and hemizygous deletion, indicating the loss of two alleles, or one (or less than one) allele for a certain gene from genome in a diploid organism. In detail, hemizygous deletion of METTL16 (82 cases) was the most frequent alteration in all kinds of CNAs of m6A regulatory genes (Table 2), followed by the hemizygous deletions of WTAP (79 cases) and ALKBH5 (69 cases). The second most frequent CNA event was copy number gain (35.98%, 390/1084), which happened to VIRMA and HNRNPA2B1 in 40 patients, tied for the first place, followed by the “reader” gene IGF2BP3 (38 cases). To verify the reliability of this cohort, we also detected KRAS (all 149 cases with mutations and 39 cases with CNAs) and TP53 (94 cases with mutations and 72 with CNAs) as control, which were also widely recognized by the published literatures.

Table 2. Different CNV patterns occur in TCGA_PAAD samples (n=125).

Gene namesHomozygous deletionHemizygous deletionCopy number gainHigh level amplificationCNA sumDiploid

Alterations of m6A regulatory genes were associated with clinicopathological characteristics and survival of PAAD patients

The relationship between alterations (CNAs and/or mutations) of m6A regulatory genes and the clinicopathological characteristics of PAAD patients were analyzed. Results showed that alterations of m6A genes were significantly associated with the patients’ gender, tumor grade and the maximum diameter of resected tumor. More concretely, male patients had a higher frequency of mutations or CNAs than female patients; patients with larger tumor volumes and higher histopathological grade also tended to show increased alterations of m6A genes (Table 3).

Table 3. Clinical pathological parameters of PAAD patients with or without mutations/CNAs of m6A regulatory genes.

With mutations and/or CNAs*Without mutations and CNAs*p value
Metastasis (M)M05810p=0.549
Nodes (N)N0354p=0.671
Primary tumor (T)T141p=0.471
Maxium diameter of resected tumor≤3cm438p<0.001
Histological grade&G1137p=0.049
Colloid Carcinoma11
Other Subtype122
Primary siteunknown10
*With mutations and/or CNAs: Cases have mutant or CNA or mutant+CNV, confirmed through TCGA database. Without mutant and CNV: Cases with neither mutant nor CNV, confirmed through TCGA database. #The reason for grouping by age 65 was because 65 is the median age of this cohort. &Histological grade indicating the degree of tumor differentiation, G1: well differentiated; G2: middle differentiated; G3: poorly differentiated; G4: undifferentiated. Ambiguous variables (Nx, Mx, N/A, discrepancy and Gx) were excluded from chi-square test or non-parametric test.

The above results made us interested in the impact of these alterations on patients’ prognosis. Thus, we analyzed the effects of overall alterations of m6A regulatory genes and some certain high-frequency CNAs on patients’ overall survival (OS) and disease-free survival (DFS). However, results did not seem to be ideal. Individuals with or without mutations or CNAs of m6A regulatory genes didn’t have any significant correlation with OS and DFS (Figure 4A, 4B). Furthermore, separate analysis of the included 24 genes revealed that most single CNA events didn’t affect OS and DFS (Supplementary Figure 1, 2). The only exception is IGF2BP2, a m6A “reader” gene. Patients with copy number gain of IGF2BP2 showed significantly poorer OS and DFS than other patients without this alteration (Figure 4C, 4D).

Overall survival and disease-free survival of PAAD patients with different kinds of CNA and/or mutation patterns. (A, B) OS and DFS for patients with any CNAs or mutations of m6A regulatory genes or with diploid genes. (C, D) OS and DFS for patients with copy number gain of IGF2BP2 or other patients except for them. (E) OS of PAAD patients with simultaneous alterations of writer genes and reader genes. (F) OS of PAAD patients with simultaneous alterations of eraser genes and reader genes.

Figure 4. Overall survival and disease-free survival of PAAD patients with different kinds of CNA and/or mutation patterns. (A, B) OS and DFS for patients with any CNAs or mutations of m6A regulatory genes or with diploid genes. (C, D) OS and DFS for patients with copy number gain of IGF2BP2 or other patients except for them. (E) OS of PAAD patients with simultaneous alterations of writer genes and reader genes. (F) OS of PAAD patients with simultaneous alterations of eraser genes and reader genes.

