TPD52 promotes the proliferation and metastasis of gastric cancer cells.

Introduction
Currently, according to the data released by the International Agency for Research on Cancer, cancer has been a major public health problem worldwide, with its morbidity and mortality rates increasing year by year and gradually becoming the number one killer threatening human health [1–3]. However, with a deeper understanding of the mechanisms of tumorigenesis and progression, we have opened up more options to fight cancer. In addition to conventional chemotherapy and targeted therapies, immunotherapy has emerged as an emerging treatment [4–6]. For example, immune checkpoint inhibitors (ICIs: anti-programmed death-1, anti-PD-1; anti-programmed death-ligand 1, anti-PD-L1; anti-cytotoxic T-lymphocyte-associated protein 4, anti-CTLA-4) can restore the efficacy of T cells and thus enhance the anti-tumor immunological response, which is one of the most crucial breakthroughs in the field of oncology, and has now become a powerful strategy for anti-tumor therapies [7–11]. However, despite the great success of ICIs, a considerable number of tumor patients do not benefit from immunotherapy [5, 12–14]. Therefore, new immune checkpoints have been explored that can target malignant tumors with better anti-tumor therapeutic efficiency, resulting in higher response rates and better treatment outcomes, dramatically improving the prognosis of tumor patients [7, 15–17].

As a tumor-associated gene, tumor protein D52 (TPD52) is located on chromosome 8, q21.13, with a sequence of 80034745–80231232, and it is commonly genotyped with three genotypes, TPD52L1 (D53, hD53, TPD53), TPD52L2 (D54, hD54, TPD54), and TPD52L3 (D55, NYD-SP25, TPD55). It has been reported that TPD52 is highly expressed in a wide range of tumors and plays an important modulating role in the proliferation, apoptosis, and metastasis of tumor cells. For example, Kang et al. found that the high expression of TPD52 was clearly observed during the G2/M transition in breast cancer cells, and thus, TPD52 could be involved in the modulation of the tumor cell cycle [18]. In addition, Roslan et al. similarly reported that in breast cancer, TPD52 chromosome 8q21 amplification was notably associated with ERBB2 amplification and poor patient prognosis [19]. Notably, Chen et al. reported that TPD52, a novel regulator of the LKB1-AMPK signaling pathway, regulates exosomal vesicle trafficking and extracellular secretion. Namely, it affects the metabolism of tumor cells by negatively regulating AMPK [20]. Not only that, Fan et al. revealed that TPD52 could interact with TPD52L1, which is commonly amplified in prostate cancer and is strongly associated with poor prognosis of tumor patients and is involved in prostate cancer progression as a prostate-specific and androgen-responsive gene [21]. Interestingly, Elizondo et al. discovered that TPD52-induced CD8+ T cells may facilitate suppression of the observed memory cytotoxic T-lymphocyte (CTLs) response and suppression of persistent tumor immunity and may represent a unique subpopulation of suppressor cells [22, 23]. In conclusion, TPD52 is an oncogenic tumor auto-protein in multiple tumors and is an effective target for anti-tumor therapeutic purposes in preclinical studies.

Materials and methods

Patient samples
Serum was collected from 60 pairs of GC patients and healthy donors between 20 November 2023 and 1 June 2024 at the Affiliated Suqian First People’s Hospital of Nanjing Medical University (Jiangsu, China). All patients were diagnosed by pathologists and did not undergo chemotherapy or radiotherapy before surgery. Approval for this research was granted by the Research Ethics Committee of the Affiliated Suqian First People’s Hospital of Nanjing Medical University, and informed consent was signed by the participants.

Data source

The Cancer Genome Atlas (TCGA) pan-cancer datasets were downloaded from the UCSC Cancer Genome Browser (https://xenabrowser.net/datapages/). The datasets contained 33 cancer types, including Adrenocortical carcinoma (ACC), Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon adenocarcinoma (COAD), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Acute Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Liver hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Mesothelioma (MESO), Ovarian serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), Pheochromocytoma and Paraganglioma (PCPG), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), Sarcoma (SARC), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT), Thyroid carcinoma (THCA), Thymoma (THYM), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), and Uveal Melanoma (UVM).”

