LncRNA CASC19 Regulates mTOR Pathway to Affect Aerobic Glycolysis and Promote the Proliferation of Gastric Cancer in Vitro and in Vivo

LncRNA CASC19 Regulates mTOR Pathway to Affect Aerobic Glycolysis and Promote the Proliferation of Gastric Cancer in Vitro and in Vivo | 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 LncRNA CASC19 Regulates mTOR Pathway to Affect Aerobic Glycolysis and Promote the Proliferation of Gastric Cancer in Vitro and in Vivo Chang-An Guo, Ruo-Fei Sun, Cheng-Bin Tao, Hong An, Hong-Wu Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6450229/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 Aim This study aimed to investigate the function and mechanism of LncRNA CASC19 in gastric cancer (GC) in vitro and vivo. Method Metabolomics and bioinformatics methods were used to study the changes of metabolites and metabolic pathways in GC cells, wherein CASC19 expression was knocked down. The expression levels of phosphorylated mammalian target of rapamycin (p-mTOR) and different aerobic glycolysis related proteins were detected in cell experiments and animal experiments. After overexpression or knockdown of CASC19 in GC cells, we measured lactate levels and glucose consumption. In the nude mice experiment, the growth curve of the transplanted tumor was created, and the tumor weight difference between the two groups was finally compared. Result Various metabolites were screened after knocking down CASC19 expression in GC cells. Bioinformatics analysis showed that different metabolites were significantly enriched in the mTOR pathway. The results of in vitro and in vivo experiments showed that p-mTOR expression levels and various downstream proteins of the mTOR pathway related to aerobic glycolysis were significantly decreased after knocking down CASC19 expression in GC cells. CASC19 expression in GC cells was positively correlated with glucose uptake and lactate production. Conclusion Metabolomics and bioinformatics analyses showed that CASC19 could regulate the levels of various metabolites and was related to different metabolic pathways. CASC19 can regulate the expression of aerobic glycolysis related proteins by affecting the mTOR pathway and controls gastric cancer cell proliferation in vitro and in vivo. Long non-coding RNA CASC19 Gastric cancer Metabolomics mTOR pathway Aerobic glycolysis Xenograft tumor in nude mice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gastric cancer (GC) is one of the most common cancers worldwide and is particularly prevalent in Asian countries [ 1 ] . Currently, no effective treatment is available for advanced GC. Due to the lack of specific biomarkers, early diagnosis of GC is challenging [ 2 ] . In addition, patients with advanced GC have a high recurrence rate of metastasis and poor prognosis. Although multimodal treatment with surgery and chemotherapy has improved patient survival, the prognosis for patients with advanced GC remains poor. Therefore, early diagnosis and treatment are important [ 3 ] . Identifying important regulatory molecules in GC can provide valuable information in the search for diagnostic, therapeutic, and prognostic biomarkers, as well as therapeutic targets [ 4 , 5 ] . LncRNAs are a group of RNA transcripts > 200 nt, but they cannot encode proteins [ 6 ] . They are involved in tumor development through various ways (digestive, respiratory, reproductive, urinary, central nervous, and other systems) [ 7 , 8 ] . Moreover, lncRNAs are closely related to the occurrence, metastasis, and prognosis of GC [ 9 , 10 ] . LncRNAs have become the focus of cancer research due to their high specificity and easy detection in different tissues and body fluids [ 11 – 13 ] . The study on the function of lncRNAs in GC may provide an important theoretical basis for the occurrence, development, and prognosis of GC. Recent metabolomic studies have promoted our understanding of the relationship between metabolic reprogramming and GC progression and identified potential metabolic targets for clinical applications and therapeutic interventions [ 14 ] . As a powerful tool for metabolite flux measurement, metabolomics can comprehensively analyze metabolites and related metabolic pathways and play an important role in GC diagnosis and therapeutic target discovery [ 15 ] . Our previous studies found that CASC19 was highly expressed in GC cells, and the results of in vitro cell experiments showed that proliferation, invasion, and metastasis of GC cells were inhibited after downregulation of CASC19 expression [ 16 ] . In this study, we initially transfected the lectin virus to downregulate CASC19 expression in GC cells and then clarified the effects of CASC19 on key metabolites and metabolic pathways of GC through metabolomic analysis. Thus, metabolomics was used to explain the mechanism of CASC19 in the occurrence and development of GC. In vitro and in vivo experiments verified the changes of important metabolite pathways. Lactic acid and glucose concentrations in GC cells before and after CASC19 knockdown were measured using lactic acid and glucose kits. Western blot was used to detect the effect of CASC19 knockdown on the aerobic glycolysis pathway of GC cells. By conducting animal experiments, the effect of CASC19 knockdown on the proliferation and aerobic glycolysis pathway of GC cells was detected. Therefore, we preliminarily explored the mechanism of CASC19 in GC. Material and method Cell culture We used the human GC cell line AGS, NCI-N87, MKN-45, and KATO-III cell line. The cells were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). All cells were cultivated in fetal bovine serum (FBS) (10%) medium and were kept in a CO 2 (25%) incubator at 37℃. CASC19 expression levels in cell lines of normal gastric epithelial mucosa and gastric cancer To investigate the expression level of CASC19 in human GC-associated cells, we examined the CASC19 expression in the normal gastric epithelial mucosa cell line GES-1 and four human GC cell lines (i.e., AGS, NCI-N87, MKN-45, and KATO-III) using qRT-PCR. As shown in Fig. 1 A, CASC19 expression was significantly higher in the human GC cell lines (extremely higher in MKN-45 and NCI-N87 and slightly higher in AGS and KATO-III) than in the normal gastric epithelial mucosa cell line GES-1. Construction of lentivirus and cell transfection In this experiment, MKN-45 cells were selected as the target for CASC19 knockdown, because they had the highest CASC19 expression among the four GC cells. The sh-RNA sequences of sh-CASC19 and negative control sh-NC (sh-blank) were synthesized by Jikai Gene Co. Ltd. (Shanghai, China). The sh-NC sequence was 5′-TTCTCCGAACGTGTCACGT-3′. We designed three sh-RNA targets, including sh-CASC19-1 , sh-CASC19-2 , and sh-CASC19-3 , with the sequences 5′-CAGCATTTGCCATACTACATT-3′, 5′-CAGCACAATGATGGAAGGCTT-3′, and 5-CTGCATGCTTCTGATGTGAGT3', respectively. After the subsequent experiments, sh-CASC19-2 had the highest gene knockdown efficiency (79.4%) in MKN-45 cells and was selected for the other succeeding experiments (Fig. 1 B). Meanwhile, KATO-III cells were selected as the target for CASC19 gene upregulation, because they had the lowest CASC19 expression among the four GC cells. Lentivirus with CASC19 overexpression (i.e., pLV- CASC19 ) was synthesized by Jikai Gene Co. Ltd., and a vector was used as the negative control for overexpression. The final qRT-PCR results showed that the target gene expression level was 12.6 times higher in the pLV- CASC19 group than in the vector group (Fig. 1 C). Transfection was performed using Lipofectamine 3000 reagent (Thermo Fisher Scientific, USA), following the manufacturer’s instructions. Metabolite extraction For the transcriptomic analysis, we used the human GC cell line MKN-45 GC. The control and knockdown groups had six samples each. The cell samples were removed from the refrigerator (− 80°C), and 10 times the volume of the extraction buffer MeOH:ACN:H 2 O (2:2:1, V/V) was added, fully vortexed, and sonicated. The cells were flash-frozen in liquid nitrogen for 1 min and then thawed at room temperature, sonicated again, and this step was repeated thrice. The precipitate was centrifuged at 18000g for 15 min using a low-temperature centrifuge. The centrifuged supernatant was taken and drained using a concentrator, then sonicated with an equal volume of ACN:H 2 O (1:1, V/V) to redissolve. The samples were centrifuged at 18000 g for 15 min at 4°C, and the supernatant was transferred to a new centrifuge tube and stored in a refrigerator (− 80°C) or analyzed with high performance liquid chromatography (Waters, USA). Liquid chromatography–mass spectrometry analysis The metabolites were separated using the Waters ACQUITY UPLC ultra-high performance liquid phase system and combined using the chromatographic column (Waters ACQUITY UPLC BEH C18 Column, 1.7 µm, 2.1 × 100 mm) with a 10 µL sample size. Elution was performed at a flow rate of 400 µL/ min (column temperature 40 C). Mobile phase A is an aqueous solution, containing formic acid (0.1%), and mobile phase B is an aqueous solution, containing formic acid (0.1%) and acetonitrile (1%). The liquid phase gradient (mobile phase B) was set as follows: 0–11 min, 5–90%; 11.0–12.0 min, 90%; 12.0–12.1 min, 90–5%; 12.1–15.0 min, 5%. For the metabolite treatment process, we used the ultra-high performance liquid phase system for separation, followed by electron spray ionization (ESI) implantation into an ESI source. Subsequently, the timsTOF Pro mass spectrometer (Waters, USA) was used for analysis. The input voltage of the ion source was expected to be 1.6 kV, and the parent ion of the peptide segment and its secondary fragments was determined on the timsTOF Pro mass spectrometer. The mass spectrum scanning range was set at 20–1,300 m/z. Data were collected using the parallel cumulative serial fragmentation mode. After successfully collecting the first stage mass spectrum, the secondary spectrum of the charge number of the parent ion within 0–1 was collected using PASEF mode. To avoid secondary scanning of the parent ion, the dynamic exclusion time of the series mass spectrometry scan should be set to 6 s. Database retrieval We used MetaboScape 2022 for peak extraction, alignment, and retention time correction of the original data, and the primary and secondary mass errors were controlled within 20 ppm to ensure the accuracy of the identification results. The structure and annotation information of metabolites were obtained by spectrogram comparison of the National Institute of Standards and Technology (NIST), Human Metabolomics (HMDB), Owned, and Integrated Public databases. Bioinformatics analysis Data screening and statistical algorithms were combined to fill and correct missing data values based on the quantitative information of metabolites obtained through database matching. For samples with multiple repetitions, the corrected expression level was used to calculate the fold change of the metabolite difference between the two groups, and the P-value of the univariate t-test analysis was combined. Multivariate statistical analysis and orthogonal partial least square discriminant (OPLS-DA) analysis were used to calculate the variance importance (VIP) values and obtain the metabolites with significant differences. Subsequently, multilevel bioinformatics and functional analyses were conducted for the differential metabolites. When the number of sample groups is > 3, all intergroup analysis of variance (ANOVA) of P-values can also be provided concurrently to screen differential metabolites. Expression cluster and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed for the selected differential metabolites. Glucose concentration testing in target cell culture medium The change of glucose concentration in the cell culture medium of different groups was completed based on the kit instructions (Biyuntian Biotechnology Co., Ltd.). The experiment was performed in a 96-well plate, and a microplate reader was used to detect the absorbance at 505 nm in each well. Glucose content (mmol/L) = (absorbance of sample wells − absorbance of blank wells)/(absorbance of standard wells − absorbance of blank wells) × 5.55 (standard concentration, mmol/L). Lactate concentration testing in target cell culture medium The change of lactic acid concentration in the cell culture medium of different groups was completed based on the kit instructions (Biyuntian Biotechnology Co., Ltd.). The experiment was performed in a 96-well plate, and a microplate reader was used to detect the absorbance of each sample at 530 nm. The lactate content in each group was calculated using the following formula: lactate content (mmol/L) = (absorbance of sample wells − absorbance of blank wells)/(absorbance of standard wells − absorbance of blank wells) × 3 (standard concentration, mmol/L). Western blotting MKN-45 GC cells of the sh-NC group and sh- CASC19 were lysed for 30 min on ice in the radioimmunoprecipitation assay buffer. Subsequently, the supernatants were extracted for the Western blotting analysis of glucose transporter-1 (GLUT1), lactate dehydrogenase A (LDHA), hexokinase-2 (HK2), and pyruvate kinase M2 (PKM2) proteins. The samples were subjected to sodium dodecyl sulfate (12%) polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes. The blots were detected using the primary antibodies (Abcam, UK). Secondary antibodies were added after washing four times with PBS-Tween 20, horseradish peroxidase-conjugated. The remaining steps were as previously reported [ 17 ] . The expression of β-actin acts as an internal reference. The gray bands were quantified using the ImageJ software. Each blot-related experiment was repeated thrice. Animal experiment Experimental nude mice were purchased from Guangdong Vitong Lihua Experimental Animal Technology Co. Ltd. The Lanzhou University Second Hospital approved this (approval number: 2021A-312). A specific amount of GC cells (MKN-45 GC cell) in the sh-NC and sh- CASC19 groups with good growth states were injected into the posterior subcutaneous area of the left upper limb of nude mice. The long and short diameters of the transplanted tumor were measured and recorded with an electronic vernier caliper every other day. On the 28th day after injection, the nude mice were killed (carbon dioxide euthanasia) and photographed. Subcutaneous grafts of nude mice were exfoliated, grouped, weighed by electronic scale, and photographed. Subsequently, some transplanted tumor tissues of the sh-NC and sh-CASC1 groups were fixed with formaldehyde for subsequent pathological study. Hematoxylin eosin (HE) staining The transplanted tumor tissues of nude mice were washed with NS thrice, fixed with neutral formaldehyde buffer (10%), dehydrated, treated with paraffin wax, sliced, baked, dewaxed, and stained using HE. Morphological changes were observed under a bifocal microscope. Immunohistochemistry The expression level of aerobic glycolytic-related protein in transplanted tumor tissues of nude mice was detected by immunohistochemical staining. After conventional paraffin embedding, transplanted tumor tissue sections were obtained, placed on slides, dewaxed, and hydrated. The first (Abcam, UK) and second antibodies were added successively. Finally, the antibody was treated with diaminobenzidine (DAB) color development solution and hematoxylin as the reverse stain, followed by the transparent step. After completing transparency, resin tablets were sealed, and the immunohistochemical staining effect was eventually observed under a microscope. Statistical analysis Metabolite concentrations were normalized to the sample volume used for extraction. Each metabolite was readjusted, and the median was set to one. Normalized metabolite concentrations were transformed in log base 10. Log-transformed metabolomics data were analyzed using ANOVA and t-test (assuming unpaired variance) to generate volcano plots, and VIP values in projections were determined using partial least squares-discriminant analysis (PLS-DA). The Benjamini–Hochberg false discovery rate method was used to adjust for multiple comparisons. VIP scores were determined using PLS-DA, and metabolites with VIP scores > 1.5 were statistically significant. Leave-one-out cross-validation and permutation tests were used to evaluate the robustness of the model and the amount of variance represented by principal components, with a permutation significance threshold set at P 1.5, |log2|>1.5-fold, and a change in false discovery rate of ≤ 10% was used as an important feature for further analysis. AUROC curve analysis was used to quantify the diagnostic performance of the selected classifiers. Metabolite pathway analysis was performed using Fisher calculation and KEGG metabolomics reference library as previously described, and the operation was performed in Metabo Analyst 4.0. Statistical significance was defined as P < 0.05. Data analysis was performed using a combination of JMP™ statistical discovery software 1515.0.0 (SAS Institute) and Metabo Analyst 4.0. ANOVA LSD comparison tests were used to analyze the statistical significance of the continuous variables between multiple groups. The chi-square test was used to compare the rates between multiple groups, and statistical significance was set at P < 0.05. Statistical Packages for the Social Sciences version 23.0 software (IBM Corp.; Armonk, NY, USA) was used for data analysis. Results Metabolome data analysis We used Analyst 1.6.3 to analyze the mass spectrum data. One quality control sample was added to every six holes for repeatability analysis. The basic peak ion flow diagram (Fig. 2 A) is generated through quality control analysis of GC samples. The quality control samples in this experiment showed a high overlap of metabolites in the basic peak ion flow diagram, indicating relatively stable GC cell samples that could be used for subsequent studies. Principal component analysis of GC cell samples showed clustering of the treatment and control groups in relatively concentrated areas. The principal component analysis (PC) of PC1 (49.0%), PC2 (8.2%), and PC3 (7.3%) showed that PC scores varied widely between groups, indicating differences in metabolomics between the two sample sets of GC cells (Fig. 2 B, C). The relative standard deviation (RSD) distribution of features in the quality control sample was < 0.2, highlighting the excellent data quality (Fig. 2 D), indicating reliable metabolomics data. Screening of differential metabolites Differential metabolites in different GC cells can be initially analyzed using the OPLS-DA model. Differential metabolites were screened using a t-test combined with the OPLS-DA model. Metabolomics data showed that 160 different metabolites were detected between CASC19 lentivirus knockdown GC cells and un-knockdown GC cells, including 134 and 26 up and 26 downregulated metabolites, respectively. Specific visualizations are shown in Figs. 2 E and 3 A. The main components of differential metabolites include steroids, peptides, lipids, nucleic acids, vitamins, cofactors, and antibiotics (Fig. 3 B). Moreover, more metabolites act as vitamin cofactors and antibiotics. The differential metabolites after screening were also cluster analyzed, and heat maps were obtained (Fig. 3 C). We created a chart of the top 20 differential metabolites through differential metabolism analysis, including inositol, phospholipids, 2-methyl-3-pyrimidine-2-phenylpropionic acid, adenylate succinic acid, serine, guanylate, etc. (Fig. 3 D and Table 1 ). Table 1 Statistical table of differential metabolites between the knockdown group and the control group (The top 20 differential metabolites with the smallest P -values are listed below in the order from smallest to largest P -values). Index Compounds K/C Fold_Change K/C P value K/C VIP Regulated Type PTM_761 D-Myo-Inositol, 1-[2-hydroxy-3-[(1-oxo-9,12-octadecadienyl)oxy]propyl hydrogen phosphate], [S-(Z,Z)]- 14.725 8.15346E-16 1.42895108 Up PTM_2623 PS(18:1(9Z)/0:0) 7.03 2.97785E-15 1.42910664 Up PTM_1316 LysoPE(16:1(9Z)/0:0) 7.929 7.26162E-15 1.42852157 Up PTM_1288 LPE 18:2 6.785 1.94425E-14 1.42886974 Up PTM_1464 n-Pentadecylamine 2.836 2.82146E-14 1.42872596 Up PTM_1721 PC 20:2e 6.631 5.85416E-14 1.42796943 Up PTM_1435 N-Fructosyl phenylalanine 0.413 3.46138E-13 1.42668092 Down PTM_540 Adenosine Monophosphate 5.447 4.65967E-13 1.42738323 Up PTM_751 Cys Glu Glu 3.825 5.2978E-13 1.42674102 Up PTM_744 CocamidopropylBetaine 0.339 8.85534E-13 1.42603260 Down PTM_1299 LPE 20:4 5.014 2.14854E-12 1.42606004 Up PTM_648 BENZALKONIUM 3.427 8.82196E-12 1.42504027 Up Index Compounds K/C Fold_Change K/C P value K/C VIP Regulated Type PTM_1251 LPC 20:2 2.575 1.12252E-11 1.42417684 Up PTM_395 3-Hydroxy-2-((9Z,12Z)-octadeca-9,12-dienoyloxy)propyl 2-(trimethylazaniumyl)ethyl phosphate 2.077 1.70019E-11 1.42344671 Up PTM_888 Folinic acid 2.61 1.70196E-11 1.42415279 Up PTM_1728 PC 20:4e 6.96 2.11613E-11 1.42283171 Up PTM_2735 Succinoadenosine 2.329 2.8518E-11 1.42283434 Up PTM_301 2-[2-(4-fluorophenoxy)ethyl]-8-pyrrolidin-1-ylsulfonyl-[1,2,4]triazolo[4,3-a]pyridin-3-one 3.768 3.97536E-11 1.42277134 Up PTM_1303 LPE 22:5 4.576 6.39427E-11 1.42179284 Up PTM_990 Ile Asn Val Asp Tyr 5.467 1.55994E-10 1.42009351 Up KEGG annotation and differential metabolite classification In this study of differential metabolites in GC cells, the KEGG functional classification of downregulated differential metabolites is mainly focused on the global metabolite pathway. Other functions are classified as the digestive system, cofactor metabolism, vitamins, energy metabolism, biosynthesis of other secondary function metabolites, chemical structure transformation, amino acid metabolism, neurodegenerative diseases, drug resistance in cancer, signaling transduction and membrane transport, etc. (Fig. 4 A). Upregulated differential metabolites were categorized mainly as global and overview maps, nucleotide metabolism, cofactor and vitamin metabolism, and chemical structure transformation in KEGG functional classification (Fig. 4 B). Overall, after lentiviral transfection downregulated CASC19 expression in GC cells, the KEGG functional categories were global and overview maps, metabolic profiles of cofactors and vitamins, chemical structure transformation maps, nucleotide metabolism, antitumor drug resistance, and signaling transduction (Fig. 4 C). KEGG annotation was further performed for GC cell differential metabolites showed that the upregulated differential metabolites were mainly enriched in olfactory transduction, cGMP-PKG signaling pathway, histidine, purine-derived alkaloid synthesis, antihardeners, flavor transduction, purine metabolism, and cofactor biosynthesis (Fig. 5 A). The downregulated differential metabolites were mainly enriched in the folate-carbon pool, stress resistance, biosynthesis of various antibiotics and pathways of neurodegenerative diseases, amino acid biosynthesis, cofactors, and other biological pathways (Fig. 5 B). The main enrichment pathways of differential metabolites were the mammalian target of rapamycin (mTOR) signaling pathway, olfactory conduction, antifolate resistance, cofactor biosynthesis, metabolic pathways, and purine metabolism (Fig. 5 C). Effects of CASC19 expression changes on glucose consumption and lactic acid production in GC cells To investigate the effect of CASC19 on aerobic glycolysis of GC cells. Glucose consumption and lactate production in KATO-III cells and MKN-45 cells were detected using glucose and lactate kits. Compared with the sh-NC group, KATO-III cells had increased glucose consumption and lactic acid production after upregulation of CASC19 expression. Conversely, compared with the sh-NC group, MKN-45 cells had decreased glucose consumption and lactic acid production after downregulating CASC19 expression (Fig. 6 A,B). Effect of CASC19 on aerobic glycolysis related proteins in GC cells Western blot assay was used to detect the changes of total mTOR and p-mTOR protein and protein expression related to aerobic glycolysis (GLUT1, LDHA, HK2, and PKM2) after downregulating CASC19 expression in MKN-45 cells. Moreover, MKN-45 cells had decreased protein expression (p-mTOR, GLUT1, LDHA, HK2, and PKM2) after downregulating CASC19 expression (Fig. 6 C,D). Effect of downregulated CASC19 