Additional file 1 of Comprehensive analysis of EMT-related genes and lncRNAs in the prognosis, immunity, and drug treatment of colorectal cancer

Additional file 1: Figure S1. The correlation of EMT-RDGs in CRC. Figure S2. The cluster analysis based on EMT-RDGs of CRC in TCGA set. (A) Consensus matrix of cluster analysis in CRC. (B) The CDF curve of cluster analysis in CRC. (C) Heat map of prognostic EMT-RDGs and clinical parameters at two clusters by R package “pheatmap”. (D) Survival curve comparing cluster 1 and 2 by R package “survival”. (E) 22 types of immune cells infiltration of two clusters in TCGA data by R package “1071”, “parallel” and “preprocessCore”. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Figure S3. Risk prognosis model verification of 9 prognostic EMT-RGDs in three GEO data. (A) Overall survival curve comparing high-risk and low-risk groups by R package “survival” in the GSE12954 set. (B) The distribution of risk score and the scatterplot of the relationship between risk scores and overall survival time by R package “ggplot” in the GSE12954 set. (C) Disease-free survival curve comparing high-risk and low-risk groups by R package “survival” in the GSE12954 set. (D) ROC curve of risk sore and other clincial characteristics in the GSE12954 set by R package “survivalROC”. (E) Overall survival curve comparing high-risk and low-risk groups by R package “survival” in the GSE17536 set. (F) The distribution of risk score and the scatterplot of the relationship between risk scores and overall survival time by R package “ggplot” in the GSE17536 set. (G) Disease-free survival curve comparing high-risk and low-risk groups by R package “survival” in the GSE17536 set. (H) ROC curve of risk sore and other clincial characteristics in the GSE17536 set by R package “survivalROC”. (I) Overall survival curve comparing high-risk and low-risk groups by R package “survival” in the GSE17537 set. (J) The distribution of risk score and the scatterplot of the relationship between risk scores and overall survival time by R package “ggplot” in the GSE17537 set. (K) Disease-free survival curve comparing high-risk and low-risk groups by R package “survival” in the GSE17537 set. (L) ROC curve of risk sore and other clincial characteristics in the GSE17537 set by R package “survivalROC”. Figure S4. Risk prognosis model construction of the prognostic EMT-RlncRNAs in the TCGA data by unicox and lasso regression. (A) The network diagram of EMT-RlncRNAs by R packege “WGCNA”. (B) ROC curve of risk sore by R package “survivalROC”. (C) The overall survival curve comparing high-risk and low-risk groups by R package “survival”. (D) Heat map of prognostic EMT-RlncRNAs and clinical parameters at high risk and low risk groups by R package “pheatmap”. (E) The univariate cox forest map of the clinical characteristics in the training set by R package “survival” and “forestplot”. (F) The multivariate cox forest plot of the clinical characteristics in the training set by R package “survival” and “forestplot”. (G) The nomogram baseline of multivariate cox analysis by R package “rms”. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Figure S5. The prognosis of 9 prognostic EMT-RGDs in CRC. (A) The overall survival curve comparing high-expression and low-expression of 9 prognostic EMT-RGDs in TCGA set by R package “survival”. (B) The overall survival curve comparing high-expression and low-expression of 9 prognostic EMT-RGDs in the GSE40967 set by R package “survival”. (C) The overall survival curve comparing high-expression and low-expression of 9 prognostic EMT-RGDs in the GEPIA database. (D) The effects of high expression of 9 prognostic EMT-RGDs on survival risk in the GCSC database. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Figure S6-a. The relationship between the status of FGF8 and immune cells in CRC from TIMER database. (A) The correlation between the expression of FGF8 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of FGF8 in CRC. (C) The comparism of immune cells infiltration in different CNA types of FGF8 in CRC. (D) The cumulative survival of the expression level of FGF8 and the immune cells infiltration in CRC. Figure S6-b. The relationship between the status of NOG and immune cells in CRC from TIMER database. (A) The correlation between the expression of NOG and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of NOG in CRC. (C) The cumulative survival of the expression level of NOG and the immune cells infiltration in CRC. Figure S6-c. The relationship between the status of PHLDB2 and immune cells in CRC from TIMER database. (A) The correlation between the expression of PHLDB2 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of PHLDB2 in CRC. (C) The cumulative survival of the expression level of PHLDB2 and the immune cells infiltration in CRC. Figure S6-d. The relationship between the status of SIX2 and immune cells in CRC from TIMER database. (A) The correlation between the expression of SIX2 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of SIX2 in CRC. (C) The cumulative survival of the expression level of SIX2 and the immune cells infiltration in CRC. Figure S6-e. The relationship between the status of SNAI1 and immune cells in CRC from TIMER database. (A) The correlation between the expression of SNAI1 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of SNAI1 in CRC. (C) The comparism of immune cells infiltration in different CNA types of SNAI1 in CRC. (D) The cumulative survival of the expression level of SNAI1 and the immune cells infiltration in CRC. Figure S6-f. The relationship between the status of TBX5 and immune cells in CRC from TIMER database. (A) The correlation between the expression of TBX5 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of TBX5 in CRC. (C) The cumulative survival of the expression level of TBX5 and the immune cells infiltration in CRC Figure S6-g. The relationship between the status of TCF15 and immune cells in CRC from TIMER database. (A) The correlation between the expression of TCF15 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in different CNA types of TCF15 in CRC. (C) The cumulative survival of the expression level of TCF15 and the immune cells infiltration in CRC. Figure S6-h. The relationship between the status of TIAM1 and immune cells in CRC from TIMER database. (A) The correlation between the expression of TIAM1 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of TIAM1 in CRC. (C) The cumulative survival of the expression level of TIAM1 and the immune cells infiltration in CRC. Figure S6-i. The relationship between the status of TWIST1 and immune cells in CRC from TIMER database. (A) The correlation between the expression of TWIST1 and the immune cell infiltration in CRC. (B) The comparism of immune cells infiltration in wild-type and mutated-type of TWIST1 in CRC. (C) The cumulative survival of the expression level of TWIST1 and the immune cells infiltration in CRC. Figure S7. Single-cell analysis of 11 CRC patients in the GSE81861 set. (A) The function heat map of single-cell analysis. (B) The correlation between EMT and expression of PHLDB2. Figure S8. The mutation verification analysis of 9 prognostic EMT-RGDs in CRC from the GCSC database. (A) The SNV frequency of 9 prognostic EMT-RGDs based on the 60 CRC patients. (B) The mutation frequency of 9 prognostic EMT-RGDs in colon adenorcarcinoma and rectal adenorcarcinoma. Figure S9. The mutation verification analysis of 9 prognostic EMT-RGDs in CRC from the Cbiportal database. (A) The SNV frequency of 9 prognostic EMT-RGDs based on the 60 CRC patients. (B) The comparism of expression Z-score of the prognostic EMT-RGDs in mutated-type and wild-type. (C) The mutated sites of 9 prognostic EMT-RGDs. D. The comprehensive comparison of mutated counts and disease-free survival of 9 prognostic-related EMT-RDGs. Figure S10. The CNA verification analysis of 9 prognostic EMT-RGDs in CRC from the Cbiportal database. Figure S11. The expression comparism of 9 prognostic EMT-RGDs in CRC treated with capecitabine, capecitabine + irinotecan, and XELOX (capecitabine+oxaliplatin) + bevacizumab group in the GSE36864 set. Figure S12. The expression verification of 9 prognostic EMT-RGDs in the DONIVD database. Table S1. All the CpG sites and DNA methylation status of 9 prognostic EMT-RGDs from DNMIVD database. Table S2. All the drugs information of 9 prognostic EMT-RGDs from GDSC database.