Gastric cancer (stomach adenocarcinoma, STAD) RNAseq database and clinical information were downloaded from TCGA (https://cancergenome.nih.gov/). As of 30 June 2018, there were 443 samples with useable gastric cancer data in the TCGA database, of which 416 were mRNA chip or RNAseq data samples, and 32 pairs were RNAseq v2 paired sample data with pathological information. Biological coefficient of variation (BCV) was observed for quality control, and 26 paired samples with stable data were selected. Expression spectrum analysis was performed based on this paired sample RNAseq data.

AGS, SGC7901, MGC803, and BGC823 gastric cancer cell lines were purchased from Shanghai Genechem. RPMI 1640 culture medium, PBS, and fetal bovine serum were purchased from Hyclone (United States), and Opti-MEM culture medium was purchased from Gibco (United States). NCAPD3 overexpression lentivirus (LV-NCAPD3), blank control vector lentivirus (LV-NC1), shRNAs targeting the NCAPD3 gene (shRNA-NCAPD3-1/2/3), single guide RNAs (sgRNAs) targeting human NCAPD3 (sgRNA-NCAPD3-1/2/3) and their negative control (shRNA-NC and sgRNA-NC) were purchased from Shanghai Genechem. Supplementary Table S1 shows the sequences of shRNAs and sgRNAs. Knockout and Mutation Detection Kit were purchased from Shanghai Genesci Medical Technology. The TRIzol reagent was purchased from Shanghai Pufei Biotechnology. The reverse transcription kit was purchased from Promega (United States). PCR primers were synthesized by Shanghai Genechem. The MTT assay kit was purchased from Genview (United States). The AnnexinV-APC apoptosis assay kit was purchased from eBioscience (United States). Transwell chambers with matrigel were purchased from Corning (United States). NCAPD3, TNFAIP3, FADD, IRS1, SMAD3, CD44, and MAP1LC3B antibodies were purchased from Abcam (United Kingdom). The CDK6 antibody was purchased from CST (United States). The FLAG antibody was purchased from Sigma-Aldrich (Germany). The GAPDH antibody was purchased from Santa-Cruz Biotechnology (United States). The marker (catalogue number 26619) was purchased from Thermo (United States).

All clinical samples, including 67 pairs of gastric cancer and paracancerous tissues, were obtained from the tissue bank of Fujian Medical University Union Hospital. The application of archived cancer samples was approved by the Ethics Committee of Fujian Medical University Union Hospital (No. 2021WSJK042). In this study, no subjects received preoperative radiotherapy or chemotherapy. All resected specimens were stored at −80 °C for long-term storage. Written informed consents were obtained from all of patients or their guardians.

RPMI-1640/DMEM containing 10% fetal bovine serum and 1% penicillin and streptomycin solution was used for culture of the four gastric cancer cell lines (AGS, MGC803, BGC823, and SGC7901) in a 37 °C, 5% CO₂ incubator.

The lentivirus plasmid containing full-length NCAPD3 was used for NCAPD3 overexpression, and the empty plasmid was used as a negative control. Additionally, shRNA plasmids targeting NCAPD3 were used for NCAPD3 knockdown, and shRNA plasmids containing non-specific scrambled shRNA sequences were used as a negative control (shNCs). Lentiviruses were used to transfect knockdown plasmids into GC cells. The culture medium was changed 6 h after lentivirus vectors were transfected into GC cells. Fetal bovine serum (FBS, 10%) was added to the new culture medium, and total RNA and total protein were extracted after 48 h of culture.

The CRISPR/Cas9 system contains the LV-cas9-puro and LV-sgRNA-EGFP recombinant lentiviruses vectors. LV-cas9-puro carries a puromycin resistance gene and LV-sgRNA-EGFP contains an enhanced green fluorescent protein (EGFP) tag. The three single guide RNAs (sgRNAs) targeting human NCAPD3 were designed and synthesized by Shanghai Genechem. Sequencing was used to validate the sequences of the synthesized sgRNAs. The LV-cas9-puro and LV-sgRNA-EGFP vectors were constructed by Shanghai Genechem.

