Identification of cuproptosis-related gene CDKN2A as a molecular diagnostic target in gastric carcinoma based on transcriptomic data

Identification of cuproptosis-related gene CDKN2A as a molecular diagnostic target in gastric carcinoma based on transcriptomic data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of cuproptosis-related gene CDKN2A as a molecular diagnostic target in gastric carcinoma based on transcriptomic data Guo Chen, Wenli Zhang, Di Wei, Zeng Li, Jun Lu, Wenying Liu, Lei Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5756178/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gastric Carcinoma (GC) is the world’s third-highest cause of death by cancer. Cuproptosis is a newly discovered programmed cell death dependent on overload copper-induced mitochondrial respiration dysregulation. We speculated this regulatory cell death (RCD) mechanism might serve as a potential prognostic predictors and therapy for GC patients. The expression and mutation patterns of 12 cuproptosis-related genes were systematically evaluated in the GC training group. Through unsupervised clustering analysis and developing a cuproptosis-related scoring system, we further explored the relationship between cuproptosis and GC progression, prognosis, immune cell infiltration, and immunotherapy. Molecular docking was used to screen the drugs which had the best binding affinity with cuproptosis target proteins. CCK8, invasion and migration assay were used to explore the anticancer effect of the drug which binging to the cuproptosis target protein and then verify it in nudes. Our results revealed three genes (CDKN2A, GLS, and MTF1) have predictive value for the prognosis. Patients from low-CRG score group were characterized by higher immune cell infiltration, immune checkpoint expression. Via molecular docking, CCK8, invasion and migration assay, saquinavir had the best binding affinity with CDKN2A,which could inhibit the proliferation, invasion, and migration of gastric carcinoma cells in vitro. Ani-mal experiment showed that saquinavir treated group had smaller volume and weight tumors. Our results confirmed the essential function of cuproptosis in regulating the progression, prognosis, immune cell infiltration, and response to immunotherapy. CDKN2A as the potential target for gastric carcinoma showed the anticancer effect in vitro and vivo. cuproptosis gastric carcinoma (GC) tumor microenvironment biomarkers prognostic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction According to recent reports, worldwide, GC which was responsible worldwide for one in 13 deaths, was the fifth most common cancer in 2020 [1], and was the third leading cause of cancer-related death [2]. GC is a heterogeneous disease, and the lack of GC screening techniques hampers its early diagnosis and leads to poor five-year survival rates [3]. Notably, the prognosis of GC has not improved in recent years [1]. Despite the significant progress in our understanding of the molecular causes of GC, the exact mechanism underlying its development remain unknown. A deeper understanding of novel gene candidates is crucial for better understanding the molecular mechanism of pathogenesis, which would improve patient survival [3]. Therefore, there is an urgent need to identify more convincing and suitable biomarkers for GC. Copper (Cu) is an essential cofactor for all organisms. However, it becomes toxic if its concentration exceeds a threshold that is maintained by evolutionarily conserved homeostatic mechanism [4]. Tsvetkov et al. [5] recently identified a novel Cu-induced cell death pathway known as cuproptosis, which was distinct from all other known mechanisms of RCD, including apoptosis, ferroptosis, pyroptosis, and necroptosis [6]. The investigators found that Cu-induced cell death is mediated by an ancient mechanism, namely protein lipoylation. Previous research has established a link between Cu homeostasis and various types of cancer [7–10]. Despite this, there remains the gap in our understanding of the connection between the recently described process of cellular proptosis and the development of GC, as well as its influence on the tumor immune microenvironment and response to immunotherapy. Consequently, it is essential to investigate the biological and pathological activities related to cuproptosis, clarify the mechanisms by which cuproptosis affects GC progression, and identify potential targets for its diagnosis and effective treatment. Such insights are vital for the early detection, diagnosis, and management of GC. In our present study, we intended to comprehensively investigate the molecular alterations and clinical relevance of cuproptosis-related genes (CRGs) in GC through accessing and analysing a public health database. Next, we explored the anticancer effects of CRGs as potential targets both in vivo and in vitro. Our analysis highlights the importance of CRGs in GC development and lays the foundation for the therapeutic application of cuproptosis regulators in the treatment of GC. 2 Materials and Methods 2.1 Data preprocessing RNA sequencing data of 443 patients diagnosed with STAD were extracted from TCGA dataset, available at https://portal.gdc.cancer.gov/projects/TCGA-STAD . Normal tissue samples were procured from the GTEx data portal at https://www.gtexportal.org/home/datasets . To further confirm the expression levels of CRGs, we downloaded and utilized datasets GSE19826 and GSE29272 from the Gene Expression Omnibus database, accessible at https://www.ncbi.nlm.nih.gov/geo/ . The protein expression level of CRGs in tumors compared to normal tissue was obtained from the Human Protein Atlas database ( https://www.proteinatlas.org/ ). The web-based resource at http://ualcan.path.uab.edu/analysis.html , known for its extensive capabilities, was employed to evaluate the expression patterns and prognostic significance of DEGs. A Student’s t-test was utilized to calculate the corresponding p-values. We used the cBioPortal ( www.cbioportal.org ), a comprehensive web resource, can visualize and analyze multidimensional cancer genomics data, to analyze the mutation and CNV data of all CRGs in 478 total GC samples (TCGA, PanCancer Atlas). 2.2 Functional enrichment analysis of CRGs To functionally annotate CRGs identified by the aforementioned comparison groups, annotation and visualization of GO terms was used by GO enrichment analysis and metascape. The overlaps between differently expressed gene lists of GO terms were performed by enrichment analysis circle diagram. The DEGs were then introduced into the FunRich (functional enrichment analysis tool) ( http://www.funrich.org/ ) for KEGG pathway analysis. To further elucidate the functions of the potential targets, we conducted a functional enrichment analysis. This involved examining the GO annotations for the targets to identify their biological processes and also enriching for KEGG pathways. For visual representation, boxplots were created using the ggplot2 package in R software. 2.3 GENEMANIA GENEMANIA ( http://genemania.org/search/ ) was used to construct a gene–gene interaction net-work for DEGs to evaluate the functions of these genes. 2.4 Kaplan–Meier plotter database analysis The Kaplan–Meier Plotter database ( http://kmplot.com/analysis/ ), a comprehensive online platform offering survival analysis of 54,675 genes in 21 various tumor types. We executed analyses for overall survival (OS), free-progression survival (FPS) and post-progression survival (PPS) of CRGs in GC. 2.5 CRGs mutations and prognosis cBioPortal ( https://www.cbioportal.org ) can be used to explore, visualize, and analyze multidimensional cancer genome data [30]. 2.6 Analysis of correlation with immune infiltration Tumor Immune Estimation Resource (TIMER http://timer.comp-genomics.org/ ) is a data source for comprehensive analysis of tumor-infiltrating immune cells. The Immune infiltration cells include Neutrophils, Macrophages, B cells, CD4 + T cells, CD8 + T cells and Dendritic cells, etc. Which can effectively predict the prognosis of patients. We have studied the correlation between CRGs expression and these immune infiltrating cells by employing TIMER. 2.7 RNA extraction and reverse transcription-quantitative PCR (RT-qPCR) Total RNA was extracted with Trizol reagent (Invitrogen, USA), and then reverse transcription was performed using the HiScript II Q RT SuperMix kit for qPCR (Vazyme, R223) according to the manufacturer’s instructions. qPCR performed using the ChamQ SYBR qPCR Master Mix kit (Vazyme, Q311) in accordance with the manufacturer’s instructions. The primers for all PCR primers, and their internal reference sequences were designed using Primer 5. The thermocycling protocol consisted of an initial denaturation step at 95°C for 10 min, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 min. All amplifications and detections were performed using a real-time PCR machine (Roche, LightCycler®96). The expression level of each target gene was determined using β-actin as the normalization control. Relative gene expression was calculated using the 2 −ΔΔCt method [7]. Every experiment was repeated at least three times independently. The following primers were used: CDKN2A Forward, 5’-3’: GATCCAGGTGGGTAGAAGGTC and reverse 5’-3’: CCCCTGCAAACTTCGTCCT, GLS forward 5’-3’ AGGGTCTGTTACCTAGCTTGG and reverse 5’-3’: ACGTTCGCAATCCTGTAGATTT, MTF1 forward 5’-3’: CACAGTCCAGACAACAACATCA and reverse 5’-3’: GCACCAGTCCGTTTTTATCCAC, β-Actin forward 5’-3’: CATGTACGTTGCTATCCAGGC, and reverse 5’-3’: CTCCTTAATGTCACGCACGAT. 2.8 Western blotting Total protein was extracted using RIPA Lysis Buffer (Beyotime, Guangzhou, China), and the protein concentration was determined using a BCA protein assay kit (Pierce, Rockford, USA) according to the manufacturer’s instructions. Then, a western blot assay was performed as previously described. The primary antibodies used are listed in The following antibodies were used: CDKN2A (1:1000; Abways, CY8312) GLS (1:1000; Aways, CY5719), MTF1(1:1000; Proteintech,25383-1-AP) and β-actin (1:4000, Proteintech,20536-1-AP). Subsequently, the membranes were immunoblotted with secondary antibody (1:10000, Proteintech, SA00001-2). The protein expression was visualized by enhanced chemiluminescence (Millipore, USA). Images were captured using a ChemiDoc XRS imaging system (Bio-Rad, USA), and Quantity One image software was used for the densitometry analysis of each band; β-actin was used as an internal loading control. 2.9 Molecular docking In NCGC Pharmaceutical Collection database, 7929 FDA-approved drugs were selected for drug screening. Download the crystal structures numbered 7OZT (CDKN2A), 3voz (GLS), AF-Q14872-F1-model (MTFI) from the PDB database ( https://www.rcsb.org/ ). Using Discovery Studio 2019 software to prepare for molecular docking. The Discovery Studio Libdock program was used for molecular docking, and the scores were sorted according to Libdock score. The top 20 Libdock score compounds were selected for the next step of research. Using Pubchenm ( https://pubchem.ncbi.nlm.nih.gov/ ) download ligand structure database. AutoDock Vina software ( http://vina.scripps.edu/ ) was used to prepare ligands and proteins required for molecular docking. Prepare the ligands and proteins required for molecu-lar docking with AutoDock Vina software ( http://vina.scripps.edu/ ). Pyrx soft-ware ( https://pyrx.sourceforge.io/ ) was used for docking. 