Identification of LINC00482/mir-22-3p/UNC5D regulatory axis associated with diagnostic and prognostic value of colon cancer

Identification of LINC00482/mir-22-3p/UNC5D regulatory axis associated with diagnostic and prognostic value of colon cancer | 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 Article Identification of LINC00482/mir-22-3p/UNC5D regulatory axis associated with diagnostic and prognostic value of colon cancer Weijie Wang, Zhigang Xiao, Mingxi Jia, Yi Shi, Qingming Cao, Jing Deng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7221309/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In the present paper, we built a competitive endogenous RNA (ceRNA) network in colon cancer (CC) through bioinformatics and experimental validation, and discussed the functional effect and regulatory mechanism of this network in CC, as well as their clinical application potential as biomarkers. First, we obtained DElncRNAs, DEmiRNAs and DEmRNAs by analyzing the RNA expression profiles of CRC retrieved from the TCGA database, respectively. Then, those differentially expressed genes with the same trend in expression analysis and survival analysis were used to construct the LINC00482/mir-22-3p/UNC5D axis ceRNA regulatory network by a comprehensive bioinformatics approach. In addition, the results were validated in both correlation analysis and immunohistochemical experiment. The univariate and multivariate cox regression analyses yielded that low expression of LINC00482/UNC5D may be an independent prognostic clinical factor affecting overall survival (OS) in CC patients. Finally, the regulatory mechanism of the ceRNA network affecting the occurrence and development of CC was explored through immune cell infiltration and DNA methylation analysis. In conclusion, the construction of a novel LINC00482/mir-22-3p/ UNC5D axis ceRNA regulatory network may provide new insights into CC pathogenesis and may serve as a promising target for CC therapy. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology ceRNA network colon cancer immune cells methylation prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Colon cancer (CC) is one type of the most general cancers, which ranks the second and third place in mortality and incidence among global cancers, respectively 1 . In China, colon cancer was both ranked fifth in the terms of mortality and incidence in 2015 2 . Colon cancer has the characteristics of rapid metastasis and difficult treatment, and most CC patients are asymptomatic at early diagnosis or are found at an advanced stage, which has caused a huge burden on society and medical care. The standard treatments for CC are radical resection and adjuvant chemoradiotherapy 3 . Statistics found that despite with the implementation of early screening and surgical operations, the 5-year survival rate of CC patients with early-stage reached 90%, however, when patients are diagnosed as advanced-stage or develop distant metastases, the treatment options are extremely limited, and the 5-year survival rate significantly decreased to 14% 4 . Consequently, early diagnosis and valid treatment are the key to accurately predict CC and reduce its mortality. Although the current treatment strategies for CC patients have been improved, there are still shortcomings such as delayed diagnosis of imaging and low sensitivity and specificity of serological markers 5 . Hence, there is badly need of exploring novel sensitive and effective biomarkers related to the growth and metastasis of CC, which are crucial for elucidating pathogenesis of CC and effectively improving the prognosis of colon cancer patients. As we all know, non-coding RNA (ncRNA) is ubiquitous in eukaryotes and was once considered as a useless transcriptional "noise" without protein-coding potential and specific biological function 6 , 7 . In recent years, two kinds of noncoding RNAs, long noncoding RNA (lncRNA) and microRNA (miRNA), have become the focus of attention by interacting with diverse molecules to regulate gene expression and participate in several biological processes 8 . MicroRNA (miRNA) is endogenously produced, evolutionarily conserved, and small single-stranded non-coding RNA (ncRNA) with a size of about 19 ~ 24 nucleotides 9 . Nowadays, there are many reports on microRNA in tumors, and over 2,000 miRNAs have been proved to have the capacity to regulate ex-pression, but the level of research on lncRNA needs to be improved 10 . LncRNA, a class of transcripts with a length of over 200 nucleotides, have been found to offer emerging roles in many cancer-related biological processes by regulating target genes, such as proliferation, differentiation, invasion and metastasis 11 , 12 . Nevertheless, the mechanism of lncRNA how to mediate gene expression and exert different biological functions in tumors is unclear. Then, salmena and his colleagues posed the competitive endogenous RNA (ceRNA) hypothesis for the first time in 2011, explained the interaction mechanism between lncRNA and other molecules, and described its impact on the changed protein expression level 13 .A growing list of research have indicated that lncRNA is considered to be a key factor in the ceRNA network, which due to it serves as a ceRNA to regulate mRNA expression by competitively sharing miRNA response elements (MREs) 14 . More importantly, increasing researches have suggested that ceRNA plays a key role in colorectal carcinoma, lung adenocarcinoma, kidney cancer, Hepatocellular Carcinoma and other diseases 15 – 17 . For example, Meg3, as a ceRNA, increases SOCS3 expression by specifically binding to miR-708, further inhibiting the proliferation of colon cancer cells and influencing the outcome of patients 18 . In addition, LINC00460 upregulates ANXA2 expression by sponging miR-433-3p to accelerate the development of CC 19 . However, the molecular mechanism underlying ceRNA regulatory network analysis in CC remains poorly understood. Hence, we are sure that the function of ceRNA in CC is well worthy of further exploration. In the paper, we firstly screened the differentially expressed RNAs (DERs) in CC based on the The Cancer Genome Atlas (TCGA) database, including DEmRNAs, DEmiRNAs and DElncRNAs. Then, applied multiple prediction databases and survival analysis, the survival-related lncRNA-miRNA-mRNA triple regulatory networks were built. Next, the key ceRNA network related to CC was successfully constructed by combining expression analysis and survival analysis. Functional enrichment analysis was used to explore the biological behaviors of related target genes in the ceRNA network. The prognostic significance of RNA in ceRNA network was analyzed by clinical information. Furthermore, we also used patient tissue samples to validate certain analytical results. Finally, the molecular mechanisms of target genes in the ceRNA network affecting CC were explored through immune cell infiltration and DNA methylation analysis. Our findings may furnish valuable clues for the treatment of colon cancer. The roadmap for the analysis performed in this study is shown in Fig. 1 . Results Identification of differentially expressed RNAs (DERs) in CC In this study, total RNA expression profiles of 456 (and 439) CC tissues and 41 (and 8) normal tissues samples from 445 CC patients were obtained from the TCGA-COAD database. 