First, LV-cas9-puro lentiviruses were used to transfect AGS cells. Three days after transfection, a suitable amount of puromycin was used for 3 days of selection to obtain AGS cells with stable Cas9 expression. Following that, the three LV-sgRNA-EGFP lentiviruses were used to transfect Cas9-AGS cells. After 3 days of transfection, an inverted microscope was used to look for green fluorescent protein (GFP), and the percentage of green, fluorescent cells was calculated.

The Knockout and Mutation Detection Kit was used to detect gene knockout 5 days after LV-sgRNA-EGFP lentivirus infection according to the manufacturer’s instructions. In brief, the genomic DNA extraction kit was used to extract genomic DNA for PCR amplification according to the manufacturer’s instructions. The PCR conditions used were as follows: pre-denaturation at 94 °C for 90 s, followed by 40 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, and extension at 72 °C for 60 s, followed by final extension at 72 °C for 5 min. The PCR products were cooled to <40 °C. Then, 1 μL Cruiser™ was added to 3 μL PCR product, and the mixture was incubated at 45 °C for 20 min for enzymatic cleavage. Finally, 2% agarose gel electrophoresis was used to observe enzymatic cleavage. Supplementary Table S2 shows the PCR primer sequences.

Gastric cancer cells in the logarithmic growth phase were harvested and seeded at 1,500 cells/well in a 96-well plate. Triplicates were set up for every group, and the final volume of culture medium in each well was 100 µL. The cells were cultured under normal conditions. After 24, 48, 72, 96, and 120 h, 20 µL MTT (5 mg/mL) was added, and the 96-well plates were cultured normally for 4 h in an incubator. After that, the culture medium was carefully aspirated and 100 µL DMSO was added. The plates were incubated with shaking for 2–5 min before a microplate reader was used to read the optical density (OD) of each well at 490 nm.

Pre-cooled PBS was used to wash the cells before trypsin was used for digestion and cells were collected. Following that, 4 °C pre-cooled D-Hanks solution was used to wash the cells, and then 1 × binding buffer was added. Cells were collected by centrifugation before 200 µL 1 × binding buffer was used to resuspend the cell pellet. Next, 10 µL AnnexinV-APC was added for staining. After incubating at room temperature in the dark for 10–15 min, a flow cytometer was used to measure changes in apoptosis rate in the various groups. This procedure was carried out in triplicate for each sample.

After cells had undergone trypsin digestion, serum-free culture medium was used for washing. Following that, serum-free culture medium was used to resuspend cells and enumeration was carried out. A cell suspension of 10 × 10⁴ cells/200 μL was added to every Transwell chamber, and 650 μL of 30% FBS complete culture medium was added to the lower chamber, with triplicate wells for each group. The cells were incubated in a 37 °C incubator for 20 h. After 20 h of culture, 1 mL of 4% formaldehyde was added to every well, and room temperature fixation was carried out for 10 min. The cells were stained, the fixing solution was discarded, and 1× PBS was used to wash the cells once. Subsequently, 1 mL of 0.5% crystal violet solution was added to every well. At 30 min after staining, 1× PBS was used to wash the wells thrice. The plate was dried and observed. A cotton bud was used to gently remove cells that had not migrated in the Transwell chambers, and the chambers were observed under a 200 × microscope. Three random fields were selected per well for enumeration, and ImageJ was used for enumeration. Each expriment was done in triplicate.

Cells in the logarithmic growth phase from the various experimental groups were digested with trypsin before complete culture medium was used to resuspend the cells (plating density was determined based on cell size to achieve >90% confluency on the following day). Next, cells were cultured in 96-well plates in a 37 °C and 5% CO₂ incubator, with quintuplicate wells per group and 100 μL/well. On the following day, a wound making tool was used to gently scratch the center of the lower part of wells in the 96-well plates. PBS was used to gently wash the plates 2–3 times, and 0.5% FBS culture medium was added. Photographs were taken, and Celigo was used to analyze the migration area at 0 h and 8 h after scratching.

