Downregulation of TMEM220-AS1, a novel long noncoding RNA, is associated with Gastric Cancer development

Downregulation of TMEM220-AS1, a novel long noncoding RNA, is associated with Gastric Cancer development | 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 Downregulation of TMEM220-AS1, a novel long noncoding RNA, is associated with Gastric Cancer development Elaheh Paki, AmirHossein Yari, Samin Abdolazadeh, Vida Ebrahimi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8758621/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in a wide array of biological mechanisms. Recent studies have demonstrated their strong association with both functional and pathological aspects of gastric cancer progression. Nonetheless, the specific role of TMEM220-AS1 in gastric tumorigenesis remains poorly characterized. This study aimed to elucidate the functional involvement of TMEM220-AS1 in gastric cancer and assess its potential as a diagnostic and prognostic biomarker. The novelty of this work lies in its exclusive focus on TMEM220-AS1 within the context of gastric cancer, a setting not previously explored. TMEM220-AS1 was identified through integrated bioinformatic screening, followed by validation of its expression profile in gastric cancer specimens. Materials and Methods TCGA genomic repository was used to analyze the expression levels of TMEM220-AS1. Furthermore, the results for a subset of clinical specimens were validated using qRT-PCR. LncRNA TMEM220-AS1’s clinical relevance was analyzed using the TCGA-derived transcriptomic data and matched clinical profiles. The diagnostic utility of this lncRNA was further explored using ROC curve analysis. As a last step, the functional analysis of TMEM220-AS1 was evaluated by bioinformatics approaches. Results The current investigation indicates that gastric cancer samples exhibited significant downregulation of LncRNA TMEM220-AS1 expression compared to non-neoplastic gastric tissues, and these reduced levels were not related to the clinical characteristics of the patients. Additionally, ROC curve analysis suggests that the expression pattern of LncRNA TMEM220-AS1's may serve as a promising diagnostic indicator for gastric cancer. Functional annotation via in silico tools revealed TMEM220-AS1’s involvement in diverse biological pathways, such as complement and coagulation cascades, peroxisomal activity, histidine catabolism, cholesterol homeostasis, and fatty acid breakdown. Conclusion In conclusion, our finding demonstrates that TMEM220-AS1 is significantly under-expressed in gastric cancer are not associated to clinicopathological characteristics of gastric cancer patients. Based on ROC curve evaluation, TMEM220-AS1 may hold value as a novel biomarker for the early detection of gastric cancer. Gastric cancer LncRNA TMEM220-AS1 ROC curve Diagnostic biomarker TCGA qPCR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Cancer arises from a multifactorial cascade that gives rise to extensive genetic changes and is characterized by uncontrolled cellular growth, invasion, and metastasis [ 1 ]. Worldwide, Gastric cancer (GC) ranks as the fourth most prevalent malignancy and the third leading cause of cancer-related death and the fourth most prevalent type of cancer [ 2 ]. The poor prognosis associated with GC is primarily due to late-stage diagnosis in the majority of patients [ 3 ]. Treatment for stomach cancer primarily involves surgical resection along with adjuvant chemotherapy or combined chemoradiotherapy [ 4 ]. Despite radical resection, over 50% of patients experience local recurrence or distant metastases, or they are diagnosed with stomach cancer after the tumor spreads. As a result, the 5-year survival rate is less than 10%, and the median survival is rarely exceeds 12 months [ 5 ]. Treatment for gastric cancer remains challenging [ 6 ]. The management of GC remains challenging, owing to its considerable heterogeneity in both phenotypic presentation and molecular characteristics [ 7 ]. Several factors contribute to the advancement and progression of malignancies, including mutational events, epigenetic changes, and environmental factors [ 8 ]. In addition, hereditary predisposition plays a significant role in the development of stomach cancer [ 9 ]. Long non-coding RNAs (lncRNAs), exceeding 200 nucleotides in length, have emerged as critical regulators in various biological processes and pathological conditions, including cancer [ 10 ]. The vast majority of the human genome is consists predominantly of non-coding RNAs, which are RNA molecules that are not translated into proteins [ 11 ]. LncRNAs, are RNA molecules associated with several crucial cellular processes as well as diverse pathological conditions [ 12 ]. These RNA molecules, are involved in the modulation of gene expression at multiple levels [ 13 ]. LncRNAs can influence chromatin structure, thereby affecting epigenetic gene repression, and can also regulate gene expression post-transcriptionally. Additionally, by influencing chromatin structure, they affect epigenetic gene repression [ 14 ]. A wide range of cancer forms has been associated with dysregulated expression of lncRNA genes [ 15 ]. Functionally, lncRNAs control the expression of genes at all stages, involving transcription, post-transcriptional processing, and chromatin modification [ 2 ]. Furthermore, by altering transcriptional activity, they are involved in the processes such as cell differentiation, cell cycle, proliferation, apoptosis, migration, and invasion. The expression of oncogenes and tumor suppressor genes is significantly influenced by dysregulation of lncRNA expression [ 11 ]. Several lncRNAs have also been identified as potential biomarkers for the detection of GC [ 16 – 18 ]. TMEM220-AS1 is an antisense lncRNA transcribed from the opposite strand of the TMEM220 gene locus [ 19 ]. In hepatocellular carcinoma (HCC), downregulation of TMEM220-AS1 has been associated with poor prognosis, as reported by Du et al; this suggests that TMEM220-AS1 could serve as a valuable prognostic biomarker [ 20 ]. However, the role of TMEM220-AS1 in GC remains poorly understood. Our study aims to address this research gap by examining the relationship between TMEM220-AS1 long noncoding RNA expression and gastric tumorigenesis. We analyzed RNA sequencing datasets from The Cancer Genome Atlas - Stomach Adenocarcinoma (TCGA-STAD) collection to determine the expression levels of TMEM220-AS1 in GC samples. Subsequently, the results were validated using GC tissue samples and paired normal tissue samples. Additionally, numerous databases were reviewed to elucidate the functional roles associated with TMEM220-AS1, and the correlation between TMEM220-AS1 expression levels and clinicopathological characteristics was assessed. 2. Material And Methods 2.1. Examination of TMEM220-AS1 expression in-silico with the TCGA database RNA sequencing (RNA-seq) gene expression data for stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks package in R. Normalization and processing of the data were performed employing the limma package within the R programming environment [ 21 ]. The expression levels of the long non-coding RNA (lncRNA) TMEM220-AS1 and corresponding receiver operating characteristic (ROC) curves were analyzed using GraphPad Prism software ( https://www.graphpad.com/ ). Normal tissue expression data were obtained from the Genotype-Tissue Expression (GTEx) project, while tumor tissue data were sourced from the TCGA dataset [ 22 ]. Comparative analysis of transcriptomic profiles was conducted using the GEPIA tool ( https://gepia.cancer-pku.cn/ ). Additionally, the UALCAN online platform ( https://ualcan.path.uab.edu/ ), was utilized to investigate TMEM220-AS1 expression across various cancer types and to examine correlations between its expression levels and clinicopathological features using TCGA RNA-seq data [ 23 , 24 ]. 2.2. Sample preparation With written informed consent, 25 GC samples were collected from the Iranian National Tumor Biobank in Tehran, Iran. Liquid nitrogen was employed for the preservation of tissue samples until RNA extraction. No radiotherapy or chemotherapy was administered to the enrolled subjects before surgical intervention. The data assessed for this study consist of clinical and demographic parameters, such as gender, age, tumor size, primary tumor site, lymphatic invasion, histological grade, perineural, serosal and vascular invasion, clinical stage, smoking status, and family history of gastric cancer. 