Certification of novel lncRNA based on analysis of ceRNA network in digestive system pan-cancer and characteristics of tumor immune microenvironment

Certification of novel lncRNA based on analysis of ceRNA network in digestive system pan-cancer and characteristics of tumor immune microenvironment | 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 Certification of novel lncRNA based on analysis of ceRNA network in digestive system pan-cancer and characteristics of tumor immune microenvironment Zikang He, Shuang Liang, Guoli Li, Xueyan Wang, Ping Shen, Huan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3054408/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Based on analysis of competitive endogenous RNA (ceRNA) and immune microenvironment, we screened their specific Long non-coding RNA (lncRNA) from the perspective of digestive system pan-cancer, and performed preliminary experimental validation. Methods: The transcriptome data of were downloaded from The Cancer Genome Atlas (TCGA) database, including esophageal carcinoma(ESCA), stomach adenocarcinoma (STAD), colorectal carcinoma (CRC) and liver hepatocellular adenocarcinoma (LIHC). We screened and predicted co-expressed differentially lncRNAs, miRNAs, and mRNAs of four tumors using R language. CeRNA networks were constructed by Cytoscape software.LASSO and Cox regression analysis were used to construct prognostic model. The application value of the prognostic model was assessed by combining clinicopathological features. The relationship between prognostic models and immune micro-environment was evaluated using Wilcoxon signed rank test. CCK8, scratch and Transwell assays were performed to analyze the effects of overexpression of lncRNA on CRC cells lines SW837 and SW620. The effect of overexpression of lncRNA on target proteins was detected using western blot. Results: Co-expressed lncRNAs 256, miRNAs 36, mRNAs 921 were obtained to construct the ceRNA network. LncRNA (WDFY3-AS2 and HOTAIR), miRNA (hsa-miR-21), and mRNA (OSR1) were screened using LASSO and Cox regression analysis to construct prognostic model. The survival rate of patients in the low-risk group was better than that in the high-risk group (P<0.001). The risk score and clinical stage could be used as independent prognostic factors for the digestive system pan-cancer. The risk score was positively correlated with the infiltration of multiple immune cells. The high-risk groups of CRC and LIHC were positively correlated with the expression of CD274, CTLA4, PDCD1, and HAVCR2 (P<0.05). Cellular experiments showed that the overexpression of WDFY3-AS2 reduced the survival rates of colorectal cancer cells, increased the healing time of scratched cells, and decreased the passage rate of transwell cells. Western blot assay suggested that WDFY3- AS2 can positively regulated the expression of OSR1. Conclusions: The prognostic model constructed based on the WDFY3-AS2/HOTAIR /hsa-miR-21/OSR1 ceRNA regulatory axis was able to assess the prognosis of pan-cancer of the digestive system, and the specific LncRNA WDFY3-AS2 inhibited the proliferation, invasion and metastasis of colon cancer cells. digestive system pan-cancer long non-coding RNA (lncRNA) competitive endogenous RNA (ceRNA) prognostic model tumor immune microenvironment bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Background Digestive system pan-cancer is a general term of digestive cancers, including esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colorectal carcinoma (CRC) and liver hepatocellular adenocarcinoma (LIHC). In China, digestive system tumors are common malignant tumors and have a relatively poor prognosis [ 1 ] . By 2020 statistics, the new incidence and mortality rates were 3.1% and 5.5% for ESCA, 5.6% and 7.7% for STAD, 9.8% and 9.2% for CRC, and 4.7% and 8.3% for LIHC [ 2 ] . It has been found that tumors of similar tissue origin may share common pathogenic molecular mechanisms to influence tumors progression, such as gynecologic and breast pan-cancer [ 3 ] . However, it remains to be explored whether there may be common molecular mechanisms in the digestive system pan-cancer. Long non-coding RNA (lncRNA) are a class of RNA molecules with transcripts greater than 200 nucleotides that are not directly involved in protein synthesis, but can regulate gene expression at the transcriptional and post-transcriptional levels. It has been shown that the regulatory effects of lncRNAs influence gene expression activity and contribute to tumorigenesis and development by altering cellular biology [ 4 , 5 ] . In addition, lncRNAs may also act as different functional molecules such as signals, decoys, scaffolds and molecular sponges, and some lncRNAs can encode functional small peptides [ 6 , 7 ] , which act as important regulators of various human life activities and play a key role in the progression of diseases including cancer. In 2011, Selmena et al. proposed the competing endogenous RNA (ceRNA) hypothesis, which assumes that different RNAs regulate the function of targeted genes by competing to bind to specific molecular site [ 8 ] . When lncRNAs and downstream mRNA shared the same miRNA response element (MREs), lncRNAs can act as ceRNAs and compete to bind miRNAs through MREs to inhibit or enhance the expression of mRNAs. In recent years, an increasing number of studies have shown that aberrant gene expression in the ceRNA regulatory network interferes with the progression and prognosis of digestive malignancies [ 9 – 11 ] . However, no studies have yet explored the value of ceRNA network regulatory axis on the diagnosis and prognosis of digestive malignancies from pan-cancer perspective. Cancer immunotherapy is a new treatment method in which the anti-tumor antibody or immune tumor vaccine is used to activate the autoimmune system to fight against tumors. The purpose of the treatment is to inhibit the growth of tumor cells by activating the immune effector cells or anti-tumor immune response in the body, thus improving the survival rate of patients [ 12 , 13 ] . A growing body of evidence suggests that cancer immunotherapy displays great advantages in patients with gastrointestinal malignancies. For example, Overman et al. found the combination of Nivolumab and Ipilimumab achieves disease control rates of around 80% in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer [ 14 ] . CheckMate 032 study suggested for the first time that the combination of Nivolumab and Ipilimumab has significant benefits in the treatment of advanced esophageal cancer [ 15 ] . In this study, we utilized bioinformatics approaches to screen pivotal lncRNAs and their corresponding miRNAs and mRNAs in the digestive system pan-cancer and construct a ceRNA network. The prognostic model of ceRNA network molecules were constructed based on univariate cox analysis, LASSO regression and multivariate cox analysis, and risk scores were calculated. The predictive value of the prognostic model was evaluated by combining survival information, clinicopathological characteristics, tumor-infiltrating immune cells, immune checkpoint genes and mutation data. Finally, cell experiments validated the biological function of specific lncRNA WDFY3-AS2 in colorectal cancer cells. To sum up, the construction of ceRNA network in the digestive system pan-cancer and its role in the immune microenvironment provide new insights into the prognosis of digestive system cancers and their immunotherapy. Material and methods Data preparation and collation. The transcriptome data and mutation data were downloaded from The Cancer Genome Altas (TCGA, https://tcgadata.nci.nih.gov/tcga/ ) database. Among them, 11 tumor-adjacent tissues and 160 tumor tissues were included in ESCA, 32 tumor-adjacent tissues and 375 tumor tissues in STAD, 50 tumor-adjacent tissues and 374 tumors tissues from LIHC, 51 tumor-adjacent tissues and 641 tumor tissues from CRC. In addition, the complete clinical information of patients with four digestive tract tumors was downloaded and combined from the TCGA database, and samples with less than 50 days of follow-up were excluded. Mutation data in varscan 2 format were downloaded and extracted from the TCGA database. Then, we merged the mutation data from four tumors and analyzed them using the R software “maftools” package. Identification of lncRNA, miRNA, and mRNA. The Ensembl IDs of trascriptome expression matrix were converted to gene IDs using Perl language. Then, the expression matrix of lncRNA, miRNA, and mRNA were extracted for each of four digestive tumors. Applying the edgeR package of R software, the differentially expressed lncRNAs, miRNAs, and mRNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were derived in cancer tissues and adjacent normal tissues (set P < 0.05 and | logFC | ≥ 1 as the threshold). Next, the DElncRNAs, DEmiRNAs and DEmRNAs of the four digestive tumors were intersected separately and a Wayne diagram ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) was drawn to obtain the differential co-expressed lncRNAs, miRNAs and mRNAs of the four tumors. GO and KEGG enrichment analysis. To explore the biological functions of co-expressed genes in digestive pan-cancer, we performed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis by the DAVID functional annotation tool ( https://david-d.ncifcrf.gov/ ). With P < 0.05 as the screening criterion, the top 10 GO and KEGG enrichment pathways with significant enrichment were visualized using the “clusterProfiler” package of R software. Construction of ceRNA network. The complementary relationship pairs of DElncRNAs and DEmiRNAs were compared and obtained using the miRcode database. Second, the three target gene prediction databases, Targetscan, miRTarBase, and miRDB were used to retrieve the target mRNAs of DEmiRNAs in the above pairs. Then, we plotted the intersection of target mRNAs and co-expressed DEmRNAs using the "VennDiagram" package of R software. Finally, the corresponding lncRNA-miRNA pairs and miRNA-mRNA pairs were imported into Cytoscape 3.8.1 software to construct ceRNA networks. Optimization of ceRNA network. First, the gene expression data of the four digestive system tumors were integrated and standardized. Combined with the survival information, we performed survival analysis of the molecules in the ceRNA network using "survivor" package in the R software. Molecules with significant differences in prognosis were selected and the ceRNA network was optimized. Constructed the prognostic model from ceRNA network. The prognostic value of all molecules in the ceRNA network was analyzed using univariate cox analysis. Next, molecules with P < 0.05 were included in the least absolute shrinkage and selection operator (LASSO) regression and multivariate cox analysis to calculate gene coefficients and construct the prognostic model (riskScore). The formula was as follows: Evaluated the prognostic model of ceRNA network. Based on the survival information and clinicopathological characteristics, we evaluated the value of the prognostic model for clinical application. To begin with, the patients with digestive system pan-cancer were divided into high- and low-groups according to median value of risk scores. Kaplan-Meier survival analysis was used to assess the survival prognosis between the high- and low-risk groups, and survival curve were plotted. Chi-square test was used to analyze the relationship between the prognostic model and clinicopathological characteristics. The differences between clinicopathological characteristics and high- and low-risk scores were validated using Wilcoxon signed-rank test. Subsequently, we performed univariate and multivariate cox analysis in conjunction with clinicopathological features to evaluate whether this prognostic model could be used as an independent prognostic factor for digestive pan-cancer. Analysis of the relationship between prognostic model and the immune microenvironment. In order to explore the correlation of prognostic model and immune microenvironment, we used immune databases from XCELL, TIMER, QuanTIseq, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT to analyze the relationship between the prognostic model and tumor-infiltration immune cells. Wilcoxon signed-rank test was used to verify the difference in the prognostic model and tumor-infiltration immune cells. Spearman correlation analysis was used to predict the relationship between prognostic models and immune checkpoint marker genes. p < 0.05 was set as the threshold of significance. Mutation landscape Cell culture and cell transfection. The human colorectal cancer cell lines (SW837 and SW620) were obtained from the cell bank of Zhonghong Boyuan Biotech Co. Ltd. All cells were cultured in DMEM medium (KeyGEN BioTECH, China) with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% antibodies (streptomycin/penicillin) at 37℃ and 5% CO 2 . LncRNA WDFY3-AS2 sequence was cloned into pcDNA3.1 