1324 DElncRNAs (838 over-expression and 486 down-regulated), 261 DEmiRNAs (189 over-expression and 72 down-regulated), and 2022 DEmRNAs (1068 over-expression and 954 down-regulated) were obtained by differential expression analysis. The distributions of DEmiRNAs (Fig. 2 B), DEmRNAs (Fig. 2 C) and DElncRNAs (Fig. 2 A) were described by volcano plots and clustering heatmaps. Construction of Survival-Associated lncRNA-miRNA-mRNA triple regulatory networks for CC To better understand the pathogenesis and target prediction of colon cancer, the survival-related lncRNA-miRNA-mRNA triple regulatory network was established according to the above results. First, the target relationship between lnRNAs and miRNAs was evaluated based on DElncRNAs and DEmiRNAs by the miRcode database. Next, miRDB and TargetScan were applied to forecast the target mRNAs of miRNAs. Then, the candidate target mRNAs were compared with DEmRNAs, and the overlapping genes were picked. The findings suggested that the co-expression of 30 miRNAs, 216 lncRNAs and 563 mRNAs in DERNAs. Next, the survival analysis was performed to constitute a survival-related lncRNA-miRNA-mRNA triple regulatory network (including 7 lncRNAs, 5 miRNAs and 12 mRNAs). Finally, the co-expression results were visualized by using Cytoscape 3.6.0 software (Fig. 3 A). Development of a ceRNA regulatory network and Functional Enrichment analysis For constructing a ceRNA network, the above positive co-expressed lncRNA-miRNA pair and miRNA-mRNA pair were excluded, and only the expression and overall survival of lncRNA-miRNA pairs and mRNA-miRNA pairs conform to the negative regulation mode could constitute ceRNA network (Figure. 3B, Figure. 4, and Figure. S1). At last, we built the ceRNA regulatory network of CC, which is composed of LINC00482/mir-22-3p/UNC5D axis (Fig. 5 A). The prediction of potential binding sites between mir-22-3p and LINC00482 as well as mir-22-3p and UNC5D were shown in Fig. 5 B. Based on the ceRNA theory that lncRNA acts as a natural miRNA sponge to inhibit the function of miRNA, the expression of lncRNA-mRNA should be positively correlated. Then, the correlation between LINC00482, mir-22-3p and UNC5D was analyzed using expression profile data from TCGA database. The showed a positive correlation between LINC00482 and UNC5D was validated in CC clinical samples (P = 0.0001, r = 0.181, Fig. 5 C). Moreover, the effect of the combination of LINC00482, mir-22-3p and UNC5D on survival was also analyzed. The findings suggested that patients with high-LINC00482/UNC5D expression and low-mir-22-3p expression had better prognosis than patients with other combinations. The target gene UNC5D in ceRNA network was analyzed by function enrichment, for the sake of further probed the potential biological behaviors of the ceRNA. The KEGG pathways were mainly enriched in "axon guidance" 20 . GO analysis results showed that the top five GO terms in which target genes mainly clustered were “regulation of neuron migration”, “netrin receptor activity”, “netrin activated signaling pathway”, “cell surface”, “cell-cell adhesion via plasma-membrane adhesion molecules” (Figure. S2). Building a predictive model based on independent prognostic clinical factors and Preliminary validation in clinical samples For further probing the influence of clinical features on overall survival (OS), univariate and multivariate Cox regression analysis were applied to evaluate. Univariate analysis showed that Diameter, TNM stage, Lymph-node metastasis, Distant metastasis, low expression of LINC00482/UNC5D, and high expression of mir-22-3p were positively associated with poor patient prognosis (Table 1 , 2 , S1). In multivariate analysis, TNM stage, Distant metastasis, and low expression of LINC00482/UNC5D may be independent prognostic clinical factors affecting OS in CC patients (Table 1 , 2 ). These results suggest that LINC00482/UNC5D was low expression in CC and related to poor prognosis. We also estimated the expression levels of UNC5D in several tumor tissues. The findings revealed that UNC5D exhibited a trend of down-regulation in several tumor types (Fig. 6 A). Further validation was demonstrated by IHC analysis of clinical samples, which indicated that UNC5D was significantly over-expression in paracancerous tissues than CC tissue samples (Fig. 6 B). Relationship between UNC5D expression and immune cell infiltration in CC For exploring the influential mechanism of UNC5D on CC, we probed the relationship between UNC5D expression and immune cell infiltration. Firstly, altered UNC5D gene copy numbers seemed to significantly associate with Several immune cell infiltration levels (including B cell, CD8 + T cell, and dendritic cell) in COAD (Fig. 7 A). Next, the relevance between UNC5D expression and tumor-infiltrating immune cell adjusted by purity was examined using TIMER. As shown in Fig. 7 B, the expression level of UNC5D positively related to infiltration levels of CD4 + T cells (r = 0.207, p = 2.81e − 05) and Dendritic Cell (r = 0.141, p = 4.64e − 03) in CC but negatively correlated with tumor purity (r = − 0.111, p = 2.58e − 02) (Fig. 7 B). In addition, we also probed the influence of immune cells on clinical prognosis of patients with COAD., The findings indicated that low levels of CD8 + T cell (p = 0.035) and neutrophil (p = 0.038) had worse outcome for COAD patients with survival time within 30 months (Fig. 7 C). For further validating the relevence between UNC5D expression and immune cell infiltration in CC, we identified the relationship between UNC5D and 57 immune cell markers in 16 immune cells using the TIMER, GEPIA, and TCGA-COAD database. As shown in Table S2, all results suggested that SLC2A1 expression was related to B cell, Th17, Treg immune marker genes, including CD19 (r = 0.117, p < 0.05), CD79A (r = 0.219, p < 0.0001), STAT3 (r = 0.205, p < 0.0001), IL17A (r = 0.147, p < 0.01), FOXP3 (r = 0.150, p < 0.01), CCR8 (r = 0.172, p < 0.001), STAT5B (r = 0.228, p < 0.0001). These findings supported that UNC5D could offer a key role as an immunomodulatory factor in CC. UNC5D methylation in patients with CC To further elucidate the potential mechanisms underlying the aberrant lowly expressed of UNC5D in CC tissues, we probed the relationship between the expression and methylation status of UNC5D by multiple methods. It was found that DNA methylation could affect the behavior of cancer cells by three DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) to regulate gene expression. The results revealed that DNMT1, DNMT3A and DNMT3B had higher expression in the UNC5Dhigh group compared with UNC5Dlow (P = 0.0004 for DNMT1、DNMT3A and DNMT3B) (Fig. 8 A). Besides, DNA methylation levels of UNC5D were obviously higher in CC tissues than normal tissue samples by using UALCAN (Fig. 8 B). Consistently, the same results were obtained with DiseaseMeth 2.0 (Fig. 8 C). To further substantiate our conclusions, the relationship between UNC5D expression and its methylation levels at different methylation sites was estimated. A heatmap of different methylation regions related to UNC5D were obtained by MethSurv database analysis. The map showed that the distributions of low, medium and high methylation sites were 14.29% (4/28), 21.43% (6/28) and 64.28% (18/28), respectively (Fig. 8 D). In addition, the analysis of TCGA data in the MEXPRESS database indicated that there was a total of 9 CpG sites whose methylation levels were inversely correlated with expression (Pearson correlation coefficients range from − 0.0117 to − 0.175 and the p values were statistically significant). Then, the role of UNC5D