TPD52 and immune cell infiltration
The Tumor Immune Estimation Resource (TIMER; http://timer.cistrome.org/), the ESTIMATE algorithm, and CIBERSORT were employed to study the correlation of TPD52 expression with markers of immune cell subpopulations, including CD8 + T cells, B cells, natural killer cells (NK cells), monocytes, myeloid-derived suppressor cells (MDSCs), neutrophils, endothelial cells, and fibroblasts.

Cell culture
AGS, HGC-27, MKN-45, and the GES-1 were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). All cell Lines were cultured in RPMI 1640 medium (Procell, Hubei, Wuhan, China), supplemented with 10% fetal bovine serum (Procell) and 1% penicillin and streptomycin. The culture medium was refreshed every two days, and the cells were Maintained in an incubator set at 37 °C with 5% CO2.

Cell transfection
The small interfering RNA (siRNA) of TPD52 (si-TPD52) were synthesized by GeneAdv (Huzhou, Zhejiang, China) and performed according to the manufacturer’s instructions.

CCK-8 and clone formation assays
Two days after cell transfection, 2000 cells were seeded per well in a 96-well plate. Once the cells adhered to the surface, 10 µL of CCK-8 reagent was added to each well. After a 2-hour incubation, absorbance at 450 nm was measured using a microplate reader. For the clone formation assays, 1000 transfected cells were seeded per well in a 6-well plate. The culture medium was replaced every 4 days to support normal cell growth for 2 weeks. Cells were then fixed with 1 mL of 4% paraformaldehyde for 12 h, stained with crystal violet for 10 min, washed with PBS, and photographed.

Transwell assay
The Transwell assay consisted of both migration and invasion assays. Cells were harvested 24–48 h post-transfection, and 50,000 cells were seeded per migration well, while 80,000 cells were seeded per invasion well. For migration, cells were uniformly seeded into a 24-well plate, while for invasion, cells were seeded into a 24-well plate pre-coated with Matrigel. The plates were then gently shaken and cultured at 37 °C with 5% CO2. After 48 h, cells were fixed with 4% paraformaldehyde for 12 h. After fixation, cells were stained with crystal violet for 10 min and photographed under a microscope. For quantification, three random fields from each sample were selected and counted using ImageJ software.

Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted from the sample using TRIzol (Invitrogen Life Technology, USA) and diluted with 30 µL of nuclease-free water per sample. The RNA concentration was measured using the nanodrop method. Complementary DNA (cDNA) was obtained from total RNA using the RevertAid First Strand cDNA Synthesis Kit (K1622, Thermo Scientific, USA) by selecting the samples required for the reverse transcription reaction. A Mastercycler nexus PCR machine (Eppendorf, Germany) was used, and the following conditions were set: one cycle of 10 s at 95 °C (pre-denaturation), followed by 40 cycles of 10 s each at 95 °C, 30 s at 60 °C, and 30 s at 70 °C. The qRT-PCR primer sequences are provided in the Supplementary Table. GAPDH was selected as the internal reference, and the 2–ΔΔCt method was applied to calculate the relative quantitative value of RNA.

Statistical analysis
Four of the clinical survival types, including overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI), were selected for this pan-cancer analysis. Kaplan-Meier (KM) survival curves, P-values, and hazard ratios (HR) at 95% confidence intervals (CIs) were derived by applying the Log-rank tests and Cox proportional hazards regression. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to investigate the potential signaling pathways related to TPD52 expression and biological functions in GC. Tumor Immune Dysfunction and Exclusion (TIDE) was employed to investigate the efficacy of the TPD52 and ICIs combination on tumor patient prognosis. R language was used for all analyses, and P < 0.05 was considered to indicate a statistically significant difference. Each experiment is repeated three times and is repeatable.