2.3. RNA extraction and cDNA synthesis The samples of tissue were homogenized using a syringe and needle after being treated with lysis buffer, ground with a mortar and pestle apparatus, and then frozen in liquid nitrogen. Total RNA was extracted using using Qiagen's AllPrep DNA/RNA Kit (Germany), following the manufacturer's guidelines. To evaluate the yield and quality of the RNA samples', a ThermoFisher Scientific Life Sciences, USA NanoDrop spectrophotometer was employed. The integrity of RNA was assessed by means of a agarose gel (1%) electrophoretic assay. For cDNA synthesis, 1 µg of total RNA was reverse-transcribed using the PrimeScript™ RT Reagent Kit (TaKaRa Bio, Japan) in a final reaction volume of 20 µL. 2.4. Quantitative real-time PCR (qPCR) In a reaction volume of 10 ml, gene-specific primers and BioFACTTM 2X Real-Time PCR Master Mix (Korea) were utilized for qPCR. The Step One Plus Real-Time PCR System (Applied Biosystems, USA) was utilized to perform three quantitative PCR stages; the first denaturation at 95°C lasted for 15 minutes. This was followed by 45 cycles of denaturation at 95°C for 10 seconds, primer annealing at 60°C for 30 seconds, and elongation at 72°C for 20 seconds. After each run was completed, melting curve profiles were generated. To standardize gene expression levels, the reference gene utilized in this study was GAPDH. To determine the TMEM220-AS1 relative expression levels between groups, the 2 −ΔΔCt technique was employed. Table 1 provides the primer details. Table 1 The sequences of qPCR primers. Target Forward (5′ – 3′ direction) Reverse (5′ – 3′ direction) TMEM220-AS1 5´ CAGGGTCATCACCATAGCACC 3´ 5´ TCAGAAGGGGACTTGGAGCA 3´ GAPDH 5´ AAGGTGAAGGTCGGAGTCAAC 3´ 5´ GGGGTCATTGATGGCAACAA 3´ 2.5. Functional enrichment analysis To identify enriched pathways related to TMEM220-AS1, we first obtained the LncRNAs and coding genes co-expressed with TMEM220-AS1 (both negatively and positively correlated genes) associated with TMEM220-AS1 as well as pharmacological agents predicted to upregulate this lncRNA and CRISPR-based regulatory predictions for its upregulation, utilizing the web resource lncHUB2 ( https://maayanlab.cloud/lncHUB2/ ) [ 25 ]. To determine biological pathways influenced by this lncRNA, we accessed the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the LNCHUB2 online application [ 26 – 28 ]. The Cytoscape software ( https://cytoscape.org/ ) provides network-based visual outputs of all the data [ 29 – 38 ]. 2.6. Statistical analysis The qPCR data and associated results were analyzed, and the visual plots were generated using GraphPad Prism V8. The relative TMEM220-AS1 expression between groups were statistically evaluated using two-tailed unpaired t-tests. The SEM (standard error of the mean), or mean ± SEM, is used to present all data. Statistical significance was set at P < 0.05. 3. Results 3.1. Validation of TMEM220-AS1 overexpression in TCGA-STAD dataset Confirmation of the qPCR experiment results was conducted through analysis of the TCGA-STAD database. Based on the information obtained from the TCGA-STAD analysis and Gepia web tools, TMEM220-AS1 appears to be markedly downregulated in gastric cancer tissues compared to non-tumorous tissues (p < 0.0001) (Fig. 1 a, b). In addition, as shown in Fig. 1 c, with an area under the curve (AUC) of 0.9 (p < 0.0001), the expression of the TMEM220-AS1 gene serves as a promising diagnostic biomarker to distinguish GC from normal samples. A cross-cancer evaluation using the Ualcan database revealed that TMEM220-AS1 expression was downregulated in multiple malignancies, particularly in gastric cancer (Fig. 2 ). The following studies in TCGA-STAD dataset indicated that in all types and stages of gastric cancer, the expression level of TMEM220-AS1 was consistently diminished (Fig. 3 ). 3.2. TMEM220-AS1 downregulation in GC samples As part of the experiment, 25 GC tissues and 25 adjacent non-cancerous gastric tissues were used. Based on quantitative PCR findings (Fig. 4 ), Comparing GC tissue specimens to normal tissue specimens revealed a demonstrated a marked reduction (p < 0.0001) of TMEM220-AS1, in agreement with data derived from the TCGA database. Furthermore, in GC patients, the association between clinical-pathological parameters and TMEM220-AS1 expression was examined. Gender, histological grade, tumor size, initial tumor location, perineural and serosal invasion, clinical stage, smoking status, and family history of GC did not exhibit any statistically significant relationship with TMEM220-AS1 levels (Table 2 ). 3.3. TMEM220-AS1 clinicopathological characteristics in GC In GC patients, the relationship between TMEM220-AS1 transcript levels and clinicopathological variables was examined. Gender, histological grade, tumor size, initial tumor location, perineural and serosal invasion, clinical stage, smoking status, and family history of GC showed no statistically meaningful association with TMEM220-AS1 expression (Table 2 ). Table 2 The associations between the clinicopathological features of GC patients and the expression of TMEM220-AS1. Characteristics Number of samples (%) Significance Age =60 13 (52%) Gender Male 21 (84%) 0.3980 Female 4 (16%) Site of primary tumor Stomach 16 (64%) 0.5229 Antrum 5 (20%) Gastric Cardia 4 (16%) Tumor size < 5 11 (44%) 0.6707 ≥ 5 12 (48%) Unknown 2 (8%) Histology grade I, II 17 (68%) 0.3045 III, IV 8 (32%) Lymphatic invasion Yes 19 (76%) 0.1894 No 6 (24%) Vascular invasion Yes 19 (76%) 0.1894 No 6 (24%) Serosal invasion Yes 14(56%) 0.2919 No 11(44%) Perineural invasion Yes 16 (64%) 0.1983 No 9 (36%) Clinical Stage I, II 9 (36%) 0.1942 III, IV 16 (64%) Family history Yes 9 (36%) 0.4826 No 16 (64%) Smoking status Yes 14 (56%) 0.5476 No 11 (44%) 3.4. TMEM220-AS1 is vital in a broad number of biological functions By employing the lncHUB2 web platform, TCGA datasets were queried to identify co-expressed genes associated with TMEM220-AS1. The interaction networks of positively and negatively correlated genes are shown in Figs. 5 and 6 , respectively. Table 3 presents the top-ranking co-expressed lncRNAs, along with negatively and positively co-expressed genes with related to TMEM220-AS1, including their Pearson's Correlation Coefficient. Additionally, the table 4 lists the candidate drugs and CRISPR-based regulatory predictions that upregulate TMEM220-AS1. The analysis of KEGG resource using LNCHUB2 online tool revealed the involvement of TMEM220-AS1 in various biological pathways including complement and coagulation cascades, peroxisome, histidine metabolism, cholesterol metabolism, fatty acid degradation, as well as additional biologically relevant pathways. Figure 7 visualizes the functional pathways associated with the TMEM220-AS1. Table 3 High rank co-expressed lncRNAs, negatively co-expressed, and positively co-expressed genes with TMEM220-AS1. positively co-expressed genes Pearson's Correlation Coefficient negatively co- expressed genes Pearson's Correlation Coefficient co-expressed LncRNAs Pearson's Correlation Coefficient ENSG00000277190 0.431948215 HNRNPUL1 -0.098580256 ENSG00000277190 0.431948215 AC024704.2 0.418479145 KPNB1 -0.096339531 AC024704.2 0.418479145 CTA-292E10.8 0.404167265 LARP1 -0.093646139 CTA-292E10.8 0.404167265 CTAGE7P 0.379795015 SF3A1 -0.093314506 RP11-58K22.5 0.36222592 RP11-58K22.5 0.36222592 IQGAP1 -0.092940845 SIGLEC10-AS1 0.343316197 ETS2P1 0.357353926 SSRP1 -0.092497654 RP11-549D18.1 0.338969141 ENSG00000213727 0.356051892 ACTR2 -0.091310322 CTD-2532D12.4 0.29595238 UBE2FP3 0.350379676 ANKRD11 -0.089667536 CTD-2006K23.2 0.274490029 SIGLEC10-AS1 0.343316197 DHX9 -0.089218177 RP11-160N1.10 0.271753609 RP11-549D18.1 0.338969141 DYNC1H1 -0.088495292 AC091770.3 0.256620586 4. Discussion This study aimed to establish an association between the expression of TMEM220-AS1 and gastric cancer development. Until now, to assess the correlation between TMEM220-AS1 expression and GC, no research has been conducted. The methods used to assess the expression value of TMEM220-AS1 are shown in the results section. Comparing gastric cancer to normal tissues, the obtained data demonstrated a reduced expression of TMEM220-AS1. Thus, it is suggested that TMEM220-AS1 may have potential tumor-suppressive function in gastric cancer and changes in expression and results show that the TMEM220-AS1 plays important role in various pathways. Among the main reasons for global cancer mortality is still gastric cancer, despite reductions in both incidence and fatality rates [ 39 ]. It is challenging to detect early recurrence in gastric cancer, and patients who have recurrent illness have limited prospects of survival [ 40 ]. A comprehensive insight of the molecular mechanisms of GC helps to improve clinical outcomes for GC patients and to create therapeutic methods that are effective [ 41 ]. In the past 10 years, studies have linked long noncoding RNAs (lncRNAs) to a range of illnesses and developmental processes, including cancer [ 42 ]. The expression of genes is actively regulated by lncRNAs [ 43 ]. Over the last few years, Long non-coding RNA (lncRNA) has been shown in several studies to contribute substantially in the advancement and pathophysiology of GC [ 44 ]. It was believed a decade ago that long noncoding RNA (lncRNA) regulates both the transcript and post-transcript stages of gastric cancer