Finding the cross-tissue biomarkers gastric cancer diagnosis by transcriptome high throughput data analysis

Gastric cancer (GC) is one of the most common gastrointestinal malignancies. Although several approacheshave been developed to diagnose GC in recent years, still, there is a long distance to provide rapid, noninvasive,and inexpensive detections. The present study aimed to detect cross-tissue biomarkers in tissue,blood, and salivary samples of GC patients. The GC-related terms were searched in array express and GeneExpression omnibus to collect and analyze appropriate datasets. Each dataset was analyzed with GEO2Rtools to calculate differentially expressed genes. Then, a defined formula [-log(p.value)×|log(fold change)|]was applied to select the 2000 top significant probs. In the end, we selected the intersection genes which wererepeated at least in four out of eight tissue datasets. At last, we integrated two GC blood and salivary datasetsand found the common genes in all datasets. The analysis showed 190 and 144 common genes in GC tissueand blood datasets. The evaluation of VENN diagram results demonstrated that common gene expressionbetween tissue and blood, salivary and blood, tissue and salivary, was 6, 17, and 17. Also, the common genesbetween tissue, salivary, and blood were 4 genes, including AKR1C1, SORBS2, CKM, and PDLIM3. Ourfinding indicated that explored genes would manipulate oxidoreductase activity, production of structuralproteins, and transferase activity in favor of GC pathogenesis. We concluded that four candidate commongenes which have been explored in the present study between salivary, tissue, and blood samples of GCpatients would develop promising diagnostic approaches in the future. If laboratory investigations confirmthe results of the present study in the future, it is possible to obtain rapid and inexpensive tests to accuratelydiagnose GC patients.

کلیدواژه های Finding the cross-tissue biomarkers gastric cancer diagnosis by transcriptome high throughput data analysis:

gastric cancer ، biomarkers ، transcriptome ، data analysis ، diagnostic approach