Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.