methylation in clinical features was investigated by UALCAN analysis. The following results obtained that the promoter methylation level of UNC5D in CC patients was obviously higher (according to age, sex, weight, race, tumor histology, stage and lymph node metastasis status) than the normal group (Fig. 9 A). Eventually, the relationship between UNC5D methylation and the prognostic value of patient were investigated using the MethSurv database. The Kaplan-Meier plot suggested the prognosis information of 28 methylation CpG sites. The methylation level of 5 CpG sites (cg26764980、cg01311313、cg22386073、cg08754088、cg26679047) were related to patient outcome (p < 0.05) (Fig. 9 B). Patients with high HSPA4 methylation in four of these CpG sites had a worse prognosis. However, the prognosis of cg08754088 was reversed. Therefore, DNA methylation could offer a pivotal impact in the mechanism resulted in aberrant downregulation of UNC5D in tumor tissues. Discussion CRC is one of the most common leading causes of cancer death worldwide, and COAD is a common type of CRC 1 . In recent years, the main treatment methods for colon cancer are radical therapy and radiotherapy and chemotherapy. Although the five-year survival rate of CC patients has significantly improved with advances in medical technology, the mortality rate remains high 21 . Previous research has revealed that abnormally expressed genes could exert a major impact in cancer and have potential as biomarkers 22 . Therefore, the identification of effective biomarkers and potential regulatory networks provides a great help for selective therapeutic strategies for CC patients. With the expeditious growth of high-throughput sequencing technology, a growing list of research have shown that lncRNAs are Aberrant expression in tumor tissues and exert a major impact in tumor progression, some of which are expected to be biomarkers with better prognosis and diagnosis 23 , 24 . LncRNAs have complex functions through multiple pathways, among which the ceRNA hypothesis elucidates a new regulatory mechanism, that is, lncRNAs can serve as miRNA sponges to competitively bind miRNAs with mRNAs 14 . This hypothesis better explains the interaction of multiple types of RNAs at the genetic level. So far, some studies have clarified several potential lncRNAs and their mediated ceRNA regulatory networks in colon cancer, but the regulatory mechanism of ceRNA regulatory networks on CC has not been deeply explored. In this research, DElncRNAs, DEmiRNAs and DEmRNAs in COAD samples from the TCGA database was identified. Those differentially expressed genes with the same trend in expression analysis and survival analysis were used to construct the LINC00482/mir-22-3p/UNC5D axis ceRNA regulatory network by integrative bioinformatics approach. Previous studies have indicated that lncRNA LINC00482 was up-regulated in bladder cancer and liver cancer 25 , 26 . While, there is no data on the biological role and behavior of LINC00482 in CC. Mir-22-3p is a 22-nucleotide noncoding RNA with paradoxical roles in various cancers. It has been reported that mir-22-3p was down-expressed in both lung cancer tissues and cell lines, as well as exerts a tumor suppressor effect by inhibiting MET-STAT3 signaling 27 . In addition, mir-22-3p was lowly ex-pressed in breast cancer and targets PLAGL2 to the invasion and migration of breast cancer cells 28 . However, one study showed that mir-22-3p inhibits hepatocellular carcinoma cell growth by targeting SP1 and its downstream target genes CCND1 and BCL2 29 . Another study demonstrated that mir-22-3p was high expression in cervical cancer tissues and induced cervical cancer cell proliferation by inhibiting its target, eIF4EBP3 30 . UNC5D was a member of the UNC5s family of human-dependent receptors, which shared the ligand netrin-1 with other UNC5s family members and the tumor growth inhibitor DCC 31 , 32 . Previous research has reported that, UNC5D, a direct transcriptional target of p53, which cooperates with E2F1 and p53 to form feedback to induce programmed cell death (PCD) in neuroblastoma (NB) cells using DNA dam-age signals, and high-level expression of UNC5D correlates with the better outcomes of NB 33 , 34 . At present, there are surprisingly few studies of the role and potential molecular mechanism of UNC5D in CRC. We further analyzed the function and pathway of DEmRNA UNC5D in the ceRNA network by Using GO and KEGG. Based on the ceRNA hypothesis, specific lncRNAs may also function or focus on potential pathways in an mRNA-like manner. The findings of the GO biological process revealed that the top 5 processes in which UNC5D was mainly enriched include “regulation of neuron migration”, “netrin receptor activity”, “netrin activated signaling pathway”, “cell surface”, “cell-cell adhesion via plasma-membrane adhesion molecules”. KEGG pathway analysis is mainly enriched in axon guidance. The axon guidance molecule Netrin protein has been reported to be strongly correlated with tumorigenesis, metastasis, and apoptosis 35 . More importantly, UNC-5s are the receptor for netrins 36 . To determine the possible important role of this ceRNA network in CRC tumorigenesis and prognosis, a combination of correlation analysis, survival analysis, and univariate and multivariate cox regression analysis was carried out for further exploration. The result proved that there was a positive relevance between the expression of LINC00482 and UNC5D in CC samples. Moreover, patients with LINC00482high/mir-22-3plow/UNC5Dhigh had the best prognosis than patients with other combinations. In addition, the target gene was validated by IHC analysis experiments based on clinical samples. Finally, the mechanism of the ceRNA network affecting the occurrence and development of CC was explored through immune cell infiltration and DNA methylation analysis. According to the analysis of TIMER online database, the UNC5D gene copy number in CC appeared to be obviously related to the expression levels of B cells, CD8 + T cells and dendritic cells. Next, the expression level of UNC5D was positively correlated with the infiltration level of CD4 + T cells and dendritic cells in CC. Moreover, low levels of CD8 + T cells and neutrophils were related to poor outcome in patients with COAD (survival time within 30 months). More importantly, the analysis of three databases probed that UNC5D expression was relevant to B cells, Th17 cells, and Treg cells immune marker genes. Th17 and Treg cells are CD4 + T cells subsets with significant immunosuppressive effect. It has been claimed that CD4 + T cells exert a crucial impact in coordinating and improving the immune response against colon cancer cells 37 . The above analysis suggested that UNC5D may affect the immune filtration of the ceRNA regulatory network by influencing the expression of CD4 + T cells. Abnormal methylation of gene promoter is strongly relevant to the progression and outcome of tumor, and can be used as a marker for early diagnosis and prognosis evaluation of CC. A growing list of research revealed that aberrant DNA methylation exerts a crucial impact in the induction and development of CC 38 , 39 . In this paper, the analysis of various bioinformatics tools based on the TCGA database yielded consistent results indicating that UNC5D is hypermethylated in CC tissues. Next, the UNC5Dhigh group was obviously positively correlated with the high expression of DNMT1, DNMT3A and DMNT3B. These can well