development [ 2 ]. TMEM220-AS1 was selected as the focal lncRNA for this investigation and its function in GC assessed because lncRNAs have a significant role in the onset, metastatic spread, and progression of cancers. The expression level of TMEM220-AS1 was examined in gastric cancer and normal tissue samples from TCGA so as to evaluate the relationship between gastric carcinogenesis and TMEM220-AS1. Between GC patients and normal samples, there was a reduction in the expression of TMEM220-AS1. All tumor and marginal tissue samples in this experiment underwent RNA extraction and cDNA synthesis after the tissues were pathologically inspected. The gene expression was analyzed via quantitative real-time PCR. The qPCR analysis revealed that the expression levels of TMEM220-AS1 were markedly decreased in the GC tissue specimens compared to the normal tissue samples. In addition, No statistically significant correlation was observed between reduced levels of TMEM220-AS1 gene expression with other pathological criteria (age, gender, site of primary tumor, tumor size, TNM stage, lymphatic invasion, vascular invasion, serosal invasion, perineural invasion, family history and smoking status), This likely reflects limitations in sample size used in the research; therefore, To get precise findings, it is necessary to examine the pathogenic parameters in several samples spread across a vast geographical region. Therefore, based on our research, TMEM220-AS1 could represent a potential diagnostic marker for stomach cancer. In HCC cell lines, downregulation of TMEM220-AS1 was verified. Therefore, TMEM220-AS1 may function as a prognostic marker for HCC, according to results [ 19 ]. Long noncoding RNAs are integral to various tumorigenic processes, including HCC, as recent research has shown [ 45 ]. A poor prognosis for HCC patients is correlated with downregulation of TMEM220-AS1. TMEM220-AS1 is an antisense lncRNA of TMEM220 that positively regulates TMEM220 expression in HCC. In HCC tissue samples, TMEM220 expression and TMEM220-AS1 levels were positively correlated, and a significant association was discovered between TMEM220 downregulation and reduced patient survival. TMEM220-AS1's inhibitory impact was removed in HCCLM3 cells by TMEM220 knockdown. The mechanistic investigation revealed that the overexpression of TMEM220 reduced the nuclear accumulation of β-catenin and the mRNA levels of MYC, Cyclin D1, and Snail1 in HCCLM3 cells. In addition to decreasing HCC cell motility and proliferation, the GSK3β inhibitor BIO removed the Wnt/β-catenin pathway inactivation induced by TMEM220. Finally, it can be said that HCC patients have low expression levels of TMEM220-AS1 and TMEM220. By increasing the expression of TMEM220, TMEM220-AS1 suppressed the Wnt/β-catenin pathway and inhibited malignant phenotypes of HCC cells [ 19 ]. According to studies conducted by Bo et al. (2020) and Lyu et al. (2020), lncRNAs can interact with miRNAs and modulate downstream mRNA targets. It is known that the TMEM220-AS1/miR-484 axis controls HCC cell activity. Furthermore, TMEM220-AS1 shRNA's effects on HCC cell invasion, proliferation, cell cycle, and apoptosis may partially reversed by miR-484 inhibitors. Only MAGI1 expression was repressed in HCC cells by miR-484 overexpression. According to Zhang and Wang (2011), MAGI1 also inhibited cell motility and metastatic potential of HCC via controlling PTEN. In conclusion, their findings demonstrated that MAG1 functioned as a direct downstream effector of miR-484 and that TMEM220-AS1 competitively bound miR-484 to release MAGI1. The growth, invasion, and tumor development of HCC were all reduced by MAGI1. Finally, HCC advancement and metastasis are stimulated by low levels of TMEM220-AS1 via the miR-484/MAGI1 axis [ 45 ]. The development of biomarkers and early detection diagnostic methods for cervical cancer might lead to an improvement in patient survival outcomes. The successful identification of six lncRNAs, TMEM220-AS1, TRAM2‐AS1, C5orf66‐AS1, RASSF8‐AS1, AC126474, and AC004908, has been achieved through a comprehensive analysis, indicating their potential significance in the advancement of cervical cancer. The levels of their expression reduced along with the tumor progression. Analysis demonstrated that these hub lncRNAs were correlated with immune-related and keratinization-related pathways, suggesting that these two mechanisms are essential for regulating how cervical cancer develops [ 46 ]. A variety of molecular signaling cascades, including complement and coagulation cascades, peroxisome, histidine metabolism, cholesterol metabolism, and fatty acid degradation, were found by bioinformatics tools to be associated with the TMEM220-AS1 gene. Chemotherapeutic sensitivity and overall survival (OS) in cancer patients are associated with activation of the complement, coagulation, and coagulation cascades (Zhang et al., 2020). New perspectives and targets for the diagnosis, prognosis prediction, and therapeutic treatment of GC patients are offered by coagulation-related gene models [ 47 ]. By activating peroxisome proliferator-activated receptor γ (PPARγ), the malignant properties of gastric cancer cells may be reduced in vitro, indicating that this mechanism may also influence human gastric tumorigenesis [ 48 ]. On a range of cancer types, interruption of peroxisomal fatty acid oxidation has also demonstrated anti-tumorigenic characteristics [ 49 ]. The only enzyme that is known to convert histidine to histamine is histidine decarboxylase (HDC). The production and release of histamine in the stomach has a major impact on the pathophysiology of gastric illnesses as well as the secretion of gastric acid [ 50 ]. Changes in cholesterol metabolism are thought to have a role in the advancement of cancer, according to clinical and experimental findings [ 51 ]. According to Zhu et al., the gene sterol regulatory element-binding protein 1 (SREBP1), which is involved in the metabolism of cholesterol, was increased by SOAT1 during the development of gastric cancer [ 52 ]. Therefore, it can be said that these pathways are important in gastric cancer. Conclusion According to our findings, aberrant low expression of TMEM220-AS1 may help to the progression of GC, and TMEM220-AS1 may also be oncogenic in GC. Based on study findings, TMEM220-AS1 appears to be a likely candidate for GC diagnosis, according to a ROC curve analysis. Additionally, our findings indicated a low expression of TMEM220-AS1 in other prevalent malignancies, such as GC, as well as an essential part that TMEM220-AS1 plays in the advancement of gastric cancer. Nevertheless, the outcomes of additional studies could offer insights into the molecular pathways through which TMEM220-AS1 participates in carcinogenesis and its potential use in medicine. Declarations Ethical Approval The ethical committee of the Immunology Research Center, Tabriz University of Medical Sciences approved the study. Written informed consent was obtained from all patients. This study was conducted in accordance with the principles of the Declaration of Helsinki. All data used in this research were obtained from publicly available, de-identified databases (TCGA and GTEx). Therefore, no additional ethical approval or informed consent was required. Competing interests The authors declare that they have no conflicts of interest. Clinical trial number Not applicate Authors' contributions E.P. was responsible for RNA extraction, cDNA synthesis, real-time PCR, and writing the manuscript. A.H.Y. contributed to bioinformatics, systems biology, and data analysis. S.A. and V.E. assisted with manuscript editing. A.A.M. conceived the study, managed the research, and performed the final manuscript editing. Funding This study was supported by Tabriz University of Medical Science. Consent to publish Not applicable. Availability of data and materials RNA-sequencing data and corresponding clinical information for stomach adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) through the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/ ) under project ID TCGA-STAD. Data were retrieved from the GDC Data Portal (current data release as of February 2026; accessed on 21 February 2026). Normal tissue RNA-sequencing data were obtained from the Genotype-Tissue Expression (GTEx) Project via the GTEx Portal (https://gtexportal.org/ ), GTEx Analysis Release V8 (dbGaP accession: phs000424.v8.p2; accessed on 21 February 2026). Gene expression comparisons between TCGA and GTEx datasets were performed using the GEPIA platform (http://gepia.cancer-pku.cn/ ; accessed on 21 February 2026), which integrates TCGA and GTEx RNA-seq data processed using a unified pipeline. Independent validation and subgroup analyses were conducted using the UALCAN portal (http://ualcan.path.uab.edu/ ; accessed on 21 February 2026), based on TCGA level 3 RNA-seq and clinical data. lncRNA-associated analyses were performed using lncHUB2 (https://lnchub.org/ ; accessed on 21 February 2026), which integrates TCGA-derived transcriptomic datasets. Functional enrichment analyses were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.kegg.jp/ ; accessed on 21 February 2026). Specific KEGG pathway identifiers analyzed in this study are provided in the Results section. No new datasets were generated during the current study. All data are publicly available from the respective repositories listed above. References Visone R, Croce CM. MiRNAs and cancer. Am J Pathol. 