2.10 Cell proliferation capacity The cell proliferation capacity was evaluated using the CCK-8 assay. The NCI-N87 and MKN-45 cells were diluted to 5×10 3 per well before being plated into a 96-well plate, The cells were incubated for 24h at 37°C in an atmosphere comprising 5% CO 2 . various concentrations of saquinavir, folic acid, GHRP were added and cultivated with cells for an additional 48 h. The CCK-8 reaction solution was added according to the instructions, and the OD at 450 nm (denoted as A450) was measured. Each experimental condition was replicated three times to ensure reliable results. All experiments were performed with mycoplasma-free cells. 2.11 Transwell migration and invasion assays The transwell chamber (Millipore, USA) was used for migration and invasion assays. To evaluate cell invasion, the chamber was pre-coated with Matrigel (Corning NY, USA). Matrigel was diluted in pre-cooled culture medium according to the manufacturer’s instructions. Then, cells (2.5 × 10 4 cells) were seeded in the upper chamber without serum, saquinavir was added into the upper chamber, while medium with 10% serum was added to the lower chamber for 48 h, then fixed with 4% paraformaldehyde for 10 min and stained with crystal violet at room temperature. The migration or invasion cells were photographed by a light microscope (Leica, Germany). Five random fields (400×) were select and the number of migration or invasion cells were counted. All samples were conducted with three repeats. The migration assay is the same with invasion assay excepting no matrigel was used. 2.12 Xenograft transplantation in vivo Specific-pathogen-free (SPF) 4-week-old male BALB/c-nu mice were purchased from Beijing Vital River Laboratory Animal Technology with SPF-grade rearing environment. Animals were housed in a 12/12 h of light/dark cycle at 22℃ with 45–55% humidity and adaptively provided with free access to water and food. The animal studies were approved (approval no. IACUC-20241372) by the Laboratory Animal Welfare and Ethics Committee of the Forth Military Medical University (Xi’an, China). Cells of the NCI-N87 cells were adjusted to 5×10 6 and suspended in 200 µL PBS, followed by inoculation under the dorsal skin of the nude mice. The tumor size was recorded every three days, and the tumor volume was calculated according to the formula V = 0.5×a×b 2 . The experimental group was administered saquinavir via Intraperitoneal injection, at a dose of 600 mg/kg, in accordance with previous research for 2 weeks. The control group was administered PBS. All animals were sacrificed after 21 days, the tumors excised and weighed. The tumor tissue, the heart, liver, spleen, lung, and kidney were fixed in 4% paraformaldehyde, embedded in paraffin, and cut into 5 µm thick paraffin sections. 2.13 Statistical analysis The relationships between variables were assessed using either Spearman or Pearson correlation tests. To compare differences in gene expression between adjacent normal and tumor samples, the Wilcoxon test was employed. Survival data were analyzed using Kaplan-Meier estimations and univariate Cox proportional hazards regression models. R software (Version 4.1.2) was utilized for the statistical analysis of bioinformatics outcomes (with statistical significance set at P < 0.05). 3. Results 3.1 CRGs that exhibit varied expression levels in gastric tissue compared to normal biopsies As mentioned earlier, a set of 12 genes (FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, SLC31A1, and ATP7B) were identified as being linked to the process of cuproptosis [5]. To verify the role of these genes associated with cuproptosis in GC, we analyzed and compared the expression profiles of the 12 CRGs in both tumor and normal tissue samples, as obtained from the TCGA and GTEx databases. Our analysis included 470 samples of gastric tissue and 1809 samples of normal tissue. As a result, we observed that the expression levels of all 12 genes varied significantly between gastric and normal biopsies (Fig. 1 A, B; Table 1 ). Among these, the expression of 11 genes (FDX1, LIPT1, DLD, DLAT, PDHA1, MTF1, GLS, CDKN2A, SLC31A1 and ATP7B) was increased, whereas the expression of one gene (LIAS) remained unchanged in STAD samples (Fig. 1 A). Furthermore, we examined the relationships between the expression levels of various genes and discovered several strong correlations (Fig. 1 B). For example, a high positive correlation was observed between the expression of DLD and DLAT (r = 0.631, P < 0.001) (Fig. 1 C). Subsequently, we conducted a thorough examination of the molecular properties of the CRGs using the TCGA Pan Cancer Atlas. The results showed that the top five genes—CDKN2A, ATP7B, LIPT1, GLS and DLAT were altered in 17%, 6%, 5%, 5%, 4%, and 4% of the queried GC samples, respectively (Fig. 1 D). Additionally, the alteration frequency of tubular pathological type showed higher than other types (Fig. 1 E). 3.2 Functional enrichment and protein–protein interaction analysis of CRGs To demonstrate the biological functions of CRGs, relevant pathways were analyzed by GO and KEGG databases. The biological processes of the 10 CRGs mainly involved in the GO analysis were iron-sulfur cluster binding, transition metal ion transmembrane transporter activity, oxidoreductase activity, acting on the aldehyde or oxo group pf donors, NAD or NADP as acceptor, oxidoreductase complex, mitochondrial matrix, citrate metabolic process, tricarboxylic acid cycle, acetyl-CoA biosynthetic process and acetyl-CoA biosynthetic process from pyruvate (Fig. 2 A). Additionally, in the KEGG pathway enrichment analysis, the 5 CRGs were largely related to Carbon metabolism, Glycolysis/Gluconeogenesis, Pyruvate metabolism, Citrate cycle (TCA cycle) and Central carbon metabolism in cancer (Fig. 2 B). An analysis of PPIs was conducted to study the relationships among CRGs, and it was found that DLD, DLAT, PDHA1, and PDHB emerged as central or hub genes ( Supplementary Fig. 1 ). 3.3 Differential expression of CRGs in different pathologic stages and histological grades of GC Moreover, we used the TCGA database to analyze the differential expression of CRGs in different pathologic stages and histological grades of GC. The expression of CDKN2A differed significantly between stage2, stage3 and stage 4 and normal tissues respectively (Fig. 3 A), GLS differed significantly between stage2, stage3, stage 4 and normal tissues (Fig. 3 B), MTF1 revealed no significantly differed among the different stage groups (Fig. 3 C). Furthermore, the expression of CDKN2A differed significantly between grade2, grade3 and normal tissues respectively (Fig. 3 D), GLS differed significantly between grade1, grade2, grade3 and normal tissues respectively (Fig. 3 E) while that of MTF1 differed no significantly among the different grade groups (Fig. 3 F). Within the HPA database, our investigation revealed that with the exception of MTF1, CDKN2A and GLS protein expression levels were notably elevated in GC tissues in comparison to the normal tissues (Fig. 3 G). Similarly, we confirmed the mRNA and protein levels of these three genes in the GC cell lines. CDKN2A and MTF1 mRNA and protein levels were overexpressed in the both cancer cell lines compared to the GES-1 Cell (Fig. 3 H–I). However, Fig. 3 H showed that there was no difference in the mRNA expression level of GLS between GES-1 and MKN-45 cells. 3.4 Predictive value of CRGs for GC diagnosis and prognosis We then evaluated the prognostic value of CRGs expression by Kaplan–Meier plotter. The results revealed that lower mRNA level of MTF1, LGS and CDKN2A correlated with preferable OS, FPS and PPS in GC samples, respectively (Fig. 4 A-I). The data suggested these three CRGs to be the potential biomarkers for predicting gastric cancer prognosis. 3.5 Nomogram development and validation for GC To simplify the use of the prognostic model in clinical practice, we incorporated four clinical and genetic characteristics from patients in the TCGA dataset and utilized the multivariable Cox regression analysis to create the nomogram. We then employed discrimination and calibration techniques for OS, progression-free interval (PFI) and disease-specific survival (DSS) outcomes (Fig. 5 ). A nomogram integrating CRGs expression and independent clinical risk factors (age, pathological, stage, N stage and M stage) was constructed (Fig. 5 A-C). A worse prognosis was represented by a higher total number of points on the nomogram. Meanwhile, calibration plots were closed to the idea curve (i.e., a 45°line), showed the favorable concordance between the predicted OS, DSS or PFI and the observed OS, DSS, PFI at 1, 3 and 5 years of survival (Fig. 5 D-F). 3.6 Correlation between expression of CRGs and immune infiltration levels in GC It remains unclear if CRGs have an impact on the recruitment of immune cells within the tumor microenvironment, which could in turn influence the prognosis of GC. TIMER database was used to estimate immune infiltration levels in GC. CDKN2A expression was negatively correlated with CD8 + T cell infiltration (r = -0.11, p = 3.41e-02), macrophage (r = -0.079, p = 1.31-01) and dendritic cell (r = -0.112, p = 3.08e-02). No correlation was observed with tumor purity, B cell, CD4 + T cell, neutrophil (Fig. 6 A), GLS expression was positively correlated with tumor purity (r = 0.041, p = 4.3e-01), B cell (r = 0.127, p = 1.43e-02), CD4 + T cell (r = 0.188, p = 2.97e-04), Macrophage (r = 0.077, p = 1.41e-01). GLS expression was negatively correlated with CD8 + T cell infiltration (r = 0.1888, p = 2.97-04) and neutrophil (r = -0.049, p = 3.46e-01). No correlation was observed with dendritic cell (r = 0.022, p = 6.77e-01) (Fig. 6 B). MTF1 expression was positively correlated with B cell (r = 0.057, p = 2.74e-01), CD4 + T cell (r = 0.194, p = 1.94e-04), macrophage (r = 0.128, p = 1.34e-02), neutrophil (r = 0.134, p = 9.61e-03), dendritic cell (r = 0.136, p = 8.74e-03). MTF1 expression was negatively correlated with tumor purity (r = -0.08, p = 1.17e-01). No correlation was observed with CD8 + T cell infiltration (r = 0.015, p = 7.75e-01) (Fig. 6 C). 3.7 Molecular docking In order to confirm if CDKN2A, GLS, MTFI can become a potential therapeutic target for gastric cancer, we employed the molecular docking to mimic the interaction between the drugs and these three proteins. Fast molecular docking screening protein was performed using Libdock score ( Supplementary Table 1, 3, 5 ). Using PyRx software for finally docking and the top 5 binding affinity score compounds were selected ( Supplementary Table 2, 4, 6 ). From 7929 FDA-approved drugs, we found that saquinavir, folic acid, and GHRP were respectively docked into the CDKN2A, GLS, MTFI, which were demonstrated the best binding affinity (Fig. 7 A-F). 