Then we divided the 24 m6A regulatory genes into 3 classes (writers, readers and erasers) based on their functions in m6A modification and detected whether the loss or gain of overall copy number for a class of m6A genes could influence patients’ OS and DFS. We found that copy number loss mainly happened to “writers” and “erasers”, yet “readers” mainly gained copy number. Therefore, patients were divided by whether “writer loss”, “eraser loss” or “reader gain” happened. It turned out that patients with “reader gain” combined with “writer loss”, and those with “reader gain” and “eraser loss” had worse OS (Figure 4E, 4F). Besides, no significant difference was found about DFS of these groups (Supplementary Figure 3A3D). These results highlighted the potential importance of m6A reader genes on the prognosis of PAAD patients.

CNAs of m6A regulatory genes were associated with mRNA and protein expression levels

We wondered whether CNAs of m6A regulatory genes at the genomic level were related to their mRNA expression status. The results of correlation analysis showed that mRNA expression level was significantly associated with most of the CNA patterns in PAAD samples. In 21 of 24 detected genes, copy number gains and high-level amplifications were related to higher mRNA expression; while homozygous and hemizygous deletions resulted in a declined level of mRNA (Figure 5A5R and Supplementary Figure 4A4C). No significant relation was found between the CNAs of FTO, RBM15 and ZC3H13 and their mRNA expression level (Supplementary Figure 4D4F).

Correlation between different CNA patterns and mRNA expression levels of m6A regulatory genes respectively. (A–R) A total of 18 m6A regulatory genes showed significant correlation between their CNA patterns and mRNA expression. Only when p 6A genes were listed in Supplementary Figure 4.

Figure 5. Correlation between different CNA patterns and mRNA expression levels of m6A regulatory genes respectively. (AR) A total of 18 m6A regulatory genes showed significant correlation between their CNA patterns and mRNA expression. Only when p < 0.001 the gene was included in this figure, correlation plots of other m6A genes were listed in Supplementary Figure 4.

Then we detected whether mRNAs of these 24 m6A regulatory genes are differentially expressed between PAAD patients and normal individuals. RNA-sequencing data from TCGA_PAAD, ICGC_AU_PAAD and ICGC_CA_PAAD dataset, and normal pancreas transcriptome data from Genotype-Tissue Expression (GTEx) database were enrolled, including 279 tumor samples and 328 normal pancreas samples. Unfortunately, none of the m6A regulatory genes met the conventional criterion of differential expression (log2FoldChange > 1.5, P < 0.05) (Supplementary Table 2). There were only 4 genes which showed statistically significant differential expression (P <0.05), among which the most differentially expressed one was Insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) (log2FoldChange = 0.341, P = 0.048). Heatmap showed that the expression of m6A -regulated genes cannot fully distinguish patients from normal individuals through clustering analysis, either (Supplementary Figure 5). However, some interesting hints drew our attention. First, the mRNA expression level of m6A regulatory genes were consistent with their copy number status; those genes that mostly showed copy number loss, like METTL3, ALKBH5 and YTHDF family, all showed a tendency of lower expression compared with normal tissues; On the contrary, IGF2BP family, which mainly suffered copy number gain in PAAD patients, showed a tendency of mRNA over-expression in PAAD compared with normal pancreas. Besides, it was not difficult to notice from the heatmap that the most capable genes to distinguish tumors from normal tissues in Cluster Analysis were from IGF2BP protein family as well.

