N6-methyladenosine (m6A) messenger RNA methylation is a gene regulatory mechanism affecting cell differentiation and proliferation in development and cancer. To study the roles of m6A mRNA methylation in cell proliferation and tumorigenicity, we investigated human endometrial cancer in which a hotspot R298P mutation is present in a key component of the methyltransferase complex (METTL14). We found that about 70% of endometrial tumours exhibit reductions in m6A methylation that are probably due to either this METTL14 mutation or reduced expression of METTL3, another component of the methyltransferase complex. These changes lead to increased proliferation and tumorigenicity of endometrial cancer cells, likely through activation of the AKT pathway. Reductions in m6A methylation lead to decreased expression of the negative AKT regulator PHLPP2 and increased expression of the positive AKT regulator mTORC2. Together, these results reveal reduced m6A mRNA methylation as an oncogenic mechanism in endometrial cancer and identify m6A methylation as a regulator of AKT signalling.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Liu, N. & Pan, T. N 6-methyladenosine-encoded epitranscriptomics. Nat. Struct. Mol. Biol. 23, 98–102 (2016).
Zhao, B. S., Roundtree, I. A. & He, C. Post-transcriptional gene regulation by mRNA modifications. Nat. Rev. Mol. Cell Biol. 18, 31–42 (2017).
Jia, G. et al. N 6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol. 7, 885–887 (2011).
Bokar, J. A., Rath-Shambaugh, M. E., Ludwiczak, R., Narayan, P. & Rottman, F. Characterization and partial purification of mRNA N 6-adenosine methyltransferase from HeLa cell nuclei. Internal mRNA methylation requires a multisubunit complex. J. Biol. Chem. 269, 17697–17704 (1994).
Liu, J. et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).
Ping, X.-L. et al. Mammalian WTAP is a regulatory subunit of the RNA N 6-methyladenosine methyltransferase. Cell Res. 24, 177–189 (2014).
Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5' sites. Cell Rep. 8, 284–296 (2014).
Wang, Y. et al. N 6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).
Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013).
Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).
Zhao, X. et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 24, 1403–1419 (2014).
Liu, N. et al. N 6-methyladenosine-dependent RNA structural switches regulate RNA–protein interactions. Nature 518, 560–564 (2015).
Xiao, W. et al. Nuclear m6A reader YTHDC1 regulates mRNA splicing. Mol. Cell 61, 507–519 (2016).
Roundtree, I. A. et al. YTHDC1 mediates nuclear export of N 6-methyladenosine methylated mRNAs. eLife 6, e31311 (2017).
Meyer, K. D. et al. 5' UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015).
Wang, X. et al. N 6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).
Zhou, J. et al. Dynamic m6A mRNA methylation directs translational control of heat shock response. Nature 526, 591–594 (2015).
Li, A. et al. Cytoplasmic m6A reader YTHDF3 promotes mRNA translation. Cell Res. 27, 444–447 (2017).
Shi, H. et al. YTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA. Cell Res. 27, 315–328 (2017).
Wang, X. et al. N 6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).
Geula, S. et al. Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347, 1002–1006 (2015).
Batista, P. J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).
Zhao, B. S. et al. m6A-dependent maternal mRNA clearance facilitates zebrafish maternal-to-zygotic transition. Nature 542, 475–478 (2017).
Lin, S., Choe, J., Du, P., Triboulet, R. & Gregory, R. I. The m6A methyltransferase METTL3 promotes translation in human cancer cells. Mol. Cell 62, 335–345 (2016).
Zhang, C. et al. Hypoxia induces the breast cancer stem cell phenotype by HIF-dependent and ALKBH5-mediated m6A-demethylation of NANOG mRNA. Proc. Natl Acad. Sci. USA 113, E2047–E2056 (2016).
Ma, J. Z. et al. METTL14 suppresses the metastatic potential of HCC by modulating m6A-dependent primary miRNA processing. Hepatology 65, 529–543 (2017).
Li, Z. et al. FTO plays an oncogenic role in acute myeloid leukemia as a N 6-methyladenosine RNA demethylase. Cancer Cell 31, 127–141 (2017).
Zhang, S. et al. m6A demethylase ALKBH5 maintains tumorigenicity of glioblastoma stem-like cells by sustaining FOXM1 expression and cell proliferation program. Cancer Cell 31, 1–16 (2017).
Cui, Q. et al. m6A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells. Cell Rep. 18, 2622–2634 (2017).
Li, X. et al. The m6A methyltransferase METTL3: acting as a tumor suppressor in renal cell carcinoma. Oncotarget 8, 96103–96116 (2017).
Wang, X. et al. Reduced m6A mRNA methylation is correlated with the progression of human cervical cancer. Oncotarget 8, 98918–98930 (2017).