expression on growth of subcutaneous GC grafted tumor in nude mice. The nude mice were killed 28 days after the tumor formation in the animal experiment, followed by transplanted tumor removal. The weight and volume of the transplanted tumor in the sh-NC group were significantly higher than those in the sh- CASC19 group. Thus, knockdown CASC19 expression can significantly inhibit the proliferation of subcutaneous GC transplanted tumors in nude mice (Fig. 6 E). During the growth of the transplanted tumor, the long and short diameters were measured regularly. After calculating the measured data, the growth curves of the transplanted tumor in the sh-NC and sh- CASC19 groups were created, which showed a statistically significant difference in the growth curves of the transplanted tumor between both groups (Fig. 6 F). We conducted a statistical comparative analysis of the final weight of transplanted tumors in the sh-NC and sh- CASC19 groups and found a statistically significant difference in the tumor weight between both groups (Fig. 6 G). Use of HE staining to observe the histomorphology changes of the transplanted tumors The tumor tissue was stained with HE. After staining, the difference between sh- CASC19 and sh-NC groups was observed under the microscope. The sh- CASC19 group showed characteristics of reducing the number of tumor giant cells, nuclear fission image, nuclear irregularity, and cell volume, indicating that the malignant proliferation of tumors is inhibited after CASC19 gene knockdown (Fig. 7 ). Effect of downregulated CASC19 expression on p-mTOR, GLUT1, HK2, and PKM2 protein expression in transplanted tumors We further investigated the effect of CASC19 downregulation on p-mTOR, GLUT1, HK2, and PKM2 protein expression in subcutaneous transplanted tumors of nude mice by using the IHC staining assay. The experimental results showed that p-mTOR, GLUT1, HK2, and PKM2 protein expressions in the sh- CASC19 group were significantly lower than those in the sh-NC group (Fig. 7 ). In conclusion, downregulating CASC19 gene expression in subcutaneous GC transplanted tumor tissues of nude mice would correspondingly inhibit p-mTOR, GLUT1, HK2, and PKM2 protein expressions in transplanted tumor tissue of nude mice. Discussion Many studies have shown that lncRNAs are closely related to the occurrence and development of GC [ 9 , 10 ] . Our research team and other scholars have conducted preliminary studies on the role of lncRNAs CASC19 in GC [ 16 , 18 ] but yielded few relevant research results. The effect of CASC19 on GC as an oncogene needs further studies. In this study, after downregulating CASC19 expression in GC cells, metabolomics was used to analyze the changes in metabolite levels and bioinformatics analysis was performed for differential metabolites. Finally, cell and animal experiments were used to further verify the key signaling molecules in the signaling pathways with the most significant differences obtained from metabolomics and bioinformatics analyses, and initially explore the mechanism of CASC19 in GC, providing directions for developing new GC therapeutic targets and diagnostic biomarkers. In this metabolomics study, the metabolites sh- CASC19 and sh-NC groups mainly included steroids, peptides, lipids, nucleic acids, vitamins, cofactors, and antibiotics, but the proportion of vitamins, cofactors, and antibiotic metabolites was higher. Vitamins are closely related to the occurrence, development, and prognosis of GC. Vitamins D and B6 have important effects on the occurrence, development, and prognosis of tumors [ 19 , 20 ] . Metabolic reprogramming is one of the important characteristics of tumors recently discovered [ 21 , 22 ] , and the research on peptide and amino acid metabolism in this aspect has gained more attention. In this study, obvious changes were found in serine and guanylic acid in the differential metabolites. Serine is a nonessential amino acid; however, it is an essential amino acid in specific tumor situations. Reducing exogenous serine concentration can effectively play an antitumor role [ 23 , 24 ] . Increased guanylate-binding protein 1 (GBP1) expression is associated with the decreased aggressiveness of colorectal cancer, and GBP1 can inhibit tumor cell proliferation and promote tumor cell apoptosis [ 25 ] . Among all metabolites studied in this experiment, the difference between inositol and phospholipid is the most significant. Phosphatidylinositol 3-kinase (PI3K) pathway is an important signaling pathway and is often activated in cancer cells [ 26 ] . The main components of PI3K are inositol and phospholipids. Activation of PI3K/AKT and PI3K/mTOR pathways plays a key role in tumor evolution [ 27 , 28 ] , and inositol inhibitors have been proposed as potential new therapies for many cancers [ 27 , 29 ] . Inositol and phospholipids showed the greatest variation, indicating that CASC19 likely affects GC development through the PI3K/AKT and PI3K/mTOR pathways. To further investigate the effect of CASC19 on GC metabolites, we classified and annotated the differential metabolites using KEGG. The classification of upregulated differential metabolite pathways mainly focused on the digestive system, cofactors, vitamin metabolism, and energy metabolism. The downregulated differential metabolites mainly focused on nucleotide metabolism, cofactors, vitamin metabolism, and chemical structure transformation. KEGG enrichment analysis showed upregulated biosynthesis of histidine and purine-derived alkaloids and downregulated biosynthesis of folic acid one-carbon pool, stress resistance, and various antibiotics. The log2-fold enrichment value of the PI3K/Akt and mTOR signaling pathways is large, with an enrichment factor of 8.33. However, the Fisher exact test showed P = 0.12 for PI3K-Akt, which was not significantly different, but it was P = 0.01333 for the mTOR signaling pathway, which was significantly different. Comprehensive metabolomics and bioinformatics analysis results showed that CASC19 knockdown in GC cells significantly affects the mTOR signaling pathway, but mTOR is a serine/threonine protein kinase belonging to the PI3K family; thus, theoretically, CASC19 knockdown affects the PI3K/Akt pathway. Earlier studies found that the mTOR signaling pathway was closely related to aerobic glycolysis [ 30 ] ; thus, this GC cell and animal experiments closely focused on CASC19 regulation on PI3K/mTOR pathway and aerobic glycolysis. The mTOR protein can regulate physiological and pathological activities, including cell function, protein synthesis, metabolism, and cell proliferation. It consists of two complexes, mTORC1 and mTORC2. Although rapamycin can inhibit mTORC1, mTORC2 is not sensitive to rapamycin inhibition. mTORC1/mTORC2 signaling can be activated by different oncogenic signaling pathways and is highly expressed in most cancers [ 31 , 32 ] . The PI3K signaling pathway with mTOR as the core kinase plays an important role in the occurrence, proliferation, invasion, and metastasis of cancer cells and other biological behaviors [ 31 , 32 ] . Aerobic glycolysis of tumor cells, known as the Warburg effect, indicates that cancer cells produce energy through aerobic glycolysis even with sufficient oxygen, which accelerates glucose consumption and produces a large amount of lactic acid. Aerobic glycolysis is a cancer-specific energy access mode [ 33 ] . Aerobic glycolysis in tumor cells can be clearly distinguished from normal cells undergoing anaerobic glycolysis. Aerobic glycolysis of tumor cells plays an important role in the biological behavior of tumor cells. mTOR signaling pathway is closely related to aerobic glycolysis [ 30 ] . Additionally, mTOR has a regulatory effect on different proteins related to aerobic glycolysis (PKM2, HK2, LDHA, and GLUT1) and tumor development by affecting aerobic glycolysis of tumor cells [ 34 – 37 ] . Moreover, phosphorylated mTOR (p-MTOR) is the activated form of mTOR involved in signaling transduction. We selected p-mTOR as an indicator to detect p-mTOR expression in cell and animal experiments, and the results showed inhibition of p-mTOR expression in the sh- CASC19 group, indicating that CASC19 knockdown affected the mTOR pathway. Our findings were consistent with the results of metabolomics and bioinformatics analyses. PKM2 is a key metabolic enzyme in the Warburg effect, which is closely related to tumor growth, development, prognosis, and transformation. PKM2 is one of the hotspots of PKM family research. PKM has four subtypes: PKL, expressed in the liver and kidney [ 38 ] and plays a role in gluconeogenesis; PKR, the only pyruvate kinase subtype expressed in erythrocytes [ 39 ] ; PKM1, commonly found in the bone marrow, muscle, and brain tissues requiring rapid energy [ 40 ] ; and PKM2, a splice isoform of PKM1 expressed in proliferating cells, particularly in tumor cells with stable and specific expression [ 41 ] . The expression of other isoforms of pyruvate kinase gradually loses their respective tissue specificity during tumor formation, eventually changing to PKM2 isoform dominant. Therefore, PKM2 can also act as tumor-type pyruvate kinase. PKM2 is overexpressed in various human tumors [ 42 ] . PKM2 converts pyruvate into lactate and ATP in the state of aerobic glycolysis, which can provide sufficient energy for rapid tumor cell proliferation [ 42 ] . HK2, the rate-limiting enzyme that catalyzes the first step of glycolysis, is commonly overexpressed in most cancer cells. The conversion of glucose to pyruvate and lactate is inhibited after silencing HK2 in liver cancer cells, thereby inhibiting tumor development and inducing tumor cell death. HK2 loss can lead to decreased Snail protein transcriptional activity and reduced Snail-mediated EMT, thereby affecting cancer cell metastasis [ 43 ] . LDHA catalyzes the conversion of pyruvate to lactate, plays an important role in nontumor and tumor cells, and is a key enzyme in aerobic glycolysis in cancer cells. LDHA expression is increased in GC, which is significantly correlated with GC differentiation status, vascular invasion, and TNM stage. Other studies have shown that increased LDHA can promote matrix metalloproteinase-2 (MMP-2) and MMP-9 production, accelerate extracellular matrix degradation, and promote tumor metastasis and invasion [ 44 ] . GLUT1 is a membrane protein that promotes glucose uptake, and its high expression is related to the occurrence, development, and prognosis of different tumors [ 45 ] , including GC cells, and positively correlated with tumor proliferation [ 46 ] . The results of cell and animal experiments showed inhibition of p-mTOR expression in cells and xenografts after downregulating CASC19 expression, indicating that CASC19 knockdown significantly affects the mTOR pathway, which was consistent with the results of the metabolomics and bioinformatics analyses. The mTOR pathway plays an important role in regulating aerobic glycolysis. To verify whether CASC19 knockdown in GC cells affects aerobic glycolysis through the mTOR pathway, the changes of p-mTOR protein and various aerobic glycolysis pathway related proteins (GLUT1, LDHA, HK2, and PKM2) were investigated in cell and animal models. The results showed that the expression of p-mTOR and various glycolysis related proteins was significantly decreased after downregulating CASC19 expression in GC cells in vitro and in vivo. Increased lactate levels in the tumor microenvironment will promote the malignant biological behavior of tumor cells through various mechanisms, such as histone lactylation, which will accelerate lactate production, eventually forming a vicious cycle [ 44 ] . Glucose consumption and lactate production in the GC cell culture medium were two direct indicators reflecting the effect of CASC19 expression changes on the aerobic glycolysis of GC cells. Compared with the sh-NC group, glucose consumption and lactic acid production were increased after upregulating CASC19 expression, but decreased after downregulating CASC19 expression in GC cells. These results indicated that CASC19 could regulate the p-mTOR expression and the changes of aerobic glycolysis pathway related proteins to affect the biological behavior of GC cells. The results of animal experiments showed that the growth of GC xenograft in nude mice was significantly inhibited after CASC19 knockdown, which was consistent with those of our previous in vitro proliferation experiments [ 16 ] . In summary, metabolomic studies have shown that CASC19 can regulate the levels of multiple metabolites, indicating that it can promote GC development. Comprehensive metabolomics, bioinformatics, and cell and animal experiment results proved that CASC19 could regulate the expression of aerobic glycolysis related proteins by regulating the mTOR pathway and could affect the biological behavior of GC cells. LncRNA CASC19 may potentially become an effective important molecule for GC diagnosis and treatment. Declarations Authors’ contributions Chang-An Guo, Ruo-Fei Sun and Hong-Wu Ma designed this research. Chang-An Guo, Hong-Wu Ma, Cheng-Bin Tao, Hong An and Ruo-Fei Sun conducted experiments and Data Analysis. Chang-An Guo and Hong-Wu Ma wrote this manuscript. Ethics statement The Lanzhou University Second Hospital Ehtics Committee approved this study (approval number: 2021A-312). The size or diameter of the transplanted tumors in nude mice in this study did not exceed the size stipulated by the relevant ethical regulations. Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article. All methods were carried out in accordance with relevant guidelines and regulations. Funding This research was supported by the Gansu Provincial Natural Science Foundation Project 21JR11RA106 and 22JR5RA974. 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GLUT1 production in cancer cells: a tragedy of the commons[J]. NPJ Syst biology Appl. 2022;8:22. Dai Z, Zhang X, Li W, et al. Salidroside Induces Apoptosis in Human Gastric Cancer Cells via the Downregulation of ENO1/PKM2/GLUT1 Expression[J]. Volume 44. Biological & pharmaceutical bulletin; 2021. pp. 1724–31. Additional Declarations No competing interests reported. Supplementary Files PmTOR.tif TmTOR.tif 2PKM2.tif 2HK2.tif GLUT1.tif ALDHA.tif 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. 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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-6450229","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458973823,"identity":"8ac909e6-28a9-4566-852f-df558a3d6599","order_by":0,"name":"Chang-An Guo","email":"","orcid":"","institution":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chang-An","middleName":"","lastName":"Guo","suffix":""},{"id":458973824,"identity":"1ac2cd8f-dd27-473e-8cd6-fbb0a4639887","order_by":1,"name":"Ruo-Fei Sun","email":"","orcid":"","institution":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruo-Fei","middleName":"","lastName":"Sun","suffix":""},{"id":458973825,"identity":"f8612a11-c6a6-45f0-b25a-f38609ddc7fa","order_by":2,"name":"Cheng-Bin Tao","email":"","orcid":"","institution":"Lanzhou University Second Hospital, The Second Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Cheng-Bin","middleName":"","lastName":"Tao","suffix":""},{"id":458973826,"identity":"08304db9-f651-40cc-b23c-5c747bbf343c","order_by":3,"name":"Hong An","email":"","orcid":"","institution":"Lanzhou University Second Hospital, The Second Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"An","suffix":""},{"id":458973827,"identity":"b1c82696-3a68-49ee-9091-e32b7a88eb3c","order_by":4,"name":"Hong-Wu Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACCQbGBwkGNgkMDMwHDnz4QZwWZoMPFWlALWyJB2f2EKeFTXLGmcNALTzGhznYiNChO7vHQJq3jTmPXyLnw2EGHgZ5frED+LWY3TljYMzbxlYsOSN3w+ECCwbDmbMTCGi5kbshmbeNJ3EDkHF4Bg9DgsFtIrQc5m2TAGrJeXCYh404LRsbZ5wxAGlhIFZL/meGDxUJxZI9zwyAgSxBjF/S0n8kGPzP42dPfvzhww8beX5pAloQQACsUoJY5SDAf4AU1aNgFIyCUTCSAACXZE3bila7EwAAAABJRU5ErkJggg==","orcid":"","institution":"Lanzhou University Second Hospital, The Second Clinical Medical College of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Hong-Wu","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-04-15 03:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6450229/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6450229/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83244764,"identity":"267e0f7f-f76a-4eb7-a9ea-926b89c93f84","added_by":"auto","created_at":"2025-05-21 16:47:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272664,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The \u003cem\u003eCASC19\u003c/em\u003e expression levels in the normal gastric epithelial mucosa cell line GES-1 and different human GC cell lines are shown. (B) \u003cem\u003eCASC19\u003c/em\u003e knockdown is detected in MKN-45 cells. (C)\u003cem\u003e CASC19\u003c/em\u003e overexpression is detected in KATO-III cells.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/adc5560b9cab38e118924777.jpg"},{"id":83244766,"identity":"0fdec5a1-8f7c-45aa-aac5-1126e97570f9","added_by":"auto","created_at":"2025-05-21 16:47:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2634699,"visible":true,"origin":"","legend":"\u003cp\u003e(A) A diagram of the base peak ion current on essence spectrum detection and analysis of the different quality control (QC) samples are shown. (B, C) The results of the principal component analysis (PCA) of the different GC samples are shown. (D) The distribution of RSD in the GC samples is shown. (E) A volcano plot of the expressions of the differential metabolites (green dots for decreased, orange dots for increased, and gray dots for no difference) in the GC cell groups is shown.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/6475747b03efdbdfa718fd2b.jpg"},{"id":83245257,"identity":"6c549d26-0441-4d9c-95ad-5d78635b7ea7","added_by":"auto","created_at":"2025-05-21 16:55:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3353137,"visible":true,"origin":"","legend":"\u003cp\u003eThis figure shows the (A) bar graph of the differential metabolites in the GC cell lines, (B) percentage of the expression of the different kinds of metabolites, (C) clustering heatmap of the differential metabolites, and (D) violin plot of the top 20 differential metabolites in the GC cells.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/dc65303290786a89fc272ca7.jpg"},{"id":83245265,"identity":"8901b3dc-a41a-4d60-a081-cb32e709c02d","added_by":"auto","created_at":"2025-05-21 16:55:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3605925,"visible":true,"origin":"","legend":"\u003cp\u003eThis figure shows the KEGG classifications of the (A) downregulated metabolites, (B) upregulated metabolites, and (C) all differential metabolites in the GC cells.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/f9aa8e83c2c869f34ef92403.jpg"},{"id":83245259,"identity":"18c7a5b4-900d-4724-8eed-a46309ac8f67","added_by":"auto","created_at":"2025-05-21 16:55:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3678747,"visible":true,"origin":"","legend":"\u003cp\u003eThis figure shows the KEGG enrichment analyses of the (A) upregulated differential metabolites, (B) downregulated differential metabolites, and (C) all differential metabolites in the GC cells.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/c69df328e9d9914c71d7c769.jpg"},{"id":83244769,"identity":"f637e447-4a8c-4328-9466-a5e1ab78e985","added_by":"auto","created_at":"2025-05-21 16:47:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":950402,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of (A) lactic acid production and (B) glucose content in the medium after upregulation of \u003cem\u003eCASC19\u003c/em\u003e expression in KATO-III cells and knockdown of \u003cem\u003eCASC19\u003c/em\u003e expression in MKN-45 cells are shown. (C, D) Western blot assay detects changes in the expressions of total mTOR, p-mTOR, and aerobic glycolysis-related proteins (i.e., GLUT1, LDHA, HK2, and PKM2) after downregulation of the target gene expression in MKN-45 cells. Comparisons of the (E) subcutaneous graft tumor morphology, (F) transplanted tumor growth curves, and (G) final weight of the transplanted tumors between the sh-NC group and sh-CASC19 group are shown.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/be9ba24f0225d5034a915ab9.jpg"},{"id":83245258,"identity":"a3bd05b2-5395-4ab9-af16-204cfd72eb6e","added_by":"auto","created_at":"2025-05-21 16:55:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2267409,"visible":true,"origin":"","legend":"\u003cp\u003eHE staining demonstrates the morphological changes in the two groups of transplanted tumor tissues and arrows indicate giant tumor cells (400×). IHC staining detects p-mTOR, GLUT1, HK2, and PKM2 protein expressions in the subcutaneous tissues with transplanted tumor in nude mice (400×).\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/15eb0dd8348ca5d1e9183fb8.jpg"},{"id":86673510,"identity":"5237c469-e3ae-4f7b-9ed0-41341d1aede7","added_by":"auto","created_at":"2025-07-14 11:47:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17922175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/053e41cb-f5f7-4d3f-8db2-dad50c78485b.pdf"},{"id":83245261,"identity":"890f08ee-a3a0-46e3-9681-79b54b160740","added_by":"auto","created_at":"2025-05-21 16:55:02","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":264228,"visible":true,"origin":"","legend":"","description":"","filename":"PmTOR.tif","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/84f8e6bc6960a09ae09bb098.tif"},{"id":83244767,"identity":"b74730a6-e205-4484-a1e4-c7f96d97f97f","added_by":"auto","created_at":"2025-05-21 16:47:02","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":579328,"visible":true,"origin":"","legend":"","description":"","filename":"TmTOR.tif","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/597578b4279529b10332afcb.tif"},{"id":83244777,"identity":"2ff9e492-d4ce-4aa3-84c6-4b01c1e6d452","added_by":"auto","created_at":"2025-05-21 16:47:02","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":283460,"visible":true,"origin":"","legend":"","description":"","filename":"2PKM2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/266767a9bb952cd2e4415147.tif"},{"id":83245391,"identity":"ae54094c-e989-4a99-b3c3-6670afb20201","added_by":"auto","created_at":"2025-05-21 17:03:03","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":440920,"visible":true,"origin":"","legend":"","description":"","filename":"2HK2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/d9b57f63a047da0bb9f0af66.tif"},{"id":83245263,"identity":"5faf202c-7f3e-4cb8-b982-c828fd35b6de","added_by":"auto","created_at":"2025-05-21 16:55:02","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":179100,"visible":true,"origin":"","legend":"","description":"","filename":"GLUT1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/382a55ea217fdb0ba45f3436.tif"},{"id":83245390,"identity":"7dcfc74f-7bbd-461b-a6d7-e44a76f2a7b6","added_by":"auto","created_at":"2025-05-21 17:03:02","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":338300,"visible":true,"origin":"","legend":"","description":"","filename":"ALDHA.tif","url":"https://assets-eu.researchsquare.com/files/rs-6450229/v1/26bbcfa65861313707e7a2d7.