Based on the above results, we had a strong interest in the expression status and mechanism of IGF2BP family in PAAD pathogenesis. IGF2BP family contained three members. As mentioned in our previous results, IGF2BP1 had the highest mutation frequency, while IGF2BP3 showed the highest CNA frequency; however, only IGF2BP2 was associated with patient prognosis, and most likely there was a significant increase in its mRNA expression. Thus, IGF2BP2 was chosen as the candidate gene for further experimental detection. Immunohistochemistry staining for IGF2BP2 expression was firstly performed in 3 pairs of PAAD tissues and their adjacent normal tissues. We found that IGF2BP2 protein was highly expressed in all three pairs of PAAD tissues than that of normal (Figure 6A and Supplementary Figure 6). Then, 20 pairs of PAAD clinical specimens with their adjacent normal pancreatic tissue were extracted to detect the mRNA expression level of IGF2BP2. It turned out that IGF2BP2 was significantly overexpressed in PAAD cells from clinical samples than normal pancreatic cells (Figure 6B). We further utilized 5 cell lines (four PAAD cell lines: Panc-1, CFPAC-1, BXPC-3, SW1990; one normal pancreatic ductal epithelial cell line: HPDE) to verify IGF2BP2 expression at mRNA and protein level in vitro. qRT-PCR and Western Blotting results further confirmed the presence of up-regulated mRNA and protein expression of IGF2BP2 in PAAD (Figure 6C, 6D).

Experimental validation of IGF2BP2 over-expression in PAAD tissue array and cell lines in vitro. (A) Immunohistochemistry staining (IHC) for a pair of PAAD tissues and their adjacent normal tissues for IGF2BP2 protein. The upper part of the figure showed normal pancreas tissues and the lower part showed the corresponding PAAD tissue staining. The two pictures in left panel were magnified 200 times (200x), and the right panel, 400 times (400x). (B) Quantitative real-time PCR (qRT-PCR) for mRNA expression of IGF2BP2 in 20 pairs of PAAD cells and their corresponding normal pancreatic cells. (C) qRT-PCR of IGF2BP2 for 4 PAAD cell lines and one normal pancreatic ductal epithelial cell line (HPDE) in vitro. (D) Western Blotting (WB) showed the protein level of IGF2BP2 in 4 PAAD cell lines and HPDE cell line in vitro (**, P

Figure 6. Experimental validation of IGF2BP2 over-expression in PAAD tissue array and cell lines in vitro. (A) Immunohistochemistry staining (IHC) for a pair of PAAD tissues and their adjacent normal tissues for IGF2BP2 protein. The upper part of the figure showed normal pancreas tissues and the lower part showed the corresponding PAAD tissue staining. The two pictures in left panel were magnified 200 times (200x), and the right panel, 400 times (400x). (B) Quantitative real-time PCR (qRT-PCR) for mRNA expression of IGF2BP2 in 20 pairs of PAAD cells and their corresponding normal pancreatic cells. (C) qRT-PCR of IGF2BP2 for 4 PAAD cell lines and one normal pancreatic ductal epithelial cell line (HPDE) in vitro. (D) Western Blotting (WB) showed the protein level of IGF2BP2 in 4 PAAD cell lines and HPDE cell line in vitro (**, P < 0.01; ***, P < 0.001).

Apart from the above research, survival analysis was also performed by dividing patients of TCGA_PAAD cohort into two groups according to the median mRNA expression level of every single gene. However, none of the mRNA expression of the 24 genes was found to significantly influence the OS and DFS of PAAD patients (Supplementary Figure 7).

Predictive potential of IGF2BP2 for the prognosis of PAAD patients and downstream Enrichment Analysis for IGF2BP2 overexpression

The results so far made us very interested in the m6A “reader” gene IGF2BP2. To preliminarily explore the clinical and biological function of IGF2BP2, univariate and multivariate Univariate and multivariate Cox regression analysis was applied to further confirm the relationship between copy number gain of IGF2BP2 and prognosis of PAAD patients. Results suggested that copy number gain of IGF2BP2 alone was an independent risk factor for worse OS (for multivariate Cox regression, HR = 2.392, 95%CI: 1.392-4.112, p<0.001) and DFS (for multivariate Cox regression, HR = 2.400, 95%CI: 1.236-4.659, p=0.010) of PAAD patients (Table 4). In summary, we believed that IGF2BP2 was an important indicator leading to higher severity and poorer prognosis of PAAD patients.

Table 4. Univariate and Multivariate COX regression analysis of IGF2BP2 for PAAD patients' overall survival (OS) and disease-free survival (DFS)*.

