## 3 Results
### Variant Effects Analysis Using IPA  
We identified 213 unique CNVs in the control group, 420 unique CNVs in the GC group, and 225 common variants between the two groups. The total number of unique variants in the control and GC groups and the common variants are shown in a Venn diagram (Figure 1). The list of unique CNVs found in the control and patient groups and the common CNVs between these groups are provided in Supplementary Tables S1-S3. apoptosis, T-cell exhaustion, and Type 1 regulatory T-cell (Tr1 cells) specialization were activated in GC (Figure 3a) but not in the healthy control group. Cellular functions, such as T-cell activation, effector functions of T cells, and T-cell proliferation, were inhibited in the GC group compared to the control group (Figure 3b). The mechanism of PD-1/PD-L1 immune checkpoint inhibition of T-cell activation by the tumor cell and the effectiveness of anti PD-1 antibodies in restoring T-cell activation and subsequent immune attack on the tumor cell, leading to its death, is depicted in Figure 3c. Figure 1: Venn diagram created based on the comparison module in IPA to find the unique and common CNVs in the control (**A**) and GC (**B**) groups. Figure 2: The top canonical pathways significantly affected by CNVs in GC group based on activation Z-score. The PD-1/PD-L1 cancer immunotherapy signaling pathway was significantly activated by CNVs in the GC patients. The height of the bars indicates how significant the overlap is with the known targets in one’s dataset, as calculated by [PERSON]’s exact test. \(p\leq 0.05\) was used as a cut-off to compute the Benjamini–Hochberg (B-H) correction. The color of the bar indicates whether the canonical pathway is expected to be activated (orange) or inhibited (blue). This is calculated using a z-score compared to an idealized activated pattern for the pathway. Figure 3: _Cont._  
Using the \"Diseases and Functions\" tab, a bar graph and a categorically organized heatmap were generated to investigate groups of ontologically related diseases and biofunctions predicted to be increased (orange) or decreased (blue) in control and GC groups (Figure 4). We found that cancer, gastrointestinal diseases, and organismal injury and abnormalities were the three diseases or disorders most affected in the GC group (Figure 4a). The least affected diseases and disorders, including neurological disease, dermatological disease and conditions, endocrine system disorders, etc., are shown in Figure 4a. The heatmap-based activation Z-score algorithm of IPA showed that diseases such as cancer, organismal injury and abnormalities, gene expression, cell death, and survival were significantly inhibited compared to the GC group (Figure 4b). In the GC group, disease categories such as cancer, incidence of cancer, and neoplasia were significantly activated based on the Z-score (Figure 4c). The disease and biofunctions activated by the CNVs unique to the control group included cellular development, cellular maintenance, cellular assembly and organization, cell morphology, and tissue development (Figure 4d). The diseases and biofunctions that were significantly activated by the CNVs common in the control and GC groups included cell development, cell movement, cell growth and development, cellular function, and cellular assembly (Figure 4e). The diseases and biofunctions significantly affected by the CNVs unique to the GC group included cancer, solid tumor, abdominal cancer, cell death, and survival (Figure 4f). The diseases and biofunctions affected by CNVs in the control and GC patients are provided in Supplementary Figures S2-S6 and Supplementary Tables S4-S10. Additionally, the meta-analysis of high-throughput RNAseq data obtained from GC showed that PD-1/PD-L1 pathway was significantly upregulated compared to normal control (Figures S7-S9). Figure 3: (**a**) Effects of CNVs on the PD-1/PD-L1 checkpoint pathway in the healthy control group based on the Molecule Activity Predictor (MAP) tool in IPA.