Additional file 1 of A microRNA-based liquid biopsy signature for the early detection of esophageal squamous cell carcinoma: a retrospective, prospective and multicenter study
Creators: name: Miyoshi, Jinsei
name: Zhu, Zhongxu
name: Luo, Aiping
name: Toden, Shusuke
name: Zhou, Xuantong
name: Izumi, Daisuke
name: Kanda, Mitsuro
name: Takayama, Tetsuji
name: Parker, Iqbal M.
name: Wang, Minjie
name: Gao, Feng
name: Zaidi, Ali H.
name: Baba, Hideo
name: Kodera, Yasuhiro
name: Cui, Yongping
name: Wang, Xin
name: Liu, Zhihua
name: Goel, Ajay
Publisher: name: figshare
Year: 2022
Subjects: subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Supplementary Figure 1. Study design for the identification and validation of the circulating miRNA panel for ESCC detection. Supplementary Figure 2. 18-miRNAs can distinguish between tumor and normal tissues. Supplementary Figure 3. In silico validation for 18-miRNA panel and Silhouette analysis. Supplemental Figure 4. miRNA regulatory network analysis and functional analysis of miRNA target genes. Supplemental Figure 5. Tissue validation for initial miRNA candidates. Supplemental Figure 6. Selection of circulating miRNAs in the serum biomarker prioritization cohort. Supplementary Figure 7. The robustness of the miRNA-classifier in training and validation cohorts. Supplemental Figure 8. Confusion matrices analysis for validation cohort 2. Supplemental Figure 9. Specificity analysis for the 8-miRNA panel on multiple cancer types. Supplemental Figure 10. The robustness of the miRNA-classifier in the prospectively collected cohorts. Supplemental Figure 11. Specificity analysis for the 8-miRNA panel and individual miRNAs. Supplemental Figure 12. Confusion matrices analysis for the Beijing-2 prospective cohort. Supplemental Table 1. Characteristics of in silico discovery sets. Supplemental Table 2. miRNA–mRNA interactions in the regulatory network. Supplemental Table 3. Functional analysis of miRNA target genes identified 31 significantly enriched signaling pathways and Hallmark gene sets (BH-adjusted p-value &lt; 0.05). Supplemental Table 4. miRNA panel selection and logistic regression model in serum biomarker prioritization and training phases. Supplemental Table 5. Prediction of serum 8-miR panel and serum SCC-Ag for the differential diagnosis of ESCC from healthy participants in serum training and serum validation cohorts. Supplemental Table 6. Comparison of the performance of the circulating miRNA signature against SCC-Ag, CEA, CA72–4, and CYFRA21-1 for non-invasive detection of ESCC across all stages in randomized prospective serum cohorts. Supplemental Table 7. Benchmark the performance of the circulating miRNA signature against SCC-Ag and CEA for non-invasive detection of stage I ESCC in randomized prospective serum cohorts. Supplemental Table 8. Univariate and multivariate analyses of the circulating miRNA signature with SCC-Ag, CEA, CA72–4, and CYFRA21-1 for non-invasive detection of ESCC across all stages in randomized prospective serum cohorts. Supplemental Table 9. Univariate and multivariate analyses of the circulating miRNA signature with SCC-Ag and CEA for non-invasive detection of stage I ESCC in randomized prospective serum cohorts. Supplemental Table 10. Results of cost-effectiveness analysis for non-invasive screening for Chinese men in China (&gt; 40 years old). Supplemental Table 11. Base-case values in cost-effectiveness modeling.