2009;174(4):1131–8. Hao N-B, et al. The role of miRNA and lncRNA in gastric cancer. Oncotarget. 2017;8(46):81572. Miao Y, et al. Comprehensive analysis of a novel four-lncRNA signature as a prognostic biomarker for human gastric cancer. Oncotarget. 2017;8(43):75007. Lazăr DC, et al. New advances in targeted gastric cancer treatment. World J Gastroenterol. 2016;22(30):6776. Orditura M, et al. Treatment of gastric cancer. World J gastroenterology: WJG. 2014;20(7):1635. Meyer H-J, Wilke H. Treatment strategies in gastric cancer. 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Long non-coding RNA TMEM220-AS1 suppressed hepatocellular carcinoma by regulating the miR-484/MAGI1 axis as a competing endogenous RNA. Front Cell Dev Biology. 2021;9:681529. Luo W, et al. Identification of a six lncRNAs signature as novel diagnostic biomarkers for cervical cancer. J Cell Physiol. 2020;235(2):993–1000. Wang B, et al. Construction and validation of a novel coagulation-related 7-gene prognostic signature for gastric cancer. Front Genet. 2022;13:957655. Cho SJ, et al. Peroxisome proliferator-activated receptor γ upregulates galectin‐9 and predicts prognosis in intestinal‐type gastric cancer. Int J Cancer. 2015;136(4):810–20. Kim J-A. Peroxisome metabolism in cancer. Cells. 2020;9(7):1692. Ku HJ, et al. Bile acid increases expression of the histamine-producing enzyme, histidine decarboxylase, in gastric cells. World J Gastroenterology: WJG. 2014;20(1):175. Ding X, et al. The role of cholesterol metabolism in cancer. Am J cancer Res. 2019;9(2):219. Cui M-Y, et al. The role of lipid metabolism in gastric cancer. Front Oncol. 2022;12:916661. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor invited by journal 02 Mar, 2026 Editor assigned by journal 26 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 21 Feb, 2026 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. 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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-8758621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601210235,"identity":"c1959803-35df-419e-8f25-5a780dbaa43a","order_by":0,"name":"Elaheh Paki","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Elaheh","middleName":"","lastName":"Paki","suffix":""},{"id":601210236,"identity":"5511346d-0219-4616-badd-a18400df861a","order_by":1,"name":"AmirHossein Yari","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"AmirHossein","middleName":"","lastName":"Yari","suffix":""},{"id":601210237,"identity":"4bb6113a-dfc2-4dad-8e47-258237451056","order_by":2,"name":"Samin Abdolazadeh","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Samin","middleName":"","lastName":"Abdolazadeh","suffix":""},{"id":601210239,"identity":"336100b4-ac99-48bf-8dbf-178bd5ba90da","order_by":3,"name":"Vida Ebrahimi","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vida","middleName":"","lastName":"Ebrahimi","suffix":""},{"id":601210241,"identity":"6230eff7-21d0-4fc4-9f1d-31252c7662cb","order_by":4,"name":"Amir Ali Mokhtarzadeh","email":"data:image/png;base64,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","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"Ali","lastName":"Mokhtarzadeh","suffix":""}],"badges":[],"createdAt":"2026-02-01 20:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8758621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8758621/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104338324,"identity":"04f52b6b-2c36-4540-b3dd-9c1d92dd1f3b","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136009,"visible":true,"origin":"","legend":"\u003cp\u003ea) TMEM220-AS1 expression level in TCGA-STAD dataset. A comparison of GC samples to normal samples shows that TMEM220-AS1 is significantly downregulated in GC samples (p \u0026lt; 0.0001). b) The TMEM220-AS1 expression level in the TCGA and GTEx using the Gepia web tool. \u0026nbsp;c) The ROC curve findings for TMEM220-AS1 biomarker strength evaluation from the TCGA-STAD dataset can be utilized as a diagnostic target, with an AUC of 0.9 (p \u0026lt; 0.0001), to differentiate GC from normal samples (p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/0cd59bc291cd079c985a81b0.png"},{"id":104338328,"identity":"553a5f03-7dd7-4b31-b401-2e7f4e0e5fb4","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104326,"visible":true,"origin":"","legend":"\u003cp\u003eTMEM220-AS1 expression value between normal and gastric cancer samples and their correlation with various histological subtypes (a), different stages of disease (b) and diverse tumor grades(c).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/a017b618d38300cdc5ae83c7.png"},{"id":104338323,"identity":"9ec7d378-ac68-4995-8b79-28020a353d34","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72521,"visible":true,"origin":"","legend":"\u003cp\u003eTMEM220-AS1 has been demonstrated to be downregulated in several malignancies, particularly gastric cancer.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/8e49ecd59e3919c0cabc6b4f.png"},{"id":104338325,"identity":"7f7c5e7a-b3c2-4aa8-b9c0-4a469d9f2059","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89462,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant reduction of TMEM220-AS1 expression in collected gastric cancer tissue specimens (N = 25); (* p = 0.0001), compared to the adjacent tissue specimens (N = 25) in q-PCR analysis(a). According to the ROC curve data, with an AUC of 0.9 (p \u0026lt; 0.0001), the biomarker intensity of TMEM220-AS1 may serve as a diagnostic target to differentiate GC from normal samples.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/a745bf1665700edbaa445aa4.png"},{"id":104338330,"identity":"b14d7d52-53bd-4b60-b6fa-e96df5cb7682","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":300561,"visible":true,"origin":"","legend":"\u003cp\u003eThe positively co-expressed genes with TMEM220-AS1. Orange nodes show the positively co-expressed genes with LINC00162 (green node) according to Pearson's Correlation Coefficient.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/cb567d120998f46a8261fd49.png"},{"id":104338326,"identity":"7b183104-17a0-4dc2-9fcb-a973c7ae51fc","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":258717,"visible":true,"origin":"","legend":"\u003cp\u003eThe negatively co-expressed genes with TMEM220-AS1. cyan nodes show the negatively co-expressed genes that negatively correlated with TMEM220-AS1 according to Pearson's Correlation Coefficient.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/fb809a4b41098a60114531e5.png"},{"id":104338329,"identity":"779ae15d-08c9-4f01-ab88-57eae0adf680","added_by":"auto","created_at":"2026-03-10 16:18:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2975241,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted KEGG pathways associations for the lncRNA TMEM220-AS1. Terms are classified by the right-tailed p-value for the mean Pearson association measurement calculated between by each gene set and TMEM220-AS1.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/f0a68b69d6332513bad43dd8.png"},{"id":104406056,"identity":"f6a1b117-0687-4c57-92c6-750874d6721b","added_by":"auto","created_at":"2026-03-11 12:24:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5257593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8758621/v1/462b178c-44f5-437a-af41-ef68e94a6c82.