Univariate Cox RegressionMultivariate Cox RegressionUnivariate Cox RegressionMultivariate Cox Regression
Age1.218 (0.780-1.902)0.3861.337 (0.838-2.132)0.2231.026 (0.641-1.641)0.9151.143 (0.698-1.872)0.596
Gender0.804 (0.519-1.245)0.3280.774 (0.482-1.242)0.2880.811 (0.506-1.298)0.3820.777 (0.459-1.313)0.346
M#1.031 (0.666-1.598)0.890.889 (0.553-1.5280.6260.701 (0.435-1.129)0.1440.660 (0.396-1.102)0.112
N#1.799 (1.053-3.073)0.0321.212 (0.350-4.250)0.7561.395 (0.828-2.351)0.2110.928 (0.198-4.347)0.924
T#0.879 (1.053-3.807)0.0961.398 (0.634-3.079)0.4061.212 (0.635-2.313)0.5601.387 (0.759-2.533)0.932
Stage1.839 (1.048-3.227)0.0341.253 (0.332-4.724)0.7391.374 (0.808-2.334)0.2411.339 (0.282-6.364)0.714
Grade1.263 (0.796-2.003)0.3221.278 (0.774-4.724)0.3371.250 (0.759-2.058)0.3811.283 (0.736-2.238)0.380
Primary Site1.779 (0.980-3.231)0.0581.653 (0.871-3.139)0.1241.482 (0.846-2.597)0.1691.387 (0.759-2.533)0.288
Maximum Diameter of resected tumor1.295 (0.792-2.116)0.3021.172 (0.694-1.979)0.5531.284 (0.758-2.057)0.3841.265 (0.734-2.179)0.397
Copy Number Gain of IGF2BP2 or Other Patients2.399 (1.436-4.008)<0.0012.392(1.392-4.112)0.0022.433 (1.317-4.495)0.0052.400 (1.236-4.659)0.010
#TNM: T, primary tumor status; N, lymph node metastasis; M, distant metastasis. *Ambiguous variables (Nx, Mx, N/A, discrepancy and Gx) were excluded.

We then determined to explore the role of genetic alteration of IGF2BP2 in the pathogenesis of PAAD. Gene Set Enrichment Analysis (GSEA) suggested that high expression of IGF2BP2 was related to some critical biological processes including 1) regulation of centrosome cycle; 2) telomere organization; 3) β-catenin destruction complex disassembly; 4) cytokinesis (Figure 7A7D and Table 5). All these 4 processes were highly conserved and related to the malignant behavior of tumors, including cell proliferation, cell immortalization and tumor immunization. To preliminarily explore how many m6A sites IGF2BP2 could bind to as a m6A “reader” protein, RMBase V2.0 ( based on epitranscriptome sequencing data was then explored. Result of the data mining showed that IGF2BP2 protein was capable of binding to a total of 60,935 m6A sites on tens of thousands of mRNAs. These results might provide some new ideas for subsequent researchers who would like to study the molecular mechanism of IGF2BP2 in pancreatic cancer.

Table 5. Gene sets enrichment of high IGF2BP2 mRNA expression level in PAAD cohort.

GS DETAILSSIZEES (absolute value)NES (absolute value)NOM p-valFDR q-val
Abbreviations: ES: enrichment score; NES: normalized enrichment score; NOM p-val: normalized p-value; FDR q-val: false discovery rate q value
GSEA results of different expression level of IGF2BP2. Gene set enrichment plots of (A) regulation of centrosome cycle; (B) telomere organization; (C) β-catenin destruction complex disassembly; (D) cytokinesis. Group of IGF2BP2 low expression was compared with group of high expression.

Figure 7. GSEA results of different expression level of IGF2BP2. Gene set enrichment plots of (A) regulation of centrosome cycle; (B) telomere organization; (C) β-catenin destruction complex disassembly; (D) cytokinesis. Group of IGF2BP2 low expression was compared with group of high expression.


