SCIENCE CHINA Life Sciences, Volume 65 , Issue 1 : 19-32(2022) https://doi.org/10.1007/s11427-020-1928-0

Identification of A-to-I RNA editing profiles and their clinical relevance in lung adenocarcinoma

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  • ReceivedDec 17, 2020
  • AcceptedApr 3, 2021
  • PublishedMay 27, 2021



the National Natural Science of China(81922061,82072579,81521004,81973123)

the National Key Research and Development Project(2017YFC0907905)

and Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancer

Chinese Academy of Medical Sciences(2019RU038)


This work was supported by the National Natural Science Foundation of China (81922061, 82072579, 81521004, 81973123 and 81871885), the National Key Research and Development Project (2017YFC0907905), and Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancer, Chinese Academy of Medical Sciences (2019RU038). We are grateful to the patients who participated in this study and all the researchers, clinicians and technical and administrative staff who have made this work possible.

Interest statement

The author(s) declare that they have no conflict of interest. All participants signed an informed consent form that was approved by the local internal review boards or ethics committees (Nanjing, China).

Supplementary data


The supporting information is available online at https://10.1007/s11427-020-1928-0. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.


[1] Agarwal V., Bell G.W., Nam J.W., Bartel D.P.. Predicting effective microRNA target sites in mammalian mRNAs. eLife, 2015, 4e05005 CrossRef PubMed Google Scholar

[2] Amin E.M., Liu Y., Deng S., Tan K.S., Chudgar N., Mayo M.W., Sanchez-Vega F., Adusumilli P.S., Schultz N., Jones D.R.. The RNA-editing enzyme ADAR promotes lung adenocarcinoma migration and invasion by stabilizing FAK. Sci Signal, 2017, 10eaah3941 CrossRef PubMed Google Scholar

[3] Bass B.L.. RNA editing by adenosine deaminases that act on RNA. Annu Rev Biochem, 2002, 71817-846 CrossRef PubMed Google Scholar

[4] Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., Jemal A.. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68394-424 CrossRef PubMed Google Scholar

[5] Cancer Genome Atlas Research N.. Comprehensive molecular profiling of lung adenocarcinoma. Nature, 2014, 511543-550 CrossRef PubMed ADS Google Scholar

[6] Chan T.H.M., Qamra A., Tan K.T., Guo J., Yang H., Qi L., Lin J.S., Ng V.H.E., Song Y., Hong H., et al. ADAR-mediated RNA editing predicts progression and prognosis of gastric cancer. Gastroenterology, 2016, 151637-650.e10 CrossRef PubMed Google Scholar

[7] Chen F., Zhang Y., Parra E., Rodriguez J., Behrens C., Akbani R., Lu Y., Kurie J.M., Gibbons D.L., Mills G.B., et al. Multiplatform-based molecular subtypes of non-small-cell lung cancer. Oncogene, 2017a, 361384-1393 CrossRef PubMed Google Scholar

[8] Chen J., Wang L., Wang F., Liu J., Bai Z.. Genomic identification of RNA editing through integrating omics datasets and the clinical relevance in hepatocellular carcinoma. Front Oncol, 2020, 1037 CrossRef PubMed Google Scholar

[9] Chen L., Li Y., Lin C.H., Chan T.H.M., Chow R.K.K., Song Y., Liu M., Yuan Y.F., Fu L., Kong K.L., et al. Recoding RNA editing of AZIN1 predisposes to hepatocellular carcinoma. Nat Med, 2013, 19209-216 CrossRef PubMed Google Scholar

[10] Chen Y.B., Liao X.Y., Zhang J.B., Wang F., Qin H.D., Zhang L., Shugart Y.Y., Zeng Y.X., Jia W.H.. ADAR2 functions as a tumor suppressor via editing IGFBP7 in esophageal squamous cell carcinoma. Int J Oncol, 2017b, 50622-630 CrossRef PubMed Google Scholar

[11] Dobin A., Davis C.A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T.R.. Star: Ultrafast universal RNA-seq aligner. Bioinformatics, 2013, 2915-21 CrossRef PubMed Google Scholar

[12] Ellrott K., Bailey M.H., Saksena G., Covington K.R., Kandoth C., Stewart C., Hess J., Ma S., Chiotti K.E., McLellan M., et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst, 2018, 6271-281.e7 CrossRef PubMed Google Scholar

[13] Friedman J., Hastie T., Tibshirani R.. Regularization paths for generalized linear models via coordinate descent. J Stat Soft, 2010, 331-22 CrossRef Google Scholar