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Downregulation of TMEM220-AS1, a novel long noncoding RNA, is associated with Gastric Cancer development","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer arises from a multifactorial cascade that gives rise to extensive genetic changes and is characterized by uncontrolled cellular growth, invasion, and metastasis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Worldwide, Gastric cancer (GC) ranks as the fourth most prevalent malignancy and the third leading cause of cancer-related death and the fourth most prevalent type of cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The poor prognosis associated with GC is primarily due to late-stage diagnosis in the majority of patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Treatment for stomach cancer primarily involves surgical resection along with adjuvant chemotherapy or combined chemoradiotherapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite radical resection, over 50% of patients experience local recurrence or distant metastases, or they are diagnosed with stomach cancer after the tumor spreads. As a result, the 5-year survival rate is less than 10%, and the median survival is rarely exceeds 12 months [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Treatment for gastric cancer remains challenging [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The management of GC remains challenging, owing to its considerable heterogeneity in both phenotypic presentation and molecular characteristics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several factors contribute to the advancement and progression of malignancies, including mutational events, epigenetic changes, and environmental factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, hereditary predisposition plays a significant role in the development of stomach cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Long non-coding RNAs (lncRNAs), exceeding 200 nucleotides in length, have emerged as critical regulators in various biological processes and pathological conditions, including cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The vast majority of the human genome is consists predominantly of non-coding RNAs, which are RNA molecules that are not translated into proteins [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. LncRNAs, are RNA molecules associated with several crucial cellular processes as well as diverse pathological conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These RNA molecules, are involved in the modulation of gene expression at multiple levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. LncRNAs can influence chromatin structure, thereby affecting epigenetic gene repression, and can also regulate gene expression post-transcriptionally. Additionally, by influencing chromatin structure, they affect epigenetic gene repression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A wide range of cancer forms has been associated with dysregulated expression of lncRNA genes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Functionally, lncRNAs control the expression of genes at all stages, involving transcription, post-transcriptional processing, and chromatin modification [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, by altering transcriptional activity, they are involved in the processes such as cell differentiation, cell cycle, proliferation, apoptosis, migration, and invasion. The expression of oncogenes and tumor suppressor genes is significantly influenced by dysregulation of lncRNA expression [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Several lncRNAs have also been identified as potential biomarkers for the detection of GC [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTMEM220-AS1 is an antisense lncRNA transcribed from the opposite strand of the TMEM220 gene locus [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In hepatocellular carcinoma (HCC), downregulation of TMEM220-AS1 has been associated with poor prognosis, as reported by Du et al; this suggests that TMEM220-AS1 could serve as a valuable prognostic biomarker [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the role of TMEM220-AS1 in GC remains poorly understood. Our study aims to address this research gap by examining the relationship between TMEM220-AS1 long noncoding RNA expression and gastric tumorigenesis. We analyzed RNA sequencing datasets from The Cancer Genome Atlas - Stomach Adenocarcinoma (TCGA-STAD) collection to determine the expression levels of TMEM220-AS1 in GC samples. Subsequently, the results were validated using GC tissue samples and paired normal tissue samples. Additionally, numerous databases were reviewed to elucidate the functional roles associated with TMEM220-AS1, and the correlation between TMEM220-AS1 expression levels and clinicopathological characteristics was assessed.\u003c/p\u003e"},{"header":"2. Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Examination of TMEM220-AS1 expression in-silico with the TCGA database\u003c/h2\u003e \u003cp\u003eRNA sequencing (RNA-seq) gene expression data for stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks package in R. Normalization and processing of the data were performed employing the limma package within the R programming environment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The expression levels of the long non-coding RNA (lncRNA) TMEM220-AS1 and corresponding receiver operating characteristic (ROC) curves were analyzed using GraphPad Prism software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Normal tissue expression data were obtained from the Genotype-Tissue Expression (GTEx) project, while tumor tissue data were sourced from the TCGA dataset [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Comparative analysis of transcriptomic profiles was conducted using the GEPIA tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"https://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, the UALCAN online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was utilized to investigate TMEM220-AS1 expression across various cancer types and to examine correlations between its expression levels and clinicopathological features using TCGA RNA-seq data [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample preparation\u003c/h2\u003e \u003cp\u003eWith written informed consent, 25 GC samples were collected from the Iranian National Tumor Biobank in Tehran, Iran. Liquid nitrogen was employed for the preservation of tissue samples until RNA extraction. No radiotherapy or chemotherapy was administered to the enrolled subjects before surgical intervention. The data assessed for this study consist of clinical and demographic parameters, such as gender, age, tumor size, primary tumor site, lymphatic invasion, histological grade, perineural, serosal and vascular invasion, clinical stage, smoking status, and family history of gastric cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. RNA extraction and cDNA synthesis\u003c/h2\u003e \u003cp\u003eThe samples of tissue were homogenized using a syringe and needle after being treated with lysis buffer, ground with a mortar and pestle apparatus, and then frozen in liquid nitrogen. Total RNA was extracted using using Qiagen's AllPrep DNA/RNA Kit (Germany), following the manufacturer's guidelines. To evaluate the yield and quality of the RNA samples', a ThermoFisher Scientific Life Sciences, USA NanoDrop spectrophotometer was employed. The integrity of RNA was assessed by means of a agarose gel (1%) electrophoretic assay. For cDNA synthesis, 1 \u0026micro;g of total RNA was reverse-transcribed using the PrimeScript\u0026trade; RT Reagent Kit (TaKaRa Bio, Japan) in a final reaction volume of 20 \u0026micro;L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quantitative real-time PCR (qPCR)\u003c/h2\u003e \u003cp\u003eIn a reaction volume of 10 ml, gene-specific primers and BioFACTTM 2X Real-Time PCR Master Mix (Korea) were utilized for qPCR. The Step One Plus Real-Time PCR System (Applied Biosystems, USA) was utilized to perform three quantitative PCR stages; the first denaturation at 95\u0026deg;C lasted for 15 minutes. This was followed by 45 cycles of denaturation at 95\u0026deg;C for 10 seconds, primer annealing at 60\u0026deg;C for 30 seconds, and elongation at 72\u0026deg;C for 20 seconds. After each run was completed, melting curve profiles were generated. To standardize gene expression levels, the reference gene utilized in this study was GAPDH. To determine the TMEM220-AS1 relative expression levels between groups, the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e technique was employed. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the primer details.\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\u003eThe sequences of qPCR primers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward (5\u0026prime; \u0026ndash; 3\u0026prime; direction)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse (5\u0026prime; \u0026ndash; 3\u0026prime; direction)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMEM220-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026acute; CAGGGTCATCACCATAGCACC 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026acute; TCAGAAGGGGACTTGGAGCA 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026acute; AAGGTGAAGGTCGGAGTCAAC 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026acute; GGGGTCATTGATGGCAACAA 3\u0026acute;\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=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo identify enriched pathways related to TMEM220-AS1, we first obtained the LncRNAs and coding genes co-expressed with TMEM220-AS1 (both negatively and positively correlated genes) associated with TMEM220-AS1 as well as pharmacological agents predicted to upregulate this lncRNA and CRISPR-based regulatory predictions for its upregulation, utilizing the web resource lncHUB2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/lncHUB2/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/lncHUB2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To determine biological pathways influenced by this lncRNA, we accessed the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the LNCHUB2 online application [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Cytoscape software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provides network-based visual outputs of all the data [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe qPCR data and associated results were analyzed, and the visual plots were generated using GraphPad Prism V8. The relative TMEM220-AS1 expression between groups were statistically evaluated using two-tailed unpaired t-tests. The SEM (standard error of the mean), or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM, is used to present all data. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Validation of TMEM220-AS1 overexpression in TCGA-STAD dataset\u003c/h2\u003e \u003cp\u003eConfirmation of the qPCR experiment results was conducted through analysis of the TCGA-STAD database. Based on the information obtained from the TCGA-STAD analysis and Gepia web tools, TMEM220-AS1 appears to be markedly downregulated in gastric cancer tissues compared to non-tumorous tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). In addition, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, with an area under the curve (AUC) of 0.9 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), the expression of the TMEM220-AS1 gene serves as a promising diagnostic biomarker to distinguish GC from normal samples. A cross-cancer evaluation using the Ualcan database revealed that TMEM220-AS1 expression was downregulated in multiple malignancies, particularly in gastric cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The following studies in TCGA-STAD dataset indicated that in all types and stages of gastric cancer, the expression level of TMEM220-AS1 was consistently diminished (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. TMEM220-AS1 downregulation in GC samples\u003c/h2\u003e \u003cp\u003eAs part of the experiment, 25 GC tissues and 25 adjacent non-cancerous gastric tissues were used. Based on quantitative PCR findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), Comparing GC tissue specimens to normal tissue specimens revealed a demonstrated a marked reduction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) of TMEM220-AS1, in agreement with data derived from the TCGA database. Furthermore, in GC patients, the association between clinical-pathological parameters and TMEM220-AS1 expression was examined. Gender, histological grade, tumor size, initial tumor location, perineural and serosal invasion, clinical stage, smoking status, and family history of GC did not exhibit any statistically significant relationship with TMEM220-AS1 levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. TMEM220-AS1 clinicopathological characteristics in GC\u003c/h2\u003e \u003cp\u003eIn GC patients, the relationship between TMEM220-AS1 transcript levels and clinicopathological variables was examined. Gender, histological grade, tumor size, initial tumor location, perineural and serosal invasion, clinical stage, smoking status, and family history of GC showed no statistically meaningful association with TMEM220-AS1 expression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe associations between the clinicopathological features of GC patients and the expression of TMEM220-AS1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of samples (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSite of primary tumor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.5229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric Cardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.6707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI, II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII, IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphatic invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascular invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSerosal invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.2919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerineural invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI, II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII, IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.4826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.5476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (44%)\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. TMEM220-AS1 is vital in a broad number of biological functions\u003c/h2\u003e \u003cp\u003eBy employing the lncHUB2 web platform, TCGA datasets were queried to identify co-expressed genes associated with TMEM220-AS1. The interaction networks of positively and negatively correlated genes are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the top-ranking co-expressed lncRNAs, along with negatively and positively co-expressed genes with related to TMEM220-AS1, including their Pearson's Correlation Coefficient. Additionally, the table 4 lists the candidate drugs and CRISPR-based regulatory predictions that upregulate TMEM220-AS1. The analysis of KEGG resource using LNCHUB2 online tool revealed the involvement of TMEM220-AS1 in various biological pathways including complement and coagulation cascades, peroxisome, histidine metabolism, cholesterol metabolism, fatty acid degradation, as well as additional biologically relevant pathways. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e visualizes the functional pathways associated with the TMEM220-AS1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHigh rank co-expressed lncRNAs, negatively co-expressed, and positively co-expressed genes with TMEM220-AS1.\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositively\u003c/p\u003e \u003cp\u003eco-expressed\u003c/p\u003e \u003cp\u003egenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson's\u003c/p\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegatively\u003c/p\u003e \u003cp\u003eco- expressed\u003c/p\u003e \u003cp\u003egenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePearson's\u003c/p\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eco-expressed\u003c/p\u003e \u003cp\u003eLncRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePearson's Correlation Coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000277190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.431948215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHNRNPUL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.098580256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eENSG00000277190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.431948215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC024704.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.418479145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKPNB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.096339531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC024704.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.418479145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTA-292E10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.404167265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLARP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.093646139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTA-292E10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.404167265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTAGE7P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.379795015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSF3A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.093314506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-58K22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36222592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP11-58K22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36222592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIQGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.092940845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSIGLEC10-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.343316197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETS2P1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.357353926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSRP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.092497654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-549D18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.338969141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000213727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.356051892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.091310322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTD-2532D12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29595238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBE2FP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.350379676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANKRD11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.089667536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTD-2006K23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274490029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIGLEC10-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.343316197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDHX9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.089218177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-160N1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.271753609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP11-549D18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.338969141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDYNC1H1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.088495292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC091770.