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"LncRNA CASC19 Regulates mTOR Pathway to Affect Aerobic Glycolysis and Promote the Proliferation of Gastric Cancer in Vitro and in Vivo","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is one of the most common cancers worldwide and is particularly prevalent in Asian countries \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Currently, no effective treatment is available for advanced GC. Due to the lack of specific biomarkers, early diagnosis of GC is challenging \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In addition, patients with advanced GC have a high recurrence rate of metastasis and poor prognosis. Although multimodal treatment with surgery and chemotherapy has improved patient survival, the prognosis for patients with advanced GC remains poor. Therefore, early diagnosis and treatment are important \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Identifying important regulatory molecules in GC can provide valuable information in the search for diagnostic, therapeutic, and prognostic biomarkers, as well as therapeutic targets \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLncRNAs are a group of RNA transcripts\u0026thinsp;\u0026gt;\u0026thinsp;200 nt, but they cannot encode proteins \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. They are involved in tumor development through various ways (digestive, respiratory, reproductive, urinary, central nervous, and other systems) \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Moreover, lncRNAs are closely related to the occurrence, metastasis, and prognosis of GC \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. LncRNAs have become the focus of cancer research due to their high specificity and easy detection in different tissues and body fluids \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The study on the function of lncRNAs in GC may provide an important theoretical basis for the occurrence, development, and prognosis of GC. Recent metabolomic studies have promoted our understanding of the relationship between metabolic reprogramming and GC progression and identified potential metabolic targets for clinical applications and therapeutic interventions \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. As a powerful tool for metabolite flux measurement, metabolomics can comprehensively analyze metabolites and related metabolic pathways and play an important role in GC diagnosis and therapeutic target discovery \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur previous studies found that \u003cem\u003eCASC19\u003c/em\u003e was highly expressed in GC cells, and the results of in vitro cell experiments showed that proliferation, invasion, and metastasis of GC cells were inhibited after downregulation of \u003cem\u003eCASC19\u003c/em\u003e expression \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In this study, we initially transfected the lectin virus to downregulate \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells and then clarified the effects of \u003cem\u003eCASC19\u003c/em\u003e on key metabolites and metabolic pathways of GC through metabolomic analysis. Thus, metabolomics was used to explain the mechanism of \u003cem\u003eCASC19\u003c/em\u003e in the occurrence and development of GC. In vitro and in vivo experiments verified the changes of important metabolite pathways. Lactic acid and glucose concentrations in GC cells before and after \u003cem\u003eCASC19\u003c/em\u003e knockdown were measured using lactic acid and glucose kits. Western blot was used to detect the effect of \u003cem\u003eCASC19\u003c/em\u003e knockdown on the aerobic glycolysis pathway of GC cells. By conducting animal experiments, the effect of \u003cem\u003eCASC19\u003c/em\u003e knockdown on the proliferation and aerobic glycolysis pathway of GC cells was detected. Therefore, we preliminarily explored the mechanism of \u003cem\u003eCASC19\u003c/em\u003e in GC.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eWe used the human GC cell line AGS, NCI-N87, MKN-45, and KATO-III cell line. The cells were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). All cells were cultivated in fetal bovine serum (FBS) (10%) medium and were kept in a CO\u003csub\u003e2\u003c/sub\u003e (25%) incubator at 37℃.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCASC19\u003c/b\u003e \u003cb\u003eexpression levels in cell lines of normal gastric epithelial mucosa and gastric cancer\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the expression level of \u003cem\u003eCASC19\u003c/em\u003e in human GC-associated cells, we examined the \u003cem\u003eCASC19\u003c/em\u003e expression in the normal gastric epithelial mucosa cell line GES-1 and four human GC cell lines (i.e., AGS, NCI-N87, MKN-45, and KATO-III) using qRT-PCR. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cem\u003eCASC19\u003c/em\u003e expression was significantly higher in the human GC cell lines (extremely higher in MKN-45 and NCI-N87 and slightly higher in AGS and KATO-III) than in the normal gastric epithelial mucosa cell line GES-1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction of lentivirus and cell transfection\u003c/h3\u003e\n\u003cp\u003eIn this experiment, MKN-45 cells were selected as the target for \u003cem\u003eCASC19\u003c/em\u003e knockdown, because they had the highest \u003cem\u003eCASC19\u003c/em\u003e expression among the four GC cells. The sh-RNA sequences of \u003cem\u003esh-CASC19\u003c/em\u003e and negative control \u003cem\u003esh-NC\u003c/em\u003e (sh-blank) were synthesized by Jikai Gene Co. Ltd. (Shanghai, China). The \u003cem\u003esh-NC\u003c/em\u003e sequence was 5\u0026prime;-TTCTCCGAACGTGTCACGT-3\u0026prime;. We designed three sh-RNA targets, including \u003cem\u003esh-CASC19-1\u003c/em\u003e, \u003cem\u003esh-CASC19-2\u003c/em\u003e, and \u003cem\u003esh-CASC19-3\u003c/em\u003e, with the sequences 5\u0026prime;-CAGCATTTGCCATACTACATT-3\u0026prime;, 5\u0026prime;-CAGCACAATGATGGAAGGCTT-3\u0026prime;, and 5-CTGCATGCTTCTGATGTGAGT3', respectively. After the subsequent experiments, \u003cem\u003esh-CASC19-2\u003c/em\u003e had the highest gene knockdown efficiency (79.4%) in MKN-45 cells and was selected for the other succeeding experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eMeanwhile, KATO-III cells were selected as the target for \u003cem\u003eCASC19\u003c/em\u003e gene upregulation, because they had the lowest \u003cem\u003eCASC19\u003c/em\u003e expression among the four GC cells. Lentivirus with \u003cem\u003eCASC19\u003c/em\u003e overexpression (i.e., pLV-\u003cem\u003eCASC19\u003c/em\u003e) was synthesized by Jikai Gene Co. Ltd., and a vector was used as the negative control for overexpression. The final qRT-PCR results showed that the target gene expression level was 12.6 times higher in the pLV-\u003cem\u003eCASC19\u003c/em\u003e group than in the vector group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTransfection was performed using Lipofectamine 3000 reagent (Thermo Fisher Scientific, USA), following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eMetabolite extraction\u003c/h3\u003e\n\u003cp\u003eFor the transcriptomic analysis, we used the human GC cell line MKN-45 GC. The control and knockdown groups had six samples each. The cell samples were removed from the refrigerator (\u0026minus;\u0026thinsp;80\u0026deg;C), and 10 times the volume of the extraction buffer MeOH:ACN:H\u003csub\u003e2\u003c/sub\u003eO (2:2:1, V/V) was added, fully vortexed, and sonicated. The cells were flash-frozen in liquid nitrogen for 1 min and then thawed at room temperature, sonicated again, and this step was repeated thrice. The precipitate was centrifuged at 18000g for 15 min using a low-temperature centrifuge. The centrifuged supernatant was taken and drained using a concentrator, then sonicated with an equal volume of ACN:H\u003csub\u003e2\u003c/sub\u003eO (1:1, V/V) to redissolve. The samples were centrifuged at 18000 g for 15 min at 4\u0026deg;C, and the supernatant was transferred to a new centrifuge tube and stored in a refrigerator (\u0026minus;\u0026thinsp;80\u0026deg;C) or analyzed with high performance liquid chromatography (Waters, USA).\u003c/p\u003e\n\u003ch3\u003eLiquid chromatography–mass spectrometry analysis\u003c/h3\u003e\n\u003cp\u003eThe metabolites were separated using the Waters ACQUITY UPLC ultra-high performance liquid phase system and combined using the chromatographic column (Waters ACQUITY UPLC BEH C18 Column, 1.7 \u0026micro;m, 2.1 \u0026times; 100 mm) with a 10 \u0026micro;L sample size. Elution was performed at a flow rate of 400 \u0026micro;L/ min (column temperature 40 C). Mobile phase A is an aqueous solution, containing formic acid (0.1%), and mobile phase B is an aqueous solution, containing formic acid (0.1%) and acetonitrile (1%). The liquid phase gradient (mobile phase B) was set as follows: 0\u0026ndash;11 min, 5\u0026ndash;90%; 11.0\u0026ndash;12.0 min, 90%; 12.0\u0026ndash;12.1 min, 90\u0026ndash;5%; 12.1\u0026ndash;15.0 min, 5%. For the metabolite treatment process, we used the ultra-high performance liquid phase system for separation, followed by electron spray ionization (ESI) implantation into an ESI source. Subsequently, the timsTOF Pro mass spectrometer (Waters, USA) was used for analysis. The input voltage of the ion source was expected to be 1.6 kV, and the parent ion of the peptide segment and its secondary fragments was determined on the timsTOF Pro mass spectrometer. The mass spectrum scanning range was set at 20\u0026ndash;1,300 m/z. Data were collected using the parallel cumulative serial fragmentation mode. After successfully collecting the first stage mass spectrum, the secondary spectrum of the charge number of the parent ion within 0\u0026ndash;1 was collected using PASEF mode. To avoid secondary scanning of the parent ion, the dynamic exclusion time of the series mass spectrometry scan should be set to 6 s.\u003c/p\u003e\n\u003ch3\u003eDatabase retrieval\u003c/h3\u003e\n\u003cp\u003eWe used MetaboScape 2022 for peak extraction, alignment, and retention time correction of the original data, and the primary and secondary mass errors were controlled within 20 ppm to ensure the accuracy of the identification results. The structure and annotation information of metabolites were obtained by spectrogram comparison of the National Institute of Standards and Technology (NIST), Human Metabolomics (HMDB), Owned, and Integrated Public databases.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003eData screening and statistical algorithms were combined to fill and correct missing data values based on the quantitative information of metabolites obtained through database matching. For samples with multiple repetitions, the corrected expression level was used to calculate the fold change of the metabolite difference between the two groups, and the P-value of the univariate t-test analysis was combined. Multivariate statistical analysis and orthogonal partial least square discriminant (OPLS-DA) analysis were used to calculate the variance importance (VIP) values and obtain the metabolites with significant differences. Subsequently, multilevel bioinformatics and functional analyses were conducted for the differential metabolites. When the number of sample groups is \u0026gt;\u0026thinsp;3, all intergroup analysis of variance (ANOVA) of P-values can also be provided concurrently to screen differential metabolites. Expression cluster and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed for the selected differential metabolites.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlucose concentration testing in target cell culture medium\u003c/h3\u003e\n\u003cp\u003eThe change of glucose concentration in the cell culture medium of different groups was completed based on the kit instructions (Biyuntian Biotechnology Co., Ltd.). The experiment was performed in a 96-well plate, and a microplate reader was used to detect the absorbance at 505 nm in each well. Glucose content (mmol/L) = (absorbance of sample wells\u0026thinsp;\u0026minus;\u0026thinsp;absorbance of blank wells)/(absorbance of standard wells\u0026thinsp;\u0026minus;\u0026thinsp;absorbance of blank wells) \u0026times; 5.55 (standard concentration, mmol/L).\u003c/p\u003e\n\u003ch3\u003eLactate concentration testing in target cell culture medium\u003c/h3\u003e\n\u003cp\u003eThe change of lactic acid concentration in the cell culture medium of different groups was completed based on the kit instructions (Biyuntian Biotechnology Co., Ltd.). The experiment was performed in a 96-well plate, and a microplate reader was used to detect the absorbance of each sample at 530 nm. The lactate content in each group was calculated using the following formula: lactate content (mmol/L) = (absorbance of sample wells\u0026thinsp;\u0026minus;\u0026thinsp;absorbance of blank wells)/(absorbance of standard wells\u0026thinsp;\u0026minus;\u0026thinsp;absorbance of blank wells) \u0026times; 3 (standard concentration, mmol/L).