[14] Fumagalli D., Gacquer D., Rothé F., Lefort A., Libert F., Brown D., Kheddoumi N., Shlien A., Konopka T., Salgado R., et al. Principles governing A-to-I RNA editing in the breast cancer transcriptome. Cell Rep, 2015, 13277-289 CrossRef PubMed Google Scholar

[15] Galeano F., Rossetti C., Tomaselli S., Cifaldi L., Lezzerini M., Pezzullo M., Boldrini R., Massimi L., Di Rocco C.M., Locatelli F., et al. ADAR2-editing activity inhibits glioblastoma growth through the modulation of the CDC14B/Skp2/p21/p27 axis. Oncogene, 2013, 32998-1009 CrossRef PubMed Google Scholar

[16] Geeleher P., Zhang Z., Wang F., Gruener R.F., Nath A., Morrison G., Bhutra S., Grossman R.L., Huang R.S.. Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies. Genome Res, 2017, 271743-1751 CrossRef PubMed Google Scholar

[17] Group P.T.C., Calabrese C., Davidson N.R., Demircioğlu D., Fonseca N.A., He Y., Kahles A., Lehmann K.V., Liu F., Shiraishi Y., et al. Genomic basis for RNA alterations in cancer. Nature, 2020, 578129-136 CrossRef PubMed ADS Google Scholar

[18] Han L., Diao L., Yu S., Xu X., Li J., Zhang R., Yang Y., Werner H.M.J., Eterovic A.K., Yuan Y., et al. The genomic landscape and clinical relevance of A-to-I RNA editing in human cancers. Cancer Cell, 2015, 28515-528 CrossRef PubMed Google Scholar

[19] Heale B.S.E., Keegan L.P., McGurk L., Michlewski G., Brindle J., Stanton C.M., Caceres J.F., O’Connell M.A.. Editing independent effects of adars on the miRNA/siRNA pathways. EMBO J, 2009, 283145-3156 CrossRef PubMed Google Scholar

[20] Jevremovic D., Billadeau D.D., Schoon R.A., Dick C.J., Leibson P.J.. Regulation of NK cell-mediated cytotoxicity by the adaptor protein 3BP2. J Immunol, 2001, 1667219-7228 CrossRef PubMed Google Scholar

[21] Kent W.J.. BLAT—The BLAST-like alignment tool. Genome Res, 2002, 12656-664 CrossRef PubMed Google Scholar

[22] Liao Y., Smyth G.K., Shi W.. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 2014, 30923-930 CrossRef PubMed Google Scholar

[23] Liu J., Lichtenberg T., Hoadley K.A., Poisson L.M., Lazar A.J., Cherniack A.D., Kovatich A.J., Benz C.C., Levine D.A., Lee A.V., et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell, 2018, 173400-416.e11 CrossRef PubMed Google Scholar

[24] Lortet-Tieulent J., Soerjomataram I., Ferlay J., Rutherford M., Weiderpass E., Bray F.. International trends in lung cancer incidence by histological subtype: Adenocarcinoma stabilizing in men but still increasing in women. Lung Cancer, 2014, 8413-22 CrossRef PubMed Google Scholar

[25] Nakamura H., Saji H.. A worldwide trend of increasing primary adenocarcinoma of the lung. Surg Today, 2014, 441004-1012 CrossRef PubMed Google Scholar

[26] Nishikura K.. Functions and regulation of RNA editing by adar deaminases. Annu Rev Biochem, 2010, 79321-349 CrossRef PubMed Google Scholar

[27] Nishikura K.. A-to-I editing of coding and non-coding RNAs by ADARs. Nat Rev Mol Cell Biol, 2016, 1783-96 CrossRef PubMed Google Scholar

[28] Oakes E., Anderson A., Cohen-Gadol A., Hundley H.A.. Adenosine deaminase that acts on RNA 3 (ADAR3) binding to glutamate receptor subunit B pre-mRNA inhibits RNA editing in glioblastoma. J Biol Chem, 2017, 2924326-4335 CrossRef PubMed Google Scholar

[29] Osmani L., Askin F., Gabrielson E., Li Q.K.. Current WHO guidelines and the critical role of immunohistochemical markers in the subclassification of non-small cell lung carcinoma (NSCLC): Moving from targeted therapy to immunotherapy. Semin Cancer Biol, 2018, 52103-109 CrossRef PubMed Google Scholar

[30] Park E., Williams B., Wold B.J., Mortazavi A.. RNA editing in the human encode RNA-seq data. Genome Res, 2012, 221626-1633 CrossRef PubMed Google Scholar

[31] Paz-Yaacov N., Bazak L., Buchumenski I., Porath H.T., Danan-Gotthold M., Knisbacher B.A., Eisenberg E., Levanon E.Y.. Elevated RNA editing activity is a major contributor to transcriptomic diversity in tumors. Cell Rep, 2015, 13267-276 CrossRef PubMed Google Scholar