Results

Expression landscape of TPD52
To explore the expression profile of TPD52 in human tissues, we evaluated the expression of TPD52 in various human tissues using the Human Protein Atlas (HPA) dataset. As shown in Fig. 1A, TPD52 mRNA was enriched for expression in gastrointestinal tissues. Then, based on GTEx and TCGA datasets, we compared the expression of TPD52 in normal and tumor samples of different TCGA cancer types. The results are shown in Fig. 1B-C, we found that TPD52 was remarkably highly expressed in UCEC, BRCA, CESC, LUAD, ESCA, STES, COAD, PRAD, STAD, LUSC, LIHC, THCA, OV, PAAD, TGCT, UCS, ALL, LAML, PCPG, ACC, CHOL; In contrast, TPD52 expression was significantly lower in KIRP, KIPAN, KIRC, WT, KICH. Furthermore, we demonstrated through the GEO dataset that TPD52 is significantly overexpressed in STAD (Fig. 1D). Finally, the expression of TPD52 in tumor samples with different tumor stages indicated a trend of high expression in advanced stages (Fig. 1E).

Differential expression of TPD52 notably affects the prognosis of tumor patients
To investigate how TPD52 affects the prognosis of tumor patients, we used the TCGA dataset to analyze the relationship between TPD52 and the survival outcomes of tumor patients. Interestingly, aberrant expression of TPD52 significantly affected the prognosis of tumor patients. Cox regression modeling confirmed that high TPD52 expression in KIRC (HR = 0.78, 95% CI = 0.67–0.92, P = 3e-3), SKCM (HR = 0.91, 95% CI = 0.83-1.00, P = 0.05) was significantly associated with a better prognosis. In contrast, in LAML (HR = 1.22, 95% CI = 1.10–1.36, P = 2.3e-4), KIRP (HR = 1.64, 95% CI = 1.25–2.13, P = 2.8e-4), BRCA (HR = 1.26, 95% CI = 1.08–1.47, P = 3.8e-3), LIHC (HR = 1.21, 95% CI = 1.03–1.42, P = 0.02), UVM (HR = 1.25, 95% CI = 1.03–1.51, P = 0.02) and ALL (HR = 1.20, 95% CI = 1.01–1.41, P = 0.03), high expression of TPD52 was associated with a poorer prognosis (Fig. 2A; Table 1). Among them, in univariate analysis, high expression of TPD52 indicated a better prognosis in BLCA (HR = 1.391, 95% CI = 1.028–1.882, P = 0.0305) and SKCM (HR = 1.334, 95% CI = 1.107–1.749, P = 0.0338), in comparison to a poor prognosis in ESCA (HR = 0.521, 95% CI = 0.310–0.875, P = 0.0164), STAD (HR = 0.666, 95% CI = 0.463–0.959, P = 0.032), and KIPAN (HR = 0.679, 95% CI = 0.524–0.881, P = 0.0041) (Fig. 2B-G).

TPD52 expression is associated with immune infiltration in TME
To understand the relationship between TPD52 expression and immune cell infiltration in tumor microenvironment (TME), we estimated the proportion of immune cells using the TCGA pan-cancer dataset with multiple algorithms, including CIRBERSORT, TIMER, and MCPCOUNTER. The results showed that TPD52 expression was negatively correlated with the infiltration of some immune cells, including CD8+ T cells (P < 0.05) and B cells (P < 0.05). In contrast, there was a significant positive correlation between TPD52 expression and infiltration of Neutrophils and Endothelial cells (P < 0.05) (Figs. 3A-B and 4). Notably, TPD52 expression was significantly negatively correlated with the stromal score, immune component abundance, and tumor purity in ESCA, STAD, and READ, suggesting that TPD52 was highly involved in the immunological infiltration and multicomponent formation process in the tumors mentioned above (Fig. 3C). In conclusion, differences in TPD52 expression tend to indicate that TPD52 plays different roles in a wide range of different TMEs, thereby significantly affecting immune cell infiltration and the prognosis of tumor patients.