Trizol reagent was used to extract total RNA from the various groups, followed by reverse transcription to cDNA. Next, fluorescence quantitative PCR was carried out. The primer sequences for real-time PCR analysis are listed in Supplementary Table S3. The reaction conditions were pre-denaturation at 95 °C for 30 s followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Triplicate wells were set up for every sample, and the 2^−ΔΔCT method was used to analyze RT-PCR data. Relative mRNA expression changes were calculated (Bustin et al., 2009).

Cells in the logarithmic growth phase were collected and washed twice with PBS. The RIPA lysis buffer and protease inhibitor (or phosphatase inhibitor) mixture was used to extract total protein after transfection. The BCA assay kit was used for protein quantitation. Following that, SDS-PAGE was used to separate proteins (20–30 µg), and proteins were transferred to a PVDF membrane. The membrane was blocked with TBST buffer containing 5% skimmed milk at room temperature for 1 h. Next, the membrane was incubated with primary antibodies at 4 °C overnight. The membrane was washed four times with TBST followed by incubation with secondary antibody for 1.5 h. The membrane was washed four times with TBST, and the ECL reagent was used for luminescence, followed by development and imaging. Briefly, excess ECL solution was removed and membrane was put inside plastic wrap inside X-ray film cassette. Next, we expose membrane to the X-ray film in cassette in dark room for 1–2 min. We developed with the help of Carestream medical film processor using fixer and developer and then measure the band intensity using ImageJ.

Co-IP was performed using the protein A/G plus-agarose immunoprecipitation kit (Santa Cruz Biotechnology, United States), according to the manufacturer’s instructions (Liu et al., 2017). Briefly, the GC cells were lysed by RIPA lysis buffer, followed by total protein extraction. The concentration of proteins was detected using BCA assay kit according to previous studies (Liu et al., 2017; Zhang et al., 2021). Then cell lysate was incubated overnight incubation at 4 °C with IP antibody, followed by incubation with 20 μL protein A/G PLUS-Agarose for 1 h to form an immune complex. The complexes were washed twice with RIPA lysis buffer and resuspended in 6× loading buffer, denatured for 10 min. The suspensions were further analyzed by Western blotting.

Selected gastric cancer tissue samples and their paired paracancerous tissue samples, which were fixed with 4% formaldehyde embedded in paraffin blocks, were made into 3 μm thick continuous sections. The sections were adhered to poly-L-lysine-coated glass slides and dried in a 70 °C oven for 4 h. NCAPD3 monoclonal antibody was purchased from Abcam PLC (United Kingdom). SP immunohistochemistry assay kit and DAB substrate were purchased from Fuzhou Maixin Biotech. Staining was performed according to the manufacturer’s instructions, and PBS was used instead of the primary antibody for the negative control. The positive control was provided by the company. Xylene, absolute ethanol, and PBS were analytical grade.

Immunohistochemistry staining of NCAPD3 was scored by two independent experienced pathologists. For each sample, the score of staining intensity was assigned as follows: 0, negative staining; 1, weak staining (light yellow); 2, moderate staining (yellow brown) and 3, strong staining (brown) (Luo et al., 2020). And the percentage of stained cells was scored as 0 (<5% stained cells); 1 (5%–10% stained cells); 2 (11%–50% stained cells); 3 (51%–80% stained cells) and 4 (>80% stained cells). The final score was defined as staining score multiplied by proportion score (Guo et al., 2021). Final scores of 0–4 and 6–12 were considered to be low and high expression, respectively (Hou et al., 2014).

