Neuroendocrine Neoplasm Based on Multi-omics Integrated Analysis

Study Overview
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Brief Summary
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This project intends to analyze the molecular biological characteristics of NEN based on multi-omics, develop an exclusive NEN multi-omics big data platform, and carry out molecular subtypes and potential targets prediction, so as to improve the therapeutic effect of neuroendocrine tumors.

Detailed Description
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In recent years, the innovation of high-throughput sequencing technology has greatly promoted the understanding of disease mechanisms at the molecular level. It is an indisputable fact that there are large differences in the prognosis of tumors with the same pathological type and stage clinically. A large number of studies have proved that the difference in prognosis is closely related to the heterogeneity of the tumor. In the past few years, individualized precision treatment can greatly improve the prognosis of patients. Studies have shown that subgroup classification of colorectal cancer based on somatic mutations and signal pathway activation in the TCGA database has greatly improved the accuracy of diagnosis and the effectiveness of treatment. Lehmann's team divided the samples into six types based on the gene expression profile of triple-negative breast cancer: immunomodulatory type, mesenchymal type, mesenchymal stem-like type, androgen receptor type, and two basal-like types. This typing method combines the role of normal matrix and immune cell transcription levels in the tumor microenvironment, and explores their clinical characteristics and treatment strategies according to different subtypes. However, no single omics is sufficient to elucidate the complex pathogenesis of tumors. Therefore, the integrated analysis of multiple omics is a development trend, which will help clarify the pathogenesis of tumors and discover potential drug treatment targets. The interactive analysis of phenotypic data and molecular omics data can not only help us analyze the correlation between biological phenotypes and molecular phenotypes, but also allow us to understand the microscopic molecular mechanism of macro-biological phenotypes. For example, imaging phenotypes based on CT and MRI can be used to explore important protein markers related to them, which provides experimental and theoretical basis for guiding future clinical drug targeted therapy and drug resistance mechanism research. What's more interesting is that the relationship between molecular classification of tumors based on molecular omics and the establishment of phenotypic recognition models such as imaging omics can also make phenotypics such as imaging omics become a guide for targeted tumor therapy. An important method. Therefore, for neuroendocrine tumors with a high degree of heterogeneity, it is very necessary to analyze them from the perspective of multiple omics. However, in the current public databases TCGA and GEO, the exclusive NEN genomics data is extremely scarce, and there are almost no data such as proteomics, epiomics, metabolomics, and imagingomics. Therefore, it is urgent to carry out exclusive NEN multi-omics big data analysis to comprehensively and in-depth study the genesis and development mechanism of neuroendocrine tumors. This project intends to analyze the molecular biological characteristics of NEN based on multi-omics analysis, develop an exclusive NEN multi-omics big data platform, and carry out molecular subtypes. We hope that this study can find the molecular mechanism and potential intervention targets of NEN recurrence and metastasis, and provide clinicians with safe and effective treatment strategies, thereby improving the therapeutic effect of neuroendocrine tumors.

Official Title
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Research on Neuroendocrine Neoplasm in a Single Center Based on Multi-omics Integrated Analysis

Conditions
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Neuroendocrine Neoplasm

Intervention / Treatment
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* Procedure: Biopsy/surgical tissue and peripheral blood


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Received surgical treatment at Fudan University Affiliated Cancer Hospital from January 2010 to January 2021; Postoperative pathology proved to be neuroendocrine tumor; Has signed an informed consent form for tissue bank sample collection, agreeing to use the specimens and related clinical data for scientific research. Exclusion Criteria: Merge other malignant tumors; The clinical data is missing.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Gastroenteropancreatic neuroendocrine neoplasms<br> | Procedure: Biopsy/surgical tissue and peripheral blood<br>* Retrieve specimens stored in the tissue bank, the main types of samples are RNAlater specimens, liquid nitrogen frozen specimens and peripheral blood specimens<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| NEN mechanism analysis based on Multi-omics | Collect NEN tissue specimens and peripheral blood specimens for genomics, transcriptomics, proteomics, phosphorylation, metabolomics and other multiple omics sequencing analysis, so as to find the relationship between these molecular omics and phenotypes, explore NEN mechanism, including driver genes, activation of signal pathways, etc., and screen sensitive drugs based on potential targets. Use multi-omics data to establish NEN big data analysis platform, including sensitive target prediction, related gene prediction, survival analysis, and so on. | One year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| NEN immune microenvironment analysis | The GSVA software and CIBORSORT method are used to predict the content and ratio of various immune cell subtypes based on NEN mRNA expression. | One year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Neuroendocrine Neoplasm, Multi-omics Analysis, Molecular Subtypes