vector. These plasmids were then transfected into colorectal cancer cells to establish SW837/SW620 overexpression lncRNA WDFY3-AS2, SW837/SW620 overexpression on mock-vector and negative control cells. Quantitative reverse-transcription PCR (qRT-PCR). Total RNA was extracted from SW837 and SW620 cells using TRIzol Reagent (CWBIO, China) and then reverse transcribed using to HiScript II Q RT SuperMix for qPCR (Vazyme, China). RNA expression was detected by qRT-PCR using ChamQ Universal SYBR qPCR Master Mix kit (Vazyme, China) and the following primer sequences. The relative expression of RNAs was calculated using ΔΔ CT method. The primers sequences are as follows: β-actin Forward: 5’-TGGCACCCAGCACAATGAA-3’; β-actin Reverse: 5’-CTAAGTCATAGTCCGCCTAGAAGCA-3’; WDFY3-AS2 Forward: 5’- ACTGCGTCAAGTTAAGGGCA-3’; WDFY3-AS2 Reverse: 5’-CCTACCCCCAGGTGAGACTT-3’. Cell counting kit-8 proliferation (CCK-8) assay. The transfected cells were placed in 96-well plate with about 3000 cells per well and incubated for 24h at 37℃ and 5% CO 2 . CCK-8 cell proliferation reagent (KeyGEN BioTECH, China) was added to each well and the absorbance value was measured at the 450nm with the microplate reader after 4h. Wound healing assays. The transfected cells were seeded in 6-well plate to full confluence. Vertical scratches are made in the wells using the micropipette tip. After 48h of incubation at 37°C and 5% CO2, the cells were observed under a microscope. Transwell invasion assays. The cells suspended of fresh serum-free medium were seeded in the upper chamber of transwell and the lower chamber was filled with the complete medium and incubated for 24 hours at 37°C and 5% CO2. The chambers were stained with 0.1% crystal violet (Solarbio, China) for 10 minutes and 3% acetic acid was added to dissolve the staining solution in the cells. The absorbance value of the solution was measured at 562nm with the microplate reader. Western blot. The total protein was extracted from SW837 and SW620 cells with RIPA lysis buffer (Applygen, China). The protein concentrations were calculated with BCA protein assay kit and equal amount of protein were separated on 10% SDS-PAGE gels (Xilong Scientific, China). Then the proteins were transferred to PVDE memberanes (Millipore, USA). The membranes were blocked for 1h with 3% skimmed milk and incubated with primary antibody overnight. After washing the membranes, the PVDE membranes were incubated with the secondary antibody for 2h. Finally, the PVDF membranes were wetted with luminescent solution and placed in the sample placement area of the ultra-high sensitivity chemiluminescent imaging system (Bio-Rad, Shanghai) to run the procedure for developing the images. The primary antibodies and secondary antibodies are listed below: Mouse Monoclonal Anti-Actin (#TA-09, ZSGB-BIO), Rabbit Anti-OSR1 (#AF7668, Affinity), HRP anti-mouse (#ZB-2305, ZSGB-BIO), HRP anti-Rabbit (#ZB-2301, ZSGB-BIO). Statistical analysis Statistical significance was calculated using Prism GraphPad 8.0.1 software and SPSS 18.0 software. All experiments were performed at least three times from biological level. Survival analysis was performed using the Kaplan-Meier method and compared using the log-rank test. The Wilcoxon signed-rank test was used to analyze the differences of risk scores in terms of immune infiltrating cell content. Spearman correlation analysis predicted the relationship between prognostic models and tumor infiltrating immune cells. The data with P value < 0.05 were defined as statistically significant. Results Identification of co-expressed lncRNAs, miRNAs, and mRNAs for digestive system cancinomas. We download and processed the transcriptome data for ESCA, CRC, STAD and LIHC from the TCGA database. Differentially expressed lncRNAs (DELs), miRNAs (DEMRs), and mRNAs (DEMs) were screened based on filtering conditions of P 1. The co-expressed 265 DELs, 36 DEMRs, and 921 DEMs from four tumors were identified by intersection using venn diagrams (Fig. 1 ). GO and KEGG functional enrichment analysis. In order to investigate the biological function of differential expressed genes (DEGs), we performed GO and KEGG functional enrichment analysis. The KEGG analysis shows that the DEGs mainly enriched in the cell cycle, DNA replication and Neuroactive ligand-receptor interaction (Fig. 2 A). The gene ontology (GO) analysis indicated that DEGs mainly involved in nuclear division, chromosome segregation, extracelluar matrix, kinetochore and DNA-dependent ATPase activity (Fig. 2 B-D). Construction and optimization of ceRNA network. First, the miRcode database were used to screen out miRNAs associated with 265 lncRNAs. Comparing with 36 co-expressed DEMRs, we obtained 7 DEMRs and found them as the downstream targets of 17 DELs (Table 1 ). Then, 324 target genes associated with 7 DEMRs were screened by three databases including miRDB, TargetScan, and miRTarBase. Further, Intersecting the 324 target genes with the 921 co-expressed differential genes and plotting the Venn diagram, we obtained 12 common DEMs (Fig. 3 ). The ceRNA network was constructed using cytoscape software. A total of 15 DELs, 6 DEMRs, and 12 DEMs were found in the ceRNA network (Fig. 4 A). The survival analysis demonstrated that 11 DELs (ADAMTS9-AS1, ADAMTS9-AS2, AL357153.1, AP002478.1, LINC00184, MAGI2-AS3, PVT1, SOX21-AS1, LMO7-AS1, HOTAIR and WDFY3-AS2), 2 DEMRs (has-miR-21 and has-miR-217) and 7 DEMs (ATAD2, CFL2, JPH1, KPNA2, OSR1, PBK and PBM20) were significant difference in digestive pan-cancer (Figure S1 ). Based on survival analysis, the ceRNA network was optimized to contained a total of four DELs (PVT1, WDFY3-AS2, ADAMTS9-AS1 and HOTAIR), 1 DEMRs (hsa-miR-21), and 2 DEMs (OSR1 and JPH1) (Fig. 4 B). Establishment and evaluation of a prognostic model based on the ceRNA network. To identify the prognostic value of 7 molecules from the ceRNA network, we screened out four factors (HOTAIR, WDFY3-AS2, hsa-miR-21, and OSR1) by univariate cox analysis (Fig. 5 A). Further analysis using lasso regression and multivariate cox analysis revealed that four molecules could be used as independent factors for digestive system pan-cancer (Fig. 5 B-D). The nomogram and calibration showed that four factors have certain predictive value for the prognosis of digestive system pan-cancer (Fig. 6 A-C). Risk scores were calculated from the expression values and coefficients of the genes to construct a prognostic model. According to the median value, the patients of digestive pan-cancer were then divided into two groups: high- and low-risk. Figure 7 A and B demonstrates the increase in the number of patient deaths with increasing risk scores. Survival analysis demonstrated the low-risk group has a better prognostic value than high-risk group (Fig. 7 C). The ROC curve was used to analyzed the AUC of patients with digestive pan-cancer at 1, 3, and 5 years to determine the optimal model (Fig. 7 D). Relevance analysis of prognostic model and clinicopathological characteristics. To further validate the robustness of prognostic model, we evaluated the relationship between the risk of digestive pan-cancer and clinicopathological characteristics. The Wilcoxon signed-rank test showed the correction between the risk score and age (P = 0.25) (Fig. 8 A), sex (P < 0.05) (Fig. 8 B), stage T (P < 0.05) (Fig. 8 C), stage N (P < 0.05) (Fig. 8 D), stage M (P = 0.099) (Fig. 8 E) and clinical stage (P < 0.05) (Fig. 8 F). The univariate and multivariate cox analysis demonstrated that risk score and clinical stage act as independent risk factors in digestive system pan-cancer (Fig. 8 G-H). The ROC analysis suggested that risk score and clinical stage had certain predictive values to patients with digestive tumors (Fig. 8 I). Correlation analysis of prognostic model and immune microenvironment. As the advent of immunotherapy has benefited more tumor patients, we assessed the relationship between the risk score and immune microenvironment. Based on seven immune data calculation methods (TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT), we found that risk scores were positively associated with more tumor-infiltrating immune cells, such as B cell, myeloid dendritic cell, macrophage, monocyte, cancer associated fibroblast, Endothelial cell, CD4 + T cell, and CD8 + T cell. Whereas they were negatively associated with NK cell and mast cell (Fig. 9 and S2). We then compared the high- and low- risk groups in relation to common immune checkpoints, including CD274, CTLA4, PDCD1, and HAVCR2. The results suggested that the high-risk group to be positively associated with four immune checkpoints in CRC and LIHC (P < 0.05) (Fig. 10 A-B), but not statistically significant in ESCA and STAD (no show). This suggests that patients in the high-risk group with CRC and LIHC may benefit more by using immune checkpoint inhibitors. Analysis of mutation landscape between groups with high- and low-risk scores. Further analysis of the mutation characteristics of high- and low-risk groups for digestive system analysis, we found a high incidence of mutations in pan-cancer of the digestive system, with a mutation rate of 90.27% in the high-risk group of patients (Fig. 11 A) and 95.65% in the low-risk group (Fig. 11 B). Compared with other mutated genes, the highest mutation frequencies were in APC, TP53, and TTN. Among them, APC mutations were mainly multiple mutations and nonsense mutations; TP53 and TTN mutations were predominantly missense mutations. It was found that higher TMB provided tumor patients with more neoantigens and raised T cells to recognize cancer cells, which was associated with better immunotherapy outcomes [ 16 ] . Therefore, these results suggested that the prognostic model may have an advantage in immunotherapy in the clinical management of patients with digestive system pan-cancer. The relative expression of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer tissues. The ceRNA network analysis revealed that WDFY3-AS2/has-miR-21/OSR1 has a targeted regulatory relationship. In order to explore the interaction between the three factors, we have analyzed the relative expression of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer and para-cancerous tissues. The results showed that the expression of WDFY3-AS2 and OSR1 were significantly down-regulated in colorectal cancer tissues compared to para-cancerous tissues (Fig. 12 A and B), while the expression of has-miR-21 was markedly up-regulated in colorectal cancer tissues (Fig. 12 C). The findings indicated that the expression of the three factors in colorectal cancer tissues is consistent with ceRNA targeted regulation. Correlation analysis of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer tissues. To investigate the targeted regulation of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer tissues, we discovered the correction between the expression of three molecules by Spearman correlation analysis. The outcome revealed that the expression of WDFY3-AS2 and OSR1 were both negatively correlated with has-miR-21 in colorectal cancer tissues (Fig. 13 A and B), whereas the expression of WDFY3-AS2 was positively corrected with OSR1 (Fig. 13 C). The correlation between the expression of WDFY3-AS2/has-miR-21/OSR1 in colorectal cancer tissues was in accordance with the ceRNA-targeted regulatory relationship. The effects of overexpression of WDFY3-AS2 in the proliferation, migration and invasion of colorectal cancer cells. To evaluate the biological function of WDFY3-AS2 in colorectal cancer cells, we constructed and transfected WDFY3-AS2 overexpression plasmid into SW837 and SW620 cell lines of colorectal cancer. The results showed that the expression of WDFY3-AS2 was significantly upregulated in SW837 and SW620 cell lines by the overexpression WDFY3-AS2 groups compared to the blank groups (Fig. 14 A). The experiments with CCK8, Scratch assay and Transwell showed that overexpression of WDFY3-AS2 dramatically inhibited the proliferation, migration and invasion of SW837 and SW620 cells compared to the blank and overexpression null groups (Fig. 14 B-F). Overexpression of WDFY3-AS2 regulates protein expression of OSR1 in colorectal cancer cells. In order to investigate the regulatory effect of WDFY3-AS2 on the target gene OSR1, we examined the protein expression of OSR1 in colorectal cancer cells using Western blot assay. The outcomes showed that the expression of OSR1 protein in SW837 (Fig. 15 A-B) and SW620 (Fig. 15 C-D) cells was significantly higher in the overexpression WDFY3-AS2 group compared to the mock-vector group (P < 0.05), indicating that WDFY3-AS2 may suppress the development and progression of colorectal cancer cells by positively regulating the expression of the target gene OSR1. Discussion The morbidity and mortality of digestive malignancies are among the highest in China [ 1 ] . Although its clinical treatments have made stage progress, such as surgical resection, chemotherapy, molecular