Twenty five-week-old male nude mice were randomized into two groups: the negative control group in which untreated MGC803 gastric cancer cells were inoculated, and the NCAPD3-knockdown group in which MGC803 gastric cancer cells that were transfected with NCAPD3-shRNA lentivirus vector were inoculated. In each mouse, 4.0 × 10⁶ cells were inoculated subcutaneously below the axilla. Tumor volume (volume = π/6×L×W×W, L: length, W: short axis, unit: mm³) was calculated 30 days after inoculation for one to two times a week. On day 38, nude mice were euthanized by cervical dislocation after intraperitoneal injection of 2% pentobarbital sodium anesthesia, and the tumors were harvested and weighed. In order to minimize the bias from individual differences, the maximum and minimum mice in each group were removed from data analysis. All of the experimental protocols were approved by the Institutional Animal Care and Use Committee of Fujian Medical University (No. 2021-8CAARM125), and the animal experiments were conducted in Animal Center of Fujian Medical University.

In order to obtain the NCAPD3-regulated gene expression spectrum, AGS cells were transfected with shNCAPD3 lentiviruses and control vector to construct AGS-KD and AGS-NC cells. Next, qPCR was used to validate the efficiency of RNA interference. The Trizol reagent was used to extract total RNA for an RNA quality test. The quality standards were: NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, United States), 1.7 < A260/A280 < 2.2; Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, United States), RIN ≥7.0, 28S/18S > 0.7. The 3′ IVT Plus Kit (Affymetrix, Santa Clara, CA, United States), a reverse transcription kit, was used for labeling of RNA that passed the quality test according to the manufacturer’s instructions. The labeled RNAs were fragmented and hybridized. The Affymetrix GeneChip PrimeView human gene expression array was used for testing. The selection criteria for significantly differentially expressed genes were |Fold Change| ≥ 2.0 and FDR <0.05. The experiment was completed in Shanghai Genechem. GeneChip Scanner 3000 (Affymetrix) was used for data analysis.

In order to examine changes in overall biological processes and pathways after NCAPD3 knockdown, GSEA was carried out on expression data. First, the probe group with coefficient of variation >25% in the NCAPD3 knockdown group and control group were removed to obtain the filtered expression matrix. The filtered overall expression matrix was inputted into the GSEA. The MSigDB database was used for enrichment analysis of the background gene set, which is a combination of various gene sets, such as canonical pathway, cellular component, immunologic signatures, oncogenic signature, and transcription factor. The GSEA parameter settings were as follows: the permutation type was gene set, and “control vs. knockdown” was used for enrichment analysis. Therefore, the normalized enrichment score (NES) < 0 represents the degree of pathway enrichment of genes that were ranked in front of changes in the knockdown group. Default parameters were used for the other settings. FDR <0.05 was considered to indicate significant enrichment.

The Ingenuity Pathway Analysis (IPA: Ingenuity Systems; www.ingenuity.com; Redwood City, CA, United States) database was used for bioinformatics analysis of differentially expressed genes. Canonical pathway analysis was carried out by comparison of differentially expressed genes and pathways containing these genes, and comparison significance (p < 0.05) was calculated to determine which pathways contained differentially expressed genes. Following that, the upstream and downstream regulatory factors of differentially expressed genes were compared. A Z-score ≥2 means that the pathway is significantly activated, whereas a Z-score ≤ −2 means that the pathway is significantly inhibited. The activation Z-score algorithm was used to analyze upstream regulatory factors to predict activation or inhibition of upstream regulatory factors. Disease and functional analysis was carried out according to IPA internal algorithm and standards. A Z-score ≥2 means that the disease or function is significantly activated, whereas a Z-score ≤ −2 means that the disease or function is significantly inhibited. Network map analysis was used to present the relationship between the disease and the differentially expressed gene. The Consistency Score is a measurement of the consistency and dense connection of causality between upstream regulatory factors and disease or function. The higher the Consistency Score, the more accurate the regulatory effect results. Therefore, IPA results were used to interpret and visualize interactions between upstream and downstream factors and global signal transduction.