explain the down-regulated of UNC5D in CC. Then, we determined that the methylation levels of 9 out of 28 CpG sites were negatively correlated with expression. Moreover, there was a strong correlation between UNC5D methylation and clinical features as well as between different methylation sites and prognosis of CC patients. These results suggested that aberrant DNA methylation is a cause of colon cancer-related mortality. Conclusions In conclusion, we have successfully established ceRNA regulatory network that LINC00482 could modulate the expression of UNC5D by sponging mir-22-3p, and the target gene was validated in clinical samples. In addition, we also preliminarily explored the mechanism of LINC00482/mir-22-3p/UNC5D axis ceRNA network on colon cancer. The present research may offer a novel idea on the underlying mechanisms of gene regulation in CC. Materials and Methods TCGA dataset retrieval and clinical sample collection The RNA-sequencing profiles and miRNA-sequencing profiles and clinical information were downloaded from TCGA database (up to November 7, 2021) based on Il-lumina HiSeqRNASeq and Illumina HiSeqmiRNASeq platforms by using the Genomic Data Commons (GDC) Data Transfer Tool ( https://tcga-data.nci.nih.gov/).Th e TCGA-COAD dataset contains mRNA and lncRNA expression profiles (RNA-seq data) of 456 CC samples and 41 normal samples, and miRNA expression profiles (miRNA-seq data) of 439 CC samples and 8 normal samples. Cancer tissue type was 01A and duplicate tissue samples were excluded. The above data followed the TCGA publication guide-lines. For immunohistochemical (IHC) analysis, we selected 10 pairs of COAD tissue samples and their paired adjacent non-tumor tissue samples were obtained from 10 primary COAD patients who underwent surgical resection at the People's Hospital of Hunan Province (Hunan, China). These patients (aged 46–84 years) were diagnosed with COAD based on clinical history and histopathology. Prior to sample collection, written informed consent forms were acquired from COAD patients. The research was ratified by the Ethics Committee of the People's Hospital of Hunan Province (Hunan, China). All methods were performed in accordance with the relevant guidelines and regulations. Identification of differentially expressed RNAs (DERNAs) First, the downloaded raw data of lncRNAs, miRNAs and mRNAs were normalized, and then the "edge" package in R software was applied to carry out differential expression analysis on the normalized values to obtain the DERNAs (including differentially expressed lncRNAs (DElncRNAs)、Differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs)) between CC samples and normal samples. DEmiRNAs and DEmRNAs were screened by thresholds of | logFC (fold change) |≥2 and P value 0.7 and P value < 0.05 were regarded as statistically significant. The results were visualized by drawing volcano plots and heatmaps using TBtools (version 1.075) 40 and GraphPad Prism 8(version 8.3.0). Build of lncRNA-miRNA-mRNA triple networks with prognostic significance First, DElncRNA-based lncRNA-miRNA regulatory interactions were predicted by miRcode ( http://www.mircode.org/ ) 41 . Then, DEmiRNAs were compared with the candidate target miRNAs to obtain intersections. Next, miRDB ( http://www.mirdb.org/ ) 42 and TargetScan ( https://www.targetscan.org/vert_72/ ) 43 were applied to predict miRNA-mRNA regulatory interactions based on DEmRNAs. Furthermore, the triple regulatory networks based on the lncRNA-miRNA-mRNA axis with prognostic significance were screened by combining the above interaction pairs and using survival analysis. Finally, co-expression networks were built and visualized by Cytoscape (v3.7.0) 44 software. Establishment of the ceRNA regulatory network and functional enrichment analysis By the TCGA database, we compared the expression and survival outcomes of miRNAs, mRNAs and lncRNAs among the triple network in CRC, only these RNAs with opposite expression and prognostic significance in colon cancer and adjacent cancer samples were included in this study. Next, the qualified lncRNA-miRNA interaction group and miRNA-mRNA interaction group were combined to build a ceRNA network. The prediction of biological targets is accomplished through TargetScan and StarBase online analysis tools. Subsequently, to explore promising biological processes and signaling pathways, functional enrichment analysis of DEmRNA in ceRNA networks was carried out by the online analysis tool KOBAS ( http://kobas.cbi.pku.edu.cn/genelist/ ) 45 , including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analysis. The results of the enrichment analysis were displayed by the ggplot2 package of R software. P < 0.05 was set as the critical value. Immunohistochemistry (IHC) analysis 10 pairs of colon tumors and normal tissues from 10 CC patients were fixed with 10% formalin, dehydrated, paraffin embedded, and sectioned. IHC staining was applied using antibodies against UNC5D (1:100 dilution; bs-11494R; Bioss, Beijing, China). Two pathologists evaluated all pathological sections and obtained IHC analysis results under a microscope (AE41, Motic, Xiamen, China). The immunoreactive score of each sample was calculated by multiplying the tissue staining intensity score (Negative = 0, Buff = 1–4, Yellow = 5–8 and Brown = 9–12) by the degree of tissue staining (0% = 0, 1%-24% = 1, 25%-49% = 2, 50%-74% = 3, and 75%-100% = 4). Tumor Immune Cell Infiltration Analysis The Tumor Immunity Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer ) 46 database was utilized to explore the relevance in cancer be-tween the expression of target genes in the ceRNA network and six tumor-infiltrating immune cells (including B cells, neutrophils, CD4 + T cells, CD8 + T cells, macrophages and dendritic cells). The somatic copy number alteration (SCNA) module of the TIMER tool was applied to explore the relationship between genetic copy number variation (CNV) of target genes and the level of immune cell infiltration. Moreover, the survival module was used to study the effect of tumor immune infiltrating cells on the clinical outcome of CC patients. Finally, we used three databases (TIMER, Gene Expression Profile Interaction Analysis (GEPIA) 47 and TCGA) to jointly analyze the relevance between target gene and 16 marker genes of immune cells in CC samples. DNA methylation analysis of target genes The methylation level of target genes in CC was evaluated by different strategies. First, the expression of three DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) between UNC5DHigh and UNC5DLow was explored based on CC data from TCGA database. Next, the analytical tools DiseaseMeth 2.0 ( http://bioinfo.hrbmu.edu.cn/diseasemeth/ ) 48 and UALCAN ( http://ualcan.path.uab.edu/ ) 49 were used to detect the methylation levels of target genes in CC and para-cancer normal tissues. In addition, the MethSurv ( https://biit.cs.ut.ee/methsurv/ ) 50 database was applied to visualize DNA methylation of target genes at CpG sites. MEXPRESS ( http://mexpress.be ) 51 was performed to examine the relevance between the expression of target genes and their DNA methylation status by aggregating and visualizing gene expression, clinical data and methylation data from TCGA. Finally, UALCAN was also used to assess promoter methylation levels of target genes in clinicopathological features based on colon cancer staging, patient race, sex, age, body weight, lymph node metastasis, and tumor histology. MethSurv