Spots Global Cancer Trial Database for Transcriptomic Signatures in Gastric Adenocarcinoma

Study Description

Brief Summary: Gastric Adenocarcinoma (GAC) accounting for the major percentage of all stomach malignancies is associated with a poor overall survival of 25-30% despite the advancement in treatment strategies. Several factors associated with tumor microenvironment (TME) are thought to play an important role in tumorigenesis and acquired chemoresistance to therapies that are not otherwise addressed by the comprehensive molecular classification of GAC given by TCGA and ACRG. In the present study investigators intend to do transcriptome profiling of GAC tumor tissue and adjacent normal tissue to investigate differentially expressed genes between the two in relation to TME which might help in identification of gene signatures that are clinically relevant with survival outcome in Gastric Adenocarcinoma.

Detailed Description: This study on Gastric adenocarcinoma is retrospective as well as prospective that will be conducted at SGPGIMS, Lucknow, India. Patients undergoing gastric resection at the department of surgical gastroenterology who are diagnosed with GAC based on the endoscopic biopsy in the department of pathology will be recruited for the study. For the retrospective part of study, cases will be selected based on histological findings retrieved from hospital information system and patient records. For sample collection, surgically resected fresh specimens will be collected in RNAlater and stored at -80⁰C. Archived formalin fixed paraffin embedded (FFPE) tissue blocks for retrospective cases will be retrieved and reviewed histopathologically. After a confirmed diagnosis of GAC, tissues will be processed to obtain tumor and normal tissue for experimental part. RNA from the tumor and normal area will be extracted from FFPE blocks and fresh tissue specimens. Whole transcriptomic next generation sequencing will be performed after successful quality check, library preparation and amplification. The gene expression data obtained from sequencing will be bioinformatically analyzed to elucidate differential gene expression between tumor and adjacent normal tissue in relation to TME. The significantly differentially expressed genes between the tumor and normal areas will be annotated and identified using bioinformatic packages for gene annotation. Using statistical analysis, the differentially expressed genes will be correlated with the patient's clinical features and outcome to identify TME genes with significant prognostic value.

Keywords

Eligibility

Minimum Age: 18 Years

Eligible Ages: ADULT, OLDER_ADULT

Sex: ALL

Healthy Volunteers: No

Locations

Contact Details

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