Additional file 1 of Cross-talk of four types of RNA modification writers defines tumor microenvironment and pharmacogenomic landscape in colorectal cancer
Creators: name: Chen, Huifang
name: Yao, Jiameng
name: Bao, Rujuan
name: Dong, Yu
name: Zhang, Ting
name: Du, Yanhua
name: Wang, Gaoyang
name: Ni, Duan
name: Xun, Zhenzhen
name: Niu, Xiaoyin
name: Ye, Youqiong
name: Li, Hua-Bing
Publisher: name: figshare
Year: 2021
Subjects: subject: Biochemistry
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Pharmacology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Mental Health
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1 : Figure S1. Overview of study design. (A) Flowchart of the steps in the performed analyses. Figure S2. Analysis of mutation frequency and CNV in TCGA- COAD/READ. (A) The mutation frequency of RNA modification “writers” among 33 cancer types in the TCGA cohort. The horizontal axis represents cancer types, and the number of samples is given in the parentheses. The vertical axis lists the names of the genes. (B) Comparison of GSEA enrichment analysis between “writers” mutation samples and non-mutation samples. NES, Normalized enrichment score. (C) The distribution of correlation coefficient between “writers” expression and CNV in CRC. |Rs| &gt; 0.3 and p value &lt; 0.05 indicates that “writers” expression is related to CNV. (C) The expression of “writers” among CNV groups in CRC. The sample size for each group based on the CNV alteration (CFI, CNV_loss/ CNV_gain/ normal/ none_CNV = 138/18/54/305; METTL14, 143/19/54/299; RBM15: 125/17/54/319; ADARB1: 138/26/54/297; TRMT61A: 151/33/54/277; CPSF2: 155/33/54/273; PABPN1: 146/38/54/277; METTL3: 146/38/54/277). Wilcoxon test was used to assess the difference. The boxes indicate the median ± 1 quartile, with the whiskers extending from the hinge to the smallest or largest value within 1.5× IQR from the box boundaries. Figure S3. Biological characteristics of RNA modification “writers”. (A) Association of gene expression for 26 RNA modification “writers” with patient overall survival times based on Univariate Cox regression analysis in GSE39582 cohort. (B) Heatmap shows the positive (red) and the negative (blue) correlation between TME infiltration and WM_Score in CRC. *p &lt; 0.05, **p &lt; 0.01, and ***p &lt; 0.001, as determined by the Spearman correlation analysis. Figure S4. Enrichment analysis of differentially expressed genes and the relationship between survival and the WM_Score. (A-B) GO (A) and KEGG (B) enrichment analysis of the 463 DEGs. The x-axis indicates gene counts within each GO term. The brightness of the column color represents the statistical significance of enrichment. (C) Kaplan-Meier curves comparing overall survival between two DEG clusters, gene.cluster_A (red) and gene.cluster_B (blue), in the GSE39582 cohort. The grouping of CRC samples is shown under the Kaplan-Meier plot. p &lt; 0.05 in the two-sided log-rank test was considered statistically significant. (D) Heatmap shows the differences in TME infiltration between WM_Score-high and -low groups in the GEO-CRC cohort. Red, high enrichment score; blue, low enrichment score. (E-F). Overlap (E) and frequency (F) of classifiers of WM_Score-high/−low and Cluster_1/2 in CRC. (G-H). Overlap (G) and frequency (H) of classifiers of WM_Score-high/−low and gene.cluster_A/B in CRC. The Fisher test was used to determine the statistical significance of the difference. Figure S5. Relationship between the WM_Score and the molecular subtype of CRC. (A) Correlation between the EMT score and WM_Score in the TCGA-COAD/READ cohort by Spearman analysis. (B) The difference of EMT scores in WM_Score-high (red) and -low (blue) groups in the TCGA-COAD/READ cohort. (C-D) Distribution of CMS subtypes within WM_Score-high and -low groups in four GEO-CRC datasets (C) and the TCGA-COAD/READ cohort (D). (E-F) Enrichment in signaling pathways in CMS subtypes in four GEO-CRC datasets (E) and the TCGA-COAD/READ cohort (F). (G) Distribution of TNM stage within CMS subtypes in four GEO-CRC datasets. Statistical significance (p &lt; 0.05) was calculated using the fisher.test. (H) Kaplan-Meier curves show the difference in overall survival between two RNA modification patterns, WM_Score-high (red) and -low (blue), in the GSE39582 cohort. The grouping of CRC samples is shown below. Figure S6. The length of APA PDUI gene affects the survival prognosis of CRC. (A-B) The bar graphs show the difference between WM_Score-high and -low groups in PDUI (A) and A-I editing (B). The forest plots show univariate Cox regression analyses for PDUI differential genes (A) and A-I editing differential genes (B) between WM_Score-high and -low group.  Figure S7. Graphic summary. (A) m6A, m1A, APA, and A-to-I RNA editing enzymes. (B) Genetic alterations (left panel), transcriptional alterations (intermediate panel), and mutual correlation (right panel) of four types of RNA modification "writers" in CRC. (C) Two distinct RNA modification patterns based on 26 RNA modification enzymes, and constructed a scoring model WM_Score. (D) Clinical relevance of WM Score. (E) Distinct patterns of WM_Score associated with immune infiltration. (F) Molecular subtypes associated with WM_Score in CRC. (G) WM_Score involved in transcriptional and post-transcriptional regulation. (H) Therapeutic ability of the WM_Score.