A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas
Abstract
:1. Introduction
2. Results
2.1. Workflow
2.2. A Number of Sites Display Differential Editing among Different Subtypes in NHL
2.3. More Sites Are Differentially Edited between NHL and Normal
2.4. Gene Expression Is Highly Correlated with Editing Efficiency of Differentially Edited Sites in UTRs
2.5. The Clinical Status of Samples Is Predicted with High Accuracy Based on RNA Editing Profiles Alone
2.6. Unsupervised Clustering Can Differentiate NHL and Normal Samples but Not NHL Subtypes
3. Discussion and Conclusions
4. Materials and Methods
4.1. Mapping RNA-Seq Reads to the Reference
4.2. Filtering, Editing Site Selection, and Editing Efficiency
- We removed duplicate reads (defined as reads having the exact same sequence with their mate and mapping to the same position in the reference), and kept the read with the highest base quality;
- To ensure the mapping uniqueness of a read, we only counted reads with a mapping quality score of at least 10;
- We discarded a read if the editing position was within 2 bp of the 5′ or 3′ end;
- We only counted a read if the editing site of the read had a base quality score of at least 20.
4.3. Grouping Samples
4.4. Further Filtering of Editing Sites for Statistical Comparison
- (mAA + mAG + mGG)/M > 40% (a significant number of samples showed editing efficiencies between 0 and 0.1, 0.4 and 0.6, or 0.9 and 1, consistent with homozygotic or heterozygotic SNPs);
- At least two of the three conditions mAA/M > 5%, mAG/M > 5%, and mGG/M > 5% were satisfied (to ensure that there was variation between the configurations of an SNP in the sample population).
4.5. Statistical Comparison of Groups
4.6. Correlating Editing Efficiency with Gene Expression
4.7. Leave-One-Out cross Validation
- If read coverage was less than 10, we scored the site as “0”;
- If read coverage was at least 10 and editing efficiency was closer to the mean of Group I than to the mean of Group II, we scored the site as “1”;
- If read coverage was at least 10 and editing efficiency was closer to the mean of Group II than to the mean of Group I, we scored the site as “−1”;
4.8. Clustering
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Comparison of Groups | Number of Sites Tested | Number of Sites with Significant Differences (FDR p-Value < 0.05) | Known Cancer-Related Genes | Number of Sites in Each Category | |||
---|---|---|---|---|---|---|---|
UTRs | Introns | Intergenic Regions | Repetitive Elements | ||||
ABC vs. FL | 543 | 28 (16/12) 2 | CTSS, CTSB, STK4, SAMHD1 | 21 | 4 | 3 | 25 |
GCB vs. FL | 546 | 16 (14/2) 2 | CTSS, CTSB, PRKCSH | 14 | 2 | 0 | 15 |
GCB vs. ABC | 502 | 68 (62/6) 2 | NOP14, SAMHD1, VHL | 30 | 21 | 15 | 67 |
Comparison of Groups | Number of Sites Tested | Number of Sites with Significant Differences (FDR p-Value < 0.05) | Known Cancer Related Genes | Number of Sites in Each Category | ||||
---|---|---|---|---|---|---|---|---|
Coding Regions | UTRs | Introns | Intergenic Regions | Repetitive Elements | ||||
NHL vs. Normal | 398 | 59 (18/41) 2 | STK4, AZIN1, CTSS, NOP14, PRKCSH | 2 | 22 | 28 | 7 | 56 |
GCB vs. Normal | 464 | 74 (19/55) 2 | STK4, AZIN1, CTSS, NOP14, PRKCSH, VHL, TP53 | 2 | 27 | 35 | 12 | 71 |
ABC vs. Normal | 484 | 69 (20/49) 2 | STK4, AZIN1, CTSS, PRKCSH, VHL | 2 | 25 | 33 | 9 | 66 |
FL vs. Normal | 496 | 84 (32/52) 2 | AZIN1, CTSS, PRKCSH, VHL, TP53 | 2 | 35 | 35 | 12 | 79 |
Number of Sites Tested | Significant Correlation (FDR p-Value < 0.05) | Positive Correlation | Negative Correlation |
---|---|---|---|
88 | 39 | 32 | 7 |
Groups | Total Number of Samples | Number of Samples Correctly Predicted | p-Value (Fisher’s Exact Test) |
---|---|---|---|
ABC vs. FL | 45 | 38 (84%) | 2.50 × 10−5 |
GCB vs. FL | 67 | 57 (85%) | 2.41 × 10−6 |
GCB vs. ABC | 86 | 65 (76%) | 4.54 × 10−5 |
NHL vs. Normal | 140 | 130 (93%) | 4.35 × 10−25 |
GCB vs. Normal | 95 | 86 (91%) | 1.56 × 10−17 |
ABC vs. Normal | 73 | 70 (96%) | 5.26 × 10−17 |
FL vs. Normal | 54 | 53 (98%) | 1.26 × 10−11 |
Clinical Status | Number of Samples |
---|---|
GCB | 54 |
ABC | 32 |
FL | 13 |
Normal | 41 |
NHL (GCB + ABC + FL) | 99 |
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Chen, C.; Bundschuh, R. A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas. Genes 2024, 15, 864. https://doi.org/10.3390/genes15070864
Chen C, Bundschuh R. A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas. Genes. 2024; 15(7):864. https://doi.org/10.3390/genes15070864
Chicago/Turabian StyleChen, Cai, and Ralf Bundschuh. 2024. "A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas" Genes 15, no. 7: 864. https://doi.org/10.3390/genes15070864
APA StyleChen, C., & Bundschuh, R. (2024). A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas. Genes, 15(7), 864. https://doi.org/10.3390/genes15070864