High-throughput sequencing technology related to RNA epitranscriptome has developed rapidly in recent years. M6A-Seq, MeRIP-seq and many other innovations gave researchers chances to study transcriptome-wide mapping for m6A modification. However, with the explosive growth of sequencing data, how to extract m6A -related molecular pathways with biologically significance has become a major challenge facing scientists today. Given this problem, starting with a limited number of m6A regulators using bioinformatic methods might be an alternative way to solve it. In this study, we detected a very high frequency of alterations in 24 m6A related genes. Furthermore, these alterations are significantly related to the mRNA expression level of m6A regulatory genes, implying that the dysregulation of m6A regulators might play an important role in PAAD. Mutation events for 24 m6A genes occur infrequently in the included patients, with no gene got a mutation frequency of more than 1%. However, we noticed that there was one patient (TCGA ID: TCGA.IB.7651.01) who had mutations in 11 of 24 m6A regulatory genes. Limited by sample size and mutation frequency, we could not find more similar patients with large m6A mutation burden to conduct statistical studies on them. However, it was possible that the mechanism related to m6A gene mutation could explain the onset of this kind of patients which occupied a very small proportion. Digging into the relationship between m6A mutations and the pathogenesis of PAAD and designing target therapies about these mutations might have the potential to benefit these patients. High frequency of CNA events has been previously reported in similar researches relating to clear cell renal cell carcinoma (ccRCC) [17] and acute myelocytic leukemia (AML)[18]. However, as shown in our research, CNA patterns in PAAD were totally different from other malignancies that was previously reported. In ccRCC or AML patients, most observed CNA events in writer genes resulted in copy number loss with down-regulation of the corresponding mRNAs, while CNAs of the eraser genes mainly led to overexpression and gain of function. However, in our research, copy number loss of both writers and erasers dominated most of the CNA events, especially in WTAP, METTL16 and ALKBH5. We suggested that these alterations might be related to the inhibition of m6A modification and the decrease of m6A sites at global level. Further research is urged to verify our hypothesis and to interpret the specific biological significance behind it in PAAD. Besides, owing to the discovery of many new reader proteins in the past few years, our study had the most “reader” genes included compared with other similar m6A studies. CNA patterns of the reader genes showed an interesting trend in our research as well. Specifically, YTHDC and YTHDF family which promoted mRNA degradation [19] exhibited a tendency of copy number loss and down-expression of mRNAs, but genes from IGF2BP family which functioned as mRNA stabilizers [20] were dominated by the pattern of copy number increase and gain of function. This tendency implied us that there might be global improvements in mRNA stability at the whole transcriptome level of PAAD, and m6A modification, especially m6A “reader” proteins, might play a pivotal role in this mechanism. This result might yield a high value for further research.

Linking genomic and transcriptomic alterations to patients' clinicopathological manifestations and prognostic values has been an obvious trend in bioinformatic analysis over the past years. This interdisciplinary research combines the advantages of both clinicians and bioinformatic experts, showing great value of transforming medicine. In this study, we also evaluated the effect of m6A gene alterations on clinicopathological factors and survival of PAAD patients. It turned out that patients with CNAs or mutations of m6A regulatory genes were significantly corelated with the patient being male, tumor diameter greater than 3 cm, and advanced pathological grade. These results further demonstrated that changes in m6A regulatory genes were positively correlated with the increased clinical malignancy of PAAD. However, results of survival analysis were slightly different from what we expected and what other research reported. Genes with high CNA frequency did not exhibit significant relationship with the overall and disease-free survival. The only gene among the 24 regulators associated with OS and DFS was the “reader” gene IGF2BP2. Patients who over expressed IGF2BP2 had a significantly poorer prognosis. The subsequent Cox regression analysis further proved that IGF2BP2 gain of number is an independent predictor of OS and DFS in PAAD patients. This result was in line with Hu X and his colleagues’ research [21] that IGF2BP2 “gain of function” was positively correlated with the poorer prognosis of PAAD patients. Besides, worse OS in patients with copy number gain of “reader” gene cluster in combination with writer or eraser gene loss was observed, making it clearer that m6A reader genes played a more important role in PAAD patients. Given that there are far fewer studies focusing on m6A readers than those on writers and erasers like METTL3, WTAP and FTO, we believed that these results could guide the subsequent research on m6A in PAAD towards the biological function of “reader” proteins and their molecular mechanism. We had to admit that the limited case number and the partially missing data of the included datasets in our study restricted the accuracy of our results to some extent. Detecting the m6A level directly through m6A-seq and evaluating its effect on PAAD survival in a new cohort with more patients and complete information are now urged for more statistically significant results. Another potentially controversy may be our differential expression analysis with RNA-seq data, as none of the 24 m6A genes has met the conventional standard of differential expression (usually log2FoldChange > 1.5, FDR < 0.05). It is necessary for us to explain that due to the utilization of multiple datasets from different sources, we used a more rigorous solution to remove batch effects, which to a large extent made us underestimate the degree of mRNA differential expression.