Excel 2016 was used for data processing, SPSS 22.0 was used for statistical analysis, GraphPad prism 6 was used for plotting of statistical graphs, and ImageJ was used for measurement of the migration area. Statistical description of quantitative data (mean ± standard deviation) was carried out. One-way ANOVA or t-test was used for comparison of inter-group differences. Qualitative data were described using ratios, and Chi-square test or Fisher’s exact probability test were used to compare differences. A difference with p < 0.05 was considered to be statistically significant.

Deep mining of gastric cancer and paracancerous tissue gene sequences in The Cancer Genome Atlas (TCGA) was performed, and RNA sequence data of gastric cancer and paracancerous tissues in the TCGA database were analyzed. The result showed that NCAPD3 expression level in gastric cancer tissues is significantly higher than paired paracancerous tissues, with a log2-fold change of 2.242 and FDR <0.01 (Figure 1A). In 26 pairs of sequencing samples, the expression level of NACPAD3 in 17 pairs of gastric cancer tissues was significantly higher than paired paracancerous tissues (Figure 1B).

We then detected NCAPD3 protein expression in 67 pairs of gastric cancer tissues and paracancerous tissues with immunohistochemistry. Representative IHC images posited that NCAPD3 protein was primarily expressed in the cytoplasm of gastric cancer cells (Figure 1C). The analysis results of Chi-square test revealed that The NCAPD3-positive expression rate in 67 gastric cancer patients was significantly higher than in paracancerous normal gastric mucosal tissues (p = 0.004) (Figure 1D). According to the relevant NCAPD3 expression in tumor tissues, 67 gastric cancer patients were classified into two groups: low-expression group (n = 34) and high-expression group (n = 33), the correlation between NCAPD3 expression and clinicopathological characteristics in gastric cancer patients were shown in Table 1. Expression of NCAPD3 was identified to be correlated with invasion depth (p = 0.009), lymph node metastases (p = 0.029) and pathological TNM stage (p < 0.001) but not with other clinicopathological characteristics in patients with gastric cancer. As for prognosis, the patients with NCAPD3-high expression had a significantly worse overall survival (OS) and a high risk of relapse than the NCAPD3-low patients. The overall survival rate for the NCAPD3-low patients was 85.3 percent, as compared with 60.6 percent for the patients with high NCAPD3 expression (p = 0.0245); the five-year disease-free survival (DFS) rate was 85.3 percent, as compared with 63.6 percent (p = 0.0207 by the log-rank test), respectively (Figures 1E, F).

To examine the function of NCAPD3 in gastric cancer, first, we measured NCAPD3 expression in four cancer-derived GC cell lines (AGS, BGC823, MGC803, and SGC7901) by RT-qPCR and found that the expressions of NCAPD3 mRNA in these four gastric cancer were all high-abundence (Supplementary Material 1). Additionally, AGS cells had a lower level of NCAPD3 mRNA relative to the other cells, while MGC803 cells expressed NCAPD3 at a much higher level (Figure 2A). Therefore, AGS and MGC803 cells were selected for the subsequent studies.

Then we established the stable AGS NCAPD3 overexpression cell line by lentiviral transduction. As shown in Figure 2B, NCAPD3 mRNA and protein expressions were significantly increased in cells transfected with NCAPD3 virus (oeNCAPD3) compared with negative control virus (NC). Proliferative and apoptosis abilities were evaluated using MTT assays and flow cytometry assays, respectively. Indeed, the overexpression of NCAPD3 enhanced the viability and reduced the apoptosis of GC cells (Figures 2C, D). The potentials of migration and invasion were also promoted by overexpression of NCAPDS in AGS cells. These effects were validated by Transwell invasion assays and scratch assays (Figures 2E, F).

To further determine the oncogenic properties of NCAPD3 in GC, we used specific short hairpin RNAs (shRNAs) to transfect AGS and MGC803 cells. As demonstrated by RT-qPCR and Western blotting assays, NCAPD3 expression was successfully suppressed in both shNCAPD3-treated AGS and MGC 803 cells (Figure 3A). Then the Effects of NCAPD3 knockdown on malignant biological behaviors of gastric cancer cells were detected by MTT, flow cytometry assays, Transwell invasion assays and scratch assays. As shown in Figures 3B–E, NCAPD3 silencing significantly inhibited proliferation, enhanced apoptosis, and impaired the invasion and migration abilities of GC cells. The results were further verified by the CRISPR/Cas9 technology, which was used to construct AGS cell with stable NCAPD3 knockout (Supplementary Figure S1).