Chen, M. et al. RNA N 6-methyladenosine methyltransferase METTL3 promotes liver cancer progression through YTHDF2 dependent post-transcriptional silencing of SOCS2. Hepatology 67, 2254–2270 (2017).
Vu, L. P. et al. The N 6-methyladenosine (m6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nat. Med. 23, 1369–1376 (2017).
Weng, H. et al. METTL14 inhibits hematopoietic stem/progenitor differentiation and promotes leukemogenesis via mRNA m6A modification. Cell Stem Cell 22, 191–205 (2018).
Su, R. et al. R-2HG exhibits anti-tumor activity by targeting FTO/m(6)A/MYC/CEBPA signaling. Cell 172, 90–105 (2018).
Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).
Sledz, P. & Jinek, M. Structural insights into the molecular mechanism of the m6A writer complex. eLife 5, e18434 (2016).
Wang, P., Doxtader, K. A. & Nam, Y. Structural basis for cooperative function of Mettl3 and Mettl14 methyltransferases. Mol. Cell 63, 306–317 (2016).
Wang, X. et al. Structural basis of N 6-adenosine methylation by the METTL3-METTL14 complex. Nature 534, 575–578 (2016).
Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons. Cell 149, 1635–1646 (2012).
Cancer Genome Atlas Research Network, Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).
The Cancer Genome Atlas Research Network, Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185–203 (2017).
Le Gallo, M. & Bell, D. W. The emerging genomic landscape of endometrial cancer. Clin. Chem. 60, 98–110 (2014).
Manning, B. D. & Toker, A. AKT/PKB signaling: navigating the network. Cell 169, 381–405 (2017).
Vivanco, I. & Sawyers, C. L. The phosphatidylinositol 3-kinase AKT pathway in human cancer. Nat. Rev. Cancer 2, 489–501 (2002).
Jacinto, E. et al. SIN1/MIP1 maintains rictor-mTOR complex integrity and regulates Akt phosphorylation and substrate specificity. Cell 127, 125–137 (2006).
Brognard, J., Sierecki, E., Gao, T. & Newton, A. C. PHLPP and a second isoform, PHLPP2, differentially attenuate the amplitude of Akt signaling by regulating distinct Akt isoforms. Mol. Cell 25, 917–931 (2007).
Sarbassov, D. D., Guertin, D. A., Ali, S. M. & Sabatini, D. M. Phosphorylation and regulation of Akt/PKB by the rictor-mTOR complex. Science 307, 1098–1101 (2005).
Copp, J., Manning, G. & Hunter, T. TORC-specific phosphorylation of mammalian target of rapamycin (mTOR): phospho-Ser2481 is a marker for intact mTOR signaling complex 2. Cancer Res. 69, 1821–1827 (2009).
Li, H. B. et al. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 548, 338–342 (2017).
Varghese, F., Bukhari, A. B., Malhotra, R. & De, A. IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS ONE 9, e96801 (2014).
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
Stewart, S. A. et al. Lentivirus-delivered stable gene silencing by RNAi in primary cells. RNA 9, 493–501 (2003).
Zhang, Y. et al. Reversal of chemoresistance in ovarian cancer by co-delivery of a P-glycoprotein inhibitor and paclitaxel in a liposomal platform. Mol. Cancer Ther. 15, 2282–2293 (2016).
Kenny, H. A. et al. Quantitative high throughput screening using a primary human three-dimensional organotypic culture predicts in vivo efficacy. Nat. Commun. 6, 6220 (2015).
Takahashi, K. et al. Cetuximab inhibits growth, peritoneal dissemination, and lymph node and lung metastasis of endometrial cancer, and prolongs host survival. Int. J. Oncol. 35, 725–729 (2009).
Meng, J. et al. A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods 69, 274–281 (2014).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).
We thank Martin Aryee (Massachusetts General Hospital) for initial discussions and Angela Andersen for editing the manuscript. This work was supported by a Marsha Rivkin Foundation award (M.A.E.); University of Chicago Institute for Biophysical Dynamics Yen Fellowship (B.T.H.); National Natural Science Foundation of China grants 81472023 and 81271919 (S.L.); The National Basic Research Programme grants 2012CB720600 and 2012CB720605 (S.L.); the National Key Research and Development Programme of China 2017YFA0506800 (Jz.L.); the Thousands Young Talents Plan of China and Hundreds Talents Programme of Zhejiang University (Jz.L.); National Cancer Institute grants CA111882 (E.L.) and F32 CA221007 (B.T.H.); National Institutes of Health grants R01 HG008688 and RM1 HG008935 (C.H.); and University of Chicago Cancer Center Support Grant P30CA014599. M.A.E. thanks the Harris Family Foundation for their generous support. C.H. is an investigator of the Howard Hughes Medical Institute.