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eMKN-45 GC cells of the sh-NC group and sh-\u003cem\u003eCASC19\u003c/em\u003e were lysed for 30 min on ice in the radioimmunoprecipitation assay buffer. Subsequently, the supernatants were extracted for the Western blotting analysis of glucose transporter-1 (GLUT1), lactate dehydrogenase A (LDHA), hexokinase-2 (HK2), and pyruvate kinase M2 (PKM2) proteins. The samples were subjected to sodium dodecyl sulfate (12%) polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes. The blots were detected using the primary antibodies (Abcam, UK). Secondary antibodies were added after washing four times with PBS-Tween 20, horseradish peroxidase-conjugated. The remaining steps were as previously reported \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The expression of β-actin acts as an internal reference. The gray bands were quantified using the ImageJ software. Each blot-related experiment was repeated thrice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnimal experiment\u003c/h2\u003e \u003cp\u003eExperimental nude mice were purchased from Guangdong Vitong Lihua Experimental Animal Technology Co. Ltd. The Lanzhou University Second Hospital approved this (approval number: 2021A-312). A specific amount of GC cells (MKN-45 GC cell) in the sh-NC and sh-\u003cem\u003eCASC19\u003c/em\u003e groups with good growth states were injected into the posterior subcutaneous area of the left upper limb of nude mice. The long and short diameters of the transplanted tumor were measured and recorded with an electronic vernier caliper every other day. On the 28th day after injection, the nude mice were killed (carbon dioxide euthanasia) and photographed. Subcutaneous grafts of nude mice were exfoliated, grouped, weighed by electronic scale, and photographed. Subsequently, some transplanted tumor tissues of the sh-NC and sh-CASC1 groups were fixed with formaldehyde for subsequent pathological study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHematoxylin eosin (HE) staining\u003c/h2\u003e \u003cp\u003eThe transplanted tumor tissues of nude mice were washed with NS thrice, fixed with neutral formaldehyde buffer (10%), dehydrated, treated with paraffin wax, sliced, baked, dewaxed, and stained using HE. Morphological changes were observed under a bifocal microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003eThe expression level of aerobic glycolytic-related protein in transplanted tumor tissues of nude mice was detected by immunohistochemical staining. After conventional paraffin embedding, transplanted tumor tissue sections were obtained, placed on slides, dewaxed, and hydrated. The first (Abcam, UK) and second antibodies were added successively. Finally, the antibody was treated with diaminobenzidine (DAB) color development solution and hematoxylin as the reverse stain, followed by the transparent step. After completing transparency, resin tablets were sealed, and the immunohistochemical staining effect was eventually observed under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMetabolite concentrations were normalized to the sample volume used for extraction. Each metabolite was readjusted, and the median was set to one. Normalized metabolite concentrations were transformed in log base 10. Log-transformed metabolomics data were analyzed using ANOVA and t-test (assuming unpaired variance) to generate volcano plots, and VIP values in projections were determined using partial least squares-discriminant analysis (PLS-DA). The Benjamini\u0026ndash;Hochberg false discovery rate method was used to adjust for multiple comparisons. VIP scores were determined using PLS-DA, and metabolites with VIP scores\u0026thinsp;\u0026gt;\u0026thinsp;1.5 were statistically significant. Leave-one-out cross-validation and permutation tests were used to evaluate the robustness of the model and the amount of variance represented by principal components, with a permutation significance threshold set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, using Q2 and R2. Metabolites were considered VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.5, |log2|\u0026gt;1.5-fold, and a change in false discovery rate of \u0026le;\u0026thinsp;10% was used as an important feature for further analysis. AUROC curve analysis was used to quantify the diagnostic performance of the selected classifiers. Metabolite pathway analysis was performed using Fisher calculation and KEGG metabolomics reference library as previously described, and the operation was performed in Metabo Analyst 4.0. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data analysis was performed using a combination of JMP\u0026trade; statistical discovery software 1515.0.0 (SAS Institute) and Metabo Analyst 4.0. ANOVA LSD comparison tests were used to analyze the statistical significance of the continuous variables between multiple groups. The chi-square test was used to compare the rates between multiple groups, and statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical Packages for the Social Sciences version 23.0 software (IBM Corp.; Armonk, NY, USA) was used for data analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome data analysis\u003c/h2\u003e \u003cp\u003eWe used Analyst 1.6.3 to analyze the mass spectrum data. One quality control sample was added to every six holes for repeatability analysis. The basic peak ion flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) is generated through quality control analysis of GC samples. The quality control samples in this experiment showed a high overlap of metabolites in the basic peak ion flow diagram, indicating relatively stable GC cell samples that could be used for subsequent studies. Principal component analysis of GC cell samples showed clustering of the treatment and control groups in relatively concentrated areas. The principal component analysis (PC) of PC1 (49.0%), PC2 (8.2%), and PC3 (7.3%) showed that PC scores varied widely between groups, indicating differences in metabolomics between the two sample sets of GC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). The relative standard deviation (RSD) distribution of features in the quality control sample was \u0026lt;\u0026thinsp;0.2, highlighting the excellent data quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), indicating reliable metabolomics data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eScreening of differential metabolites\u003c/h2\u003e \u003cp\u003eDifferential metabolites in different GC cells can be initially analyzed using the OPLS-DA model. Differential metabolites were screened using a t-test combined with the OPLS-DA model. Metabolomics data showed that 160 different metabolites were detected between \u003cem\u003eCASC19\u003c/em\u003e lentivirus knockdown GC cells and un-knockdown GC cells, including 134 and 26 up and 26 downregulated metabolites, respectively. Specific visualizations are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. The main components of differential metabolites include steroids, peptides, lipids, nucleic acids, vitamins, cofactors, and antibiotics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Moreover, more metabolites act as vitamin cofactors and antibiotics. The differential metabolites after screening were also cluster analyzed, and heat maps were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We created a chart of the top 20 differential metabolites through differential metabolism analysis, including inositol, phospholipids, 2-methyl-3-pyrimidine-2-phenylpropionic acid, adenylate succinic acid, serine, guanylate, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical table of differential metabolites between the knockdown group and the control group (The top 20 differential metabolites with the smallest \u003cem\u003eP\u003c/em\u003e-values are listed below in the order from smallest to largest \u003cem\u003eP\u003c/em\u003e-values).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK/C Fold_Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK/C P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK/C VIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRegulated Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-Myo-Inositol, 1-[2-hydroxy-3-[(1-oxo-9,12-octadecadienyl)oxy]propyl hydrogen phosphate], [S-(Z,Z)]-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.15346E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42895108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_2623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePS(18:1(9Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.97785E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42910664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLysoPE(16:1(9Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.26162E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42852157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPE 18:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94425E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42886974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en-Pentadecylamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82146E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42872596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC 20:2e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85416E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42796943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-Fructosyl phenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.46138E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42668092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenosine Monophosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65967E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42738323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCys Glu Glu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2978E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42674102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCocamidopropylBetaine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.85534E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42603260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPE 20:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14854E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42606004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBENZALKONIUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.82196E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42504027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCompounds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eK/C Fold_Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eK/C P value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eK/C VIP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRegulated Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPC 20:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12252E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42417684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Hydroxy-2-((9Z,12Z)-octadeca-9,12-dienoyloxy)propyl 2-(trimethylazaniumyl)ethyl phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70019E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42344671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFolinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70196E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42415279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC 20:4e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11613E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42283171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_2735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuccinoadenosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8518E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42283434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-[2-(4-fluorophenoxy)ethyl]-8-pyrrolidin-1-ylsulfonyl-[1,2,4]triazolo[4,3-a]pyridin-3-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.97536E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42277134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_1303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPE 22:5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.39427E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42179284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTM_990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIle Asn Val Asp Tyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55994E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42009351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKEGG annotation and differential metabolite classification\u003c/h2\u003e \u003cp\u003eIn this study of differential metabolites in GC cells, the KEGG functional classification of downregulated differential metabolites is mainly focused on the global metabolite pathway. Other functions are classified as the digestive system, cofactor metabolism, vitamins, energy metabolism, biosynthesis of other secondary function metabolites, chemical structure transformation, amino acid metabolism, neurodegenerative diseases, drug resistance in cancer, signaling transduction and membrane transport, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Upregulated differential metabolites were categorized mainly as global and overview maps, nucleotide metabolism, cofactor and vitamin metabolism, and chemical structure transformation in KEGG functional classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Overall, after lentiviral transfection downregulated \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells, the KEGG functional categories were global and overview maps, metabolic profiles of cofactors and vitamins, chemical structure transformation maps, nucleotide metabolism, antitumor drug resistance, and signaling transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). KEGG annotation was further performed for GC cell differential metabolites showed that the upregulated differential metabolites were mainly enriched in olfactory transduction, cGMP-PKG signaling pathway, histidine, purine-derived alkaloid synthesis, antihardeners, flavor transduction, purine metabolism, and cofactor biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The downregulated differential metabolites were mainly enriched in the folate-carbon pool, stress resistance, biosynthesis of various antibiotics and pathways of neurodegenerative diseases, amino acid biosynthesis, cofactors, and other biological pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The main enrichment pathways of differential metabolites were the mammalian target of rapamycin (mTOR) signaling pathway, olfactory conduction, antifolate resistance, cofactor biosynthesis, metabolic pathways, and purine metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of\u003c/b\u003e \u003cb\u003eCASC19\u003c/b\u003e \u003cb\u003eexpression changes on glucose consumption and lactic acid production in GC cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the effect of \u003cem\u003eCASC19\u003c/em\u003e on aerobic glycolysis of GC cells. Glucose consumption and lactate production in KATO-III cells and MKN-45 cells were detected using glucose and lactate kits. Compared with the sh-NC group, KATO-III cells had increased glucose consumption and lactic acid production after upregulation of \u003cem\u003eCASC19\u003c/em\u003e expression. Conversely, compared with the sh-NC group, MKN-45 cells had decreased glucose consumption and lactic acid production after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of\u003c/b\u003e \u003cb\u003eCASC19\u003c/b\u003e \u003cb\u003eon aerobic glycolysis related proteins in GC cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWestern blot assay was used to detect the changes of total mTOR and p-mTOR protein and protein expression related to aerobic glycolysis (GLUT1, LDHA, HK2, and PKM2) after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression in MKN-45 cells. Moreover, MKN-45 cells had decreased protein expression (p-mTOR, GLUT1, LDHA, HK2, and PKM2) after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC,D).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of downregulated\u003c/b\u003e \u003cb\u003eCASC19\u003c/b\u003e \u003cb\u003eexpression on growth of subcutaneous GC grafted tumor in nude mice.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe nude mice were killed 28 days after the tumor formation in the animal experiment, followed by transplanted tumor removal. The weight and volume of the transplanted tumor in the sh-NC group were significantly higher than those in the sh-\u003cem\u003eCASC19\u003c/em\u003e group. Thus, knockdown \u003cem\u003eCASC19\u003c/em\u003e expression can significantly inhibit the proliferation of subcutaneous GC transplanted tumors in nude mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). During the growth of the transplanted tumor, the long and short diameters were measured regularly. After calculating the measured data, the growth curves of the transplanted tumor in the sh-NC and sh-\u003cem\u003eCASC19\u003c/em\u003e groups were created, which showed a statistically significant difference in the growth curves of the transplanted tumor between both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). We conducted a statistical comparative analysis of the final weight of transplanted tumors in the sh-NC and sh-\u003cem\u003eCASC19\u003c/em\u003e groups and found a statistically significant difference in the tumor weight between both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eUse of HE staining to observe the histomorphology changes of the transplanted tumors\u003c/h2\u003e \u003cp\u003eThe tumor tissue was stained with HE. After staining, the difference between sh-\u003cem\u003eCASC19\u003c/em\u003e and sh-NC groups was observed under the microscope. The sh-\u003cem\u003eCASC19\u003c/em\u003e group showed characteristics of reducing the number of tumor giant cells, nuclear fission image, nuclear irregularity, and cell volume, indicating that the malignant proliferation of tumors is inhibited after \u003cem\u003eCASC19\u003c/em\u003e gene knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of downregulated\u003c/b\u003e \u003cb\u003eCASC19\u003c/b\u003e \u003cb\u003eexpression on p-mTOR, GLUT1, HK2, and PKM2 protein expression in transplanted tumors\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe further investigated the effect of \u003cem\u003eCASC19\u003c/em\u003e downregulation on p-mTOR, GLUT1, HK2, and PKM2 protein expression in subcutaneous transplanted tumors of nude mice by using the IHC staining assay. The experimental results showed that p-mTOR, GLUT1, HK2, and PKM2 protein expressions in the sh-\u003cem\u003eCASC19\u003c/em\u003e group were significantly lower than those in the sh-NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In conclusion, downregulating \u003cem\u003eCASC19\u003c/em\u003e gene expression in subcutaneous GC transplanted tumor tissues of nude mice would correspondingly inhibit p-mTOR, GLUT1, HK2, and PKM2 protein expressions in transplanted tumor tissue of nude mice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMany studies have shown that lncRNAs are closely related to the occurrence and development of GC \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Our research team and other scholars have conducted preliminary studies on the role of lncRNAs \u003cem\u003eCASC19\u003c/em\u003e in GC \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e but yielded few relevant research results. The effect of \u003cem\u003eCASC19\u003c/em\u003e on GC as an oncogene needs further studies. In this study, after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells, metabolomics was used to analyze the changes in metabolite levels and bioinformatics analysis was performed for differential metabolites. Finally, cell and animal experiments were used to further verify the key signaling molecules in the signaling pathways with the most significant differences obtained from metabolomics and bioinformatics analyses, and initially explore the mechanism of \u003cem\u003eCASC19\u003c/em\u003e in GC, providing directions for developing new GC therapeutic targets and diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eIn this metabolomics study, the metabolites sh-\u003cem\u003eCASC19\u003c/em\u003e and sh-NC groups mainly included steroids, peptides, lipids, nucleic acids, vitamins, cofactors, and antibiotics, but the proportion of vitamins, cofactors, and antibiotic metabolites was higher. Vitamins are closely related to the occurrence, development, and prognosis of GC. Vitamins D and B6 have important effects on the occurrence, development, and prognosis of tumors \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Metabolic reprogramming is one of the important characteristics of tumors recently discovered \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, and the research on peptide and amino acid metabolism in this aspect has gained more attention. In this study, obvious changes were found in serine and guanylic acid in the differential metabolites. Serine is a nonessential amino acid; however, it is an essential amino acid in specific tumor situations. Reducing exogenous serine concentration can effectively play an antitumor role \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Increased guanylate-binding protein 1 (GBP1) expression is associated with the decreased aggressiveness of colorectal cancer, and GBP1 can inhibit tumor cell proliferation and promote tumor cell apoptosis \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Among all metabolites studied in this experiment, the difference between inositol and phospholipid is the most significant. Phosphatidylinositol 3-kinase (PI3K) pathway is an important signaling pathway and is often activated in cancer cells \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The main components of PI3K are inositol and phospholipids. Activation of PI3K/AKT and PI3K/mTOR pathways plays a key role in tumor evolution \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, and inositol inhibitors have been proposed as potential new therapies for many cancers \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Inositol and phospholipids showed the greatest variation, indicating that \u003cem\u003eCASC19\u003c/em\u003e likely affects GC development through the PI3K/AKT and PI3K/mTOR pathways.\u003c/p\u003e \u003cp\u003eTo further investigate the effect of \u003cem\u003eCASC19\u003c/em\u003e on GC metabolites, we classified and annotated the differential metabolites using KEGG. The classification of upregulated differential metabolite pathways mainly focused on the digestive system, cofactors, vitamin metabolism, and energy metabolism. The downregulated differential metabolites mainly focused on nucleotide metabolism, cofactors, vitamin metabolism, and chemical structure transformation. KEGG enrichment analysis showed upregulated biosynthesis of histidine and purine-derived alkaloids and downregulated biosynthesis of folic acid one-carbon pool, stress resistance, and various antibiotics. The log2-fold enrichment value of the PI3K/Akt and mTOR signaling pathways is large, with an enrichment factor of 8.33. However, the Fisher exact test showed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12 for PI3K-Akt, which was not significantly different, but it was \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01333 for the mTOR signaling pathway, which was significantly different. Comprehensive metabolomics and bioinformatics analysis results showed that \u003cem\u003eCASC19\u003c/em\u003e knockdown in GC cells significantly affects the mTOR signaling pathway, but mTOR is a serine/threonine protein kinase belonging to the PI3K family; thus, theoretically, \u003cem\u003eCASC19\u003c/em\u003e knockdown affects the PI3K/Akt pathway. Earlier studies found that the mTOR signaling pathway was closely related to aerobic glycolysis \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e; thus, this GC cell and animal experiments closely focused on \u003cem\u003eCASC19\u003c/em\u003e regulation on PI3K/mTOR pathway and aerobic glycolysis.