MGC803 gastric cancer cells that were transfected with the empty lentivirus vector (negative control) and NCAPD3-shRNA lentivirus vector were inoculated subcutaneously in nude mice, and tumorigenicity was observed (Figure 4). The growth curve of subcutaneous tumor xenografts was plotted based on xenograft volume at different time points. Results showed that the growth speed of the NCAPD3 knockdown group was significantly lower than the negative control group (p < 0.05).

Tumors were harvested and weighed after nude mice were euthanized. Results showed that the tumor weight in the NCAPD3 knockdown group was significantly lower than the negative control group (p < 0.05). The tumor weight inhibition rate was 70.21%.

In order to study the molecular mechanisms of malignant biological behavior in gastric cancer regulated by NCAPD3, GeneChip PrimeView human gene expression array was used to detect differentially expressed genes (DEGs) before and after NCAPD3 knockdown. The results showed that, after NCAPD3 expression was inhibited, there were significant differences in the expression levels of 1,411 genes in AGS cells, among which 562 genes were upregulated and 849 genes were downregulated after NCAPD3 silencing (Figures 5A, B). Hierarchical clustering was used to analyze the expression of differentially expressed genes. Results showed that there were significant changes in gene expression upregulation and downregulation (Figure 5C).

In order to examine changes in overall biological processes and pathways after NCAPD3 knockdown, GSEA analysis was performed on the cellular component, canonical pathway, immunologic signatures, oncogenic signature, and transcription factor (Figures 5D, E). Compared with the non-knockdown group, five significant enrichment results were obtained in the cellular component GSEA after NCAPD3 knockdown, among which cell surface sets were significantly enriched. A total of 11 significant signaling pathways were obtained from the canonical pathway analysis, such as signaling by receptor tyrosine kinases, cytokine signaling in the immune system, and cytokine-cytokine receptor interaction. Immunologic signatures enrichment analysis showed that 119 immune gene sets were significantly enriched in the NCAPD3 knockdown group, showing that NCAPD3 significantly activates immune-related gene sets; this result is consistent with the canonical pathway enrichment analysis results. Twenty-six significantly enriched oncogene sets were obtained from oncogenic signature enrichment analysis, and the top five significantly enriched gene sets include ALK, RB, BMI1, P53, and MEK. Thirteen significant enrichment results were obtained from transcription factor analysis. Figure 5D presents the top five significantly enriched transcription factor gene sets, such as P53, FOXO4, and FOXO1. These results showed that NCAPD3 knockdown affects many signaling pathways and biological processes. Furthermore, NCAPD3 can affect the expression of oncogenes and transcription factors to affect disease occurrence and development.

The IPA platform was used to analyze differentially expressed genes in the negative control (NC) group and NCAPD3 knockdown (KD) group. The canonical pathway analysis results showed that there were 10 signaling pathways that showed differences after NCAPD3 knockdown in the KD group. Pathways that were mainly activated were the p53 signaling, G1/S cell cycle checkpoint, and STAT3 pathways. Pathways that were inhibited mainly included the phosphatidylglycerol biosynthesis II (non-plastidic), IGF-1 signaling, and superpathway of cholesterol biosynthesis, among which the cholesterol biosynthesis pathway was significantly inhibited, with a Z-score of −2.449 (Figure 6A).