[32] Paz N., Levanon E.Y., Amariglio N., Heimberger A.B., Ram Z., Constantini S., Barbash Z.S., Adamsky K., Safran M., Hirschberg A., et al. Altered adenosine-to-inosine RNA editing in human cancer. Genome Res, 2007, 171586-1595 CrossRef PubMed Google Scholar

[33] Peng X., Xu X., Wang Y., Hawke D.H., Yu S., Han L., Zhou Z., Mojumdar K., Jeong K.J., Labrie M., et al. A-to-I RNA editing contributes to proteomic diversity in cancer. Cancer Cell, 2018, 33817-828.e7 CrossRef PubMed Google Scholar

[34] Peng Z., Cheng Y., Tan B.C.M., Kang L., Tian Z., Zhu Y., Zhang W., Liang Y., Hu X., Tan X., et al. Comprehensive analysis of RNA-seq data reveals extensive RNA editing in a human transcriptome. Nat Biotechnol, 2012, 30253-260 CrossRef PubMed Google Scholar

[35] Picardi E., D’Erchia A.M., Lo Giudice C., Pesole G.. Rediportal: A comprehensive database of A-to-I RNA editing events in humans. Nucleic Acids Res, 2017, 45D750-D757 CrossRef PubMed Google Scholar

[36] Piskol R., Ramaswami G., Li J.B.. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet, 2013, 93641-651 CrossRef PubMed Google Scholar

[37] Qin Y.R., Qiao J.J., Chan T.H.M., Zhu Y.H., Li F.F., Liu H., Fei J., Li Y., Guan X.Y., Chen L.. Adenosine-to-inosine RNA editing mediated by ADARs in esophageal squamous cell carcinoma. Cancer Res, 2014, 74840-851 CrossRef PubMed Google Scholar

[38] Ramaswami G., Lin W., Piskol R., Tan M.H., Davis C., Li J.B.. Accurate identification of human Alu and non-Alu RNA editing sites. Nat Methods, 2012, 9579-581 CrossRef PubMed Google Scholar

[39] Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (beta). J Stat Soft 48, 1–36. Google Scholar

[40] Serrano-Candelas E., Ainsua-Enrich E., Navinés-Ferrer A., Rodrigues P., García-Valverde A., Bazzocco S., Macaya I., Arribas J., Serrano C., Sayós J., et al. Silencing of adaptor protein SH3BP2 reduces KIT/PDGFRA receptors expression and impairs gastrointestinal stromal tumors growth. Mol Oncol, 2018, 121383-1397 CrossRef PubMed Google Scholar

[41] Shigeyasu K., Okugawa Y., Toden S., Miyoshi J., Toiyama Y., Nagasaka T., Takahashi N., Kusunoki M., Takayama T., Yamada Y., et al. AZIN1 RNA editing confers cancer stemness and enhances oncogenic potential in colorectal cancer. JCI Insight, 2018, 3 CrossRef PubMed Google Scholar

[42] Silvestris D.A., Picardi E., Cesarini V., Fosso B., Mangraviti N., Massimi L., Martini M., Pesole G., Locatelli F., Gallo A.. Dynamic inosinome profiles reveal novel patient stratification and gender-specific differences in glioblastoma. Genome Biol, 2019, 2033 CrossRef PubMed Google Scholar

[43] Song Y., An O., Ren X., Chan T.H.M., Tay D.J.T., Tang S.J., Han J., Hong H.Q., Ng V.H.E., Ke X., et al. RNA editing mediates the functional switch of COPA in a novel mechanism of hepatocarcinogenesis. J Hepatol, 2021, 74135-147 CrossRef PubMed Google Scholar

[44] Tingley, D., Yamamoto, T., Hirose, K., Keele, L., Imai, K. (2014). Mediation: R package for causal mediation analysis. J Stat Soft 59. Google Scholar

[45] Tomaselli S., Galeano F., Alon S., Raho S., Galardi S., Polito V.A., Presutti C., Vincenti S., Eisenberg E., Locatelli F., et al. Modulation of microRNA editing, expression and processing by ADAR2 deaminase in glioblastoma. Genome Biol, 2015, 165 CrossRef PubMed Google Scholar

[46] Vargas A.J., Harris C.C.. Biomarker development in the precision medicine era: Lung cancer as a case study. Nat Rev Cancer, 2016, 16525-537 CrossRef PubMed Google Scholar

