Mutations are centres of attention in cancer genetics wherever they are causally incriminated in tumorigenesis. These causal mutations can provide novel insight into cancer genetics and point towards anticancer therapy and diagnostic markers as well. Most causal mutations are missense (nonsynonymous Single Nucleotide Polymorphism, nsSNP) in nature which replace single nucleotide bases and subsequently exert deleterious effects through alteration of protein structure and function. Several studies have shown statistical evidence of linkage between a list of genes and Gastric Cancer (GC). Current study is a combined approach rooted in Systems Biology and Bioinformatics to identify such casual and deleterious nsSNPs in GC-associated genes (n=40). Using powerful computational algorithms, a total of 11,359 nsSNPs of these 40 genes were extracted from the SNP (n=9,84,945) pool. Based on sequence and structural homology–based algorithms, only one nsSNP was selected for each GC-associated gene based on their likelihood of altering the protein’s function. Also, the damaging effect of each nsSNP on the corresponding protein structure was evaluated by drug binding analysis and structural energy comparison between the wild type and the mutant structure. Thereafter, primers have been designed and restriction enzymes were selected for experimental investigation into these nsSNPs. In addition, a comprehensive GC database named ‘GasCanBase’ has been developed which incorporates the results of this study as well as other relevant information such as mapping of both the wild type and the mutant structures onto 3D models of proteins, functional consequences of damaging nsSNPs to domains and motifs, prioritization of functions and scrutinizing the gene network. This GasCanBase can be proven as a valuable resource to be exploited for future experimental research and development of better therapeutics and identification of novel biomarker(s) for gastric cancer.