3.8 CDKN2A as the therapeutic target for gastric cancer was proved the good anticancer effect Cells viability decreased as saquinavir concentration increased both in NCI-N87 and MKN-45. When the saquinavir concentration was 20µM, the cells viability was 91% and 77% both in NCI-N87 and MKN-45. When the saquinavir concentration was 70µM, the cells viability was 9% and 21% both in NCI-N87 and MKN-45. However, in folic acid and GHRP, when the concentration was 70µM, the cells viability was still from 83–91% (Fig. 8 A-C). Hence, saquinavir which targeted the CDKN2A was chose for further investigation in our study. Migration and invasion are the key regulation processes in the progression of gastric cancer. Because of MKN-45 cells was partial suspended growth, we select NCI-N87 cells for migration and invasion assay. Figure 8 D, E showed the results of a migration assay for a treatment period of 48 h. Saquinavir inhibited the migration ability of NCI-N87 cells in a minimum effective inhibitory concentration. The invasive inhibition ability of Saquinavir was proved using a Matrigel invasion assay (Fig. 8 D, F). 3.9 CDKN2A inhibitor saquinavir demonstrated the anticancer effect in vivo To investigate the CDKN2A inhibitor saquinavir whether could inhibit the gastric cancer cells in vivo, an NCI-N87 cell GC murine xenograft model was established. The nude mice received saquinavir with intraperitoneal injection for 2 weeks after NCI-N87 cells inoculation. The results showed that the tumors treated by saquinavir was lighter. The weight of tumors in treated groups and control groups were 0.16 ± 0.12 g and 0.45 ± 0.13 g, respectively. The volume of tumors was 400.9 ± 340.4 mm 3 and 99.19 ± 158.1 mm 3 (Fig. 9 A-C). The immunohistochemistry showed that the expression of CD31, Ki-67, CDKN2A were reduced in treated group (Fig. 9 D). To explore the acute toxicity of saquinavir, the heart, liver, spleen, lung, and kidney were excised for hematoxylin-eosin staining ( Fig. 9 E). The results indicated that there was no acute injure in these organs. 4. Discussion In summary, although there has been a downward trend, GC continues to be a significant contributor to cancer-related mortality. While early detection through screening is crucial for high-risk groups, addressing the prevalence of Helicobacter pylori infection and other established risk factors is essential for GC prevention. Monitoring the incidence rate among younger individuals is necessary to determine if this upward trend will persist. It is imperative that research and governmental efforts concentrate on implementing preventive strategies that can alter the prevalence of risk factors, thus providing long-term benefits to public health. It is projected that in 2020, GC will account for 770,000 fatalities and 1.1 million new cancer cases globally. Moreover, by 2040, the number of GC-related deaths is expected to rise to approximately 1.3 million, with roughly 1.8 million new diagnoses [59]. China has the highest incidence, mortality, and Disability-Adjusted Life Years (DALYs) rates for gastric cancer globally [8]. Hence, a thorough comprehension of the genetic context and the tumor microenvironment is crucial for the prevention, therapy, and prognostic assessment of GC. Cuproptosis, a novel form of RCD, is distinct from apoptosis, ferroptosis, and necroptosis, and relies on mitochondrial respiration [5]. In our study, we investigated the prognostic significance of CRGs expression in GC, given its unclear role in tumor development as a form of RCD. Our investigation revealed that 11 of the 12 CRGs exhibited increased expression, whereas 1 gene showed no significant difference in expression between GC and normal tissues. functional analyses exhibited that pathways related to TCA cycle were enriched, and the CRGs were also proven to be associated with the pathologic stages and histological grades of GC. Using the Kaplan-Meier Plotter database, we found that higher expression of CRGs in GC correlated with reduced OS, DSS, and PFI. No prior studies have explored the relationship between CRG expression and GC progression, suggesting that CRGs may serve as prognostic markers for GC. In this study, the prognostic score was formulated using three CRGs: CDKN2A, GLS, and MTF1. CDKN2A, cyclin-dependent kinase inhibitor 2A, OMIM 600160, is a well-known tumor suppressor gene that encodes p16INK4A and p14ARF proteins, which play a crucial role in cell cycle regulation. The expression of CDKN2A is closely linked to the development of various types of tumors through its involvement in cell cycle control [9–12]. In cancer, the two GLS isozymes exhibit contrasting functions in tumor development. GLS is associated with tumor growth and malignancy, being controlled by the c-MYC oncogene, while GLS2 generally exhibits tumor-suppressive properties and is governed by the p53 protein [13–16]. GLS has been found to be consistently overexpressed in various types of cancers,, including breast cancer[17–19], prostate cancer [20], colorectal cancer [21, 22], lung cancer [23]. Rapidly growing malignant cells have elevated mRNA levels and enhanced GLS protein expression [16, 24–26] and GLS enzymatic activity correlates with poor disease outcome in liver, lung, colorectal, breast and brain tumors [16, 23, 27–29]. MTF1, a zinc finger transcription factor, enhances cell survival by activating targets like metallothionein (MT1), MMPs, ZnT-1, and ZIP-1, which are involved in metal binding and zinc regulation [30, 31]. In breast, lung, and cervical cancers, MTF1 expression is increased [32]. MTF1 is elevated in colorectal cancer and linked to copper balance [33]. Our study has multiple advantages. We are the first team to develop a prognostic model on the basis of CRGs in GC. We discovered a hidden connection between CRGs levels and immune cell infiltration, with CDKN2A expression showing an inverse relationship with DCs and CD8 + T cells. GLS expression was negatively correlated with CD8 + T cell infiltration. It is widely recognized that DCs are the most potent antigen-presenting cells, activating CD8 + T cells through cross-priming and subsequently triggering antitumor immunity, Presence of CD8 + T cells in ovarian cancer is associated with prolonged survival [34]. Our findings thus highlight the significance of cuproptosis in the TME and GC immunotherapy, offering new perspectives for immune checkpoint blockade treatment. Our study showed that CDKN2A, GLS, and MTF1 were the potential targets for GC treatment. Then we used molecular docking to screen the drugs which could connect these proteins. We found that saquinavir demonstrated the ability inhibiting the proliferation, invasion, and migration of GC cells in vitro. In animal experiment, tumors treated by saquinavir were smaller in weight and volume. We also found that in tumors the expression of CDKN2A was reduced. These results indicated that saquinavir, which was the HIV protease inhibitor used in antiretroviral therapy, could also inhibit the GC [35]. The possible mechanism is that saquinavir could inhibit the expression of CDKN2A, which promoting the cuproptosis. Despite our thorough examination of cuproptosis in GC and identification of potential targets for further investigation into GC progression, this study has limitations. It is highly feasible that the emergence and progression of tumors are intricately linked not only to the microenvironment but also to the body general response. Tumors generate a variety of substances, including cytokines, immune mediators, neurotransmitters, hypothalamic hormones, and glucocorticoids. These malignancies can manipulate the central neuroendocrine and immune systems, thereby undermining the body's natural defenses against cancer[36]. For example, corticotropin-releasing hormone (CRH) is a peptide consisting of 41 amino acids, which is derived from a larger precursor, the 196-amino acid preprohormone. The principal role of CRH is to stimulate the synthesis of adrenocorticotropic hormone (ACTH) in the pituitary gland. CRH can also be correlated with the tumorigenesis of some cancers[37],such as gastric cancer[38].A challenging question is whether CRGs also impact the neuroendocrine mechanism. As our cancer samples were derived from retrospective TCGA and GEO database analyses, more prospective case studies are needed. Further research is essential to uncover the precise molecular mechanisms by which cuproptosis influences gastric cancer advancement. In conclusion, achieving a comprehensive understanding of tumors, their microenvironments, and the intricate dynamics of neuroimmunology endocrine mechanisms, along with their interrelationships, is essential for developing holistic therapeutic strategies in the future. Declarations Acknowledgements The authors would like to thank the study participants and research staff for their contributions and commitment to this study. Funding This research was funded by grants from the Key R&D Program of Ningxia Province (grant number: 2021BEG03037), the Medical Enhancement Program(grant number:2020SWAQ06)，Incubation fund of Shaanxi Provincial People's Hospital(grant number: 2023YJY-15). Availability of data and materials The data sets analyzed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), accession numbers TCGA-STAD, STAD-FPKM; GEO repository (https://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE19826 and GSE29272. Authors' contributions Conceptualization, Guo Chen, Lei Wang and Zifan Lu; Data curation, Di Wei, Jun Lu; Formal analysis, Wenying Liu; Funding acquisition, Guo Chen, Lei Wang and Zifan Lu; Investigation, Zeng Li; Methodology, Guo Chen, Wenli Zhang and Wenying Liu; Resources, Zeng Li; Software, Wenli Zhang; Supervision, Guo Chen and Lei Wang; Validation, Zeng Li; Writing – original draft, Jun Lu and Di Wei; Writing – review & editing, Guo Chen. All authors reviewed the manuscript. All authors read and approved the final manuscript. Ethics approval and consent to participate. All animal studies were approved by the Laboratory Animal Welfare and Ethics Committee of the fourth military Medical University (No. IACUC-20241372). Patient consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. 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Table 1 Table 1: Results of differential expression of cuproptosis-related genes (CRGs) in various datasets Dataset Gene symbol Log2FoldChange P value TCGA-STAD DLD -0.1499042 0.02965239 TCGA-STAD GLS 0.045654 0.64434554 TCGA-STAD LIAS -0.0908272 0.18548217 TCGA-STAD PDHA1 -0.0686398 0.33368212 TCGA-STAD FDX1 0.02238422 0.76856542 TCGA-STAD LIPT1 0.04001656 0.4753674 TCGA-STAD CDKN2A -0.0149381 0.94776395 TCGA-STAD DLAT 0.00490048 0.94191253 TCGA-STAD PDHB -0.0983485 0.06276773 TCGA-STAD MTF1 -0.1241438 0.02395843 TCGA-STAD SLC31A1 -0.1909417 0.01423745 TCGA-STAD ATP7B -0.2407601 0.09934635 GSE19826 FDX1 -0.62178 4.57E-02 GSE19826 LIAS -0.46343 8.00E-02 GSE19826 LIPT1 -0.1182 4.72E-01 GSE19826 DLD -0.27125 2.37E-01 GSE19826 DLAT -0.33165 4.12E-01 GSE19826 PDHA1 -0.8701 4.04E-04 GSE19826 PDHB -0.54182 6.72E-03 GSE19826 MTF1 -0.08725 5.99E-01 GSE19826 GLS 0.041162 8.72E-01 GSE19826 CDKN2A -0.61551 9.90E-02 GSE19826 SLC31A1 0.190571 4.11E-01 GSE19826 ATP7B 0.646853 4.35E-02 GSE29272 DLD -0.149904209 0.02965239 GSE29272 GLS 0.045654 0.64434554 GSE29272 LIAS -0.090827237 0.18548217 GSE29272 PDHA1 -0.068639847 0.33368212 GSE29272 FDX1 0.022384223 0.76856542 GSE29272 LIPT1 0.040016561 0.4753674 GSE29272 CDKN2A -0.014938116 0.94776395 GSE29272 DLAT 0.004900481 0.94191254 GSE29272 PDHB -0.098348522 0.06276773 GSE29272 MTF1 -0.124143783 0.02395843 GSE29272 SLC31A1 -0.190941661 0.01423745 GSE29272 ATP7B 0.08119421 0.24076005 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx TableS1.docx TableS2.docx TableS3.docx TableS4.docx TableS5.docx TableS6.