C.H. is a scientific founder of Accent Therapeutics and a member of its scientific advisory board. All other authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
(a) Scatter plots showing the correlation of m6A methylation levels with the expression of m6A writers, erasers and readers in tumor tissue. Linear least squares line is shown in red. The Pearson correlation coefficient (r) and p-value (p) from a two-tailed t-test of r = 0 are shown. n = 22 tumor-normal pairs for FTO and METTL14, n = 38 tumor-normal pairs for the others. (b) Box plots showing the expression of METTL3 in TCGA endometrial cancer patients with tumors wild-type for (black) or containing mutations in (red) the indicated genes. p-values were calculated by two-tailed t-test. n = 232 tumors. For the box plots, the center line represents the median, the box limits show the upper and lower quartiles, whiskers represent 1.5x the interquartile range, and outliers are represented as individual data points. (c) Survival curves showing the correlation between METTL3 expression and patient overall survival in the TCGA datasets for endometrial cancer, high grade serous ovarian cancer, and pancreatic adenocarcinoma. n indicates the number of patients in each group. p-values were calculated by two-tailed log-rank test.
Supplementary Figure 2 Effects of alterations to m6A mRNA methylation on the HEC-1-A endometrial cancer cell line.
(a) Immunoblot showing expression levels of METTL14 in the wild-type HEC-1-A cell line, the METTL14+/- knockout cell line, knockout cells rescued with stable expression of wild-type and mutant METTL14, and HEC-1-A cells stably transfected with control shRNA or shRNA targeting METTL14. Three independent experiments have been repeated with similar results. (b) Transwell invasion and migration were assessed for wild-type HEC-1-A cells, METTL14+/- knockout cells, and knockout cells rescued with wild-type or mutant METL14. (c) LC-MS/MS quantification of the m6A/A ratio from polyA-RNA purified from the METTL14 knockdown and control cells. (d-h) Cell proliferation in an MTS assay (d), anchorage-independent cell growth (e), colony formation (f), migration in a wound healing assay (g), and transwell invasion and migration (h) of HEC-1-A cells expressing control shRNA or shRNA targeting METTL14. (i) Immunoblot showing the expression of METTL3 in HEC-1-A cells expressing a control shRNA or two different shRNAs targeting METTL3. Three independent experiments have been repeated with similar results. (j) Transwell invasion and migration were assessed for HEC-1-A cells stably expressing control shRNA or shRNA targeting METTL3. For panels c-g, n = 3 biological replicates. Error bars indicate mean ± s.e.m. p-values determined by two-tailed t-test. For panel b, h and j, the bar shows the mean from n = 3 technical replicates. Raw gel images for panels a and i are provided in Supplementary Fig. 8.
(a) Relative expression of METTL3 in tumor and tumor-adjacent samples were measured by RT-qPCR. The bar shows the mean from n = 3 technical replicates, (b) m6A/A ratio of polyA RNAs isolated from tumor and tumor-adjacent samples were measured by LC-MS/MS. The bar shows the mean from n = 3 technical replicates. (c) Metagene plots showing the average distribution of m6A peaks identified across all transcripts in the tumor and tumor-adjacent samples from 5 patients. (d) Sequence logo showing the top motifs enriched across all m6A-peaks identified from n = 5 patients. (e) The number of significant m6A peaks detected and the number of transcripts containing a significant m6A peak are reported. The number of genes containing a significant m6A peak in each pair of samples is reported in the table. (f) For each pair of samples, we identified the set of transcripts showing a significant m6A peak in both normal tissue samples. We then calculated the fraction of these transcripts showing a > 2-fold decrease in enrichment in both tumor samples and divided by all transcripts showing a > 2-fold decrease in enrichment in at least one tumor sample. (g) Histograms showing the changes in m6A enrichment in the METTL3 knockdown versus control HEC-1-A cells (left) or mutant METTL14(R298P) versus wild-type METTL14 HEC-1-A cells (right).
(a) GO term analysis of transcripts with reduced m6A in knockdown METTL3 and mutant METTL14 HEC-1-A cells versus control. (b-e) m6A-IP combined with RT-qPCR was used to quantify the relative m6A levels (b,d) and mRNA levels (c,e) in the METTL14 mutant cell line (b,c) and METTL3 knockdown cells (d,e) versus control. For panels b-e, error bars indicate mean ± s.e.m from n = 3 biological replicates. p-values determined by two-tailed t-test. (f) Quantification of the immunoblots in Fig. 4a,b. Error bars indicate mean ± s.e.m from n = 3 biological replicates. p-values determined by two-tailed t-test. (g,h) Left: Immunohistochemical staining of tissue microarray cores for PRR5L (g) and p-mTOR(S2481) (h). Right: Quantification of IHC staining in normal endometrium (n = 10) and endometrial tumors (n = 30). Staining was assessed using automated software51 and scored on a scale of 0 (no staining) to 3 (high staining). The p-value was determined by a χ2-test. Scale bar = 50μm.