was used to evaluated the prognostic value of CpG methylation in target genes. Statistical analysis R software (version 4.1.1), SPSS 23.0 (SPSS, Inc., Chicago, IL, USA) and GraphPad Prism 8 were utilized to statistically analyze the experimental data. Part of the statistical analysis was utilized by bioinformatics online tools, including GEPIA. Most graphics were drawn by GraphPad Prism 8 software. Correlations between the expression of RNAs in the ceRNA network were analyzed by Spearman's rank correlation test. Mann-whitney test, Wilcoxon test and T test were applied for comparison be-tween the two groups of data. The importance of various clinical features and RNAs expression for OS was estimated by univariate and multivariate Cox regression analysis. Log-rank P < 0.05 or P < 0.05 (two-tailed) was considered statistically significant. Declarations Ethics approval This study involving human samples was approved by the Medical Ethics Committee of the First Affiliated Hospital of Hunan Normal University (protocol code: (2021) Scientific research ethics review NO: (17) and date of approval: 2021.12.10). Our study is based on public databases, so there are no ethical issues and other conflicts of interest. All methods were performed in accordance with the relevant guidelines and regulations. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at Funding: This work was supported by Hunan Provincial Natural Science Foundation (2025JJ80767). Author Contribution W. W.: Writing review & editing, Writing – original draft, Data curation. Z.X.: Visualization, Methodology, Funding acquisition, Data curation. M.J. and Q.C.: Formal analysis, Investigation, Software. Y.S.: Writing – review & editing, Writing – original draft, Conceptualization. J.D.: Writing – review & editing, Resources. W.L.: Writing – review & editing, Writing – original draft, Visualization, Validation, Data curation. Acknowledgement We thank Forevertek Biotechnology CO., LTD for providing antibody and Hunan Aifang Biotechnology CO., LTD for helping with immunohistochemistry. We are also sincerely thank the data providers of the various public databases. 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TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol. Plant. 13 , 1194–1202 (2020). Jeggari, A., Marks, D. S., Larsson, E. & miRcode A map of putative microrna target sites in the long non-coding transcriptome. Bioinformatics 28 , 2062–2063 (2012). Wang, X. Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies. Bioinformatics 32 , 1316–1322 (2016). Fromm1, B. et al. A uniform system for the annotation of vertebrate microrna genes and the evolution of the human microRNAome. Annu. Rev. Genet. 49 , 213–242 (2015). Shannon, P. et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13 , 2498–2504 (2003). Bu, D. et al. KOBAS-i: Intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res. 49 , W317–W325 (2021). Li, T. et al. 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Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.pdf Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 15 Oct, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 11 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 08 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":2514526,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the whole analysis process in CC.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/cc757e1abd0c6d6d6ac6f685.png"},{"id":94672705,"identity":"db3bdc6f-4463-4b14-b9ad-9e40ff2f6a81","added_by":"auto","created_at":"2025-10-29 13:40:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1841817,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano and Heatmap plot of differentially expressed lncRNAs(A), miRNAs(B) and mRNAs(C) between CC tissues and normal tissues samples. 15 significant DElncRNAs, DEmRNAs and DEmiRNAs were selected for clustering analysis. The red points and words represent upregulated RNAs. The blue points and words represent downregulated RNAs.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/2b8bd95d41e19f696f4dc8ca.png"},{"id":94672875,"identity":"ef280ccb-74d9-42b3-a313-c6734b5ab066","added_by":"auto","created_at":"2025-10-29 13:41:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3172286,"visible":true,"origin":"","legend":"\u003cp\u003eThe DElncRNAs-DEmiRNAs-DEmRNAs triple regulatory networks. (A) and expression analysis(B); lncRNAs, mRNA, and miRNAs are indicated by round rectangle, hexagon and ellipse. The red nodes stand for up-regulated RNAs, and the blue nodes stand for down-regulated RNAs\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/836380c88a172423e0d7e941.png"},{"id":94672644,"identity":"4c8072db-c21a-4b2d-9267-bccaa9aab3b8","added_by":"auto","created_at":"2025-10-29 13:40:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2143791,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves of lncRNAs, miRNAs and mRNAs significantly related to the prognosis of CC patients.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/71cab275b338ffd6a344f482.png"},{"id":94672740,"identity":"5e90d0fa-239f-4b77-a206-12d4879ef21d","added_by":"auto","created_at":"2025-10-29 13:40:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2347101,"visible":true,"origin":"","legend":"\u003cp\u003eThe ceRNAs network in CC and association analysis. (A) The LNC00482/mir-22-3p/UNC5D ceRNA regulatory axis; (B) targeting relationship of between mir-22-3p and LNC00482 as well as mir-22-3p and UNC5D were predicted on StarBase and TargetScan, respectively; (C) Correlation analysis results of LINC00482, mir-22-3p and UNC5D; (D) The effects of the combination of LINC01410, miR-23c and CHD7 on survival.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/888fc327727c7eecd0d8bf39.png"},{"id":94664344,"identity":"2759dee3-fc4c-4f16-8838-2c13b3f9521d","added_by":"auto","created_at":"2025-10-29 12:18:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2891243,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of UNC5D. (A) UNC5D expression levels were summarized in human cancer tissues and normal tissues from online GEPIA database; (B) The expression levels and location of UNC5D in human CC tissues and corresponding adjacent tissues were compared by IHC assay. The representative photos were showed (zoom in X100 and X200 times respectively).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/9e5bdf503ece23b1b1acbdaa.png"},{"id":94673160,"identity":"be74980f-8d60-4efe-acb8-48afd90bd170","added_by":"auto","created_at":"2025-10-29 13:41:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1423240,"visible":true,"origin":"","legend":"\u003cp\u003eThe relevance of UNC5D expression with immune infiltration level in COAD. (A) Relationship between UNC5D copy number and immune cell infiltration. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; (B) association of UNC5D expression levels with immune cell types; (C) The survival analysis of immune infiltrates in COAD.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/290caa8d0fb7fa8e6e6d1d7d.png"},{"id":94673312,"identity":"671b3f4b-7d13-490c-9440-ed91cc17f459","added_by":"auto","created_at":"2025-10-29 13:41:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3716433,"visible":true,"origin":"","legend":"\u003cp\u003eMethylation analyses of UNC5D in CC. (A) Differential expression of 3 DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) between UNC5DHigh and UNC5Dlow based on TCGA-COAD database; The promoter methylation levels of UNC5D in CC and normal tissues were texted by using UALCAN (B) and DiseaseMeth 2.0 (C); (D) The heatmap for UNC5D-related differently methylated regions was visualized.