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5756178","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397354651,"identity":"342b944a-118d-46ee-b5d7-f6562e5823cf","order_by":0,"name":"Guo Chen","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Chen","suffix":""},{"id":397354652,"identity":"58513f7c-5ae0-451b-96a7-6be72bab4a6f","order_by":1,"name":"Wenli Zhang","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Zhang","suffix":""},{"id":397354653,"identity":"d417fa19-9afc-47c4-858b-873fe5a0356b","order_by":2,"name":"Di Wei","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wei","suffix":""},{"id":397354654,"identity":"c068131b-6421-4e6e-b80a-60812726b751","order_by":3,"name":"Zeng Li","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zeng","middleName":"","lastName":"Li","suffix":""},{"id":397354655,"identity":"6e5653ed-8ef4-4b2d-90f2-2d963a574b82","order_by":4,"name":"Jun Lu","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Lu","suffix":""},{"id":397354656,"identity":"7c2607a6-2077-4a38-9c5c-74f1a2a8d877","order_by":5,"name":"Wenying Liu","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenying","middleName":"","lastName":"Liu","suffix":""},{"id":397354657,"identity":"3f0ce3ad-0e39-410c-b699-af5d77deaad4","order_by":6,"name":"Lei Wang","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":397354658,"identity":"46f01570-2dd6-447e-b8c5-5b65d5e22ecf","order_by":7,"name":"Zifan Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPiD+8MCAgYGf+QBYgLGBkBY2oKIZCUAtkm0JJGkBMgyOEa1FIv1hQ0LB4TzjY9ypm3kYbGQ3HGB+9gC/loTEhgSDw8Vmx3i33eZhSDPecIDN3ICAluMPgFoSt93vBWk5nLjhAA+bBH4tiY0gWxI3t4Ft+U+MlmRGsJYNbGAtB4jQwvMMpCW9WALol5tzDJKNZx5mM8OrhZ8dGGIf/ljn8QMdduNNhZ1s3/HmZ3i1wEAChAIFFTMx6hFaRsEoGAWjYBRgAQC3skqbH6lWCQAAAABJRU5ErkJggg==","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zifan","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-01-03 07:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5756178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5756178/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73051231,"identity":"fe5453e3-0f91-422f-b4ee-9c8a7a628380","added_by":"auto","created_at":"2025-01-06 09:24:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11067350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and genetic alteration of CRGs in GC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) the expression of 12 CRGs in GC and normal tissues (tumor in red and normal in blue). The upper and lower ends of the boxes represent the interquartile range of values. The lines in the boxes represent the median value. (B) Correlations between the expression of cuproptosis regulators. (C) Correlations between the expression of DLD and DLAT. (D) The CNV and mutation frequency and classification of 12 CRGs in GC. (E) The alteration frequency of different types in GC. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/9750fdd3a65460753d62faee.png"},{"id":73051227,"identity":"bb672bd0-80d8-4c0a-9717-44e8faf82656","added_by":"auto","created_at":"2025-01-06 09:24:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1045841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis of CRGs in GC patients of TCGA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The enriched item in the gene ontology analysis. (B) The enriched item in the KEGG analysis. The size of circles represents the number of enriched genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/3a0400973a9e485a4aa89b34.png"},{"id":73051218,"identity":"d70f4a38-9161-40d7-b073-40090e6d7e61","added_by":"auto","created_at":"2025-01-06 09:24:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4993188,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression of CRGs in different pathologic stages and histological grades of GC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Expression profile of CDKN2A, GLS and MTF1 in different pathologic stage of GC patients (UALCAN) based on TNM. (D-F) Expression profile of CDKN2A, GLS and MTF1 in different histological grades of GC patients (UALCAN) Grade1: Well differentiated (low grade), Grade 2: Moderately differentiated (intermediate grade), Grade 3: Poorly differentiated (high grade), TPM (Transcript per million). (G)The immunohistochemistry of CDKN2A, GLS, MTF1 in GC and normal tissue. (H-I) The mRNA and protein expression levels of CDKN2A, GLS and MTF1 in GC and normal cell lines. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, NS, not significant.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/7f0b52f02901a832daa57904.png"},{"id":73051189,"identity":"bd6fc589-5558-46e6-818f-385866bd77da","added_by":"auto","created_at":"2025-01-06 09:24:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2112568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis is shown for OS, FP and PPS by Kaplan–Meier Plotter.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prognostic value of CDKN2A (A-C)，MTF1 (D-F) and GLS (G-I) in GC patients.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/4c7118e6200e016605de7a51.png"},{"id":73051216,"identity":"13628e3f-e576-4423-9d15-9bf03994a9b2","added_by":"auto","created_at":"2025-01-06 09:24:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2824907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of nomograms based on CRGs expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShown are the nomograms constructed to establish CRGs expression-based risk scoring models for 1-, 3-, and 5-year overall survival (A), progression-free interval (C), and disease-specific survival (E). Calibration plots validating the efficiency of nomograms for overall survival (B), progression-free interval (D), and disease-specific survival (F). OS, overall survival; PFI, progression-free interval; DSS, disease-specific survival.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/13bff9f65d2cbfbd92ba6c26.png"},{"id":73051226,"identity":"6208381e-7a7c-42f7-afb5-59c218d1cbc8","added_by":"auto","created_at":"2025-01-06 09:24:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30504684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of immune cell infiltration and CRGs expression in GC patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression of CRGs is related to a panel of gene markers of immune cells. (A) The relation of CDKN2A expression and immune cell infiltration in GC using TIMER. (B) The relation of GLS expression and immune cell infiltration in GC using TIMER. (C) The relation of CDKN2A expression and immune cell infiltration in GC using TIMER.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/d0736ac32364986aad38cfd5.png"},{"id":73051234,"identity":"393114fb-129b-4a80-8ed2-c31661c6f6a2","added_by":"auto","created_at":"2025-01-06 09:24:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2922312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D and 2D pictures of the drugs binding to the proteins: CDKN2A and saquinavir (A-B), GLS and folic acid (C-D), MTF1 and GHRP (E-F).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/943dd34e9d1fe80f411bc79b.png"},{"id":73051210,"identity":"def0676f-a9f9-4a3a-bf9b-60df67ddde6b","added_by":"auto","created_at":"2025-01-06 09:24:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3829813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe anticancer effect of saquinavir in vitro.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cells viability of different concentration of saquinavir (A), folic acid (B) and GHRP (C). The invasion of NCI-N87 cells after 24 h (D). The NCI-N87 cells were wounded, and then, treated with or without saquinavir for 24 h. The images were taken at 0 h and 24 h (×100 magnification). The migration distance is shown in the graph (E-F). The number of invasive cells is shown (G).*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/e28e126d227e0b32a38365fb.png"},{"id":73051203,"identity":"86ad1339-211a-4b74-bbb6-ee2f363cd5d9","added_by":"auto","created_at":"2025-01-06 09:24:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":912005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe anticancer effect of saquinavir in vivo.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumors treated with or without saquinavir \u003cstrong\u003e(A)\u003c/strong\u003e. The volume \u003cstrong\u003e(B)\u003c/strong\u003e and weight \u003cstrong\u003e(C)\u003c/strong\u003eof tumors. The expression of CD31, Ki-67 and CDKN2A in tumors \u003cstrong\u003e(D)\u003c/strong\u003e. HE of the heart, liver, spleen, lung, and kidney\u003cstrong\u003e (E).\u003c/strong\u003e *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/ddceee526a4b9350762b2cc1.png"},{"id":74065297,"identity":"f3eb7f35-8073-426a-8e45-ab2fb829d168","added_by":"auto","created_at":"2025-01-17 12:01:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":49364607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/ed9a74de-66a6-481f-a9ea-7d31860bd118.pdf"},{"id":73051213,"identity":"a1146a1c-7ad9-4dc1-8085-400d10366c77","added_by":"auto","created_at":"2025-01-06 09:24:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31236,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/920e976d6e397761a4ff82e7.docx"},{"id":73051184,"identity":"232df3f0-af12-499f-9b47-4059a78fb7dd","added_by":"auto","created_at":"2025-01-06 09:24:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17218,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/6a31e9cdd3aa1a4943af7d08.docx"},{"id":73051168,"identity":"b0b1a3d2-84c2-4282-9473-d1a3d1ba9936","added_by":"auto","created_at":"2025-01-06 09:24:37","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13852,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/1ca82184482f3ce86211afa3.docx"},{"id":73051183,"identity":"c508dd03-702d-414d-bd3d-9171ff2602e0","added_by":"auto","created_at":"2025-01-06 09:24:41","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17325,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/ea82f485b2321531dace8881.docx"},{"id":73051665,"identity":"04133fb6-a252-44c9-b543-3087eced3dc8","added_by":"auto","created_at":"2025-01-06 09:32:44","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16101,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/d2a479abc5914837f75675dd.docx"},{"id":73051225,"identity":"325b9e6c-937d-4b81-8afa-d1db3273c3ef","added_by":"auto","created_at":"2025-01-06 09:24:46","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17196,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/3cea5a5c2496901d5c661ad4.docx"},{"id":73051222,"identity":"499e0e6d-f807-4a73-9751-a6c8de02ee7d","added_by":"auto","created_at":"2025-01-06 09:24:46","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16427,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.docx","url":"https://assets-eu.researchsquare.com/files/rs-5756178/v1/564ae35ded6988e9b6ccc468.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of cuproptosis-related gene CDKN2A as a molecular diagnostic target in gastric carcinoma based on transcriptomic data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAccording to recent reports, worldwide, GC which was responsible worldwide for one in 13 deaths, was the fifth most common cancer in 2020 [1], and was the third leading cause of cancer-related death [2]. GC is a heterogeneous disease, and the lack of GC screening techniques hampers its early diagnosis and leads to poor five-year survival rates [3]. Notably, the prognosis of GC has not improved in recent years [1].