targeted therapy and so on [ 17 , 18 ] . The early diagnosis of gastrointestinal malignancies still faces a great challenge due to the insidious symptoms at the initial stage of development. Therefore, it is an urgent task for scientific research and clinical work to continue to explore potential early biomarkers of digestive malignancies. The study of the digestive system pan-cancer is a breakthrough from previous studies that only targeted individual tumor by tapping into common molecular mechanisms among similar tumors, providing new clues for the research of digestive malignancies. In previous literatures on the mechanisms of ceRNA regulation, we found that the common lncRNAs were existed in the different digestive malignancies. For example, lncRNA SNHG6 acts as an oncogene. In ESCA, downregulation of SNHG6 inhibits cancer cell proliferation through the miR-186-5p/HIF1α axis [ 19 ] . In STAD and LIHC, SNHG6 competitively binds miR-101-3p, regulates ZEB1 expression and effects cancer cell growth [ 20 , 21 ] . In CRC, SNHG6 exerts an oncogenic effect on colorectal cancer cells by competitively absorbing miR-760 and positively regulating the expression of FOXC1 [ 22 ] . It follows that there may be common molecular mechanisms in the similar tissue-derived malignancies. Thus, we constructed ceRNA networks by screening common lncRNAs, miRNAs and mRNAs in four digestive malignancies, and explored their mechanisms in the digestive system pan-cancer, which have certain practical significance for the diagnosis and prognosis of digestive malignancies. In this study, the expression values of seven genes (PVT1, WDFY3-AS2, ADAMTS9-AS1, HOTAIR, hsa-miR-21, OSR1, JPH1) in the ceRNA network and their correlation coefficients were calculated based on machine learning to construct a prognostic model, breaking away from in previous studies that only quantified gene expression to analysis their prognostic value in solid tumors. We selected the most relevant genes for the prognostic model using the univariate COX analysis, LASSO regression and multi-variate COX analysis, including WDFY3-AS2, HOTAIR, has-miR-21 and OSR1. Then risk scores of the prognostic model were calculated, and patients were divided into high- and low-risk groups according to median value of the risk scores. Next, the robustness of the prognostic model was assessed utilizing survival information and clinicopathological characters of digestive pan-cancer. The results revealed that patients in the low-risk group had a better outcome than those in the high-risk group. The analysis of clinicopathological characters showed that risk score and clinical stage have significant prognostic value, indicating that risk score and clinical stage can be used as independent prognostic factors for digestive pan-cancer. Out of 7 molecules, previous studies have reported on the mechanisms of action of 4 key genes in digestive malignancies. For instance, WDFY3-AS2, located on chromosome 4q21.23, is an RNA molecule with a length of 3383 nucleotides [ 23 ] . In ESCA, WDFY3-AS2 acts as ceRNA and regulates PTEN expression by competing for binding to miR-18a, suggesting that the WDFY3-AS2/miR-18a/PTEN axis may be involved in the progression of esophageal cancer [ 24 ] . Zhang et al. [ 25 ] found that the expression of WDFY3-AS2 was strongly correlated with the molecular mechanisms regulating EMT in LIHC. HOTAIR was highly expressed in broad-spectrum tumors as well as being linked closely to metastasis and poor outcome [ 26 ] . Wang et al. [ 27 ] concluded that knockdown of HOTAIR inhibits proliferation and migration and promotes apoptosis in ESCA by competitively binding to miR-204 to regulate HOXC8 expression. Wei et al. [ 28 ] showed that HOTAIR up-regulated COL5A1 expression by “sponge-attracting” miR-217-5p, which promoted the proliferation and metastasis of gastric cancer cells. In LIHC, inhibition of HOTAIR increased p27 expression and decreased cyclin D1 expression, thus inducing G0/G1 cell cycle arrest. Knockdown of HOTAIR markedly up-regulated the expression of miR-217, resulting in a reduction in tumor size as well as a decrease in Ki67 levels, which inhibited the proliferation and growth of hepatocellular carcinoma [ 29 ] . Huang et al. [ 30 ] discovered that HOTAIR/miR-545/EGFR axis could modulate the development and progression of colorectal cancer cells. MiR-545 suppressed EGFR expression by affecting the 3’UTR activity of EGFR, and overexpression of HOTAIR inhibited miR-545 expression, while silencing of HOTAIR enhanced miR-545 expression. Moreover, when overexpression, HOTAIR eliminated the inhibited of EGFR expression by miR-545 mimic. The conclusion suggested that HOTAIR mediated miR-545 may regulate the proliferation of CRC. Has-miR-21 has 3433 nucleotides and locates in human chromosome 17q23.2, which is an intergenic region at the 3’UTR side of transmembrane protein 49 [ 31 ] . Chen et al. [ 32 ] suggested that miR-21 may target RASA1 expression through the regulation of Snail and vimentin proteins, thus impacting the proliferation, migration, invasion and tumor growth of ESCA. Research has suggested that the expression of circ_0027599 and RUNX1 was down-regulated in STAD. Circ_0027599 acted as a sponge for miR-21-5p, positively regulating the expression of RUNX1, thus reversing the malignant progression of STAD [ 33 ] . Hong et al. [ 34 ] showed that miR-21-3p promoted migration and invasion of LIHC via directly suppressing the downstream target molecule SMAD7 and upregulating the YAP1 expression. Jiang et al. [ 35 ] found the over-expression of miR-21-5p could down-regulation of TGFβ1, which in turn induces cellular scorching and acts an anti-tumor agent. OSR1, located on human chromosome 2p24.1, is a 266 amino acid protein [ 36 ] . Zhang et al. [ 37 ] reported that down-regulation of OSR1 promoted development of colon adenocarcinoma via FAK-mediated Akt and MAPK signaling. Chen et al. [ 38 ] suggested that OSR1 can affect the invasion and migration of tongue squamous carcinoma via inhibiting the NF-κB signaling pathway. To sum up, the four critical molecules had significant prognostic value in gastrointestinal malignancies. Therefore, the prognostic model obtained for these molecules may be of great potential as a novel biomarker in clinical management of digestive system pan-cancer. Over the past few years, tumor immunotherapy has brought remarkable benefits for patients with cancer [ 39 , 40 ] . For this study, we explored the relationship between the prognostic model and tumor-infiltrating immune cells in digestive pan-caner. Interestingly, we found that the most of immune cells were positively correlated with high-risk group, such as B cell, myeloid dendritic cell, macrophage, monocyte, cancer associated fibroblast, Endothelial cell, CD4 + T cell, and CD8 + T cell. Previous research revealed tumor-infiltrating B lymphocytes promoted cytotoxic T cells or inhibited downstream immunosuppressive pathway by secreting immunoglobulins, resulting in anti-tumor response [ 41 ] . Helmink et al. [ 42 ] have shown that B cells and tertiary lymphoid structures have potential roles in immune checkpoint blockade (ICB) therapy response. Alterations in tumor microenvironment can affect the differentiation of monocytes to macrophages and dendritic cells [ 43 ] . Endothelial cells and tumor-associated fibroblasts are major inducers of the migration and invasion capacity of cancer cells which could be new targets for cancer therapy [ 44 , 45 ] . To further investigate the value of prognostic model in the immunotherapy, we analyzed the relationship between prognostic model and common immune checkpoint marker genes, such as CD274, CTLA4, PDCD1 and HAVCR2. The results found that patients in the high-risk group were significantly positively associated with these marker genes (CD274, CTLA4, PDCD1 and HAVCR2) in CRC and LIHC, suggesting that the immune checkpoint inhibitor therapy have greater prognostic benefits for survival in patients with CRC and LIHC. It has been shown that Tumor Mutation Burden (TMB) may predict clinical response to immune checkpoint inhibitors [ 46 ] . We therefore analyzed the relationship between the prognostic model and the tumor mutation data. It was found that the incidence of mutational events was higher in both the high- and low-risk groups of the prognostic model (> 90%), and that the high-risk group had a higher number of TP53 mutations, which may be the main molecular mechanism for their increased clinical prognostic risk. To elucidate the biological function of action of lncRNA WDFY3-AS2 in CRC, we have used in vitro experiments to initially validate the antitumor effect of WDFY3-AS2. The results of CCK-8, Scratch and Transwell assays demonstrated that overexpression of WDFY3-AS2 inhibited the proliferation, invasion and metastasis of colorectal cancer cells. Finally, the protein expression of OSR1 was examined using western blot assay. We found that the expression of OSR1 was significantly higher in the overexpressed WDFY3-AS2 group compared to the blank load group, indicating that lncRNA WDFY3-AS2 could influence the progression of colorectal cancer via positively regulating the expression of OSR1. It has been proved that lncRNA WDFY3-AS2 acts as an antioncogene and inhibits tumor progression by directly modulating the expression of downstream target genes. For instance, WDFY3-AS2 can regulated SOCS2 to inhibit the JAK2/Stat5 signaling pathway, thus suppressing the proliferation and invasion of esophageal cancer cells [ 47 ] . The WDFY3-AS2 inhibits the progression of ovarian cancer by positively regulating the expression of the target gene RORA [ 48 ] . Nevertheless, several limitations remain in our study, mainly in two aspects. Firstly, we only validated the role of the key lncRNA WDFY3-AS2 in colorectal cancer through in vitro experiments, and lacked validation of the WDFY3-AS2/miR-21/OSR1 axis. Secondly, the key factors were only experimentally validated in colorectal cancer cells in this study. In subsequent researches, we will further validate it in other digestive malignancies to provide a more comprehensive reference for the clinical management of digestive cancers. In summary, the prognostic model constructed in this research based on the WDFY3-AS2/HOTAIR/has-miR-21/OSR1 ceRNA regulatory axis can assess the prognosis of digestive pan-cancer and may enable patients to benefit from immunotherapy. In addition, lncRNA WDFY3-AS inhibits proliferation, invasion and metastasis of colorectal cancer cells and positively regulates OSR1 expression, providing a new prognostic marker for the treatment of colorectal cancer patients. Declarations Availability of data and materials The data that support the findings of this study are openly available in TCGA database. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from the Nature Science foundation of Heilongjiang Province, China (No.SS2022H003). Authors’ contributions RC, ZH, and SL carried out the experimental design. ZH and GL wrote and revised the manuscript. 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Odd-skipped related transcription factor 1 (OSR1) suppresses tongue squamous cell carcinoma migration and invasion through inhibiting NF-κB pathway[J]. Eur J Pharmacol. 2018;839:33–9. Binnewies M, Roberts EW, Kersten K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy[J]. Nat Med. 2018;24(5):541–50. Xie Y, Xie F, Zhang L et al. Targeted Anti-Tumor Immunotherapy Using Tumor Infiltrating Cells[J]. Advanced Science (Weinheim, Baden-Wurttemberg, Germany), 2021, 8(22):e2101672. Wang S-S, Liu W, Ly D, et al. Tumor-infiltrating B cells: their role and application in anti-tumor immunity in lung cancer[J]. Volume 16. Cellular & Molecular Immunology; 2019. 1. Helmink BA, Reddy SM, Gao J, et al. B cells and tertiary lymphoid structures promote immunotherapy response[J]. Nature. 2020;577(7791):549–55. Tcyganov E, Mastio J, Chen E, et al. Plasticity of myeloid-derived suppressor cells in cancer[J]. Curr Opin Immunol. 2018;51:76–82. Kobayashi H, Enomoto A, Woods SL, et al. Cancer-associated fibroblasts in gastrointestinal cancer[J]. Nat Rev Gastroenterol Hepatol. 2019;16(5):282–95. Sobierajska K, Ciszewski WM, Sacewicz-Hofman I, et al. Endothelial Cells in the Tumor Microenvironment[J]. Adv Exp Med Biol. 2020;1234:71–86. Samstein RM, Lee C-H, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types[J]. Nat Genet. 2019;51(2):202–6. Zhang Q, Guan F, Fan T, et al. LncRNA WDFY3-AS2 suppresses proliferation and invasion in oesophageal squamous cell carcinoma by regulating miR-2355-5p/SOCS2 axis[J]. J Cell Mol Med. 2020;24(14):8206–20. Li W, Ma S, Bai X, et al. Long noncoding RNA WDFY3-AS2 suppresses tumor progression by acting as a competing endogenous RNA of microRNA-18a in ovarian cancer[J]. J Cell Physiol. 