(a) Quantification of the immunoblots in Fig. 5a. Error bars indicate mean ± s.e.m. from n = 3 biological replicates. p-values determined by two-tailed t-test. (b) The absorbance at 260 nm was measured during fractionation of polysomes from HEC-1-A cells transiently transfected with control siRNA or siRNA targeting YTHDF1. (c) The abundance of PHLPP2 transcripts was measured by RT-qPCR in each fraction from the polysome profiling. (d-f) Top: The abundance of PRR5 (d), PRR5L (e), and mTOR (f) transcripts were measured by RT-qPCR in each fraction from the polysome profiling. Bottom: Fractions from the polysome fractionation corresponding to non-ribosomal RNAs, ribosome-associated RNAs, and polysome-associated RNAs were pooled and the abundance of the PRR5 (d), PRR5L (e), and mTOR (f) transcripts were quantified by RT-qPCR. For c-f, n = 2 biological replicates, bar represents the mean value.
Supplementary Figure 6 Time course of AKT activation during EGF stimulation and effects of m6A methylation on the RL95-2 cell line.
(a-b) Immunoblots showing the time course of AKT(S473) phosphorylation after EGF stimulation in shControl versus shMETTL3 HEC-1-A cells (a) or HEC-1-A cells expressing wild-type METTL14 versus mutant METTL14 (b). Cells were either incubated in media with 10% FBS (medium) or no FBS (starved). After 16 h of starvation, cells were stimulated with 10 ng/mL of recombinant EGF for the indicated amounts of time. Plots quantifying the time-course of EGF activation show mean ± s.e.m. from n = 3 biological replicates. p-values determined by two-tailed t-test. (c-f) Effects of alterations to m6A methylation on RL95-2 endometrial cancer cells were examined after transient transfection with control siRNA, siRNA targeting METTL3, siRNA targeting METTL14, empty vector, plasmid encoding METTL3 or plasmid encoding METTL14. (c) LC-MS/MS quantification of the m6A/A ratio in polyA-RNA after transient transfection of RL95-2 cells with the indicated reagents. (d,e) Cell proliferation measured by MTS assay of RL95-2 cells transfected with the indicated reagents. Cell numbers were normalized to the MTS signal ~ 5 h after cell seeding. For panels c-e, error bars indicate mean ± s.e.m from n = 3 biological replicates. p-values determined by two-tailed t-test. (f) Immunoblot showing the effects of the indicated perturbations to m6A methylation on the expression and phosphorylation of proteins involved in the AKT pathway in RL95-2 cells. Three independent experiments have been repeated with similar results. Raw gel images for panels a, b and f are provided in Supplementary Fig. 8.
Supplementary Figure 7 Alteration of the AKT pathway rescues changes in cell proliferation due to reduced m6A methylation.
(a,b) Immunoblots analyzing the effect of FLAG-PHLPP2 overexpression (a) or RICTOR knockdown (b) on AKT phosphorylation in wild-type and METTL14+/- HEC-1-A cells. Three independent experiments have been repeated with similar results. (c-d) Proliferation measured by MTS assay of wild-type versus METTL14+/- HEC-1-A cells. Cells were transiently transfected with a PHLPP2 overexpression plasmid versus empty vector (c) or siRNAs targeting RICTOR versus negative control siRNAs (d). Error bars show mean ± s.e.m. from n = 3 biological replicates. (e) Immunoblots showing phosphorylation status of AKT(S473) and FOXO1(S256), a downstream target of AKT, after treatment of the indicated HEC-1-A cell lines with 5 µM MK-2206 for 24 h. Three independent experiments have been repeated with similar results. Plots quantifying the phosphorylation status of FOXO1(S256) show mean ± s.e.m. from n = 3 biological replicates. p-values determined by two-tailed t-test. (f) Cell proliferation measured by MTS assay for the indicated cell lines in the presence of 5 µM MK-2206 or DMSO. For proliferation assays, cell numbers were normalized to the MTS signal ~ 5 h after cell seeding. n = 3 biological replicates; error bars indicate mean ± s.e.m. Raw gel images for panels a, b and e are provided in Supplementary Fig. 8.
Unprocessed images of all immunoblots. Molecular weight markers in kDa
About this article
Cite this article
Liu, J., Eckert, M.A., Harada, B.T. et al. m6A mRNA methylation regulates AKT activity to promote the proliferation and tumorigenicity of endometrial cancer. Nat Cell Biol 20, 1074–1083 (2018). https://doi.org/10.1038/s41556-018-0174-4
Cancer Research (2021)
The EMBO Journal (2021)
Dysregulation of USP18/FTO/PYCR1 signaling network promotes bladder cancer development and progression
Molecular Therapy - Nucleic Acids (2021)
m6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma
Briefings in Bioinformatics (2021)