\u003c/p\u003e \u003cp\u003eThe mTOR protein can regulate physiological and pathological activities, including cell function, protein synthesis, metabolism, and cell proliferation. It consists of two complexes, mTORC1 and mTORC2. Although rapamycin can inhibit mTORC1, mTORC2 is not sensitive to rapamycin inhibition. mTORC1/mTORC2 signaling can be activated by different oncogenic signaling pathways and is highly expressed in most cancers \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The PI3K signaling pathway with mTOR as the core kinase plays an important role in the occurrence, proliferation, invasion, and metastasis of cancer cells and other biological behaviors \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Aerobic glycolysis of tumor cells, known as the Warburg effect, indicates that cancer cells produce energy through aerobic glycolysis even with sufficient oxygen, which accelerates glucose consumption and produces a large amount of lactic acid. Aerobic glycolysis is a cancer-specific energy access mode \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Aerobic glycolysis in tumor cells can be clearly distinguished from normal cells undergoing anaerobic glycolysis. Aerobic glycolysis of tumor cells plays an important role in the biological behavior of tumor cells. mTOR signaling pathway is closely related to aerobic glycolysis \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Additionally, mTOR has a regulatory effect on different proteins related to aerobic glycolysis (PKM2, HK2, LDHA, and GLUT1) and tumor development by affecting aerobic glycolysis of tumor cells \u003csup\u003e[\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Moreover, phosphorylated mTOR (p-MTOR) is the activated form of mTOR involved in signaling transduction. We selected p-mTOR as an indicator to detect p-mTOR expression in cell and animal experiments, and the results showed inhibition of p-mTOR expression in the sh-\u003cem\u003eCASC19\u003c/em\u003e group, indicating that \u003cem\u003eCASC19\u003c/em\u003e knockdown affected the mTOR pathway. Our findings were consistent with the results of metabolomics and bioinformatics analyses.\u003c/p\u003e \u003cp\u003ePKM2 is a key metabolic enzyme in the Warburg effect, which is closely related to tumor growth, development, prognosis, and transformation. PKM2 is one of the hotspots of PKM family research. PKM has four subtypes: PKL, expressed in the liver and kidney \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e and plays a role in gluconeogenesis; PKR, the only pyruvate kinase subtype expressed in erythrocytes \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e; PKM1, commonly found in the bone marrow, muscle, and brain tissues requiring rapid energy \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e; and PKM2, a splice isoform of PKM1 expressed in proliferating cells, particularly in tumor cells with stable and specific expression \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The expression of other isoforms of pyruvate kinase gradually loses their respective tissue specificity during tumor formation, eventually changing to PKM2 isoform dominant. Therefore, PKM2 can also act as tumor-type pyruvate kinase. PKM2 is overexpressed in various human tumors \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. PKM2 converts pyruvate into lactate and ATP in the state of aerobic glycolysis, which can provide sufficient energy for rapid tumor cell proliferation \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. HK2, the rate-limiting enzyme that catalyzes the first step of glycolysis, is commonly overexpressed in most cancer cells. The conversion of glucose to pyruvate and lactate is inhibited after silencing HK2 in liver cancer cells, thereby inhibiting tumor development and inducing tumor cell death. HK2 loss can lead to decreased Snail protein transcriptional activity and reduced Snail-mediated EMT, thereby affecting cancer cell metastasis \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. LDHA catalyzes the conversion of pyruvate to lactate, plays an important role in nontumor and tumor cells, and is a key enzyme in aerobic glycolysis in cancer cells. LDHA expression is increased in GC, which is significantly correlated with GC differentiation status, vascular invasion, and TNM stage. Other studies have shown that increased LDHA can promote matrix metalloproteinase-2 (MMP-2) and MMP-9 production, accelerate extracellular matrix degradation, and promote tumor metastasis and invasion\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. GLUT1 is a membrane protein that promotes glucose uptake, and its high expression is related to the occurrence, development, and prognosis of different tumors \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, including GC cells, and positively correlated with tumor proliferation \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of cell and animal experiments showed inhibition of p-mTOR expression in cells and xenografts after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression, indicating that \u003cem\u003eCASC19\u003c/em\u003e knockdown significantly affects the mTOR pathway, which was consistent with the results of the metabolomics and bioinformatics analyses. The mTOR pathway plays an important role in regulating aerobic glycolysis. To verify whether \u003cem\u003eCASC19\u003c/em\u003e knockdown in GC cells affects aerobic glycolysis through the mTOR pathway, the changes of p-mTOR protein and various aerobic glycolysis pathway related proteins (GLUT1, LDHA, HK2, and PKM2) were investigated in cell and animal models. The results showed that the expression of p-mTOR and various glycolysis related proteins was significantly decreased after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells in vitro and in vivo. Increased lactate levels in the tumor microenvironment will promote the malignant biological behavior of tumor cells through various mechanisms, such as histone lactylation, which will accelerate lactate production, eventually forming a vicious cycle\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Glucose consumption and lactate production in the GC cell culture medium were two direct indicators reflecting the effect of \u003cem\u003eCASC19\u003c/em\u003e expression changes on the aerobic glycolysis of GC cells. Compared with the sh-NC group, glucose consumption and lactic acid production were increased after upregulating \u003cem\u003eCASC19\u003c/em\u003e expression, but decreased after downregulating \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells. These results indicated that \u003cem\u003eCASC19\u003c/em\u003e could regulate the p-mTOR expression and the changes of aerobic glycolysis pathway related proteins to affect the biological behavior of GC cells.\u003c/p\u003e \u003cp\u003eThe results of animal experiments showed that the growth of GC xenograft in nude mice was significantly inhibited after \u003cem\u003eCASC19\u003c/em\u003e knockdown, which was consistent with those of our previous in vitro proliferation experiments \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, metabolomic studies have shown that \u003cem\u003eCASC19\u003c/em\u003e can regulate the levels of multiple metabolites, indicating that it can promote GC development. Comprehensive metabolomics, bioinformatics, and cell and animal experiment results proved that \u003cem\u003eCASC19\u003c/em\u003e could regulate the expression of aerobic glycolysis related proteins by regulating the mTOR pathway and could affect the biological behavior of GC cells. LncRNA \u003cem\u003eCASC19\u003c/em\u003e may potentially become an effective important molecule for GC diagnosis and treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChang-An Guo, Ruo-Fei Sun and Hong-Wu Ma designed this research. Chang-An Guo, Hong-Wu Ma, Cheng-Bin Tao, Hong An and Ruo-Fei Sun conducted experiments and Data Analysis. Chang-An\u0026nbsp;Guo and Hong-Wu Ma wrote this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Lanzhou University Second Hospital\u0026nbsp;Ehtics Committee\u0026nbsp;approved this study (approval number: 2021A-312).\u0026nbsp;The size or diameter of the transplanted tumors in nude mice in this study did not exceed the size stipulated by the relevant ethical regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Gansu Provincial Natural Science Foundation Project 21JR11RA106 and 22JR5RA974.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTong X, Zhi P, Lin S. 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Nat Commun. 2022;13:899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Yang S, He J, et al. Glucose transporter 3 (GLUT3) promotes lactylation modifications by regulating lactate dehydrogenase A (LDHA) in gastric cancer[J]. Cancer Cell Int. 2023;23:303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBukkuri A, Gatenby RA, Brown JS. GLUT1 production in cancer cells: a tragedy of the commons[J]. NPJ Syst biology Appl. 2022;8:22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai Z, Zhang X, Li W, et al. Salidroside Induces Apoptosis in Human Gastric Cancer Cells via the Downregulation of ENO1/PKM2/GLUT1 Expression[J]. Volume 44. Biological \u0026amp; pharmaceutical bulletin; 2021. pp. 1724\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Long non-coding RNA CASC19, Gastric cancer, Metabolomics, mTOR pathway, Aerobic glycolysis, Xenograft tumor in nude mice","lastPublishedDoi":"10.21203/rs.3.rs-6450229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6450229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the function and mechanism of LncRNA \u003cem\u003eCASC19\u003c/em\u003e in gastric cancer (GC) in vitro and vivo.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eMetabolomics and bioinformatics methods were used to study the changes of metabolites and metabolic pathways in GC cells, wherein \u003cem\u003eCASC19\u003c/em\u003e expression was knocked down. The expression levels of phosphorylated mammalian target of rapamycin (p-mTOR) and different aerobic glycolysis related proteins were detected in cell experiments and animal experiments. After overexpression or knockdown of \u003cem\u003eCASC19\u003c/em\u003e in GC cells, we measured lactate levels and glucose consumption. In the nude mice experiment, the growth curve of the transplanted tumor was created, and the tumor weight difference between the two groups was finally compared.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eVarious metabolites were screened after knocking down \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells. Bioinformatics analysis showed that different metabolites were significantly enriched in the mTOR pathway. The results of in vitro and in vivo experiments showed that p-mTOR expression levels and various downstream proteins of the mTOR pathway related to aerobic glycolysis were significantly decreased after knocking down \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells. \u003cem\u003eCASC19\u003c/em\u003e expression in GC cells was positively correlated with glucose uptake and lactate production.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMetabolomics and bioinformatics analyses showed that \u003cem\u003eCASC19\u003c/em\u003e could regulate the levels of various metabolites and was related to different metabolic pathways. \u003cem\u003eCASC19\u003c/em\u003e can regulate the expression of aerobic glycolysis related proteins by affecting the mTOR pathway and controls gastric cancer cell proliferation in vitro and in vivo.\u003c/p\u003e","manuscriptTitle":"LncRNA CASC19 Regulates mTOR Pathway to Affect Aerobic Glycolysis and Promote the Proliferation of Gastric Cancer in Vitro and in Vivo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 16:46:57","doi":"10.21203/rs.3.rs-6450229/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":"ff385c6d-145c-4a5c-8f5a-d68b8d8b285e","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T11:39:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-21 16:46:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6450229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6450229","identity":"rs-6450229","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}