IGF2BP2 was an unexpected gain in our study. Insulin-like growth factor 2 mRNA binding protein (IGF2BP) family is a class of single-stranded RNA binding proteins (RBPs) that are highly conserved in eukaryotes. The common feature of the three members in this protein family is that they consist of 6 domains, including 2 RNA recognition domains and 4 K-H motifs [22]. IGF2BP family had not been confirmed to function in m6A modification until 2018, when Huang H [20] found that IGF2BP family could recognize m6A modified “GG (m6A) C” sequence that was highly conserved in mammals. Moreover, the combination between IGF2BP family and target mRNAs enhanced mRNA stability and promoted subsequent translation processes, resulting in increased expression level of target proteins; IGF2BP proteins could also guide ribosome-bound mRNAs (ie, ribonucleoprotein complex) into stress granules to ensure stable storage of mRNAs under stress condition. Since then, there have been a few more studies exploring IGF2BP family as m6A reader to activite tumorigenesis and cancer development. Huang X et al [23] reviewed in 2018 that IGF2BP1 was over-expressed in diverse types of cancers, including lung cancer, liver cancer, leukemia and many other diseases; this up-regulation showed cancer-promoting effect by stabilizing many mRNAs and non-coding RNAs, like KRAS, MYC, let-7 et al. Similarly, IGF2BP3 [24] was also highly expressed in a variety of cancers, promoting carcinogenesis through its RBP function. However, no one has published research so far to prove their expression and function in PAAD. Although they have a relatively high incidence of mutations and CNA events in PAAD, results did not support their association with the prognosis and clinicopathological factors of PAAD patients as well. In pancreatic cancer, Hu X [21] found that IGF2BP2 recognized and bound to long non-coding RNA DANCR (lncDANCR), which increased the stability of DANCR and promoted the development of PAAD. Li T et al [25] confirmed that IGF2BP2 could recognize and bind with the m6A enriched site of the CDS region on SOX2 mRNA and further inhibited degradation of SOX2 to promote the occurrence and development of colorectal cancer. GSEA analysis in our research for the pathways affected by IGF2BP2 overexpression in PAAD suggested that high expression of IGF2BP2 was related to the following biological processes: 1) regulation of centrosome cycle; 2) telomere organization; 3) β-catenin destruction complex disassembly; 4) cytokinesis. These pathways were closely related to the pathogenesis of cancer in different ways. They had close associations with a variety of malignant biological behaviors such as abnormal mitosis, immortalization of tumor cells, epithelial-mesenchymal transition, tumor invasion and migration, and tumor immune surveillance and immune escape [2629]. These results provided preliminary clues for those who would like to study the mechanism of IGF2BP2 in PAAD. Supported by search results from database RMBase, IGF2BP2 was also capable to target tens of thousands of mRNAs because of its affinity with the highly conservative "GG(m6A)C" motif. We suggested that meRIP-seq might be applied to screen all the significant targets in PAAD and to further reveal the molecular mechanism of IGF2BP2.

In conclusion, this study explored the alterations of m6A regulators from the perspective of genomics, transcriptomics, and clinical information in PAAD for the first time by utilizing TCGA dataset of PAAD and multiple bioinformatic methods. An obvious correlation was found between the alterations of m6A regulatory genes and worse clinical characteristics including survival. A m6A reader protein, IGF2BP2, was of great importance in m6A modification of PAAD. This study has established a bridge between the microscopic RNA modification mechanism of m6A and the macroscopic clinical manifestations of PAAD patients, highlighting the value of m6A regulatory genes, especially IGF2BP2, in the pathogenesis, diagnosis and treatment of PAAD. To further confirm our results and clarify the definite target mRNA of the m6A modification during PAAD initiation and progression, studies in another larger PAAD cohort with m6A -Seq and MeRIP-seq will be helpful.

