Additional file 1 of Multi-omics profiling of papillary thyroid microcarcinoma reveals different somatic mutations and a unique transcriptomic signature
Creators: name: Li, Qiang
name: Feng, Tienan
name: Zhu, Tengteng
name: Zhang, Weituo
name: Qian, Ying
name: Zhang, Huan
name: Zheng, Xiangqian
name: Li, Dapeng
name: Yun, Xinwei
name: Zhao, Jingzhu
name: Li, Yangyang
name: Yu, Herbert
name: Gao, Ming
name: Qian, Biyun
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer

description: Additional file 1: Fig S1. Sanger sequencing validation of the PCR products of 9 fusions in the PTMETA cohort, respectively. Fig S2. Lollipop plot of the somatic mutations of the driver genes identified in the PTMETA cohort (a) and TCGA cohort (b). For each driver gene, a lollipop plot was generated depicting all amino acid changes found along the protein (grey bar) with their frequencies in the PTMETA cohort and TCGA cohort (the height). Protein motifs were shown with coloured boxes. Fig S3. Heat map showing somatic CNAs with estimated actual copy numbers between the PTMETA cohort and TCGA cohort. Red represents amplification and blue represents deletion. Fig S4. CIBERSORT analysis of PTMETA samples. Highlighted boxes indicate immune cell types that were significantly enriched in either PTMC-proliferation or PTMC-inflammatory relative to the other using a wilcoxon rank-sum test (p &lt; 0.05). Outliers not shown. The boxes in box plots indicate 25th percentile, median, and 75th percentile, while whiskers show the maximum and minimum values within 1.5 times the inter-quartile range from the edge of the box. Fig S5. Identification of hub driver genes. a, A PPI network made up of the driver mutation genes, fusion genes, and top 100 marker genes for each subgroup. The PTMC-proliferation, PTMC-inflammatory, and driving genes are represented by the red, green, and yellow nodes, respectively. b, The expression level of PTMC-inflammatory marker genes (CD4, CXCR4, IGJ, and PTPRC) associated with AFP as a function of AFP mutation type. c, The expression levels of PTMC-inflammatory marker genes IGH@ext (IGHA1, IGHA2, IGHG2, IGHG3, IGHG4, IGHM, IGHV1-18, IGHV3-21, IGHV3-23, IGHV3-30, IGHV4-39, and IGHV4-59) as a function of IGH@ext fusion type. p values determined by the wilcoxon rank-sum test. Fig S6. The diagnostic prediction model for subgroups was validated using the ESPTC-TCGA cohort. a, Comparison of the tumor immune microenvironment across different immune state according to the diagnostic prediction model in the ESPTC-TCGA cohort using two methods: ESTIMATE immune scores, GSVA using Bindea et al.’s combined immune gene set. b, Comparison of the immune cell fraction distinguished by the different immune state in the ESPTC-TCGA cohort. c, Comparison of IFN-γ scores, PDL1, PDCD1, and CTLA4 expression by the different immune state in the PTMC-TCGA cohort. p-values from the Wilcoxon rank-sum tests. d, Kaplan–Meier curves of PFS according to the diagnostic prediction model in the ESPTC-TCGA cohort. p-value from the Log-rank test.