Additional file 2 of Identification of adenoid subtype characterized with immune-escaped phenotype in lung squamous carcinoma based on transcriptomics
Creators: name: Mei, Jie
name: Cai, Yun
name: Mussafi, Ofek
name: Zheng, Mingfeng
name: Xu, Yongrui
name: Chen, Ruo
name: Jiang, Guanyu
name: Mao, Wenjun
name: Xia, Wei
name: Wan, Yuan
Publisher: name: figshare
Year: 2022
Subjects: subject: Biochemistry
subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Pharmacology
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: Additional file 2: Figure S1. Demarcation of patients using a SNN modularity optimization-based clustering algorithm. A Unsupervised clustering of NSCLC samples with four clusters. B Unsupervised clustering of LUAD and LUSC samples. C Distribution of LUAD and LUSC samples in four different clusters. Figure S2. Associations between NSCLC subtypes and TME features. A Levels of stromal score, immune score, ESTIMATE score, and tumor purity in LUAD (n = 512), LUSC (n = 430), and LASC (n = 66) subtypes. Significance was calculated with One-way ANOVA with Tukey’s multiple comparisons test. ***P &lt; 0.001. B Expression levels of 122 immunomodulators in LUAD, LUSC, and LASC subtypes. C The levels of TILs calculated using five algorithms in LUAD, LUSC, and LASC subtypes. D Expression levels of immune checkpoints in LUAD, LUSC, and LASC subtypes. Figure S3. Determination of soft-thresholding power in WGCNA. A Analysis of the scale-free fitting indices for various soft-thresholding powers (β). B Mean connectivity analysis of various soft-thresholding powers. C Histogram of the connection distribution when β = 14. D Checking the scale-free topology when β = 14. According to Figure S3C-D, k and p(k) are negatively correlated (correlation coefficient 0.78), indicating that a gene scale-free network can be resumed. Figure S4. Identification of FOLR1 as a biomarker for LASC discrimination. A Visualization of the gene network with a heatmap. B Clustering dendrograms of genes based on dissimilarity topological overlap and module colors. C Heatmap of the correlation between module eigengenes and subtypes of NSCLC. D BP enrichment analysis of genes in the turquoise module. E BP enrichment analysis of genes in the blue module. Figure S5. FOLR1 identifies the inflamed TME in LUSC. A Levels of stromal score, immune score, ESTIMATE score, and tumor purity in the high- (n = 182) and low-FOLR1 (n = 314) groups. Significance was calculated with Student’s t-test. ***P &lt; 0.001. B Expression levels of 122 immunomodulators in the high- and low-FOLR1 groups. C The levels of TILs calculated using five algorithms in the high- and low-FOLR1 groups. D Correlations between FOLR1 and common inhibitory immune checkpoints. ***P-value &lt; 0.001. Figure S6. FOLR1 can’t identify the inflamed TME in LUAD. A Levels of stromal score, immune score, ESTIMATE score, and tumor purity in the high- (n = 188) and low-FOLR1 (n = 324) groups. Significance was calculated with Student’s t-test. ns: no statistical difference, *P &lt; 0.05. B Expression levels of 122 immunomodulators in the high- and low-FOLR1 groups. (C) The levels of TILs calculated using five algorithms in the high- and low-FOLR1 groups. D Correlations between FOLR1 and common inhibitory immune checkpoints.  *P-value &lt; 0.05; ns: P &gt; 0.05. Figure S7. FOLR1 is highly expressed in tumor cells in NSCLC. A Single-cell expression profile of FOLR1 in the GSE117570 dataset. B Single-cell expression profile of FOLR1 in the GSE131907 dataset.