TPD52 is differentially expressed in tumor molecular and immune subtypes
Next, we explored the mRNA expression pattern of TPD52 in different molecular subtypes and immune subtypes. Surprisingly, in different molecular subtypes of ESCA, STAD, and COAD, such as Chromosomal instability (CIN), Epstein-Barr virus (EBV), Genetically stabilized (GS), Microsatellite instability (MSI), Hypermutation-SNV (HM-SNV) and Hypermutation-indel (HM-indel) [24, 25]. There was significant variability in the expression of TPD52, all of which were significantly highly expressed in HM-indel (Fig. 5A-C). Furthermore, relative to immune subtypes such as C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet) and C6 (TGF-β dominant) [25–28], the expression of TPD52 in ESCA, STAD and COAD was similarly significantly differentiated; namely, TPD52 was most highly expressed in the C2 subtype of STAD and COAD and the C4 subtype of ESCA (Fig. 5D-F). Interestingly, Wang et al. reported that in subtype analysis, the molecular subtypes HM-indel, HM-SNV, and the immune subtypes C2 and C6 were sensitive to cisplatin chemotherapy as well as immunotherapy [25]. Thus, the differential expression of TPD52 in different molecular subtypes and immune subtypes may partly explain why TPD52 plays different functions in the prognosis of different tumors.

Correlation analysis of TPD52 with checkpoint gene markers
To further explore the potential mechanisms of immune suppression of TPD52 signaling, we investigated the association of TPD52 mRNA expression with multiple immune checkpoint markers in different tumor types using the TCGA dataset, including Chemokine, Receptor, MHC, Immunity inhibitor, and Immunity stimulator. We found that TPD52 expression was positively correlated with the expression of BTLA, LAG3, PDCD1 (PD-1), CD274 (PD-L1), CTLA4, and TIGIT in a wide range of tumor types. Interestingly, in poor prognostic tumors, TPD52 seems positively correlated with TNFRSF4 and TNFRSF25 in ESCA and STAD. In contrast, this correlation seems weak or negative in good prognostic tumors (SKCM, BLCA) (Fig. 6). It is noteworthy that TPD52 was strongly positively related to CD276. Emerging evidence indicated that CD276 expression was negatively associated with CD8+ T cell infiltration in HNSC tissues and that blocking CD276 can greatly boost CD8+ T cell infiltration and anti-tumor immune responses [29]. That is, TPD52 may significantly affect immune cell infiltration and tumor patient prognosis by modulating the expression of checkpoint gene markers. Therefore, to explore the predictive value of TPD52 in ICIs-based immunotherapy, we used data from anti-PD-1, anti-PD-L1, and anti-CTLA-4 treatment cohorts to analyze the efficacy of TPD52 expression on immunotherapy. The results made it clear that high expression of TPD52 in SKCM (P = 0.00992) and BLCA (P = 0.00941) significantly prolonged the prognosis of tumor patients treated with ICIs (Fig. 7). Furthermore, in order to verify the correlation between TPD52 expression and the response rate to immunotherapy, we analyzed the correlation between TPD52 and PD-1/PD-L1 expression. To our surprise, we found that the expression of TPD52 was significantly negatively correlated with the expression of PD-1/PD-L1 (P < 0.05) (Figure S1). These findings comprehensively demonstrate that TPD52 may be a promising biomarker for assessing the prognosis or treatment outcome of tumor patients receiving immunotherapy with ICIs.

TPD52 can promote the proliferation, migration and invasion of GC cells
To explore whether TPD52 regulates GC progression, we first detected the expression level of TPD52 in GC cells. The results of qRT-PCR showed that the expression levels of TPD52 in AGS, HGC-27, and MKN-45 were significantly increased (Fig. 8A). We chose AGS and HGC-27, which have the highest expression levels of TPD52 for subsequent experiments. The results of qRT-PCR showed that the mRNA levels of TPD52 were significantly decreased after transfection with si-TPD52, and the effect of si-3 was the most obvious (Fig. 8B), so we chose si-3 for subsequent experiments. The results of CCK8 and colony formation experiments showed that the proliferation ability of GC cells was significantly decreased after the knockdown of TPD52 (Fig. 8C-E). The results of migration and invasion experiments showed that the migration and invasion ability of GC cells decreased significantly after the knockdown of TPD52 (Fig. 8F). The above results suggest that TPD52 can promote the proliferation, migration, and invasion of GC cells.