In order to determine the major upstream regulatory factors and explain differential gene expression between the two groups, upstream regulatory factor analysis was carried out. This analysis determined the number of known targets for each regulatory factor, and their direction of change was compared with that found in past papers. One hundred forty-five upstream regulators (including transcription factors, small RNAs, cytokines, kinases, and chemical molecules and drugs) were identified as activators, and 45 upstream regulators were predicted as suppressors. The prediction results for top 10 activated or inhibited upstream regulatory factors were listed (Table 2), among which the transcription factor NUPR1 was predicted to be significantly activated (Z-score = 2.252). Then an interaction network between the NUPR1 upstream regulatory factor and its downstream factors was constructed (Figure 6B). Among these genes, ELL2, NDRG1, HBEGF, SAT1, NR1D1, PDK1, and DDIT3 were elevated, whereas PARP9, NAPEPLD, MKKS, PIK3R1, MAGI1, HDAC8, ASPM were decreased (Figure 6B).

In the disease and functional analysis, the first analysis showed significantly enriched differentially expressed genes in disease and function, which were mainly enriched in gastrointestinal disease, cell death and survival, cell cycle, cellular development, cellular growth and proliferation, and cellular movement (Figure 6C). This shows that differentially expressed genes that are regulated by inhibiting NCAPD3 expression mainly participate in cell growth, development, and survival. Regulatory effect analysis was used to investigate differentially expressed genes participating in different cellular functions. Results showed that the regulatory factors CRNDE, EDN1, JUNB, and MAP2K1/2, ZFP36 mainly regulate differentially expressed genes (VEGFA, EGFR, PLAUR) to regulate invasion of cells, invasion of tumor, and microtubule dynamics. For example, the regulatory factor CRNDE regulates the EGFR gene to regulate invasion of cells, invasion of tumor, and microtubule dynamics. The EDI regulatory factor regulates invasion of cells, invasion of tumor, and microtubule dynamics through the PLAUR gene (Figure 6D).

In vivo and in vitro experiments showed that NCAPD3 loss can significantly inhibit gastric cancer cell proliferation, invasion, and migration, and promote apoptosis. In order to further study the effector molecular mechanisms, the NCAPD3 knockdown differentially expressed gene data in this study were combined with literature data, and IPA was employed to construct a molecular regulatory network map of tumor cell proliferation and apoptosis. In the molecular regulatory network map in which NCAPD3 knockdown inhibits tumor cell proliferation and promotes tumor cell apoptosis, genes related to tumor cell proliferation pathways showed overall decrease or downregulation, among which CCND1, MYC, ESR1 were significantly decreased and their downstream genes CDK6 and IRS1 were significantly downregulated. Therefore, NCAPD3 knockdown may inhibit CCND1, MYC, and ESR1 activity to downregulate CDK6 and IRS1 expression, thereby inhibiting the proliferation of gastric cancer cells. Additionally, genes associated with tumor cell apoptosis pathways showed overall activation, among which IRF7, DDIT3, and HBEGF were significantly activated. Therefore, NCAPD3 knockdown may activate IRF7, DDIT3, and HBEGF expression to promote gastric cancer cell apoptosis (Figure 7).

In order to further validate whether NCAPD3 directly targets CCND1, ESR1, and MYC to regulate gastric cancer cell proliferation, Co-IP experiments were conducted to examine if NCAPD3 directly interacts with CCND1, ESR1, and MYC. The results showed that NCAPD3 NCAPD3 protein may not directly interact with MYC but may directly interact with CCND1 and ESR1 (Figure 8A).

Based on the interaction network analysis, 29 differentially expressed genes (DEGs) of interest were selected for qPCR validation. The validation results showed that the gene chip variation trends of 22 genes were consistent with PCR variation trends (Table 3). From these 22 genes, three (CD44, CDK6, and IRS1) were selected for further WB validation (Figure 8B). The protein expression levels of CD44, CDK6, and IRS1 were downregulated by 32.30%, 84.91%, and 40.50%, respectively. Based on the above experimental and bioinformatics analysis results, the target gene NCAPD3 may upregulate CD44, CDK6, and IRS1 in AGS gastric cancer cells to carry out its effects. In summary, the NCAPD3 protein may target CCND1 or ESR1 to downregulate downstream factors such as CDK6 and IRSI to inhibit gastric cancer cell proliferation.