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Establishment of Molecular Classification Models for Early Diagnosis of Digestive System Cancers

ABSTRACT: This is a single blind, case control, multicenter study jointly developed by Zhongshan Hospital of Fudan University, Shanghai Public Health Clinical Center, Shanghai Xuhui Central Hospital, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, and Shanghai Singlera Genomics Company. The enrolled population will include positive group, precancerous lesions and healthy control group, which is expected to enroll 2,430 participants. The primary objective is to establish molecular testing methods for non-invasive screening and early diagnosis of digestive system cancers through ctDNA methylation and mutation, cfDNA and ctDNA fragment size, and end motif based model (for esophageal, gastric, colorectal cancer), and through ctDNA methylation detection, ctDNA low-pass WGS, miRNA7 and CTC detection and analysis technology based model (for hepatocellular carcinoma). The sensitivity and specificity of the models in cancer early detection will be evaluated.

DISEASE(S): Gastric (stomach) Cancer,Colorectal Cancer,Esophageal Cancer,Stomach Neoplasms,Digestive System Neoplasms,Hepatocellular Carcinoma

PROVIDER: 2736788 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:We here report transcriptome profiling of human embryos at six successive developmental stages (i.e., Carnegie Stages 9 to 14), representing the first comprehensive gene expression database of early human organogenesis. Through a series of data mining and comparisons with the transcriptome during mouse embryogenesis and the disparate genomic data in human embryonic stem cells, we revealed that development potential during early human organogenesis is orchestrated by two dominant categories of genes. Specifically, most gradually induced genes are largely differentiation related whereas those gradually repressed are involved in both stemness- and differentiation-relevant aspects of the developmental potential. Further through integrative mining we uncovered a molecular network that well characterizes stemness- and differentiation-relevant aspects of developmental potentials during early human organogenesis. Analysis of published data showed that the network could serve to evaluate various differentiation models. Our results make a significant step towards understanding of human embryogenesis at a molecular level and suggest that developmental potentials are under control of shared regulatory events. With the consent of subjects and of the Ethical Review Board of the Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine,we collected human post-implantation embryos at six successive time periods: Carnegie Stages 9 to 14 (E20 to E32), covering the first third of organogenesis. Using the Affymetrix HG-U133A Genechip microarrays, three replicates were independently profiled for each stage to minimize the influence of the embryo-to-embryo variation. Raw expression data were normalized using Robust Multi-array Averaging (RMA) with quantile normalization. The resultant expression data were imported into Extraction of Differential Gene Expression (EDGE) software for the detection of probesets exhibiting the consistent changes within the triplicates and differential expression (denoted as hORG expression matrix). The hORG expression matrix was subjected to Linear Models for Microarray Data (LIMMA) bioconductor library for identification of stage-transitive transcriptome changes, and self-organizing map combined with singular value decomposition (SOM-SVD) as well as SOM-based two-phase gene clustering for the topology-preserving extraction of temporal expression patterns. Hypergeometric distribution-based enrichment analyses were performed to explore the underlying biological relevance of gene groups of interest using diverse external annotated databases. The Cytoscape plug-in jActiveModules was modified to identify expression-active connected subnetworks in the compiled human interaction/association network.

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Establishment of Molecular Classification Models for Early Diagnosis of Digestive System Cancers

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