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.256620586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to establish an association between the expression of TMEM220-AS1 and gastric cancer development. Until now, to assess the correlation between TMEM220-AS1 expression and GC, no research has been conducted. The methods used to assess the expression value of TMEM220-AS1 are shown in the results section. Comparing gastric cancer to normal tissues, the obtained data demonstrated a reduced expression of TMEM220-AS1. Thus, it is suggested that TMEM220-AS1 may have potential tumor-suppressive function in gastric cancer and changes in expression and results show that the TMEM220-AS1 plays important role in various pathways.\u003c/p\u003e \u003cp\u003eAmong the main reasons for global cancer mortality is still gastric cancer, despite reductions in both incidence and fatality rates [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It is challenging to detect early recurrence in gastric cancer, and patients who have recurrent illness have limited prospects of survival [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A comprehensive insight of the molecular mechanisms of GC helps to improve clinical outcomes for GC patients and to create therapeutic methods that are effective [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In the past 10 years, studies have linked long noncoding RNAs (lncRNAs) to a range of illnesses and developmental processes, including cancer [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The expression of genes is actively regulated by lncRNAs [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Over the last few years, Long non-coding RNA (lncRNA) has been shown in several studies to contribute substantially in the advancement and pathophysiology of GC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It was believed a decade ago that long noncoding RNA (lncRNA) regulates both the transcript and post-transcript stages of gastric cancer development [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTMEM220-AS1 was selected as the focal lncRNA for this investigation and its function in GC assessed because lncRNAs have a significant role in the onset, metastatic spread, and progression of cancers.\u003c/p\u003e \u003cp\u003eThe expression level of TMEM220-AS1 was examined in gastric cancer and normal tissue samples from TCGA so as to evaluate the relationship between gastric carcinogenesis and TMEM220-AS1. Between GC patients and normal samples, there was a reduction in the expression of TMEM220-AS1. All tumor and marginal tissue samples in this experiment underwent RNA extraction and cDNA synthesis after the tissues were pathologically inspected. The gene expression was analyzed via quantitative real-time PCR. The qPCR analysis revealed that the expression levels of TMEM220-AS1 were markedly decreased in the GC tissue specimens compared to the normal tissue samples. In addition, No statistically significant correlation was observed between reduced levels of TMEM220-AS1 gene expression with other pathological criteria (age, gender, site of primary tumor, tumor size, TNM stage, lymphatic invasion, vascular invasion, serosal invasion, perineural invasion, family history and smoking status), This likely reflects limitations in sample size used in the research; therefore, To get precise findings, it is necessary to examine the pathogenic parameters in several samples spread across a vast geographical region.\u003c/p\u003e \u003cp\u003eTherefore, based on our research, TMEM220-AS1 could represent a potential diagnostic marker for stomach cancer. In HCC cell lines, downregulation of TMEM220-AS1 was verified. Therefore, TMEM220-AS1 may function as a prognostic marker for HCC, according to results [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLong noncoding RNAs are integral to various tumorigenic processes, including HCC, as recent research has shown [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A poor prognosis for HCC patients is correlated with downregulation of TMEM220-AS1. TMEM220-AS1 is an antisense lncRNA of TMEM220 that positively regulates TMEM220 expression in HCC. In HCC tissue samples, TMEM220 expression and TMEM220-AS1 levels were positively correlated, and a significant association was discovered between TMEM220 downregulation and reduced patient survival. TMEM220-AS1's inhibitory impact was removed in HCCLM3 cells by TMEM220 knockdown. The mechanistic investigation revealed that the overexpression of TMEM220 reduced the nuclear accumulation of β-catenin and the mRNA levels of MYC, Cyclin D1, and Snail1 in HCCLM3 cells. In addition to decreasing HCC cell motility and proliferation, the GSK3β inhibitor BIO removed the Wnt/β-catenin pathway inactivation induced by TMEM220. Finally, it can be said that HCC patients have low expression levels of TMEM220-AS1 and TMEM220. By increasing the expression of TMEM220, TMEM220-AS1 suppressed the Wnt/β-catenin pathway and inhibited malignant phenotypes of HCC cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to studies conducted by Bo et al. (2020) and Lyu et al. (2020), lncRNAs can interact with miRNAs and modulate downstream mRNA targets. It is known that the TMEM220-AS1/miR-484 axis controls HCC cell activity. Furthermore, TMEM220-AS1 shRNA's effects on HCC cell invasion, proliferation, cell cycle, and apoptosis may partially reversed by miR-484 inhibitors. Only MAGI1 expression was repressed in HCC cells by miR-484 overexpression.\u003c/p\u003e \u003cp\u003e According to Zhang and Wang (2011), MAGI1 also inhibited cell motility and metastatic potential of HCC via controlling PTEN. In conclusion, their findings demonstrated that MAG1 functioned as a direct downstream effector of miR-484 and that TMEM220-AS1 competitively bound miR-484 to release MAGI1. The growth, invasion, and tumor development of HCC were all reduced by MAGI1. Finally, HCC advancement and metastasis are stimulated by low levels of TMEM220-AS1 via the miR-484/MAGI1 axis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe development of biomarkers and early detection diagnostic methods for cervical cancer might lead to an improvement in patient survival outcomes. The successful identification of six lncRNAs, TMEM220-AS1, TRAM2‐AS1, C5orf66‐AS1, RASSF8‐AS1, AC126474, and AC004908, has been achieved through a comprehensive analysis, indicating their potential significance in the advancement of cervical cancer. The levels of their expression reduced along with the tumor progression. Analysis demonstrated that these hub lncRNAs were correlated with immune-related and keratinization-related pathways, suggesting that these two mechanisms are essential for regulating how cervical cancer develops [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA variety of molecular signaling cascades, including complement and coagulation cascades, peroxisome, histidine metabolism, cholesterol metabolism, and fatty acid degradation, were found by bioinformatics tools to be associated with the TMEM220-AS1 gene.