\u003c/p\u003e \u003cp\u003eDespite the significant progress in our understanding of the molecular causes of GC, the exact mechanism underlying its development remain unknown. A deeper understanding of novel gene candidates is crucial for better understanding the molecular mechanism of pathogenesis, which would improve patient survival [3]. Therefore, there is an urgent need to identify more convincing and suitable biomarkers for GC.\u003c/p\u003e \u003cp\u003eCopper (Cu) is an essential cofactor for all organisms. However, it becomes toxic if its concentration exceeds a threshold that is maintained by evolutionarily conserved homeostatic mechanism [4]. Tsvetkov et al. [5] recently identified a novel Cu-induced cell death pathway known as cuproptosis, which was distinct from all other known mechanisms of RCD, including apoptosis, ferroptosis, pyroptosis, and necroptosis [6]. The investigators found that Cu-induced cell death is mediated by an ancient mechanism, namely protein lipoylation. Previous research has established a link between Cu homeostasis and various types of cancer [7\u0026ndash;10]. Despite this, there remains the gap in our understanding of the connection between the recently described process of cellular proptosis and the development of GC, as well as its influence on the tumor immune microenvironment and response to immunotherapy. Consequently, it is essential to investigate the biological and pathological activities related to cuproptosis, clarify the mechanisms by which cuproptosis affects GC progression, and identify potential targets for its diagnosis and effective treatment. Such insights are vital for the early detection, diagnosis, and management of GC. In our present study, we intended to comprehensively investigate the molecular alterations and clinical relevance of cuproptosis-related genes (CRGs) in GC through accessing and analysing a public health database. Next, we explored the anticancer effects of CRGs as potential targets both in vivo and in vitro. Our analysis highlights the importance of CRGs in GC development and lays the foundation for the therapeutic application of cuproptosis regulators in the treatment of GC.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Data preprocessing\u003c/h2\u003e\n \u003cp\u003eRNA sequencing data of 443 patients diagnosed with STAD were extracted from TCGA dataset, available at \u003cspan\u003e\u003cspan\u003ehttps://portal.gdc.cancer.gov/projects/TCGA-STAD\u003c/span\u003e\u003c/span\u003e. Normal tissue samples were procured from the GTEx data portal at \u003cspan\u003e\u003cspan\u003ehttps://www.gtexportal.org/home/datasets\u003c/span\u003e\u003c/span\u003e. To further confirm the expression levels of CRGs, we downloaded and utilized datasets GSE19826 and GSE29272 from the Gene Expression Omnibus database, accessible at \u003cspan\u003e\u003cspan\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe protein expression level of CRGs in tumors compared to normal tissue was obtained from the Human Protein Atlas database (\u003cspan\u003e\u003cspan\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe web-based resource at \u003cspan\u003e\u003cspan\u003ehttp://ualcan.path.uab.edu/analysis.html\u003c/span\u003e\u003c/span\u003e, known for its extensive capabilities, was employed to evaluate the expression patterns and prognostic significance of DEGs. A Student\u0026rsquo;s t-test was utilized to calculate the corresponding p-values.\u003c/p\u003e\n \u003cp\u003eWe used the cBioPortal (\u003cspan\u003e\u003cspan\u003ewww.cbioportal.org\u003c/span\u003e\u003c/span\u003e), a comprehensive web resource, can visualize and analyze multidimensional cancer genomics data, to analyze the mutation and CNV data of all CRGs in 478 total GC samples (TCGA, PanCancer Atlas).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Functional enrichment analysis of CRGs\u003c/h2\u003e\n \u003cp\u003eTo functionally annotate CRGs identified by the aforementioned comparison groups, annotation and visualization of GO terms was used by GO enrichment analysis and metascape. The overlaps between differently expressed gene lists of GO terms were performed by enrichment analysis circle diagram. The DEGs were then introduced into the FunRich (functional enrichment analysis tool) (\u003cspan\u003e\u003cspan\u003ehttp://www.funrich.org/\u003c/span\u003e\u003c/span\u003e) for KEGG pathway analysis.\u003c/p\u003e\n \u003cp\u003eTo further elucidate the functions of the potential targets, we conducted a functional enrichment analysis. This involved examining the GO annotations for the targets to identify their biological processes and also enriching for KEGG pathways. For visual representation, boxplots were created using the ggplot2 package in R software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 GENEMANIA\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eGENEMANIA (\u003cspan\u003e\u003cspan\u003ehttp://genemania.org/search/\u003c/span\u003e\u003c/span\u003e) was used to construct a gene\u0026ndash;gene interaction net-work for DEGs to evaluate the functions of these genes.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Kaplan\u0026ndash;Meier plotter database analysis\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe Kaplan\u0026ndash;Meier Plotter database (\u003cspan\u003e\u003cspan\u003ehttp://kmplot.com/analysis/\u003c/span\u003e\u003c/span\u003e), a comprehensive online platform offering survival analysis of 54,675 genes in 21 various tumor types. We executed analyses for overall survival (OS), free-progression survival (FPS) and post-progression survival (PPS) of CRGs in GC.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 CRGs mutations and prognosis\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003ecBioPortal (\u003cspan\u003e\u003cspan\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003c/span\u003e) can be used to explore, visualize, and analyze multidimensional cancer genome data [30].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Analysis of correlation with immune infiltration\u003c/h2\u003e\n \u003cp\u003eTumor Immune Estimation Resource (TIMER \u003cspan\u003e\u003cspan\u003ehttp://timer.comp-genomics.org/\u003c/span\u003e\u003c/span\u003e) is a data source for comprehensive analysis of tumor-infiltrating immune cells. The Immune infiltration cells include Neutrophils, Macrophages, B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells and Dendritic cells, etc. Which can effectively predict the prognosis of patients. We have studied the correlation between CRGs expression and these immune infiltrating cells by employing TIMER.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.7 RNA extraction and reverse transcription-quantitative PCR (RT-qPCR)\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted with Trizol reagent (Invitrogen, USA), and then reverse transcription was performed using the HiScript II Q RT SuperMix kit for qPCR (Vazyme, R223) according to the manufacturer\u0026rsquo;s instructions. qPCR performed using the ChamQ SYBR qPCR Master Mix kit (Vazyme, Q311) in accordance with the manufacturer\u0026rsquo;s instructions. The primers for all PCR primers, and their internal reference sequences were designed using Primer 5. The thermocycling protocol consisted of an initial denaturation step at 95\u0026deg;C for 10 min, followed by 40 cycles of 95\u0026deg;C for 15 seconds and 60\u0026deg;C for 1 min. All amplifications and detections were performed using a real-time PCR machine (Roche, LightCycler\u0026reg;96). The expression level of each target gene was determined using \u0026beta;-actin as the normalization control. Relative gene expression was calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method [7]. Every experiment was repeated at least three times independently. The following primers were used:\u003c/p\u003e\n \u003cp\u003eCDKN2A Forward, 5\u0026rsquo;-3\u0026rsquo;: GATCCAGGTGGGTAGAAGGTC and reverse 5\u0026rsquo;-3\u0026rsquo;: CCCCTGCAAACTTCGTCCT,\u003c/p\u003e\n \u003cp\u003eGLS forward 5\u0026rsquo;-3\u0026rsquo; AGGGTCTGTTACCTAGCTTGG and reverse 5\u0026rsquo;-3\u0026rsquo;: ACGTTCGCAATCCTGTAGATTT,\u003c/p\u003e\n \u003cp\u003eMTF1 forward 5\u0026rsquo;-3\u0026rsquo;: CACAGTCCAGACAACAACATCA and reverse 5\u0026rsquo;-3\u0026rsquo;: GCACCAGTCCGTTTTTATCCAC,\u003c/p\u003e\n \u003cp\u003e\u0026beta;-Actin forward 5\u0026rsquo;-3\u0026rsquo;: CATGTACGTTGCTATCCAGGC, and reverse 5\u0026rsquo;-3\u0026rsquo;: CTCCTTAATGTCACGCACGAT.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.8 Western blotting\u003c/h2\u003e\n \u003cp\u003eTotal protein was extracted using RIPA Lysis Buffer (Beyotime, Guangzhou, China), and the protein concentration was determined using a BCA protein assay kit (Pierce, Rockford, USA) according to the manufacturer\u0026rsquo;s instructions. Then, a western blot assay was performed as previously described. The primary antibodies used are listed in The following antibodies were used: CDKN2A (1:1000; Abways, CY8312) GLS (1:1000; Aways, CY5719), MTF1(1:1000; Proteintech,25383-1-AP) and \u0026beta;-actin (1:4000, Proteintech,20536-1-AP). Subsequently, the membranes were immunoblotted with secondary antibody (1:10000, Proteintech, SA00001-2). The protein expression was visualized by enhanced chemiluminescence (Millipore, USA). Images were captured using a ChemiDoc XRS imaging system (Bio-Rad, USA), and Quantity One image software was used for the densitometry analysis of each band; \u0026beta;-actin was used as an internal loading control.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.9 Molecular docking\u003c/h2\u003e\n \u003cp\u003eIn NCGC Pharmaceutical Collection database, 7929 FDA-approved drugs were selected for drug screening. Download the crystal structures numbered 7OZT (CDKN2A), 3voz (GLS), AF-Q14872-F1-model (MTFI) from the PDB database (\u003cspan\u003e\u003cspan\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003c/span\u003e). Using Discovery Studio 2019 software to prepare for molecular docking. The Discovery Studio Libdock program was used for molecular docking, and the scores were sorted according to Libdock score. The top 20 Libdock score compounds were selected for the next step of research. Using Pubchenm (\u003cspan\u003e\u003cspan\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003c/span\u003e) download ligand structure database. AutoDock Vina software (\u003cspan\u003e\u003cspan\u003ehttp://vina.scripps.edu/\u003c/span\u003e\u003c/span\u003e) was used to prepare ligands and proteins required for molecular docking. Prepare the ligands and proteins required for molecu-lar docking with AutoDock Vina software (\u003cspan\u003e\u003cspan\u003ehttp://vina.scripps.edu/\u003c/span\u003e\u003c/span\u003e). Pyrx soft-ware (\u003cspan\u003e\u003cspan\u003ehttps://pyrx.sourceforge.io/\u003c/span\u003e\u003c/span\u003e) was used for docking.