Materials and Methods

Ethics statement

All the CNAs, mutations, mRNA expression data and clinical information were retrieved from TCGA database by cBioportal platform [30] through R package “cgdsr” and UCSC-Xena program [31] which are open sourced to the public under certain guidelines. The ICGC database was further applied to prove TCGA results. Thus, it is confirmed that all written informed consent was achieved. For patients’ tissue array from the third Xiangya hospital, written informed consent was obtained from all patients and the study was approved by the ethics committee of the third Xiangya Hospital.

Data processing

We identified 3 datasets (TCGA_PAAD, ICGC_AU_PAAD and ICGC_CA_PAAD) with complete clinical information, of which all three datasets included somatic mutation and RNA-seq raw data, while CNA data was only available in TCGA_PAAD cohort. For somatic mutation research, raw file in “MAF” format were got from R package “TCGAmutations” and ICGC data portal. The R packages “Maftools” and “Genvisr” were then applied to analyze mutation frequency and draw “waterfall plot”. For CNA research, the loss and gain of copy number changes was identified using segmentation analysis and GISTIC algorithm. When clinical significance of the status of CNA and/or mutation was discussed, this PAAD cohort was usually divided into two subgroups, “with CNAs and/or mutation of all m6A regulatory genes (or a certain m6A regulatory gene)” and “without CNA and mutation”. The mRNA expression data were calculated from RNA-Seq V2 RSEM release and log2 fold change was applied before analyzing the relationship between CNA and mRNA expression. The R package “edgeR” [32] was utilized to analyze mRNA differential expression.

Statistical analysis

All statistical data and figures were analyzed by R (Lucent Technologies, New Jersey, USA) or GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA, USA) to ensure the aesthetics and editability of the figures and tables. The association between CNAs of m6A regulatory genes and clinicopathological characteristics were analyzed using chi-square test or Mann-Whitney U test. Kaplan-Meier curve and log-rank test were used to evaluate the prognosis value of all kinds of CNAs that the frequency is greater than 15% of total sample number. Cox proportional hazard regression model was performed using R package “survival” [33] and “survminer” [34]. All statistical results with a p-value <0.05 were considered significant.

Clinical samples

A total of 20 pancreatic cancer tissues and adjacent normal tissues were collected from Department of Hepatobiliary and Pancreatic Surgery, The Third Xiangya Hospital. All tissues were frozen immediately in liquid nitrogen after surgical excision and stored at −80°C. Clinicopathological information was retrieved from the hospital database. The ethic approval was mentioned above.

Quantitative real-time PCR (qRT-PCR), Western Blotting (WB) and Immunohistochemistry (IHC)

For qRT-PCR, Total RNAs were isolated from PC tissues and cell lines by TRIzol reagent (Invitrogen) and reverse transcription was performed with a Prime Script RT reagent kit (Takara Biotechnology, China). Qrt-PCR reactions were carried out on Applied Biosystems 7500 Real-time PCR Systems (Thermo Fisher Scientific). GAPDH was used as control for IGF2BP2 mRNA expression. The relative expression level of the target RNA was calculated by 2−ΔΔCt. In our study, all involved primers were produced by Sangon Biotech (Shanghai, China).

For WB analysis, Cells were lysed in 1x RIPA buffer with 1mM PMSF and 1x complete protease inhibitor cocktail (Roche, Basel, Switzerland). Western blottings were conducted under standard procedures. Briefly, Protein lysates were separated by SDS-PAGE, and electrophoretically transferred to PVDF membrane from MilliporeTM with nominal pore size of 0.22 um. After blocking, the membrane was incubated at 4°C overnight with anti-IGF2BP2 (Proteintech) or β-Actin (Santa Cruz Biotechnology) diluted in non-fat dry-milk (Santa Cruz Biotechnology), followed by HRP-labeled secondary (Santa Cruz Biotechnology) antibody incubation. Chemiluminescence signal was developed by ECL Plus Western Blotting Detection Reagents (GE Healthcare Life Sciences, Munich, Germany).