2020;235(2):1141–54. Tables Table 1 DEmiRNAs targeted by DELncRNAs. DEmiRNAs DElncRNAs hsa-mir-183 AL357153.1/WDFY3-AS2/LINC00365/ADAMTS9-AS2/PVT1 hsa-mir-21 WDFY3-AS2/LINC00365/HOTAIR/ERVMER61-1/ADAMTS9-AS1/PVT1 hsa-mir-217 AL357153.1/WDFY3-AS2/LINC00184/PCA3/HOTAIR/MAGI2-AS3/PVT1 hsa-mir-301b AL357153.1/PCA3/SOX21-AS1/HOTAIR/ADAMTS9-AS1/ADAMTS9-AS2 hsa-mir-372 AP002478.1/LINC00184/LMO7-AS1/DLX6-AS1/MAGI2-AS3/ADAMTS9-AS2/PVT1 hsa-mir-373 AP002478.1/LINC00184/LMO7-AS1/DLX6-AS1/MAGI2-AS3/ADAMTS9-AS2/PVT1 hsa-mir-508 LINC00470/AP002478.1/LINC00114/MAGI2-AS3 Table 2 Correction coefficients for key molecules obtained by multivariate cox analysis. Gene ID coef HR HR.95L HR.95H P value HOTAIR 0.027 1.027 0.999 1.056 0.059 WDFY3-AS2 0.168 1.184 1.100 1.274 < 0.001 Hsa-miR-21 0.127 1.136 0.999 1.291 0.052 OSR1 0.013 1.013 0.980 1.046 0.446 Additional Declarations No competing interests reported. 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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-3054408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":209436341,"identity":"872b4a34-7736-4e7a-9046-25df66829309","order_by":0,"name":"Zikang He","email":"","orcid":"","institution":"Beijing Chao-yang Hospital, Capital Medical University, Beijing, China.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zikang","middleName":"","lastName":"He","suffix":""},{"id":209436342,"identity":"d18c00c9-8252-446a-848c-4357dac3f0b9","order_by":1,"name":"Shuang Liang","email":"","orcid":"","institution":"The Second Hospital Affiliated to Mudanjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Liang","suffix":""},{"id":209436343,"identity":"7e939d88-bdbf-4989-9433-0fbe5b0ff767","order_by":2,"name":"Guoli Li","email":"","orcid":"","institution":"Beijing Chao-yang Hospital, Capital Medical University, Beijing, China.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guoli","middleName":"","lastName":"Li","suffix":""},{"id":209436344,"identity":"b9864414-ef97-446d-8504-248f38b57d61","order_by":3,"name":"Xueyan Wang","email":"","orcid":"","institution":"Mudanjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xueyan","middleName":"","lastName":"Wang","suffix":""},{"id":209436345,"identity":"66acc2b2-8eb5-4282-9b75-f7fb74d96089","order_by":4,"name":"Ping Shen","email":"","orcid":"","institution":"Mudanjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Shen","suffix":""},{"id":209436346,"identity":"a3948089-bd06-405f-bf36-5c721ba8b92f","order_by":5,"name":"Huan Wang","email":"","orcid":"","institution":"Mudanjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Wang","suffix":""},{"id":209436347,"identity":"b7624ae0-438e-467e-8a10-76b194e14f4a","order_by":6,"name":"Rongjun Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFACxgYQyQxEBxh4eMBCBsRqYUsgVgsc8BgwQHQQ0MI/I7ntw8cdtewGt3u+bngjsy2xgb15mwRDzR2cWiRuJDbPnHnmOLPBnbPbbs7huZ3YwHOsTILh2DOcWgwkEpuZeduOMRvcyN12mwekRSLHTIKx4TAxWnKeQbTIvyFKSw1ICxvUFh78WiTOPGxmnNl2gFnyRpoZyC/GbTxpxRYJx3Br4W9Pf8zwsa0ume9G8rMbb3tuy/azH95440MNbi1QcDgZTDH2AGMUxEggpIGBoc4OQv8grHQUjIJRMApGHgAAD9BYEw7uu0cAAAAASUVORK5CYII=","orcid":"","institution":"Mudanjiang Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rongjun","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2023-06-12 16:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3054408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3054408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38612001,"identity":"b71744ec-9e2a-4526-9c34-35af99247b0a","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe common differentially expressed lncRNAs, miRNAs, and mRNAs in four digestive system cancers by Venn diagram.\u003c/strong\u003e A total of 265 lncRNAs, 36 miRNAs and 921 mRNAs were obtained from CRC, ESCA, STAD and LIHC.\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/41ab9be3c971f871846b8c49.jpeg"},{"id":38612002,"identity":"b66adce2-72a2-4d87-b1ea-8f5d1cb0b573","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe enrichment analysis of 921 DEmRNAs in digestive system pan-cancer. \u003c/strong\u003e(A) The top 10 terms of KEGG pathway enrichment analysis. (B–D) The top 10 terms significantly enriched in the three GO categories: (B) biological process; (C) cellular component and (D) molecular function. P-value \u0026lt; 0.05 was set as the threshold.\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/7a24ff3d5e90587c1e4790ef.jpeg"},{"id":38612010,"identity":"5acc18d5-2ebc-4aa2-b31e-2160a5e90152","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of mRNAs for the construction of ceRNA networks in digestive system pan-cancer. \u003c/strong\u003eThe bule area shows co-expressed mRNAs in digestive pan-cancer. The pink area represents the DEmiRNA-related target genes predicted from 3 databases (miRTarBase, miRDB and TargetScan). Overlapping area means DEmRNAs for constructed ceRNA networks.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/06e5aac051efbc566a6c2521.jpg"},{"id":38614259,"identity":"93565fae-1f71-4009-8be8-be04aa478eba","added_by":"auto","created_at":"2023-06-15 15:30:35","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)The construction of ceRNA regulatory network in digestive system pan-cancer. (B)The optimized ceRNA regulatory network in digestive system pan-cancer. \u003c/strong\u003eLncRNAs, miRNAs and mRNAs are represented by blue rhombus, yellow hexagon and red ellipse, respectively.\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/6ac024fd54f86049de48df28.jpeg"},{"id":38613482,"identity":"8dfccff5-48d9-46d4-b853-da58d6f7c80b","added_by":"auto","created_at":"2023-06-15 15:22:35","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFiltering of critical molecules in the ceRNA network. \u003c/strong\u003e(A) The four molecules (OSR1, HOTAIR, WDFY3-AS2 and has-miR-21) had significant prognostic value by univariate cox analysis. (B) The optimal model for LASSO regression analysis to predict the four molecules of the digestive pan-cancer. The vertical dashed curve lies at the optimal logarithm (λ value). (C) Coefficient mapping for LASSO regression. (D) The 4 molecules (OSR1, HOTAIR, WDFY3-AS2 and has-miR-21) had significant prognostic value by multivariate cox analysis.\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/046eee337d969ec605b35beb.jpeg"},{"id":38612008,"identity":"b2715c5c-8a05-4a31-88f1-724e67251a9a","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":71999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic assessment of four molecules in digestive system pan-cancer. \u003c/strong\u003e(A) The nomogram demonstrated the prognostic value of four molecules in terms of overall survival (OS) at 1, 3, and 5 years for digestive system pan-cancer. (B-C) Calibration plots for 3 and 5 years are used to assess the agreement of the predicted values with the OS. The grey reference line indicates the best calibration line and the red prediction line demonstrates that the predicted probabilities trend in line with the actual probabilities.\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/af0c7a6bbf22b43b7289ed4e.jpeg"},{"id":38612003,"identity":"079967f8-d848-4262-917a-7618f529771f","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":109125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and assessment of a prognostic model for digestive system pan-cancer. \u003c/strong\u003e(A) Deaths in patients with pan-cancer of the digestive system gradually increased as patient risk scores rose. (B) The survival time was significantly longer in the low-risk group than in the high-risk group. (C) The ROC analysis revealed AUC values greater than 0.6 at 1, 3 and 5 years in pan-cancer of the digestive system.\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/c613a0660e8c0681ff1db57b.jpeg"},{"id":38613484,"identity":"617f7a30-126e-4ee0-96df-9ffa6a871d3a","added_by":"auto","created_at":"2023-06-15 15:22:35","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":106489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of a prognostic model for pan-cancer of the digestive system in conjunction with clinicopathological characteristics. \u003c/strong\u003e(A-F) The relationship between risk score and Clinicopathological characteristics, including (A)Age, (B) Sex, (C) T stage, (D) N stage, (E) M stage, and (F) Clinical stage. (G) The result of univariate analysis showed that the risk and age, T stage, N stage, M stage, clinical stage had significant prognostic value. (H) The result of multivariate analysis showed that the risk and clinical stage had significant prognostic value. (I) ROC analysis showed that clinical stage and risk score had certain predictive value for digestive pan-cancer (AUC \u0026gt; 0.64).\u003c/p\u003e","description":"","filename":"8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/693ee4dbc7bf3907f089bd7e.jpeg"},{"id":38613485,"identity":"2b161ecb-f1e7-4b06-af2c-1f3b8781ba41","added_by":"auto","created_at":"2023-06-15 15:22:35","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":222679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between risk scores and tumor-infiltrating immune cells.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/0fb008f608ab07598a82103f.jpeg"},{"id":38612005,"identity":"3456289f-f0b6-428a-91f3-c67b40ebe0fa","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":55105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between risk scores and the common immune checkpoint inhibitors (CD274, CTLA4, PDCD1, and HAVCR2). \u003c/strong\u003e(A) CRC. (B) LIHC.\u003c/p\u003e","description":"","filename":"10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/c1162bea5fb0578b561851fb.jpeg"},{"id":38613487,"identity":"48c1f2bc-e489-4f15-bb46-bd00368db9ac","added_by":"auto","created_at":"2023-06-15 15:22:35","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":122934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe mutation analysis of the prognostic model in digestive system pan-cancer. \u003c/strong\u003eMutation information for top 30 genes in each sample is shown by waterfall plots. Different colors indicate different mutation types. The bars on the right indicate the number of mutation load. (A) mutation events in the high-risk group; (B) mutation events in the low-risk group.\u003c/p\u003e","description":"","filename":"11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/5bd77741308f790c2a0c5c83.jpeg"},{"id":38613481,"identity":"a5f166a1-24fe-4d28-a875-5fe852e009c8","added_by":"auto","created_at":"2023-06-15 15:22:35","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":44756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative expression of WDFY3-AS2/hsa-miR-21/OSR1 in colorectal cancer tissues and para-cancerous tissues. \u003c/strong\u003e(A) WDFY3-AS2, (B) Hsa-miR-21, (C) OSR1.\u003c/p\u003e","description":"","filename":"12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/7d376305132a1b53d9c5365b.jpeg"},{"id":38612014,"identity":"36119546-ab8f-4431-bd01-15ae56de13c7","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":61829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correction analysis of WDFY3-AS2/hsa-mir-21/OSR1 in colorectal cancer tissues. \u003c/strong\u003e(A) WDFY3-AS2 was negatively linked to hsa-mir-21; (B) OSR1 was negatively linked to hsa-mir-21; (C) WDFY3-AS2 was positively correlated with OSR1.\u003c/p\u003e","description":"","filename":"13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/678a4d98c1ca8c3960beb869.jpeg"},{"id":38612016,"identity":"905a352c-cbd9-477f-89c2-4c920eb66425","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"jpeg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":243752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe biological function of lncRNA WDFY3-AS2 in colorectal cancer cells. \u003c/strong\u003e(A) The overexpressed lncRNA WDFY3-AS2 was verified by qRT-PCR. (B) The effect of overexpressed lncRNA WDFY3-AS2 on the proliferation of colorectal cancer cells was measured by CCK-8 assay. (C-D) The impact of overexpressed lncRNA WDFY3-AS2 on the migration of colorectal cancer cells was examined with the Scratch assay. (E-F) The influence of overexpressed lncRNA WDFY3-AS2 on the invasion of colorectal cancer cells with the Transwell assay. (* represents the overexpression group compared with the control group, P\u0026lt;0.05; # represents the overexpression group compared with the control group, P\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/e59ed27b48996357a3d3373f.jpeg"},{"id":38613486,"identity":"85660033-5e19-4624-a212-857d11c663c4","added_by":"auto","created_at":"2023-06-15 15:22:35","extension":"jpeg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":157975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe protein expression of OSR1 in the colorectal cancer with the Western blot assay. \u003c/strong\u003e(A-B) The relative expression of OSR1 in the mock-vector group and overexpressed WDFY3-AS2 group in SW837 cells. (C-D) The relative expression of OSR1 in the mock-vector group and overexpressed WDFY3-AS2 group in SW620 cells.