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2.10 Cell proliferation capacity\u003c/h2\u003e\n \u003cp\u003eThe cell proliferation capacity was evaluated using the CCK-8 assay. The NCI-N87 and MKN-45 cells were diluted to 5\u0026times;10\u003csup\u003e3\u003c/sup\u003e per well before being plated into a 96-well plate, The cells were incubated for 24h at 37\u0026deg;C in an atmosphere comprising 5% CO\u003csub\u003e2\u003c/sub\u003e. various concentrations of saquinavir, folic acid, GHRP were added and cultivated with cells for an additional 48 h. The CCK-8 reaction solution was added according to the instructions, and the OD at 450 nm (denoted as A450) was measured. Each experimental condition was replicated three times to ensure reliable results. All experiments were performed with mycoplasma-free cells.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e2.11 Transwell migration and invasion assays\u003c/h2\u003e\n \u003cp\u003eThe transwell chamber (Millipore, USA) was used for migration and invasion assays. To evaluate cell invasion, the chamber was pre-coated with Matrigel (Corning NY, USA). Matrigel was diluted in pre-cooled culture medium according to the manufacturer\u0026rsquo;s instructions. Then, cells (2.5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells) were seeded in the upper chamber without serum, saquinavir was added into the upper chamber, while medium with 10% serum was added to the lower chamber for 48 h, then fixed with 4% paraformaldehyde for 10 min and stained with crystal violet at room temperature. The migration or invasion cells were photographed by a light microscope (Leica, Germany). Five random fields (400\u0026times;) were select and the number of migration or invasion cells were counted. All samples were conducted with three repeats. The migration assay is the same with invasion assay excepting no matrigel was used.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e2.12 Xenograft transplantation in vivo\u003c/h2\u003e\n \u003cp\u003eSpecific-pathogen-free (SPF) 4-week-old male BALB/c-nu mice were purchased from Beijing Vital River Laboratory Animal Technology with SPF-grade rearing environment. Animals were housed in a 12/12 h of light/dark cycle at 22℃ with 45\u0026ndash;55% humidity and adaptively provided with free access to water and food. The animal studies were approved (approval no. IACUC-20241372) by the Laboratory Animal Welfare and Ethics Committee of the Forth Military Medical University (Xi\u0026rsquo;an, China). Cells of the NCI-N87 cells were adjusted to 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e and suspended in 200 \u0026micro;L PBS, followed by inoculation under the dorsal skin of the nude mice. The tumor size was recorded every three days, and the tumor volume was calculated according to the formula V\u0026thinsp;=\u0026thinsp;0.5\u0026times;a\u0026times;b\u003csup\u003e2\u003c/sup\u003e. The experimental group was administered saquinavir via Intraperitoneal injection, at a dose of 600 mg/kg, in accordance with previous research for 2 weeks. The control group was administered PBS. All animals were sacrificed after 21 days, the tumors excised and weighed. The tumor tissue, the heart, liver, spleen, lung, and kidney were fixed in 4% paraformaldehyde, embedded in paraffin, and cut into 5 \u0026micro;m thick paraffin sections.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e2.13 Statistical analysis\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe relationships between variables were assessed using either Spearman or Pearson correlation tests. To compare differences in gene expression between adjacent normal and tumor samples, the Wilcoxon test was employed. Survival data were analyzed using Kaplan-Meier estimations and univariate Cox proportional hazards regression models. R software (Version 4.1.2) was utilized for the statistical analysis of bioinformatics outcomes (with statistical significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 CRGs that exhibit varied expression levels in gastric tissue compared to normal biopsies\u003c/h2\u003e \u003cp\u003eAs mentioned earlier, a set of 12 genes (FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, SLC31A1, and ATP7B) were identified as being linked to the process of cuproptosis [5]. To verify the role of these genes associated with cuproptosis in GC, we analyzed and compared the expression profiles of the 12 CRGs in both tumor and normal tissue samples, as obtained from the TCGA and GTEx databases. Our analysis included 470 samples of gastric tissue and 1809 samples of normal tissue. As a result, we observed that the expression levels of all 12 genes varied significantly between gastric and normal biopsies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B; \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). Among these, the expression of 11 genes (FDX1, LIPT1, DLD, DLAT, PDHA1, MTF1, GLS, CDKN2A, SLC31A1 and ATP7B) was increased, whereas the expression of one gene (LIAS) remained unchanged in STAD samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, we examined the relationships between the expression levels of various genes and discovered several strong correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For example, a high positive correlation was observed between the expression of DLD and DLAT (r\u0026thinsp;=\u0026thinsp;0.631, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Subsequently, we conducted a thorough examination of the molecular properties of the CRGs using the TCGA Pan Cancer Atlas. The results showed that the top five genes\u0026mdash;CDKN2A, ATP7B, LIPT1, GLS and DLAT were altered in 17%, 6%, 5%, 5%, 4%, and 4% of the queried GC samples, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Additionally, the alteration frequency of tubular pathological type showed higher than other types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Functional enrichment and protein\u0026ndash;protein interaction analysis of CRGs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo demonstrate the biological functions of CRGs, relevant pathways were analyzed by GO and KEGG databases. The biological processes of the 10 CRGs mainly involved in the GO analysis were iron-sulfur cluster binding, transition metal ion transmembrane transporter activity, oxidoreductase activity, acting on the aldehyde or oxo group pf donors, NAD or NADP as acceptor, oxidoreductase complex, mitochondrial matrix, citrate metabolic process, tricarboxylic acid cycle, acetyl-CoA biosynthetic process and acetyl-CoA biosynthetic process from pyruvate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, in the KEGG pathway enrichment analysis, the 5 CRGs were largely related to Carbon metabolism, Glycolysis/Gluconeogenesis, Pyruvate metabolism, Citrate cycle (TCA cycle) and Central carbon metabolism in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). An analysis of PPIs was conducted to study the relationships among CRGs, and it was found that DLD, DLAT, PDHA1, and PDHB emerged as central or hub genes (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differential expression of CRGs in different pathologic stages and histological grades of GC\u003c/h2\u003e \u003cp\u003eMoreover, we used the TCGA database to analyze the differential expression of CRGs in different pathologic stages and histological grades of GC. The expression of CDKN2A differed significantly between stage2, stage3 and stage 4 and normal tissues respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), GLS differed significantly between stage2, stage3, stage 4 and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), MTF1 revealed no significantly differed among the different stage groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Furthermore, the expression of CDKN2A differed significantly between grade2, grade3 and normal tissues respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), GLS differed significantly between grade1, grade2, grade3 and normal tissues respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) while that of MTF1 differed no significantly among the different grade groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Within the HPA database, our investigation revealed that with the exception of MTF1, CDKN2A and GLS protein expression levels were notably elevated in GC tissues in comparison to the normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Similarly, we confirmed the mRNA and protein levels of these three genes in the GC cell lines. CDKN2A and MTF1 mRNA and protein levels were overexpressed in the both cancer cell lines compared to the GES-1 Cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u0026ndash;I). However, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH showed that there was no difference in the mRNA expression level of GLS between GES-1 and MKN-45 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Predictive value of CRGs for GC diagnosis and prognosis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe then evaluated the prognostic value of CRGs expression by Kaplan\u0026ndash;Meier plotter. The results revealed that lower mRNA level of MTF1, LGS and CDKN2A correlated with preferable OS, FPS and PPS in GC samples, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-I). The data suggested these three CRGs to be the potential biomarkers for predicting gastric cancer prognosis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Nomogram development and validation for GC\u003c/h2\u003e \u003cp\u003eTo simplify the use of the prognostic model in clinical practice, we incorporated four clinical and genetic characteristics from patients in the TCGA dataset and utilized the multivariable Cox regression analysis to create the nomogram. We then employed discrimination and calibration techniques for OS, progression-free interval (PFI) and disease-specific survival (DSS) outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A nomogram integrating CRGs expression and independent clinical risk factors (age, pathological, stage, N stage and M stage) was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). A worse prognosis was represented by a higher total number of points on the nomogram. Meanwhile, calibration plots were closed to the idea curve (i.e., a 45\u0026deg;line), showed the favorable concordance between the predicted OS, DSS or PFI and the observed OS, DSS, PFI at 1, 3 and 5 years of survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation between expression of CRGs and immune infiltration levels in GC\u003c/h2\u003e \u003cp\u003eIt remains unclear if CRGs have an impact on the recruitment of immune cells within the tumor microenvironment, which could in turn influence the prognosis of GC. TIMER database was used to estimate immune infiltration levels in GC. CDKN2A expression was negatively correlated with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration (r = -0.