For IHC test, IHC staining of IGF2BP2 was conducted on tissue microarray consisting of 12 PDAC tissues and 12 paired normal tissues. IHC staining was carried out using Histostain-Plus kit according to manufacturer’s protocol. Briefly, antigen retrieval was conducted by heating the sections in boiling sodium citrate buffer for 20 mins. After 3% hydrogen peroxide and BSA blocking, the tissues were incubated with anti-IGF2BP2 (Proteintech, Chicago, IL) at 4°C overnight. After washing, the tissues were incubated with HRP-conjugated secondary antibody (Santa Cruz Biotechnology, Santa Cruz, CA). IHC signal was developed by DAB substrate, and counter-stained by haematoxylin. Three random fields at 200x magnification were captured per sections for evaluation. Scoring of the IHC was based on the percentage of positive cells and staining intensity under a light microscope. Four categories (0, 1+, 2+ and 3+) were denoted as negative, weak, moderate and strong respectively, and final score was calculated by averaging the score of three fields.

Downstream pathway and target analysis for IGF2BP2

Gene set enrichment analysis (GSEA) provided by the JAVA program (Version 4.0.3) with MSigDB v6.1 was applied to explore the downstream biological processes affected by differential expression of IGF2BP2. Patients in the cohort was divided into two groups according to the median expression level of IGF2BP2. Finally, 25880 genes were enrolled into the GSEA process. Hallmark gene set “c5.bp.v7.0.symbols.gmt” was used in this study [35]. Gene sets which got highest Enrichment Score (ES) with normalized p-value <0.05, and the false discovery rate (FDR) <0.25 were considered significantly enriched.


PC: pancreatic cancer; ADEX: aberrantly differentiated endocrine exocrine; TCGA: the cancer genome atlas; PPI: protein-protein interaction; TCGA_PAAD: pancreatic adenocarcinoma cohort from TCGA project; m6A: N6-methyladenosine; PAAD: pancreatic adenocarcinoma; CNA: copy number alteration; EIF3A: eukaryotic initiation factor 3a; HNRNPA2B1: heterogeneous nuclear ribonucleoprotein A2B1; KRAS: V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog; TP53: tumor protein p53; FTO: fat mass and obesity associated gene; RBM15: RNA Binding Motif Protein 15; ZC3H13: Zinc Finger CCCH-Type Containing 13; YTHDC: YTH Domain Containing family; YTHDF: YTH N6-Methyladenosine RNA Binding Protein family; GTEx: Genotype-Tissue Expression (GTEx) database; ccRCC: clear cell renal cell carcinoma; AML: acute myeloid leukemia; RBP: RNA binding protein; IGF2BP2: Insulin-Like Growth Factor 2 mRNA-Binding Protein 2; GSEA: Gene Set Enrichment Analysis; METTL3: methyltransferase like 3; ALKBH5: alkB homolog 5; WTAP: WT1 associated protein; VIRMA: vir like m m6A methyltransferase associated; OS: overall survival; DFS: disease-free survival; DANCR: Differentiation Antagonizing Non-Protein Coding RNA; lnc-DANCR: long non-coding RNA DANCR.

Author Contributions

Conceptualization, Hongwei Zhu and Xiao Yu; methodology, Wenzhe Gao; software, Wenzhe Gao; validation, Jiahao Xu and Jiamiao Liu; formal analysis, Wenzhe Gao; investigation, Wenzhe Gao; data curation, Wenzhe Gao; writing—original draft preparation, Wenzhe Gao; writing—review and editing, Liuyang Cheng, Shuhan. He, Wei Li, Chengyu Zhou and Bixia Zhou; project administration, Hongwei Zhu; funding acquisition, Xiao Yu. All authors have read and agreed to the published version of the manuscript.”


We would like to express our sincere gratitude to TCGA program, ICGC program, UCSC Xena project and cBioportal Platform, which provides the high quality genomic and RNA-seq data. We would also like to thank the founder of the "Biotrainee" forum Jimmy Zeng and his open source R code published on Github (

Conflicts of Interest

The authors declare that they have no conflicts of interest.


This work was supported by National Natural Science Foundation of China (No. 81873589 and 81570585).


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