\u003c/p\u003e","description":"","filename":"15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/45bc031784168845e0664ff9.jpeg"},{"id":38916481,"identity":"f9f3f970-2637-47a2-8085-6cc63db99fc5","added_by":"auto","created_at":"2023-06-22 09:15:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2082114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/3e6e8ae3-d2be-4b13-8fab-29d0569e9438.pdf"},{"id":38612012,"identity":"47a2f7a3-4958-489f-b1d5-24640d00fe55","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":429798,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/1dc2b249e6980493d434d18d.pdf"},{"id":38612018,"identity":"bfc9eb47-527c-4565-9d03-b4cd157489eb","added_by":"auto","created_at":"2023-06-15 15:14:35","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2046421,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3054408/v1/e211b21fb402205e2916a64d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Certification of novel lncRNA based on analysis of ceRNA network in digestive system pan-cancer and characteristics of tumor immune microenvironment","fulltext":[{"header":"Background","content":"\u003cp\u003eDigestive system pan-cancer is a general term of digestive cancers, including esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colorectal carcinoma (CRC) and liver hepatocellular adenocarcinoma (LIHC). In China, digestive system tumors are common malignant tumors and have a relatively poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. By 2020 statistics, the new incidence and mortality rates were 3.1% and 5.5% for ESCA, 5.6% and 7.7% for STAD, 9.8% and 9.2% for CRC, and 4.7% and 8.3% for LIHC\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It has been found that tumors of similar tissue origin may share common pathogenic molecular mechanisms to influence tumors progression, such as gynecologic and breast pan-cancer\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, it remains to be explored whether there may be common molecular mechanisms in the digestive system pan-cancer.\u003c/p\u003e \u003cp\u003eLong non-coding RNA (lncRNA) are a class of RNA molecules with transcripts greater than 200 nucleotides that are not directly involved in protein synthesis, but can regulate gene expression at the transcriptional and post-transcriptional levels. It has been shown that the regulatory effects of lncRNAs influence gene expression activity and contribute to tumorigenesis and development by altering cellular biology\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In addition, lncRNAs may also act as different functional molecules such as signals, decoys, scaffolds and molecular sponges, and some lncRNAs can encode functional small peptides\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, which act as important regulators of various human life activities and play a key role in the progression of diseases including cancer.\u003c/p\u003e \u003cp\u003eIn 2011, Selmena et al. proposed the competing endogenous RNA (ceRNA) hypothesis, which assumes that different RNAs regulate the function of targeted genes by competing to bind to specific molecular site\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. When lncRNAs and downstream mRNA shared the same miRNA response element (MREs), lncRNAs can act as ceRNAs and compete to bind miRNAs through MREs to inhibit or enhance the expression of mRNAs. In recent years, an increasing number of studies have shown that aberrant gene expression in the ceRNA regulatory network interferes with the progression and prognosis of digestive malignancies\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, no studies have yet explored the value of ceRNA network regulatory axis on the diagnosis and prognosis of digestive malignancies from pan-cancer perspective.\u003c/p\u003e \u003cp\u003eCancer immunotherapy is a new treatment method in which the anti-tumor antibody or immune tumor vaccine is used to activate the autoimmune system to fight against tumors. The purpose of the treatment is to inhibit the growth of tumor cells by activating the immune effector cells or anti-tumor immune response in the body, thus improving the survival rate of patients\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. A growing body of evidence suggests that cancer immunotherapy displays great advantages in patients with gastrointestinal malignancies. For example, Overman et al. found the combination of Nivolumab and Ipilimumab achieves disease control rates of around 80% in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. CheckMate 032 study suggested for the first time that the combination of Nivolumab and Ipilimumab has significant benefits in the treatment of advanced esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we utilized bioinformatics approaches to screen pivotal lncRNAs and their corresponding miRNAs and mRNAs in the digestive system pan-cancer and construct a ceRNA network. The prognostic model of ceRNA network molecules were constructed based on univariate cox analysis, LASSO regression and multivariate cox analysis, and risk scores were calculated. The predictive value of the prognostic model was evaluated by combining survival information, clinicopathological characteristics, tumor-infiltrating immune cells, immune checkpoint genes and mutation data. Finally, cell experiments validated the biological function of specific lncRNA WDFY3-AS2 in colorectal cancer cells. To sum up, the construction of ceRNA network in the digestive system pan-cancer and its role in the immune microenvironment provide new insights into the prognosis of digestive system cancers and their immunotherapy.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData preparation and collation.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe transcriptome data and mutation data were downloaded from The Cancer Genome Altas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcgadata.nci.nih.gov/tcga/\u003c/span\u003e\u003cspan address=\"https://tcgadata.nci.nih.gov/tcga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. Among them, 11 tumor-adjacent tissues and 160 tumor tissues were included in ESCA, 32 tumor-adjacent tissues and 375 tumor tissues in STAD, 50 tumor-adjacent tissues and 374 tumors tissues from LIHC, 51 tumor-adjacent tissues and 641 tumor tissues from CRC. In addition, the complete clinical information of patients with four digestive tract tumors was downloaded and combined from the TCGA database, and samples with less than 50 days of follow-up were excluded. Mutation data in varscan 2 format were downloaded and extracted from the TCGA database. Then, we merged the mutation data from four tumors and analyzed them using the R software \u0026ldquo;maftools\u0026rdquo; package.\u003c/p\u003e \u003cp\u003e \u003col start=2\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIdentification of lncRNA, miRNA, and mRNA.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe Ensembl IDs of trascriptome expression matrix were converted to gene IDs using Perl language. Then, the expression matrix of lncRNA, miRNA, and mRNA were extracted for each of four digestive tumors. Applying the edgeR package of R software, the differentially expressed lncRNAs, miRNAs, and mRNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were derived in cancer tissues and adjacent normal tissues (set P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and | logFC | \u0026ge; 1 as the threshold). Next, the DElncRNAs, DEmiRNAs and DEmRNAs of the four digestive tumors were intersected separately and a Wayne diagram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was drawn to obtain the differential co-expressed lncRNAs, miRNAs and mRNAs of the four tumors.\u003c/p\u003e \u003cp\u003e \u003col start=3\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGO and KEGG enrichment analysis.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo explore the biological functions of co-expressed genes in digestive pan-cancer, we performed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis by the DAVID functional annotation tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david-d.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david-d.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). With P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the screening criterion, the top 10 GO and KEGG enrichment pathways with significant enrichment were visualized using the \u0026ldquo;clusterProfiler\u0026rdquo; package of R software.\u003c/p\u003e \u003cp\u003e \u003col start=4\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eConstruction of ceRNA network.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe complementary relationship pairs of DElncRNAs and DEmiRNAs were compared and obtained using the miRcode database. Second, the three target gene prediction databases, Targetscan, miRTarBase, and miRDB were used to retrieve the target mRNAs of DEmiRNAs in the above pairs. Then, we plotted the intersection of target mRNAs and co-expressed DEmRNAs using the \"VennDiagram\" package of R software. Finally, the corresponding lncRNA-miRNA pairs and miRNA-mRNA pairs were imported into Cytoscape 3.8.1 software to construct ceRNA networks.\u003c/p\u003e \u003cp\u003e \u003col start=5\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOptimization of ceRNA network.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFirst, the gene expression data of the four digestive system tumors were integrated and standardized. Combined with the survival information, we performed survival analysis of the molecules in the ceRNA network using \"survivor\" package in the R software. Molecules with significant differences in prognosis were selected and the ceRNA network was optimized.\u003c/p\u003e \u003cp\u003e \u003col start=6\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eConstructed the prognostic model from ceRNA network.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003eThe prognostic value of all molecules in the ceRNA network was analyzed using univariate cox analysis. Next, molecules with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the least absolute shrinkage and selection operator (LASSO) regression and multivariate cox analysis to calculate gene coefficients and construct the prognostic model (riskScore). The formula was as follows:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAATgAAAAiCAYAAADLRcUjAAANtklEQVR4nO2dX2hb5f/H3/uxy7hxdnphVxSawzpEoX+WFBkTpIm0bi1MnE1QTHrTaXpjScumRbFFkXYmWi/WBlSYiraxQwtJimVa2dKrtSaZiGIlRZCd9CK11pwLL4Tne9E9z+/8zZ+1XWP3vCAXff5+nufkfM7nz3PSA4QQAg4HgKIoOHv2LDKZDK5du4aFhQUcP34cnZ2dey0ah3NX/N9eC8CpHmw2G9599134/X40NzfvtTgczrbhCo7D4exbDu61AJzqYnx8HIlEAi6XCxMTE6ivr8eTTz4Jm82216JxOBVzgMfgOBzOfoW7qBwOZ9/CFRyHw9m3cAXH4XD2LVzB3afE43EcOHDgrj41NTVIp9N7vQQOpyS7ruC8Xi+OHDmC1dXVivpNT0+jo6MDly5d2iXJ7m86OzsxNTXF/na5XCgUCiCEFP0kEgkAwO3bt0vOMTc3h9bWVqYYz58/D0VRivZJp9NobW1FLpfb3gL/A6TTadTU1CAej++pHIqiYGBgADU1Nfvu4VWVFtzc3BxmZ2cxPz9fUb/p6Wk4nU52Q3m9XiwuLrIPR4vX60UsFgMAfPfddzh79mxJBXT69GlcuXKl5NjhcBjDw8P4/PPPQQhBKpXCV199hffffx9utxtut9tyrs3NTaytrVW+IE7F0LdX8vk8fv/9d5w5cwbxeBxut9vSgg+Hw6y/3hNQ1wFALpeDJEms3uy676qiJ1VKMpkkAMjY2FhZ7YeGhoggCGRqaoqVTU1NEUmSCACSTCZ3S9T/PLFYjIiiSAAQl8tFCoVC0fayLJNoNGpZn0qlSENDA0mlUpo+drudhEIh0z4+n4/Y7XaSSqWI2+0miUSCiKJo2Z6zM4RCIWK324ksy4Y6+r1QX0efz0cAGK4Lvb4ASCwWM53HrFw9ps/n2+ZqjOwLBZfNZi3b5vN5IkkSV3AlqFTJFcPn8xm+rGZKz6yf0+kkHo+HK7d7QCqVIqIoWioeMwVHFZn+OyLLMhkcHCQul8vQhxBrBSfLMnG73WRgYMBS0W6HqnRRK0WWZQDAww8/bKgTRRFvv/32vRbpP0dnZyeuXLkCURTLdlfNyOVyWFxcxHPPPcfKFEXB6OgoHnvsMbS0tECSJEOMTVEU3L59G0tLS4hGo1hfX8ePP/5oGD8cDjN3h46jKIrGpfL7/cx1om2oqxQOh9kY+niT3++H2+3G9PQ0G6fYvBS1m+b3+/Hxxx+zvStWZ+WaqefSu310rX6/n/U/cOCAqXs3PT2N48ePs7XSNnQvWlpasL6+jq6urrLdw7W1NWxubprW2Ww2fPbZZzh8+DDOnTtXVhz1iy++wNGjR/HCCy9gc3MTP/zwQ1lylM12tGM+nyeRSIRZSFNTU0QQBOJwOEg+nyfZbJa5jnoikQgRBIEAIA6Hg3g8Ho2VZWbBtbe3EwDs097ermlL59WTzWZNLbhkMkk8Hg8bz+PxkEwmY2iXyWQM7fTjldoL2iYQCGjWXW2WpdqSczqdFT9RY7EYexKHQiEiiiI5efIkCx3EYjFTV8Tn8xGXy0VWVlaI2+0mqVTK4NL6fD72d6FQIC6XS2NJ+Hw+Zj0UCgXS29tLZFnWuE8nT54koVCI9bfb7WRlZYW4XC4CgDQ0NJBgMEhCoRAbu9i8siyT3t5eJkMsFiurLhaLse+T2rJRz0v74I77pl5HQ0MD6e7uZvKp+9Ayp9NJVlZWCCFb4Rq9haSfy+q7QK0xOr+ZhSbLMhkeHjbIrJ5Lb8EVCgXS3d1NUqkUG3un3dRtKbhIJEIcDgdTRB6Ph8W8rl+/TsbGxtjNrCaRSBBJkkg2myWEbG0+dHEyMwWXz+eZgqF9KVQOfRzOCqqA6JyZTIb1V4+dSCQIABKJRAghW8qSzqWep9heZDIZks/nicPhYOvJZDKa+mpiO+5qKffU5/NZukT6tvpyvcKlNxIdT618ent7NWPQOrVsehfNLB5Val4zN++jjz4ihUKhaJ3Z/FYuI31Q0PXoFZpeUZmtgyoQ/V4VUyhqJWymjNWoFRyVAapYnZmCUyt8uq6ddlO3HYMbGxvTWFP5fF5zw1KrS43H4yFDQ0OasqGhoZIKLhAIWCqvTCbDFAyVx8o6yufzRBAEQ8yuvb2dCILA5KftAoGAph2N+QmCoLEYi+3F2NgYK6dQ5enxeDT9y/3sJrFYrGTMTI/+JtKXX7x4kTidzqJjmikUKo/VPqitPKtkBr2hi7U1s2jKmZcGyfWylKrTKzSzmJdaBtpO/xBRy221fv2Do1T8TS9PKQtLr+DUa4/FYqYKTv+w069zJ9ixGNzrr78OYCvm1djYWLTtkSNHMDk5iVu3brGy7u5uy/br6+vo6OhAZ2cnvF6vaZvGxkYsLS1hbGwMgiBgfn4eTzzxBDo6Ogxn8K5evYqNjQ2cO3dOU/7NN9/gzz//ZPJfu3YNGxsbhh98tNvtCAQC2NjYwNWrVw2ymO3F6OgonnnmGU27uro6AEA0GgUAXLhwoeQ5NPVnN/n+++8RDocr+l04Gj85ceKEppzGbf755x8cOnQIx44dsxyjubkZN2/eRG1traHO6qzewMCAZq6GhgYMDg7u2LGDUvN+8sknKBQKcLlcGBwc1MToitWZsb6+bjhjWFdXB1EUy5KVXoPnn39eUz4+Po5///0XDz74IICtc4yHDx82XCsramtr8cEHH+DTTz8te19HR0dht9vR09ODpaUlTV06nUYikUBXVxeLNXZ1dQEAZmZmyhq/HPYkyfDSSy8BAJqamtDX14fV1VU0Njbi1KlThrapVAodHR04ceIETp8+XXLsCxcuYHl5GYFAAAAwPz8Ph8OhUaZff/01gC1FVYzZ2VkAwKFDhwx1VHHRsYqxurqKjY0NvPzyy5rgcVNTk6ZNtRAOh3H06NGKf8l3ZmYGp06dMiinhYUFNDU1QRRFPP3003f100t1dXX4448/8Ntvv1m2yeVymJycxMzMDHw+H1555ZWiymRtbQ0HDx5EW1vbXc+by+WQTqdhs9nw7bffIpVKsWB5sTqruURRNNzglSijX3/91VBGlUlfXx+7NjMzM6ivr8cDDzxQckxKZ2cnfD4fenp6yjoMXFtbywwA+hCnLCws4LXXXjM8NHw+HxYXF3fsoPeeKLjGxkYsLy+jvb0dk5OTkCTJ8o0FSZIAAO+88w7m5ubKGt9ut2NiYgLJZBIOhwMbGxt49tlnK5bzr7/+sqx79NFHyx6HZnkTiYSlNWa323Hp0qWKXpnaDeLxOGRZ1lhF5UCzp7/88gv78iuKgpGREUSjUUxOTuKnn35CW1sbvvzyy6KKyozm5mY8/vjjhuxcOBxGPB6Hoih48803MTIyApvNhsuXL6O+vh4vvviiJhs8MTHB+o+Pj+Ohhx4qaVEWmxfYemDTNR87dkzz4CpWZzbXmTNnNFaSoigYHx/XKKditLW14eDBg7h+/TqArUPzTz31FPx+P7umNGN9Nw+by5cvo6mpCS0tLRpLbm1tDT///LMh897c3Gw4GJ5OpxGNRg1WJgD09/djc3MTr776akVyWbJdH5fGjaziXWYxODXJZJLFztRxOXUMLpPJEEEQDAkAddtS8Ta1jFSmUhlM2o4mGPRzAtDE56z2giYw9HHHaiOVSrHMXKXQ7OmNGzeI0+kkAIgoiuS9997TxIY6OjoqiuvpUce0YBIHozEiGuTGnURJLpcjLpeLnbGj5eoAN21vFui2mleWZTI3N8eym1DFkIrV0RiYvlwvu75OLQfN7qrXWSgUyNTUlCYTnkgkNGspdSaRxivVMpglZ+g1bmhoKJmIoPE2dSZYH2/U78l2z2MSsoNJhrtVcIT8/2FcdTt9koFmWs2OgiSTyaLKIxAIaGSkMuuTB1QWmsiIRCKaJIAamiBQJz2K7YUgCESSJNNjLOVkfXcbWZaJx+O56wyWWfa0mjBLMtyvhEKhqr5WO8mOuah///132W37+vqwvr7O/hZFEefPny/ax+v1YmhoCMvLy3jjjTcM9fqkhZqlpSUIgoBHHnkEAFhyYXJyEtPT06wdTWa0traydoIgIBqNGmJk8XgckiSZJj3M9sLr9SKbzRqSHrdu3WKxvr1CURT09/fj4sWLZblBAHDjxg3mepkd7uVUJ4qi4ObNm+jv799rUe4N29GO+XyeHYD1eDwG60RtmamtlPb2duJwONgRimw2SyRJ0lhh1HrSW2y4Y74GAgFWTq09QRBIJBJhbmw2m2XWm95KUh/JcDgczNLUt1NbjnRcekhZbamVsxf6YywOh0NzLGWvCAaDFafmg8Egcy9KHRitBu5nC45a56lUigwPD5NgMLjXIt0ztqXgAOPZIApVOuoPdTc9Ho/mrQT9mTT9Gwu44/ZZlSeTSZJIJEgmkyFDQ0NMqVJlY+U+0zcPABBJkgyxCkoikdAoJ7M3HortBYW+yVBsnHtNKBSq6KbP5XIkGAyyOFWpl+irAXXcB9idl7qrGarcRVEkH3744V6Lc0/h/3TmPiYej7OzR5XicrkwOzvL/9sWp6rZFy/bcyonnU6jp6fnrvvX1dVx5capergFx+Fw9i3cguNwOPsWruA4HM6+hSs4DoCt81FvvfWW4VUb+gOLpV4S53CqER6D45REURQEg0GMjIyUfRCYw6kGuAXH0fwMNoeznzi41wJw9h6bzQav18t+K4zD2S/8D0KZgN9r8T/nAAAAAElFTkSuQmCC\"\u003e\u003cbr\u003e\u003c/p\u003e \u003col start=7\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEvaluated the prognostic model of ceRNA network.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBased on the survival information and clinicopathological characteristics, we evaluated the value of the prognostic model for clinical application. To begin with, the patients with digestive system pan-cancer were divided into high- and low-groups according to median value of risk scores. Kaplan-Meier survival analysis was used to assess the survival prognosis between the high- and low-risk groups, and survival curve were plotted. Chi-square test was used to analyze the relationship between the prognostic model and clinicopathological characteristics. The differences between clinicopathological characteristics and high- and low-risk scores were validated using Wilcoxon signed-rank test. Subsequently, we performed univariate and multivariate cox analysis in conjunction with clinicopathological features to evaluate whether this prognostic model could be used as an independent prognostic factor for digestive pan-cancer.\u003c/p\u003e \u003cp\u003e \u003col start=8\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAnalysis of the relationship between prognostic model and the immune microenvironment.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn order to explore the correlation of prognostic model and immune microenvironment, we used immune databases from XCELL, TIMER, QuanTIseq, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT to analyze the relationship between the prognostic model and tumor-infiltration immune cells. Wilcoxon signed-rank test was used to verify the difference in the prognostic model and tumor-infiltration immune cells. Spearman correlation analysis was used to predict the relationship between prognostic models and immune checkpoint marker genes. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set as the threshold of significance. Mutation landscape\u003c/p\u003e \u003cp\u003e \u003col start=9\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCell culture and cell transfection.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe human colorectal cancer cell lines (SW837 and SW620) were obtained from the cell bank of Zhonghong Boyuan Biotech Co. Ltd. All cells were cultured in DMEM medium (KeyGEN BioTECH, China) with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% antibodies (streptomycin/penicillin) at 37℃ and 5% CO\u003csub\u003e2\u003c/sub\u003e. LncRNA WDFY3-AS2 sequence was cloned into pcDNA3.1 vector. These plasmids were then transfected into colorectal cancer cells to establish SW837/SW620 overexpression lncRNA WDFY3-AS2, SW837/SW620 overexpression on mock-vector and negative control cells.\u003c/p\u003e \u003cp\u003e \u003col start=10\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuantitative reverse-transcription PCR (qRT-PCR).\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTotal RNA was extracted from SW837 and SW620 cells using TRIzol Reagent (CWBIO, China) and then reverse transcribed using to HiScript II Q RT SuperMix for qPCR (Vazyme, China). RNA expression was detected by qRT-PCR using ChamQ Universal SYBR qPCR Master Mix kit (Vazyme, China) and the following primer sequences. The relative expression of RNAs was calculated using \u003csup\u003eΔΔ\u003c/sup\u003eCT method. The primers sequences are as follows: β-actin Forward: 5\u0026rsquo;-TGGCACCCAGCACAATGAA-3\u0026rsquo;; β-actin Reverse: 5\u0026rsquo;-CTAAGTCATAGTCCGCCTAGAAGCA-3\u0026rsquo;; WDFY3-AS2 Forward: 5\u0026rsquo;- ACTGCGTCAAGTTAAGGGCA-3\u0026rsquo;; WDFY3-AS2 Reverse: 5\u0026rsquo;-CCTACCCCCAGGTGAGACTT-3\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003col start=11\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCell counting kit-8 proliferation (CCK-8) assay.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe transfected cells were placed in 96-well plate with about 3000 cells per well and incubated for 24h at 37℃ and 5% CO\u003csub\u003e2\u003c/sub\u003e. CCK-8 cell proliferation reagent (KeyGEN BioTECH, China) was added to each well and the absorbance value was measured at the 450nm with the microplate reader after 4h.\u003c/p\u003e \u003cp\u003e \u003col start=12\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWound healing assays.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe transfected cells were seeded in 6-well plate to full confluence. Vertical scratches are made in the wells using the micropipette tip. After 48h of incubation at 37\u0026deg;C and 5% CO2, the cells were observed under a microscope.\u003c/p\u003e \u003cp\u003e \u003col start=13\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTranswell invasion assays.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe cells suspended of fresh serum-free medium were seeded in the upper chamber of transwell and the lower chamber was filled with the complete medium and incubated for 24 hours at 37\u0026deg;C and 5% CO2. The chambers were stained with 0.1% crystal violet (Solarbio, China) for 10 minutes and 3% acetic acid was added to dissolve the staining solution in the cells. The absorbance value of the solution was measured at 562nm with the microplate reader.\u003c/p\u003e \u003cp\u003e \u003col start=14\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWestern blot.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe total protein was extracted from SW837 and SW620 cells with RIPA lysis buffer (Applygen, China). The protein concentrations were calculated with BCA protein assay kit and equal amount of protein were separated on 10% SDS-PAGE gels (Xilong Scientific, China). Then the proteins were transferred to PVDE memberanes (Millipore, USA). The membranes were blocked for 1h with 3% skimmed milk and incubated with primary antibody overnight. After washing the membranes, the PVDE membranes were incubated with the secondary antibody for 2h. Finally, the PVDF membranes were wetted with luminescent solution and placed in the sample placement area of the ultra-high sensitivity chemiluminescent imaging system (Bio-Rad, Shanghai) to run the procedure for developing the images. The primary antibodies and secondary antibodies are listed below: Mouse Monoclonal Anti-Actin (#TA-09, ZSGB-BIO), Rabbit Anti-OSR1 (#AF7668, Affinity), HRP anti-mouse (#ZB-2305, ZSGB-BIO), HRP anti-Rabbit (#ZB-2301, ZSGB-BIO).\u003c/p\u003e \u003col start=15\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStatistical analysis\u003c/b\u003e\u003cspan\u003e\u003c/li\u003e\u003c/ol\u003e\u003cp\u003e Statistical significance was calculated using Prism GraphPad 8.0.1 software and SPSS 18.0 software. All experiments were performed at least three times from biological level. Survival analysis was performed using the Kaplan-Meier method and compared using the log-rank test. The Wilcoxon signed-rank test was used to analyze the differences of risk scores in terms of immune infiltrating cell content. Spearman correlation analysis predicted the relationship between prognostic models and tumor infiltrating immune cells. The data with P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were defined as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIdentification of co-expressed lncRNAs, miRNAs, and mRNAs for digestive system cancinomas.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe download and processed the transcriptome data for ESCA, CRC, STAD and LIHC from the TCGA database. Differentially expressed lncRNAs (DELs), miRNAs (DEMRs), and mRNAs (DEMs) were screened based on filtering conditions of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt; 1. The co-expressed 265 DELs, 36 DEMRs, and 921 DEMs from four tumors were identified by intersection using venn diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003col start=2\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGO and KEGG functional enrichment analysis.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn order to investigate the biological function of differential expressed genes (DEGs), we performed GO and KEGG functional enrichment analysis. The KEGG analysis shows that the DEGs mainly enriched in the cell cycle, DNA replication and Neuroactive ligand-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The gene ontology (GO) analysis indicated that DEGs mainly involved in nuclear division, chromosome segregation, extracelluar matrix, kinetochore and DNA-dependent ATPase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e\u003col start=3\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eConstruction and optimization of ceRNA network.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFirst, the miRcode database were used to screen out miRNAs associated with 265 lncRNAs. Comparing with 36 co-expressed DEMRs, we obtained 7 DEMRs and found them as the downstream targets of 17 DELs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Then, 324 target genes associated with 7 DEMRs were screened by three databases including miRDB, TargetScan, and miRTarBase. Further, Intersecting the 324 target genes with the 921 co-expressed differential genes and plotting the Venn diagram, we obtained 12 common DEMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ceRNA network was constructed using cytoscape software. A total of 15 DELs, 6 DEMRs, and 12 DEMs were found in the ceRNA network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The survival analysis demonstrated that 11 DELs (ADAMTS9-AS1, ADAMTS9-AS2, AL357153.1, AP002478.1, LINC00184, MAGI2-AS3, PVT1, SOX21-AS1, LMO7-AS1, HOTAIR and WDFY3-AS2), 2 DEMRs (has-miR-21 and has-miR-217) and 7 DEMs (ATAD2, CFL2, JPH1, KPNA2, OSR1, PBK and PBM20) were significant difference in digestive pan-cancer (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Based on survival analysis, the ceRNA network was optimized to contained a total of four DELs (PVT1, WDFY3-AS2, ADAMTS9-AS1 and HOTAIR), 1 DEMRs (hsa-miR-21), and 2 DEMs (OSR1 and JPH1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e\u003col start=4\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEstablishment and evaluation of a prognostic model based on the ceRNA network.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo identify the prognostic value of 7 molecules from the ceRNA network, we screened out four factors (HOTAIR, WDFY3-AS2, hsa-miR-21, and OSR1) by univariate cox analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Further analysis using lasso regression and multivariate cox analysis revealed that four molecules could be used as independent factors for digestive system pan-cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-D). The nomogram and calibration showed that four factors have certain predictive value for the prognosis of digestive system pan-cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003eRisk scores were calculated from the expression values and coefficients of the genes to construct a prognostic model. According to the median value, the patients of digestive pan-cancer were then divided into two groups: high- and low-risk. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and B demonstrates the increase in the number of patient deaths with increasing risk scores. Survival analysis demonstrated the low-risk group has a better prognostic value than high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The ROC curve was used to analyzed the AUC of patients with digestive pan-cancer at 1, 3, and 5 years to determine the optimal model (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e\u003col start=5\u003e\u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRelevance analysis of prognostic model and clinicopathological characteristics.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo further validate the robustness of prognostic model, we evaluated the relationship between the risk of digestive pan-cancer and clinicopathological characteristics. The Wilcoxon signed-rank test showed the correction between the risk score and age (P\u0026thinsp;=\u0026thinsp;0.25) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), sex (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), stage T (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), stage N (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), stage M (P\u0026thinsp;=\u0026thinsp;0.099) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) and clinical stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). The univariate and multivariate cox analysis demonstrated that risk score and clinical stage act as independent risk factors in digestive system pan-cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-H). The ROC analysis suggested that risk score and clinical stage had certain predictive values to patients with digestive tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003col start=6\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis of prognostic model and immune microenvironment.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAs the advent of immunotherapy has benefited more tumor patients, we assessed the relationship between the risk score and immune microenvironment. Based on seven immune data calculation methods (TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT), we found that risk scores were positively associated with more tumor-infiltrating immune cells, such as B cell, myeloid dendritic cell, macrophage, monocyte, cancer associated fibroblast, Endothelial cell, CD4\u003csup\u003e+\u003c/sup\u003e T cell, and CD8\u003csup\u003e+\u003c/sup\u003e T cell. Whereas they were negatively associated with NK cell and mast cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and S2).\u003c/p\u003e \u003cp\u003eWe then compared the high- and low- risk groups in relation to common immune checkpoints, including CD274, CTLA4, PDCD1, and HAVCR2. The results suggested that the high-risk group to be positively associated with four immune checkpoints in CRC and LIHC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-B), but not statistically significant in ESCA and STAD (no show). This suggests that patients in the high-risk group with CRC and LIHC may benefit more by using immune checkpoint inhibitors.\u003c/p\u003e \u003cp\u003e \u003col start=7\u003e\u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAnalysis of mutation landscape between groups with high- and low-risk scores.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of the mutation characteristics of high- and low-risk groups for digestive system analysis, we found a high incidence of mutations in pan-cancer of the digestive system, with a mutation rate of 90.27% in the high-risk group of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA) and 95.65% in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). Compared with other mutated genes, the highest mutation frequencies were in APC, TP53, and TTN. Among them, APC mutations were mainly multiple mutations and nonsense mutations; TP53 and TTN mutations were predominantly missense mutations. It was found that higher TMB provided tumor patients with more neoantigens and raised T cells to recognize cancer cells, which was associated with better immunotherapy outcomes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Therefore, these results suggested that the prognostic model may have an advantage in immunotherapy in the clinical management of patients with digestive system pan-cancer.\u003c/p\u003e \u003cp\u003e\u003col start=8\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe relative expression of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer tissues.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe ceRNA network analysis revealed that WDFY3-AS2/has-miR-21/OSR1 has a targeted regulatory relationship. In order to explore the interaction between the three factors, we have analyzed the relative expression of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer and para-cancerous tissues. The results showed that the expression of WDFY3-AS2 and OSR1 were significantly down-regulated in colorectal cancer tissues compared to para-cancerous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA and B), while the expression of has-miR-21 was markedly up-regulated in colorectal cancer tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC). The findings indicated that the expression of the three factors in colorectal cancer tissues is consistent with ceRNA targeted regulation.\u003c/p\u003e \u003cp\u003e\u003col start=9\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer tissues.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo investigate the targeted regulation of WDFY3-AS2/has-miR-21/OSR1 in the colorectal cancer tissues, we discovered the correction between the expression of three molecules by Spearman correlation analysis. The outcome revealed that the expression of WDFY3-AS2 and OSR1 were both negatively correlated with has-miR-21 in colorectal cancer tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA and B), whereas the expression of WDFY3-AS2 was positively corrected with OSR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC). The correlation between the expression of WDFY3-AS2/has-miR-21/OSR1 in colorectal cancer tissues was in accordance with the ceRNA-targeted regulatory relationship.\u003c/p\u003e \u003cp\u003e \u003col start=10\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe effects of overexpression of WDFY3-AS2 in the proliferation, migration and invasion of colorectal cancer cells.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the biological function of WDFY3-AS2 in colorectal cancer cells, we constructed and transfected WDFY3-AS2 overexpression plasmid into SW837 and SW620 cell lines of colorectal cancer. The results showed that the expression of WDFY3-AS2 was significantly upregulated in SW837 and SW620 cell lines by the overexpression WDFY3-AS2 groups compared to the blank groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eA). The experiments with CCK8, Scratch assay and Transwell showed that overexpression of WDFY3-AS2 dramatically inhibited the proliferation, migration and invasion of SW837 and SW620 cells compared to the blank and overexpression null groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eB-F).\u003c/p\u003e \u003cp\u003e \u003col start=11\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOverexpression of WDFY3-AS2 regulates protein expression of OSR1 in colorectal cancer cells.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn order to investigate the regulatory effect of WDFY3-AS2 on the target gene OSR1, we examined the protein expression of OSR1 in colorectal cancer cells using Western blot assay. The outcomes showed that the expression of OSR1 protein in SW837 (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eA-B) and SW620 (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eC-D) cells was significantly higher in the overexpression WDFY3-AS2 group compared to the mock-vector group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that WDFY3-AS2 may suppress the development and progression of colorectal cancer cells by positively regulating the expression of the target gene OSR1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe morbidity and mortality of digestive malignancies are among the highest in China\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although its clinical treatments have made stage progress, such as surgical resection, chemotherapy, molecular targeted therapy and so on\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The early diagnosis of gastrointestinal malignancies still faces a great challenge due to the insidious symptoms at the initial stage of development. Therefore, it is an urgent task for scientific research and clinical work to continue to explore potential early biomarkers of digestive malignancies. The study of the digestive system pan-cancer is a breakthrough from previous studies that only targeted individual tumor by tapping into common molecular mechanisms among similar tumors, providing new clues for the research of digestive malignancies.\u003c/p\u003e \u003cp\u003eIn previous literatures on the mechanisms of ceRNA regulation, we found that the common lncRNAs were existed in the different digestive malignancies. For example, lncRNA SNHG6 acts as an oncogene. In ESCA, downregulation of SNHG6 inhibits cancer cell proliferation through the miR-186-5p/HIF1α axis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In STAD and LIHC, SNHG6 competitively binds miR-101-3p, regulates ZEB1 expression and effects cancer cell growth\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In CRC, SNHG6 exerts an oncogenic effect on colorectal cancer cells by competitively absorbing miR-760 and positively regulating the expression of FOXC1\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. It follows that there may be common molecular mechanisms in the similar tissue-derived malignancies. Thus, we constructed ceRNA networks by screening common lncRNAs, miRNAs and mRNAs in four digestive malignancies, and explored their mechanisms in the digestive system pan-cancer, which have certain practical significance for the diagnosis and prognosis of digestive malignancies.\u003c/p\u003e \u003cp\u003eIn this study, the expression values of seven genes (PVT1, WDFY3-AS2, ADAMTS9-AS1, HOTAIR, hsa-miR-21, OSR1, JPH1) in the ceRNA network and their correlation coefficients were calculated based on machine learning to construct a prognostic model, breaking away from in previous studies that only quantified gene expression to analysis their prognostic value in solid tumors. We selected the most relevant genes for the prognostic model using the univariate COX analysis, LASSO regression and multi-variate COX analysis, including WDFY3-AS2, HOTAIR, has-miR-21 and OSR1. Then risk scores of the prognostic model were calculated, and patients were divided into high- and low-risk groups according to median value of the risk scores. Next, the robustness of the prognostic model was assessed utilizing survival information and clinicopathological characters of digestive pan-cancer. The results revealed that patients in the low-risk group had a better outcome than those in the high-risk group. The analysis of clinicopathological characters showed that risk score and clinical stage have significant prognostic value, indicating that risk score and clinical stage can be used as independent prognostic factors for digestive pan-cancer.