\u003c/p\u003e \u003cp\u003eChemotherapeutic sensitivity and overall survival (OS) in cancer patients are associated with activation of the complement, coagulation, and coagulation cascades (Zhang et al., 2020). New perspectives and targets for the diagnosis, prognosis prediction, and therapeutic treatment of GC patients are offered by coagulation-related gene models [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. By activating peroxisome proliferator-activated receptor γ (PPARγ), the malignant properties of gastric cancer cells may be reduced in vitro, indicating that this mechanism may also influence human gastric tumorigenesis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. On a range of cancer types, interruption of peroxisomal fatty acid oxidation has also demonstrated anti-tumorigenic characteristics [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The only enzyme that is known to convert histidine to histamine is histidine decarboxylase (HDC). The production and release of histamine in the stomach has a major impact on the pathophysiology of gastric illnesses as well as the secretion of gastric acid [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Changes in cholesterol metabolism are thought to have a role in the advancement of cancer, according to clinical and experimental findings [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. According to Zhu et al., the gene sterol regulatory element-binding protein 1 (SREBP1), which is involved in the metabolism of cholesterol, was increased by SOAT1 during the development of gastric cancer [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, it can be said that these pathways are important in gastric cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to our findings, aberrant low expression of TMEM220-AS1 may help to the progression of GC, and TMEM220-AS1 may also be oncogenic in GC. Based on study findings, TMEM220-AS1 appears to be a likely candidate for GC diagnosis, according to a ROC curve analysis. Additionally, our findings indicated a low expression of TMEM220-AS1 in other prevalent malignancies, such as GC, as well as an essential part that TMEM220-AS1 plays in the advancement of gastric cancer. Nevertheless, the outcomes of additional studies could offer insights into the molecular pathways through which TMEM220-AS1 participates in carcinogenesis and its potential use in medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical committee of the Immunology Research Center, Tabriz University of Medical Sciences approved the study. Written informed consent was obtained from all patients. This study was conducted in accordance with the principles of the Declaration of Helsinki. All data used in this research were obtained from publicly available, de-identified databases (TCGA and GTEx). Therefore, no additional ethical approval or informed consent was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicate \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.P. was responsible for RNA extraction, cDNA synthesis, real-time PCR, and writing the manuscript. A.H.Y. contributed to bioinformatics, systems biology, and data analysis. S.A. and V.E. assisted with manuscript editing. A.A.M. conceived the study, managed the research, and performed the final manuscript editing. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Tabriz University of Medical Science.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-sequencing data and corresponding clinical information for stomach adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) through the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/ ) under project ID TCGA-STAD. Data were retrieved from the GDC Data Portal (current data release as of February 2026; accessed on 21 February 2026). Normal tissue RNA-sequencing data were obtained from the Genotype-Tissue Expression (GTEx) Project via the GTEx Portal (https://gtexportal.org/ ), GTEx Analysis Release V8 (dbGaP accession: phs000424.v8.p2; accessed on 21 February 2026). Gene expression comparisons between TCGA and GTEx datasets were performed using the GEPIA platform (http://gepia.cancer-pku.cn/ ; accessed on 21 February 2026), which integrates TCGA and GTEx RNA-seq data processed using a unified pipeline. Independent validation and subgroup analyses were conducted using the UALCAN portal (http://ualcan.path.uab.edu/ ; accessed on 21 February 2026), based on TCGA level 3 RNA-seq and clinical data. lncRNA-associated analyses were performed using lncHUB2 (https://lnchub.org/ ; accessed on 21 February 2026), which integrates TCGA-derived transcriptomic datasets. Functional enrichment analyses were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.kegg.jp/ ; accessed on 21 February 2026). Specific KEGG pathway identifiers analyzed in this study are provided in the Results section. No new datasets were generated during the current study. All data are publicly available from the respective repositories listed above.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVisone R, Croce CM. MiRNAs and cancer. Am J Pathol. 2009;174(4):1131\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao N-B, et al. The role of miRNA and lncRNA in gastric cancer. Oncotarget. 2017;8(46):81572.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao Y, et al. Comprehensive analysis of a novel four-lncRNA signature as a prognostic biomarker for human gastric cancer. 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The role of lipid metabolism in gastric cancer. Front Oncol. 2022;12:916661.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, LncRNA, TMEM220-AS1, ROC curve, Diagnostic biomarker, TCGA, qPCR","lastPublishedDoi":"10.21203/rs.3.rs-8758621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8758621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) have emerged as crucial regulators in a wide array of biological mechanisms. Recent studies have demonstrated their strong association with both functional and pathological aspects of gastric cancer progression. Nonetheless, the specific role of TMEM220-AS1 in gastric tumorigenesis remains poorly characterized. This study aimed to elucidate the functional involvement of TMEM220-AS1 in gastric cancer and assess its potential as a diagnostic and prognostic biomarker. The novelty of this work lies in its exclusive focus on TMEM220-AS1 within the context of gastric cancer, a setting not previously explored. TMEM220-AS1 was identified through integrated bioinformatic screening, followed by validation of its expression profile in gastric cancer specimens.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eTCGA genomic repository was used to analyze the expression levels of TMEM220-AS1. Furthermore, the results for a subset of clinical specimens were validated using qRT-PCR. LncRNA TMEM220-AS1\u0026rsquo;s clinical relevance was analyzed using the TCGA-derived transcriptomic data and matched clinical profiles. The diagnostic utility of this lncRNA was further explored using ROC curve analysis. As a last step, the functional analysis of TMEM220-AS1 was evaluated by bioinformatics approaches.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe current investigation indicates that gastric cancer samples exhibited significant downregulation of LncRNA TMEM220-AS1 expression compared to non-neoplastic gastric tissues, and these reduced levels were not related to the clinical characteristics of the patients. Additionally, ROC curve analysis suggests that the expression pattern of LncRNA TMEM220-AS1's may serve as a promising diagnostic indicator for gastric cancer. Functional annotation via in silico tools revealed TMEM220-AS1\u0026rsquo;s involvement in diverse biological pathways, such as complement and coagulation cascades, peroxisomal activity, histidine catabolism, cholesterol homeostasis, and fatty acid breakdown.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, our finding demonstrates that TMEM220-AS1 is significantly under-expressed in gastric cancer are not associated to clinicopathological characteristics of gastric cancer patients. Based on ROC curve evaluation, TMEM220-AS1 may hold value as a novel biomarker for the early detection of gastric cancer.\u003c/p\u003e","manuscriptTitle":"Downregulation of TMEM220-AS1, a novel long noncoding RNA, is associated with Gastric Cancer development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 16:18:52","doi":"10.21203/rs.3.rs-8758621/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T18:53:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T01:03:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T10:16:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T09:10:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177034564267034254928747862841130487838","date":"2026-03-30T01:56:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159224242479944355344724366763110761506","date":"2026-03-29T14:21:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311786028550699019958055411417188650154","date":"2026-03-20T09:28:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T02:49:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-02T19:03:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T06:43:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T00:46:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-02-21T08:08:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"791783a2-2748-4138-8b18-f78231a4ca93","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T19:09:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 16:18:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8758621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8758621","identity":"rs-8758621","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}