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/345f5236cf3805e9f1b70d9c.png"},{"id":94672590,"identity":"2e3481a8-98f5-4fd4-8edd-2568b1d64f3d","added_by":"auto","created_at":"2025-10-29 13:40:45","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3007846,"visible":true,"origin":"","legend":"\u003cp\u003eClinical value of UNC5D methylation. (A) The association between UNC5D methylation and clinical features was shown. ***: P \u0026lt; 0.0001; **: P \u0026lt; 0.001; (B) Kaplan-Meier plots of low and high methylation of UNC5D different DNA promoter CpG sites (cg26764980、cg01311313、cg22386073、cg08754088、cg26679047) in CC patients.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/3375645fb1957351f6cea612.png"},{"id":94728183,"identity":"7d9175ed-d08a-443b-bb23-90ea50753ef1","added_by":"auto","created_at":"2025-10-30 07:03:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3828156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/9abfb554-6e11-41b1-911c-9806e0dbc225.pdf"},{"id":94664327,"identity":"6b674c83-be15-4709-9aaa-c8af13eee28b","added_by":"auto","created_at":"2025-10-29 12:18:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1499089,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/6aaf9a4d6161fda3a2d28abd.pdf"},{"id":94664324,"identity":"23931567-c538-4ffb-863a-3f2bef2c4e28","added_by":"auto","created_at":"2025-10-29 12:18:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17370,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7221309/v1/3e21ee538b1132ea4449d656.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of LINC00482/mir-22-3p/UNC5D regulatory axis associated with diagnostic and prognostic value of colon cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColon cancer (CC) is one type of the most general cancers, which ranks the second and third place in mortality and incidence among global cancers, respectively \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In China, colon cancer was both ranked fifth in the terms of mortality and incidence in 2015 \u003csup\u003e2\u003c/sup\u003e. Colon cancer has the characteristics of rapid metastasis and difficult treatment, and most CC patients are asymptomatic at early diagnosis or are found at an advanced stage, which has caused a huge burden on society and medical care. The standard treatments for CC are radical resection and adjuvant chemoradiotherapy \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Statistics found that despite with the implementation of early screening and surgical operations, the 5-year survival rate of CC patients with early-stage reached 90%, however, when patients are diagnosed as advanced-stage or develop distant metastases, the treatment options are extremely limited, and the 5-year survival rate significantly decreased to 14% \u003csup\u003e4\u003c/sup\u003e. Consequently, early diagnosis and valid treatment are the key to accurately predict CC and reduce its mortality. Although the current treatment strategies for CC patients have been improved, there are still shortcomings such as delayed diagnosis of imaging and low sensitivity and specificity of serological markers \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Hence, there is badly need of exploring novel sensitive and effective biomarkers related to the growth and metastasis of CC, which are crucial for elucidating pathogenesis of CC and effectively improving the prognosis of colon cancer patients.\u003c/p\u003e\u003cp\u003eAs we all know, non-coding RNA (ncRNA) is ubiquitous in eukaryotes and was once considered as a useless transcriptional \"noise\" without protein-coding potential and specific biological function \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In recent years, two kinds of noncoding RNAs, long noncoding RNA (lncRNA) and microRNA (miRNA), have become the focus of attention by interacting with diverse molecules to regulate gene expression and participate in several biological processes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. MicroRNA (miRNA) is endogenously produced, evolutionarily conserved, and small single-stranded non-coding RNA (ncRNA) with a size of about 19\u0026thinsp;~\u0026thinsp;24 nucleotides \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Nowadays, there are many reports on microRNA in tumors, and over 2,000 miRNAs have been proved to have the capacity to regulate ex-pression, but the level of research on lncRNA needs to be improved \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. LncRNA, a class of transcripts with a length of over 200 nucleotides, have been found to offer emerging roles in many cancer-related biological processes by regulating target genes, such as proliferation, differentiation, invasion and metastasis \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the mechanism of lncRNA how to mediate gene expression and exert different biological functions in tumors is unclear. Then, salmena and his colleagues posed the competitive endogenous RNA (ceRNA) hypothesis for the first time in 2011, explained the interaction mechanism between lncRNA and other molecules, and described its impact on the changed protein expression level \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.A growing list of research have indicated that lncRNA is considered to be a key factor in the ceRNA network, which due to it serves as a ceRNA to regulate mRNA expression by competitively sharing miRNA response elements (MREs) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. More importantly, increasing researches have suggested that ceRNA plays a key role in colorectal carcinoma, lung adenocarcinoma, kidney cancer, Hepatocellular Carcinoma and other diseases \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For example, Meg3, as a ceRNA, increases SOCS3 expression by specifically binding to miR-708, further inhibiting the proliferation of colon cancer cells and influencing the outcome of patients \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, LINC00460 upregulates ANXA2 expression by sponging miR-433-3p to accelerate the development of CC \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, the molecular mechanism underlying ceRNA regulatory network analysis in CC remains poorly understood. Hence, we are sure that the function of ceRNA in CC is well worthy of further exploration.\u003c/p\u003e\u003cp\u003eIn the paper, we firstly screened the differentially expressed RNAs (DERs) in CC based on the The Cancer Genome Atlas (TCGA) database, including DEmRNAs, DEmiRNAs and DElncRNAs. Then, applied multiple prediction databases and survival analysis, the survival-related lncRNA-miRNA-mRNA triple regulatory networks were built. Next, the key ceRNA network related to CC was successfully constructed by combining expression analysis and survival analysis. Functional enrichment analysis was used to explore the biological behaviors of related target genes in the ceRNA network. The prognostic significance of RNA in ceRNA network was analyzed by clinical information. Furthermore, we also used patient tissue samples to validate certain analytical results. Finally, the molecular mechanisms of target genes in the ceRNA network affecting CC were explored through immune cell infiltration and DNA methylation analysis. Our findings may furnish valuable clues for the treatment of colon cancer. The roadmap for the analysis performed in this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of differentially expressed RNAs (DERs) in CC\u003c/h2\u003e\n \u003cp\u003eIn this study, total RNA expression profiles of 456 (and 439) CC tissues and 41 (and 8) normal tissues samples from 445 CC patients were obtained from the TCGA-COAD database. 