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.41e-02), macrophage (r = -0.079, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.31-01) and dendritic cell (r = -0.112, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.08e-02). No correlation was observed with tumor purity, B cell, CD4\u003csup\u003e+\u003c/sup\u003e T cell, neutrophil (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), GLS expression was positively correlated with tumor purity (r\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.3e-01), B cell (r\u0026thinsp;=\u0026thinsp;0.127, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.43e-02), CD4\u003csup\u003e+\u003c/sup\u003e T cell (r\u0026thinsp;=\u0026thinsp;0.188, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.97e-04), Macrophage (r\u0026thinsp;=\u0026thinsp;0.077, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.41e-01). GLS expression was negatively correlated with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration (r\u0026thinsp;=\u0026thinsp;0.1888, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.97-04) and neutrophil (r = -0.049, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.46e-01). No correlation was observed with dendritic cell (r\u0026thinsp;=\u0026thinsp;0.022, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.77e-01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). MTF1 expression was positively correlated with B cell (r\u0026thinsp;=\u0026thinsp;0.057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.74e-01), CD4\u003csup\u003e+\u003c/sup\u003e T cell (r\u0026thinsp;=\u0026thinsp;0.194, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.94e-04), macrophage (r\u0026thinsp;=\u0026thinsp;0.128, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34e-02), neutrophil (r\u0026thinsp;=\u0026thinsp;0.134, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.61e-03), dendritic cell (r\u0026thinsp;=\u0026thinsp;0.136, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.74e-03). MTF1 expression was negatively correlated with tumor purity (r = -0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17e-01). No correlation was observed with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration (r\u0026thinsp;=\u0026thinsp;0.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.75e-01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Molecular docking\u003c/h2\u003e \u003cp\u003eIn order to confirm if CDKN2A, GLS, MTFI can become a potential therapeutic target for gastric cancer, we employed the molecular docking to mimic the interaction between the drugs and these three proteins. Fast molecular docking screening protein was performed using Libdock score (\u003cb\u003eSupplementary Table\u0026nbsp;1, 3, 5\u003c/b\u003e). Using PyRx software for finally docking and the top 5 binding affinity score compounds were selected (\u003cb\u003eSupplementary Table\u0026nbsp;2, 4, 6\u003c/b\u003e). From 7929 FDA-approved drugs, we found that saquinavir, folic acid, and GHRP were respectively docked into the CDKN2A, GLS, MTFI, which were demonstrated the best binding affinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.8 CDKN2A as the therapeutic target for gastric cancer was proved the good anticancer effect\u003c/h2\u003e \u003cp\u003eCells viability decreased as saquinavir concentration increased both in NCI-N87 and MKN-45. When the saquinavir concentration was 20\u0026micro;M, the cells viability was 91% and 77% both in NCI-N87 and MKN-45. When the saquinavir concentration was 70\u0026micro;M, the cells viability was 9% and 21% both in NCI-N87 and MKN-45. However, in folic acid and GHRP, when the concentration was 70\u0026micro;M, the cells viability was still from 83\u0026ndash;91% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C). Hence, saquinavir which targeted the CDKN2A was chose for further investigation in our study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMigration and invasion are the key regulation processes in the progression of gastric cancer. Because of MKN-45 cells was partial suspended growth, we select NCI-N87 cells for migration and invasion assay. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, E showed the results of a migration assay for a treatment period of 48 h. Saquinavir inhibited the migration ability of NCI-N87 cells in a minimum effective inhibitory concentration. The invasive inhibition ability of Saquinavir was proved using a Matrigel invasion assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.9 CDKN2A inhibitor saquinavir demonstrated the anticancer effect in vivo\u003c/h2\u003e \u003cp\u003eTo investigate the CDKN2A inhibitor saquinavir whether could inhibit the gastric cancer cells in vivo, an NCI-N87 cell GC murine xenograft model was established. The nude mice received saquinavir with intraperitoneal injection for 2 weeks after NCI-N87 cells inoculation. The results showed that the tumors treated by saquinavir was lighter. The weight of tumors in treated groups and control groups were 0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 g and 0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 g, respectively. The volume of tumors was 400.9\u0026thinsp;\u0026plusmn;\u0026thinsp;340.4 mm\u003csup\u003e3\u003c/sup\u003e and 99.19\u0026thinsp;\u0026plusmn;\u0026thinsp;158.1 mm\u003csup\u003e3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-C). The immunohistochemistry showed that the expression of CD31, Ki-67, CDKN2A were reduced in treated group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). To explore the acute toxicity of saquinavir, the heart, liver, spleen, lung, and kidney were excised for hematoxylin-eosin staining \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). The results indicated that there was no acute injure in these organs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn summary, although there has been a downward trend, GC continues to be a significant contributor to cancer-related mortality. While early detection through screening is crucial for high-risk groups, addressing the prevalence of Helicobacter pylori infection and other established risk factors is essential for GC prevention. Monitoring the incidence rate among younger individuals is necessary to determine if this upward trend will persist. It is imperative that research and governmental efforts concentrate on implementing preventive strategies that can alter the prevalence of risk factors, thus providing long-term benefits to public health. It is projected that in 2020, GC will account for 770,000 fatalities and 1.1\u0026nbsp;million new cancer cases globally. Moreover, by 2040, the number of GC-related deaths is expected to rise to approximately 1.3\u0026nbsp;million, with roughly 1.8\u0026nbsp;million new diagnoses [59].\u003c/p\u003e \u003cp\u003eChina has the highest incidence, mortality, and Disability-Adjusted Life Years (DALYs) rates for gastric cancer globally [8]. Hence, a thorough comprehension of the genetic context and the tumor microenvironment is crucial for the prevention, therapy, and prognostic assessment of GC. Cuproptosis, a novel form of RCD, is distinct from apoptosis, ferroptosis, and necroptosis, and relies on mitochondrial respiration [5]. In our study, we investigated the prognostic significance of CRGs expression in GC, given its unclear role in tumor development as a form of RCD. Our investigation revealed that 11 of the 12 CRGs exhibited increased expression, whereas 1 gene showed no significant difference in expression between GC and normal tissues. functional analyses exhibited that pathways related to TCA cycle were enriched, and the CRGs were also proven to be associated with the pathologic stages and histological grades of GC. Using the Kaplan-Meier Plotter database, we found that higher expression of CRGs in GC correlated with reduced OS, DSS, and PFI. No prior studies have explored the relationship between CRG expression and GC progression, suggesting that CRGs may serve as prognostic markers for GC. In this study, the prognostic score was formulated using three CRGs: CDKN2A, GLS, and MTF1. CDKN2A, cyclin-dependent kinase inhibitor 2A, OMIM 600160, is a well-known tumor suppressor gene that encodes p16INK4A and p14ARF proteins, which play a crucial role in cell cycle regulation. The expression of CDKN2A is closely linked to the development of various types of tumors through its involvement in cell cycle control [9\u0026ndash;12].\u003c/p\u003e \u003cp\u003eIn cancer, the two GLS isozymes exhibit contrasting functions in tumor development. GLS is associated with tumor growth and malignancy, being controlled by the c-MYC oncogene, while GLS2 generally exhibits tumor-suppressive properties and is governed by the p53 protein [13\u0026ndash;16]. GLS has been found to be consistently overexpressed in various types of cancers,, including breast cancer[17\u0026ndash;19], prostate cancer [20], colorectal cancer [21, 22], lung cancer [23]. Rapidly growing malignant cells have elevated mRNA levels and enhanced GLS protein expression [16, 24\u0026ndash;26] and GLS enzymatic activity correlates with poor disease outcome in liver, lung, colorectal, breast and brain tumors [16, 23, 27\u0026ndash;29]. MTF1, a zinc finger transcription factor, enhances cell survival by activating targets like metallothionein (MT1), MMPs, ZnT-1, and ZIP-1, which are involved in metal binding and zinc regulation [30, 31]. In breast, lung, and cervical cancers, MTF1 expression is increased [32]. MTF1 is elevated in colorectal cancer and linked to copper balance [33]. Our study has multiple advantages. We are the first team to develop a prognostic model on the basis of CRGs in GC.\u003c/p\u003e \u003cp\u003eWe discovered a hidden connection between CRGs levels and immune cell infiltration, with CDKN2A expression showing an inverse relationship with DCs and CD8\u003csup\u003e+\u003c/sup\u003e T cells. GLS expression was negatively correlated with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration. It is widely recognized that DCs are the most potent antigen-presenting cells, activating CD8\u0026thinsp;+\u0026thinsp;T cells through cross-priming and subsequently triggering antitumor immunity, Presence of CD8\u003csup\u003e+\u003c/sup\u003e T cells in ovarian cancer is associated with prolonged survival [34]. Our findings thus highlight the significance of cuproptosis in the TME and GC immunotherapy, offering new perspectives for immune checkpoint blockade treatment.\u003c/p\u003e \u003cp\u003eOur study showed that CDKN2A, GLS, and MTF1 were the potential targets for GC treatment. Then we used molecular docking to screen the drugs which could connect these proteins. We found that saquinavir demonstrated the ability inhibiting the proliferation, invasion, and migration of GC cells in vitro. In animal experiment, tumors treated by saquinavir were smaller in weight and volume. We also found that in tumors the expression of CDKN2A was reduced. These results indicated that saquinavir, which was the HIV protease inhibitor used in antiretroviral therapy, could also inhibit the GC [35]. The possible mechanism is that saquinavir could inhibit the expression of CDKN2A, which promoting the cuproptosis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eDespite our thorough examination of cuproptosis in GC and identification of potential targets for further investigation into GC progression, this study has limitations. It is highly feasible that the emergence and progression of tumors are intricately linked not only to the microenvironment but also to the body general response. Tumors generate a variety of substances, including cytokines, immune mediators, neurotransmitters, hypothalamic hormones, and glucocorticoids. These malignancies can manipulate the central neuroendocrine and immune systems, thereby undermining the body's natural defenses against cancer[36]. For example, corticotropin-releasing hormone (CRH) is a peptide consisting of 41 amino acids, which is derived from a larger precursor, the 196-amino acid preprohormone. The principal role of CRH is to stimulate the synthesis of adrenocorticotropic hormone (ACTH) in the pituitary gland. CRH can also be correlated with the tumorigenesis of some cancers[37],such as gastric cancer[38].A challenging question is whether CRGs also impact the neuroendocrine mechanism. As our cancer samples were derived from retrospective TCGA and GEO database analyses, more prospective case studies are needed. Further research is essential to uncover the precise molecular mechanisms by which cuproptosis influences gastric cancer advancement.