Additional file 2 of Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies
Creators: name: Yu, Xiao-wan
name: She, Pei-wei
name: Chen, Fang-chuan
name: Chen, Ya-yu
name: Zhou, Shuang
name: Wang, Xi-min
name: Lin, Xiao-rong
name: Liu, Qiao-ling
name: Huang, Zhi-jun
name: Qiu, Yu
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Cancer

description: Additional file 2: Supplementary Figure 1. Clinical characteristics of samples from different subtypes in the TCGA and GEO cohorts. (A) T Staging ratio distribution in the three ESCA subtypes in TCGA-ESCA cohort. (B) N Staging ratio distribution in the three ESCA subtypes in TCGA-ESCA cohort. (C) M Staging ratio distribution in the three ESCA subtypes in TCGA-ESCA cohort. (D) Tumor stage ratio distribution in the three ESCA subtypes in the TCGA-ESCA cohort. (E) Age distribution in the three ESCA subtypes in the TCGA-ESCA cohort. (F) Gender ratio distribution in the three ESCA subtypes in the TCGA-ESCA cohort. (G) Grade ratio distribution in the three ESCA subtypes in the GEO-ESCA cohort. (H) Gender ratio distribution in the three ESCA subtypes in the GEO-ESCA cohort. Supplementary Figure 2. Different responses to immunotherapy for 3 ESCA metabolic subtypes. (A) Differences in TIDE scores among 3 subtypes of samples from the TCGA-ESCA cohort. (B) Differences in response status to immunotherapy among 3 subtypes of samples from TCGA-ESCA cohort. (C) Differences in TIDE scores among 3 subtypes of samples from the GEO-ESCA cohort. (D) Differences in response status to immunotherapy among 3 subtypes of samples from the GEO-ESCA cohort. (E) Submap analysis showed that MC3 was not sensitive to the PD-1 inhibitor (Bonferroni-corrected P &lt; 0.05) in the TCGA cohort. (F) Submap analysis manifested that MC1 could be sensitive to the PD-1 inhibitor in the GSE19417 cohort. (G) The box plots of the estimated IC50 for gemcitabine, Cisplatin, paclitaxel and docetaxel on samples in 3 subtypes from the TCGA-ESCA cohort. Supplementary Figure 3. Construction of the metabolic subtype characteristic index model. (A) The first 2 characteristics of the model were able to distinctly classify the TCGA-ESCA samples into 3 different subtypes. (B) The characteristic index of samples in three ESCA subtypes from the TCGA cohort. (C) The ROC curve of metabolic subtype characteristic index in TCGA-ESCA cohort. (D) The first 2 characteristics of the model were able to distinctly classify the GEO-ESCA samples into 3 different subtypes. (E) The characteristic index of samples in three ESCA subtypes from the GEO cohort. (F) The ROC curve of metabolic subtype characteristic index in GEO-ESCA cohort. Supplementary Figure 4. Kaplan–Meier curves showing the distinct outcomes of ESCA patients with different expression levels of SLC40A1, GSTP1, MTHFD2, NT5DC4, ABCF2, SLC5A, CDS1, SLC37A1, IVD, DPEP2, TAP2, PDE1A and FAR1. Supplementary Figure 5. Expression levels of MTHFD2, SLC5A1 and NT5DC4 in ESCA. qRT-PCR analysis for validating the expresion of (A) MTHFD2, (B) SLC5A1 and (C) NT5DC4 in ESCA. **p &lt; 0.01.