1324 DElncRNAs (838 over-expression and 486 down-regulated), 261 DEmiRNAs (189 over-expression and 72 down-regulated), and 2022 DEmRNAs (1068 over-expression and 954 down-regulated) were obtained by differential expression analysis. The distributions of DEmiRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), DEmRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) and DElncRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA) were described by volcano plots and clustering heatmaps.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eConstruction of Survival-Associated lncRNA-miRNA-mRNA triple regulatory networks for CC\u003c/h3\u003e\n\u003cp\u003eTo better understand the pathogenesis and target prediction of colon cancer, the survival-related lncRNA-miRNA-mRNA triple regulatory network was established according to the above results. First, the target relationship between lnRNAs and miRNAs was evaluated based on DElncRNAs and DEmiRNAs by the miRcode database. Next, miRDB and TargetScan were applied to forecast the target mRNAs of miRNAs. Then, the candidate target mRNAs were compared with DEmRNAs, and the overlapping genes were picked. The findings suggested that the co-expression of 30 miRNAs, 216 lncRNAs and 563 mRNAs in DERNAs. Next, the survival analysis was performed to constitute a survival-related lncRNA-miRNA-mRNA triple regulatory network (including 7 lncRNAs, 5 miRNAs and 12 mRNAs). Finally, the co-expression results were visualized by using Cytoscape 3.6.0 software (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003ch3\u003eDevelopment of a ceRNA regulatory network and Functional Enrichment analysis\u003c/h3\u003e\n\u003cp\u003eFor constructing a ceRNA network, the above positive co-expressed lncRNA-miRNA pair and miRNA-mRNA pair were excluded, and only the expression and overall survival of lncRNA-miRNA pairs and mRNA-miRNA pairs conform to the negative regulation mode could constitute ceRNA network (Figure. 3B, Figure. 4, and Figure. S1). At last, we built the ceRNA regulatory network of CC, which is composed of LINC00482/mir-22-3p/UNC5D axis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The prediction of potential binding sites between mir-22-3p and LINC00482 as well as mir-22-3p and UNC5D were shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB. Based on the ceRNA theory that lncRNA acts as a natural miRNA sponge to inhibit the function of miRNA, the expression of lncRNA-mRNA should be positively correlated. Then, the correlation between LINC00482, mir-22-3p and UNC5D was analyzed using expression profile data from TCGA database. The showed a positive correlation between LINC00482 and UNC5D was validated in CC clinical samples (P\u0026thinsp;=\u0026thinsp;0.0001, r\u0026thinsp;=\u0026thinsp;0.181, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, the effect of the combination of LINC00482, mir-22-3p and UNC5D on survival was also analyzed. The findings suggested that patients with high-LINC00482/UNC5D expression and low-mir-22-3p expression had better prognosis than patients with other combinations.\u003c/p\u003e\n\u003cp\u003eThe target gene UNC5D in ceRNA network was analyzed by function enrichment, for the sake of further probed the potential biological behaviors of the ceRNA. The KEGG pathways were mainly enriched in \u0026quot;axon guidance\u0026quot;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. GO analysis results showed that the top five GO terms in which target genes mainly clustered were \u0026ldquo;regulation of neuron migration\u0026rdquo;, \u0026ldquo;netrin receptor activity\u0026rdquo;, \u0026ldquo;netrin activated signaling pathway\u0026rdquo;, \u0026ldquo;cell surface\u0026rdquo;, \u0026ldquo;cell-cell adhesion via plasma-membrane adhesion molecules\u0026rdquo; (Figure. S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBuilding a predictive model based on independent prognostic clinical factors and Preliminary validation in clinical samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor further probing the influence of clinical features on overall survival (OS), univariate and multivariate Cox regression analysis were applied to evaluate. Univariate analysis showed that Diameter, TNM stage, Lymph-node metastasis, Distant metastasis, low expression of LINC00482/UNC5D, and high expression of mir-22-3p were positively associated with poor patient prognosis (Table\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, S1). In multivariate analysis, TNM stage, Distant metastasis, and low expression of LINC00482/UNC5D may be independent prognostic clinical factors affecting OS in CC patients (Table\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These results suggest that LINC00482/UNC5D was low expression in CC and related to poor prognosis.\u003c/p\u003e\n\u003cp\u003eWe also estimated the expression levels of UNC5D in several tumor tissues. The findings revealed that UNC5D exhibited a trend of down-regulation in several tumor types (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Further validation was demonstrated by IHC analysis of clinical samples, which indicated that UNC5D was significantly over-expression in paracancerous tissues than CC tissue samples (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003eRelationship between UNC5D expression and immune cell infiltration in CC\u003c/h3\u003e\n\u003cp\u003eFor exploring the influential mechanism of UNC5D on CC, we probed the relationship between UNC5D expression and immune cell infiltration. Firstly, altered UNC5D gene copy numbers seemed to significantly associate with Several immune cell infiltration levels (including B cell, CD8\u0026thinsp;+\u0026thinsp;T cell, and dendritic cell) in COAD (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Next, the relevance between UNC5D expression and tumor-infiltrating immune cell adjusted by purity was examined using TIMER. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB, the expression level of UNC5D positively related to infiltration levels of CD4\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;0.207, p\u0026thinsp;=\u0026thinsp;2.81e\u0026thinsp;\u0026minus;\u0026thinsp;05) and Dendritic Cell (r\u0026thinsp;=\u0026thinsp;0.141, p\u0026thinsp;=\u0026thinsp;4.64e\u0026thinsp;\u0026minus;\u0026thinsp;03) in CC but negatively correlated with tumor purity (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.111, p\u0026thinsp;=\u0026thinsp;2.58e\u0026thinsp;\u0026minus;\u0026thinsp;02) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). In addition, we also probed the influence of immune cells on clinical prognosis of patients with COAD., The findings indicated that low levels of CD8\u0026thinsp;+\u0026thinsp;T cell (p\u0026thinsp;=\u0026thinsp;0.035) and neutrophil (p\u0026thinsp;=\u0026thinsp;0.038) had worse outcome for COAD patients with survival time within 30 months (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eFor further validating the relevence between UNC5D expression and immune cell infiltration in CC, we identified the relationship between UNC5D and 57 immune cell markers in 16 immune cells using the TIMER, GEPIA, and TCGA-COAD database. As shown in Table S2, all results suggested that SLC2A1 expression was related to B cell, Th17, Treg immune marker genes, including CD19 (r\u0026thinsp;=\u0026thinsp;0.117, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), CD79A (r\u0026thinsp;=\u0026thinsp;0.219, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), STAT3 (r\u0026thinsp;=\u0026thinsp;0.205, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), IL17A (r\u0026thinsp;=\u0026thinsp;0.147, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), FOXP3 (r\u0026thinsp;=\u0026thinsp;0.150, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CCR8 (r\u0026thinsp;=\u0026thinsp;0.172, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), STAT5B (r\u0026thinsp;=\u0026thinsp;0.228, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings supported that UNC5D could offer a key role as an immunomodulatory factor in CC.