Expression level and diagnostic value of serum TPD52 in GC
We then investigated the expression level and diagnostic role of serum TPD52 in GC to confirm whether TPD52 can be utilized as an emerging diagnostic marker. We examined the expression of TPD52 in 60 pairs of serum from GC patients and healthy donors using qRT-PCR. The results showed smooth single-peak melting curves indicating high stability and specificity of TPD52 and significantly higher expression of TPD52 mRNA in serum from GC patients relative to healthy donors (P = 0.0061) (Fig. 9A-C). Moreover, pathologic images of GC tissues with TPD52 from the Human Protein Atlas (https://www.proteinatlas.org/) demonstrated that the protein expression of TPD52 in GC tissues was significantly higher than that in paracancerous tissues (Fig. 9D). Currently, carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 199, CA724, CA125, CA242, pepsinogen, and alpha-fetoprotein (AFP) are commonly and widely utilized for screening and auxiliary diagnosis of GC. However, their specificity and sensitivity are low, and it is also difficult to accurately assess the prognosis of GC, such as recurrence, metastasis, and survival after surgery. The ROC curves showed that the area under the curve of serum TPD52 by qRT-PCR was 0.653 (P = 0.04), and the areas under the curves of the clinical indicators CEA and CA199 were 0.713 (P = 0.004) and 0.647 (P = 0.049) (Fig. 9E). Furthermore, we analyzed the diagnostic efficacy of TPD52 combined with CEA and CA199 in GC. Compared with the indices alone, the combined diagnosis of these three indices had a higher AUC of 0.743 (P = 0.001) in differentiating between GC patients and healthy blood donors (Fig. 9F). In conclusion, the combined diagnosis of TPD52 with CEA and CA199 can significantly improve the diagnostic efficiency of GC.

Functional analysis of gene set enrichment involving TPD52 in GC
Finally, we explored the potential mechanism of TPD52 involved in GC development. Firstly, we analyzed the Top50 genes positively or negatively correlated with TPD52 expression in GC by GSEA and then predicted the molecular pathways in which TPD52 might be involved. We found that TPD52 was positively correlated with the CDH17 gene in GC (P < 0.05) (Fig. 10A-B). Feng et al. reported that CAR-T cells can effectively kill gastrointestinal tumor cells in a CDH17-dependent manner. Thus, targeting CDH17 as a novel tumor-associated antigen is expected to lead to the development of safer anti-tumor immunotherapies [30]. Moreover, functional enrichment analysis of TPD52-related genes suggested that TPD52 expression was significantly related to Oxidative phosphorylation and MAPK signaling pathways (Fig. 10C).

Discussion
Our current work elucidates the workflow of TPD52 pan-cancer analysis and delves into the role of TPD52 in multiple tumors. First of all, we assessed the expression of TPD52 in multiple tumor types by employing the TCGA dataset. We were surprised to find significant variability in the expression of TPD52 in tumor and normal tissues. Furthermore, the expression of TPD52 was significantly higher in ESCA, COAD, and STAD than in paracancerous tissues but significantly lower in KIRP, SCKM, and KICH. Secondly, the differential expression of TPD52 also significantly affected the prognosis of tumor patients, and representative K-M survival curves showed that high expression of TPD52 had a better prognosis in BLCA and SKCM; on the contrary, high expression of TPD52 had a worse prognosis in ESCA and STAD. Notably, poor prognosis may be associated with cancer-associated fibroblasts (CAFs) in the TME.CAFs have a variety of pro-tumorigenic functions and play a crucial role in drug resistance through several mechanisms, including extracellular matrix remodeling, production of growth factors, cytokines, and chemokines, and regulation of metabolism and angiogenesis. Thus, specific CAF subpopulations modulate the immunosuppressive microenvironment characterized by immune cell evasion [31–34].