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn conclusion, achieving a comprehensive understanding of tumors, their microenvironments, and the intricate dynamics of neuroimmunology endocrine mechanisms, along with their interrelationships, is essential for developing holistic therapeutic strategies in the future.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study participants and research staff for their contributions and commitment to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by grants from the\u0026nbsp;Key R\u0026amp;D Program of Ningxia Province (grant number: 2021BEG03037), the Medical Enhancement Program(grant number:2020SWAQ06)，Incubation fund of Shaanxi Provincial People\u0026apos;s Hospital(grant number: 2023YJY-15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets analyzed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), accession numbers TCGA-STAD, STAD-FPKM; GEO repository (https://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE19826 and GSE29272.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Guo Chen, Lei Wang and Zifan Lu; Data curation, Di Wei, Jun Lu; Formal analysis, Wenying Liu; Funding acquisition, Guo Chen, Lei Wang and Zifan Lu; Investigation, Zeng Li; Methodology, Guo Chen, Wenli Zhang and Wenying Liu; Resources, Zeng Li; Software, Wenli Zhang; \u0026nbsp; Supervision, Guo Chen and Lei Wang; Validation, Zeng Li; Writing \u0026ndash; original draft, Jun Lu and Di Wei; Writing \u0026ndash; review \u0026amp; editing, Guo Chen. All authors reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal studies were approved by the Laboratory Animal Welfare and Ethics Committee of the fourth military Medical University (No. IACUC-20241372).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of our study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eH\u003cstrong\u003e. \u003c/strong\u003eSung, J. 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Cancer Invest 31 (2013) 167-71.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eResults of differential expression of cuproptosis-related genes (CRGs) in various datasets\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog2FoldChange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eDLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.1499042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.02965239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eGLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.045654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.64434554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eLIAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.0908272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.18548217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003ePDHA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.0686398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.33368212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eFDX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.02238422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.76856542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eLIPT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.04001656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.4753674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eCDKN2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.0149381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.94776395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eDLAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.00490048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.94191253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003ePDHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.0983485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.06276773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eTCGA-STAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eMTF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.1241438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.02395843\u003c/p\u003e\n \u003c/td\u003e\n 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\u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eDLAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.33165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e4.12E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003ePDHA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.8701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e4.04E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003ePDHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.54182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e6.72E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eMTF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.08725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e5.99E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eGLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.041162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e8.72E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eCDKN2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.61551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e9.90E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eSLC31A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.190571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e4.11E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE19826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eATP7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.646853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e4.35E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eDLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.149904209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.02965239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eGLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.045654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.64434554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eLIAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.090827237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.18548217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003ePDHA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.068639847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.33368212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eFDX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.022384223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.76856542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eLIPT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.040016561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.4753674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eCDKN2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.014938116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.94776395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eDLAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e0.004900481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.94191254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003ePDHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.098348522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.06276773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5887%;\"\u003e\n \u003cp\u003eGSE29272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3952%;\"\u003e\n \u003cp\u003eMTF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8387%;\"\u003e\n \u003cp\u003e-0.124143783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1774%;\"\u003e\n \u003cp\u003e0.02395843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 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\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cuproptosis, gastric carcinoma (GC), tumor microenvironment, biomarkers, prognostic analysis","lastPublishedDoi":"10.21203/rs.3.rs-5756178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5756178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGastric Carcinoma (GC) is the world\u0026rsquo;s third-highest cause of death by cancer. Cuproptosis is a newly discovered programmed cell death dependent on overload copper-induced mitochondrial respiration dysregulation. We speculated this regulatory cell death (RCD) mechanism might serve as a potential prognostic predictors and therapy for GC patients. The expression and mutation patterns of 12 cuproptosis-related genes were systematically evaluated in the GC training group. Through unsupervised clustering analysis and developing a cuproptosis-related scoring system, we further explored the relationship between cuproptosis and GC progression, prognosis, immune cell infiltration, and immunotherapy. Molecular docking was used to screen the drugs which had the best binding affinity with cuproptosis target proteins. CCK8, invasion and migration assay were used to explore the anticancer effect of the drug which binging to the cuproptosis target protein and then verify it in nudes. Our results revealed three genes (CDKN2A, GLS, and MTF1) have predictive value for the prognosis. Patients from low-CRG score group were characterized by higher immune cell infiltration, immune checkpoint expression. Via molecular docking, CCK8, invasion and migration assay, saquinavir had the best binding affinity with CDKN2A,which could inhibit the proliferation, invasion, and migration of gastric carcinoma cells in vitro. Ani-mal experiment showed that saquinavir treated group had smaller volume and weight tumors. Our results confirmed the essential function of cuproptosis in regulating the progression, prognosis, immune cell infiltration, and response to immunotherapy. CDKN2A as the potential target for gastric carcinoma showed the anticancer effect in vitro and vivo.\u003c/p\u003e","manuscriptTitle":"Identification of cuproptosis-related gene CDKN2A as a molecular diagnostic target in gastric carcinoma based on transcriptomic data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 09:23:36","doi":"10.21203/rs.3.rs-5756178/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bee3e07c-32ea-4195-b023-4da2d13644e7","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T11:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 09:23:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5756178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5756178","identity":"rs-5756178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}