\u003c/p\u003e \u003cp\u003eOut of 7 molecules, previous studies have reported on the mechanisms of action of 4 key genes in digestive malignancies. For instance, WDFY3-AS2, located on chromosome 4q21.23, is an RNA molecule with a length of 3383 nucleotides\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In ESCA, WDFY3-AS2 acts as ceRNA and regulates PTEN expression by competing for binding to miR-18a, suggesting that the WDFY3-AS2/miR-18a/PTEN axis may be involved in the progression of esophageal cancer\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e found that the expression of WDFY3-AS2 was strongly correlated with the molecular mechanisms regulating EMT in LIHC. HOTAIR was highly expressed in broad-spectrum tumors as well as being linked closely to metastasis and poor outcome\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e concluded that knockdown of HOTAIR inhibits proliferation and migration and promotes apoptosis in ESCA by competitively binding to miR-204 to regulate HOXC8 expression. Wei et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e showed that HOTAIR up-regulated COL5A1 expression by \u0026ldquo;sponge-attracting\u0026rdquo; miR-217-5p, which promoted the proliferation and metastasis of gastric cancer cells. In LIHC, inhibition of HOTAIR increased p27 expression and decreased cyclin D1 expression, thus inducing G0/G1 cell cycle arrest. Knockdown of HOTAIR markedly up-regulated the expression of miR-217, resulting in a reduction in tumor size as well as a decrease in Ki67 levels, which inhibited the proliferation and growth of hepatocellular carcinoma\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Huang et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e discovered that HOTAIR/miR-545/EGFR axis could modulate the development and progression of colorectal cancer cells. MiR-545 suppressed EGFR expression by affecting the 3\u0026rsquo;UTR activity of EGFR, and overexpression of HOTAIR inhibited miR-545 expression, while silencing of HOTAIR enhanced miR-545 expression. Moreover, when overexpression, HOTAIR eliminated the inhibited of EGFR expression by miR-545 mimic. The conclusion suggested that HOTAIR mediated miR-545 may regulate the proliferation of CRC. Has-miR-21 has 3433 nucleotides and locates in human chromosome 17q23.2, which is an intergenic region at the 3\u0026rsquo;UTR side of transmembrane protein 49\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e suggested that miR-21 may target RASA1 expression through the regulation of Snail and vimentin proteins, thus impacting the proliferation, migration, invasion and tumor growth of ESCA. Research has suggested that the expression of circ_0027599 and RUNX1 was down-regulated in STAD. Circ_0027599 acted as a sponge for miR-21-5p, positively regulating the expression of RUNX1, thus reversing the malignant progression of STAD\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Hong et al.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e showed that miR-21-3p promoted migration and invasion of LIHC via directly suppressing the downstream target molecule SMAD7 and upregulating the YAP1 expression. Jiang et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e found the over-expression of miR-21-5p could down-regulation of TGFβ1, which in turn induces cellular scorching and acts an anti-tumor agent. OSR1, located on human chromosome 2p24.1, is a 266 amino acid protein\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Zhang et al. \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003ereported that down-regulation of OSR1 promoted development of colon adenocarcinoma via FAK-mediated Akt and MAPK signaling. Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e suggested that OSR1 can affect the invasion and migration of tongue squamous carcinoma via inhibiting the NF-κB signaling pathway. To sum up, the four critical molecules had significant prognostic value in gastrointestinal malignancies. Therefore, the prognostic model obtained for these molecules may be of great potential as a novel biomarker in clinical management of digestive system pan-cancer.\u003c/p\u003e \u003cp\u003eOver the past few years, tumor immunotherapy has brought remarkable benefits for patients with cancer\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. For this study, we explored the relationship between the prognostic model and tumor-infiltrating immune cells in digestive pan-caner. Interestingly, we found that the most of immune cells were positively correlated with high-risk group, such as B cell, myeloid dendritic cell, macrophage, monocyte, cancer associated fibroblast, Endothelial cell, CD4\u003csup\u003e+\u003c/sup\u003e T cell, and CD8\u003csup\u003e+\u003c/sup\u003e T cell. Previous research revealed tumor-infiltrating B lymphocytes promoted cytotoxic T cells or inhibited downstream immunosuppressive pathway by secreting immunoglobulins, resulting in anti-tumor response\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Helmink et al.\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e have shown that B cells and tertiary lymphoid structures have potential roles in immune checkpoint blockade (ICB) therapy response. Alterations in tumor microenvironment can affect the differentiation of monocytes to macrophages and dendritic cells\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Endothelial cells and tumor-associated fibroblasts are major inducers of the migration and invasion capacity of cancer cells which could be new targets for cancer therapy\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. To further investigate the value of prognostic model in the immunotherapy, we analyzed the relationship between prognostic model and common immune checkpoint marker genes, such as CD274, CTLA4, PDCD1 and HAVCR2. The results found that patients in the high-risk group were significantly positively associated with these marker genes (CD274, CTLA4, PDCD1 and HAVCR2) in CRC and LIHC, suggesting that the immune checkpoint inhibitor therapy have greater prognostic benefits for survival in patients with CRC and LIHC. It has been shown that Tumor Mutation Burden (TMB) may predict clinical response to immune checkpoint inhibitors\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. We therefore analyzed the relationship between the prognostic model and the tumor mutation data. It was found that the incidence of mutational events was higher in both the high- and low-risk groups of the prognostic model (\u0026gt;\u0026thinsp;90%), and that the high-risk group had a higher number of TP53 mutations, which may be the main molecular mechanism for their increased clinical prognostic risk.\u003c/p\u003e \u003cp\u003eTo elucidate the biological function of action of lncRNA WDFY3-AS2 in CRC, we have used in vitro experiments to initially validate the antitumor effect of WDFY3-AS2. The results of CCK-8, Scratch and Transwell assays demonstrated that overexpression of WDFY3-AS2 inhibited the proliferation, invasion and metastasis of colorectal cancer cells. Finally, the protein expression of OSR1 was examined using western blot assay. We found that the expression of OSR1 was significantly higher in the overexpressed WDFY3-AS2 group compared to the blank load group, indicating that lncRNA WDFY3-AS2 could influence the progression of colorectal cancer via positively regulating the expression of OSR1. It has been proved that lncRNA WDFY3-AS2 acts as an antioncogene and inhibits tumor progression by directly modulating the expression of downstream target genes. For instance, WDFY3-AS2 can regulated SOCS2 to inhibit the JAK2/Stat5 signaling pathway, thus suppressing the proliferation and invasion of esophageal cancer cells\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. The WDFY3-AS2 inhibits the progression of ovarian cancer by positively regulating the expression of the target gene RORA\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations remain in our study, mainly in two aspects. Firstly, we only validated the role of the key lncRNA WDFY3-AS2 in colorectal cancer through in vitro experiments, and lacked validation of the WDFY3-AS2/miR-21/OSR1 axis. Secondly, the key factors were only experimentally validated in colorectal cancer cells in this study. In subsequent researches, we will further validate it in other digestive malignancies to provide a more comprehensive reference for the clinical management of digestive cancers.\u003c/p\u003e \u003cp\u003eIn summary, the prognostic model constructed in this research based on the WDFY3-AS2/HOTAIR/has-miR-21/OSR1 ceRNA regulatory axis can assess the prognosis of digestive pan-cancer and may enable patients to benefit from immunotherapy. In addition, lncRNA WDFY3-AS inhibits proliferation, invasion and metastasis of colorectal cancer cells and positively regulates OSR1 expression, providing a new prognostic marker for the treatment of colorectal cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in TCGA database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Nature Science foundation of Heilongjiang Province, China (No.SS2022H003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRC, ZH, and SL carried out the experimental design. ZH and GL wrote and revised the manuscript. XW, PS, and HW participated in the preliminary analysis for the online database. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the TCGA database for providing their platforms and contributors for uploading their meaningful datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng R-M, Zong Y-N, Cao S-M et al. Current cancer situation in China: good or bad news from the 2018 Global Cancer Statistics?[J]. 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J Cell Physiol. 2020;235(2):1141\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \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\u003eDEmiRNAs targeted by DELncRNAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEmiRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDElncRNAs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" 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\u003c/table\u003e\u003c/div\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\u003eCorrection coefficients for key molecules obtained by multivariate cox analysis.\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOTAIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWDFY3-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHsa-miR-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digestive system pan-cancer, long non-coding RNA (lncRNA), competitive endogenous RNA (ceRNA), prognostic model, tumor immune microenvironment, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-3054408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3054408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBased on analysis of competitive endogenous RNA (ceRNA) and immune microenvironment, we screened their specific Long non-coding RNA (lncRNA) from the perspective of digestive system pan-cancer, and performed preliminary experimental validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The transcriptome data of were downloaded from The Cancer Genome Atlas (TCGA) database, including esophageal carcinoma(ESCA), stomach adenocarcinoma (STAD), colorectal carcinoma (CRC) and liver hepatocellular adenocarcinoma (LIHC). We screened and predicted co-expressed differentially lncRNAs, miRNAs, and mRNAs of four tumors using R language. CeRNA networks were constructed by Cytoscape software.LASSO and Cox regression analysis were used to construct prognostic model. The application value of the prognostic model was assessed by combining clinicopathological features. The relationship between prognostic models and immune micro-environment was evaluated using Wilcoxon signed rank test. CCK8, scratch and Transwell assays were performed to analyze the effects of overexpression of lncRNA on CRC cells lines SW837 and SW620. The effect of overexpression of lncRNA on target proteins was detected using western blot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCo-expressed lncRNAs 256, miRNAs 36, mRNAs 921 were obtained to construct the ceRNA network. LncRNA (WDFY3-AS2 and HOTAIR), miRNA (hsa-miR-21), and mRNA (OSR1) were screened using LASSO and Cox regression analysis to construct prognostic model. The survival rate of patients in the low-risk group was better than that in the high-risk group (P\u0026lt;0.001). The risk score and clinical stage could be used as independent prognostic factors for the digestive system pan-cancer. The risk score was positively correlated with the infiltration of multiple immune cells. The high-risk groups of CRC and LIHC were positively correlated with the expression of CD274, CTLA4, PDCD1, and HAVCR2 (P\u0026lt;0.05). Cellular experiments showed that the overexpression of WDFY3-AS2 reduced the survival rates of colorectal cancer cells, increased the healing time of scratched cells, and decreased the passage rate of transwell cells. Western blot assay suggested that WDFY3- AS2 can positively regulated the expression of OSR1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe prognostic model constructed based on the WDFY3-AS2/HOTAIR /hsa-miR-21/OSR1 ceRNA regulatory axis was able to assess the prognosis of pan-cancer of the digestive system, and the specific LncRNA WDFY3-AS2 inhibited the proliferation, invasion and metastasis of colon cancer cells.\u003c/p\u003e","manuscriptTitle":"Certification of novel lncRNA based on analysis of ceRNA network in digestive system pan-cancer and characteristics of tumor immune microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-15 15:14:26","doi":"10.21203/rs.3.rs-3054408/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bcfc7fce-349b-4239-9bdd-23113f84f7fd","owner":[],"postedDate":"June 15th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-22T09:15:50+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-15 15:14:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3054408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3054408","identity":"rs-3054408","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}