Meanwhile, the expression of TPD52 in clinicopathologic stages and molecular immune subtypes was similarly significant and differentiated. TPD52 was most highly expressed in the C2 (IFN-γ dominant) subtypes of STAD and COAD and the C4 (lymphocyte depleted) subtype of ESCA. Wang et al. found that the genotyping of TPD52, TPD52L2 was up-regulated in colorectal cancer and was significantly associated with poor prognosis of colorectal cancer. Thus, TPD52L2 is expected to be a potentially valuable immunotherapy and efficacy assessment target [35]. In this research, the differential expression of TPD52 in different molecular and immune subtypes and the high involvement of TPD52 in a variety of different tumor microenvironments in the process of immune cell infiltration and multifunctional component formation, are results that suggest malignant biology and a complex prognostic value of TPD52 in pan-carcinomas, which may partially explain why TPD52 plays a play different functions in the prognosis of various tumors [26, 36].
Furthermore, the function of a gene is usually achieved through the cooperation of genes that co-express and interact with it, and TME plays a crucial role in tumor progression through complex molecular interactions [35, 37]. Therefore, by exploring the potential mechanism of immunosuppression of TPD52 signaling, we found that TPD52 was positively correlated with BTLA, LAG3, PDCD1 (PD-1), CD274 (PD-L1), CTLA4, and TIGIT, and negatively correlated with CXCR5, TGFB1, and TNFRSF8. Then, the functional enrichment analysis of TPD52-related genes showed that the expression of TPD52 in GC was significantly correlated with Oxidative phosphorylation, the MAPK signaling pathway, and Chemical carcinogenesis. These pathways are widely recognized for their important roles in modulating the malignant biological behaviors of tumors [38–41]. Cao et al. reported that the heterodimer composed of human epidermal growth factor receptor (HER) family members HER2-HER3 is regarded as the most effective mitogenic complex of HER. It plays a carcinogenic role in tumor by activating the MAPK signaling pathway [42, 43]. Notably, Yan et al. showed that TAK1 can lead to activation of the MAPK signaling pathway through selective RNA splicing, enhance hepatocellular carcinoma cell migration, accelerate tumorigenesis, and predict poor prognosis of hepatocellular carcinoma patients [38]. Xiong et al. reported that tipranavir can target and kill GC cells by activating the MAPK signaling pathway and the mitochondrial apoptosis pathway induced by IL24 [39]. In addition, Ma et al. reported that both glycolysis and Oxidative phosphorylation have been shown to control the production of IFN-γ in NK cells. Chimeric antigen receptor CAR-T cell therapy can promote the recruitment of dendritic cells and NK cells to tumors. This process is accompanied by a shift in CAR-T metabolism to Oxidative phosphorylation, effectively treating human tumors [40].
Overall, this research identified that TPD52 may be involved in tumor development, chemotherapy resistance, and recurrent metastasis by modulating the malignant biological behavior of tumor cells and infiltration of immune cells and is an independent diagnostic risk factor affecting the prognosis of tumor patients. More interestingly, we also discovered that TPD52 may have a synergistic effect with existing immune checkpoint inhibitors (anti-PD-1, anti-PD-L1, anti-CTLA-4), thus helping personalized diagnosis and treatment of tumor patients, and is expected to be an emerging biomarker. However, we have identified some limitations in this research. Firstly, the research utilized a limited number of gastric cancer cell lines, which rendered the research insufficiently rigorous. Additionally, it is necessary to further confirm the immunomodulatory role of TPD52 in the tumor microenvironment through in vivo functional experiments such as oxaliplatin, decitabine, or anti-PD-1 combination therapy [44–47]. In summary, the immunomodulatory mechanism of TPD52 in TME still requires further investigation to provide a more solid scientific basis for potential clinical therapeutic targets.

Conclusions
TPD52 was involved in multiple immune responses, influenced immune cell infiltration, and affected the malignant properties of multiple tumors. We found that TPD52 was significantly differentially expressed in gastrointestinal tissues through the TCGA dataset and could effectively influence the prognosis of tumor patients. Elevated TPD52 expression in GC cell lines promoted cell proliferation, migration, and invasion. qRT-PCR detection of TPD52 expression in serum in conjunction with the tumor markers CEA and CA199 could effectively improve the diagnosis of GC. Thus, it can be utilized as a potential clinical therapeutic target and biomarker to determine the prognosis and immune infiltration of GC patients.

Supplementary Information