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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

Abstract


Funding

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)


Acknowledgment

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

SUPPORTING INFORMATION

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.


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  • 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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