[47] Wang C., Yin R., Dai J., Gu Y., Cui S., Ma H., Zhang Z., Huang J., Qin N., Jiang T., et al. Whole-genome sequencing reveals genomic signatures associated with the inflammatory microenvironments in chinese NSCLC patients. Nat Commun, 2018, 92054 CrossRef PubMed ADS Google Scholar

[48] Wilkerson M.D., Hayes D.N.. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics, 2010, 261572-1573 CrossRef PubMed Google Scholar

[49] Yu G., Wang L.G., Han Y., He Q.Y.. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS, 2012, 16284-287 CrossRef PubMed Google Scholar

[50] Zhang M., Fritsche J., Roszik J., Williams L.J., Peng X., Chiu Y., Tsou C.C., Hoffgaard F., Goldfinger V., Schoor O., et al. RNA editing derived epitopes function as cancer antigens to elicit immune responses. Nat Commun, 2018, 93919 CrossRef PubMed ADS Google Scholar

  • Figure 1

    RNA editing profiles in LUAD. A, Distribution of RNA editing sites in different gene regions, Alu regions and overlap with known sites based on REDIportal public database. B, The violin plot of Alu editing index (AEI) distributions in normal and tumor tissues. The box plot displays the first and third quartiles (top and bottom of the boxes), the median (band inside the boxes), and the lowest and highest point within 1.5 times the interquartile range of the lower and higher quartile (whiskers). C, The significant correlation between AEI and ADAR1 mRNA expression and mutation number in tumor tissues. The rank-based Spearman correlations were used and plotted. D, Structural equation modeling shows RNA instability and DNA instability both were significantly associated with RNA editing level in NJLCC data. Green arrows show associations between two factors. Standardized β coefficients are reported. CFI, comparative fix index; RSMEA, root-mean-square error of approximation; CI, confidence interval; SRMR, standardized root-mean residuals. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

  • Figure 2

    Candidate driver RNA editing sites in coding regions and 3′ UTRs in LUAD. A, Heatmap of RNA editing levels in coding regions in paired NJLCC LUAD samples. Each column represents one of the 80 paired tumor tissues and 80 normal tissues, and rows show the 6 differentially RNA editing sites. Top bar annotations show ADAR1 and ADAR2 expression levels. B, Heatmap of RNA editing levels in 3′ UTR in paired NJLCC LUAD samples. Similar to (A), rows show 18 differentially RNA editing sites in 3′ UTR. Gene expression follows corresponding RNA editing sites.

  • Figure 3

    Identification of RNA editing subtypes in LUAD patients. A, NJLCC LUAD patients were classified into three molecular subtypes by RNA editing sites. B, Differences in AEI and mutation number among three RNA editing subtypes in NJLCC and TCGA patients. C, Kaplan-Meier plot for RNA editing subtypes and overall survival of TCGA LUAD patients. EC1 cluster was used as the reference group. HR and P value were calculated from Cox regression analysis, adjusting for age at diagnosis, gender, smoking status, and tumor stage. D, GSEA pathway enrichment analysis reveals that genes differentially expressed between RNA editing subtypes were enriched in cell proliferation and immune pathways. E, MKI67 mRNA expression, STAT1 mRNA expression, and cytolytic (CYT) score in three RNA editing subtypes. MKI67 mRNA expression was used as a cell proliferation marker, STAT1 mRNA expression as type I interferon response marker, and CYT score as type II interferon response marker. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

  • Figure 4

    RNA editing subtype predicted model and its application in estimating drug sensitivity. A, Receiver operating characteristics (ROC) curve and AUCs were used to assess the classification accuracy (non-EC3 subtype or EC3 subtype) of the editing score from LASSO regression. The red dots on each ROC curve indicate the cut-off value of the editing score. B, Kaplan-Meier plot shows predicted RNA editing subtypes and overall survival in TCGA patients. HR and P values were generated from the Cox regression analysis. C, Volcano plot depicts the change in drug sensitivity values (ln IC50, the natural logarithm of IC50) between different predicted RNA editing subtypes (x-axis) and the degree of significance (y-axis). P-value is calculated using Student’s t-test. D, Drug sensitivity comparison of Gemcitabine, Doxorubicin, Etoposide, Bleomycin, and Palbociclib between non-EC3 subtype and EC3 subtype in CCLE LUAD cell lines with GDSC drug sensitivity data. E, Drug sensitivity comparison of Gemcitabine, Doxorubicin, Etoposide, Bleomycin, and Palbociclib between non-EC3 subtype and EC3 subtype in NJLCC patients with predicted drug sensitivity data. F, Drug sensitivity comparison of Gemcitabine, Doxorubicin, Etoposide, Bleomycin, and Palbociclib between non-EC3 subtype and EC3 subtype in TCGA patients with predicted drug sensitivity data. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.


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