\u003c/p\u003e\n\u003ch3\u003eUNC5D methylation in patients with CC\u003c/h3\u003e\n\u003cp\u003eTo further elucidate the potential mechanisms underlying the aberrant lowly expressed of UNC5D in CC tissues, we probed the relationship between the expression and methylation status of UNC5D by multiple methods. It was found that DNA methylation could affect the behavior of cancer cells by three DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) to regulate gene expression. The results revealed that DNMT1, DNMT3A and DNMT3B had higher expression in the UNC5Dhigh group compared with UNC5Dlow (P\u0026thinsp;=\u0026thinsp;0.0004 for DNMT1、DNMT3A and DNMT3B) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). Besides, DNA methylation levels of UNC5D were obviously higher in CC tissues than normal tissue samples by using UALCAN (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). Consistently, the same results were obtained with DiseaseMeth 2.0 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC). To further substantiate our conclusions, the relationship between UNC5D expression and its methylation levels at different methylation sites was estimated. A heatmap of different methylation regions related to UNC5D were obtained by MethSurv database analysis. The map showed that the distributions of low, medium and high methylation sites were 14.29% (4/28), 21.43% (6/28) and 64.28% (18/28), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD). In addition, the analysis of TCGA data in the MEXPRESS database indicated that there was a total of 9 CpG sites whose methylation levels were inversely correlated with expression (Pearson correlation coefficients range from \u0026minus;\u0026thinsp;0.0117 to \u0026minus;\u0026thinsp;0.175 and the p values were statistically significant).\u003c/p\u003e\n\u003cp\u003eThen, the role of UNC5D methylation in clinical features was investigated by UALCAN analysis. The following results obtained that the promoter methylation level of UNC5D in CC patients was obviously higher (according to age, sex, weight, race, tumor histology, stage and lymph node metastasis status) than the normal group (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA). Eventually, the relationship between UNC5D methylation and the prognostic value of patient were investigated using the MethSurv database. The Kaplan-Meier plot suggested the prognosis information of 28 methylation CpG sites. The methylation level of 5 CpG sites (cg26764980、cg01311313、cg22386073、cg08754088、cg26679047) were related to patient outcome (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). Patients with high HSPA4 methylation in four of these CpG sites had a worse prognosis. However, the prognosis of cg08754088 was reversed. Therefore, DNA methylation could offer a pivotal impact in the mechanism resulted in aberrant downregulation of UNC5D in tumor tissues.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRC is one of the most common leading causes of cancer death worldwide, and COAD is a common type of CRC \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent years, the main treatment methods for colon cancer are radical therapy and radiotherapy and chemotherapy. Although the five-year survival rate of CC patients has significantly improved with advances in medical technology, the mortality rate remains high \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Previous research has revealed that abnormally expressed genes could exert a major impact in cancer and have potential as biomarkers \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Therefore, the identification of effective biomarkers and potential regulatory networks provides a great help for selective therapeutic strategies for CC patients.\u003c/p\u003e\u003cp\u003eWith the expeditious growth of high-throughput sequencing technology, a growing list of research have shown that lncRNAs are Aberrant expression in tumor tissues and exert a major impact in tumor progression, some of which are expected to be biomarkers with better prognosis and diagnosis \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. LncRNAs have complex functions through multiple pathways, among which the ceRNA hypothesis elucidates a new regulatory mechanism, that is, lncRNAs can serve as miRNA sponges to competitively bind miRNAs with mRNAs \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This hypothesis better explains the interaction of multiple types of RNAs at the genetic level. So far, some studies have clarified several potential lncRNAs and their mediated ceRNA regulatory networks in colon cancer, but the regulatory mechanism of ceRNA regulatory networks on CC has not been deeply explored.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this research, DElncRNAs, DEmiRNAs and DEmRNAs in COAD samples from the TCGA database was identified. Those differentially expressed genes with the same trend in expression analysis and survival analysis were used to construct the LINC00482/mir-22-3p/UNC5D axis ceRNA regulatory network by integrative bioinformatics approach.\u003c/p\u003e\u003cp\u003ePrevious studies have indicated that lncRNA LINC00482 was up-regulated in bladder cancer and liver cancer \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. While, there is no data on the biological role and behavior of LINC00482 in CC. Mir-22-3p is a 22-nucleotide noncoding RNA with paradoxical roles in various cancers. It has been reported that mir-22-3p was down-expressed in both lung cancer tissues and cell lines, as well as exerts a tumor suppressor effect by inhibiting MET-STAT3 signaling \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition, mir-22-3p was lowly ex-pressed in breast cancer and targets PLAGL2 to the invasion and migration of breast cancer cells \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, one study showed that mir-22-3p inhibits hepatocellular carcinoma cell growth by targeting SP1 and its downstream target genes CCND1 and BCL2 \u003csup\u003e29\u003c/sup\u003e. Another study demonstrated that mir-22-3p was high expression in cervical cancer tissues and induced cervical cancer cell proliferation by inhibiting its target, eIF4EBP3 \u003csup\u003e30\u003c/sup\u003e. UNC5D was a member of the UNC5s family of human-dependent receptors, which shared the ligand netrin-1 with other UNC5s family members and the tumor growth inhibitor DCC \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Previous research has reported that, UNC5D, a direct transcriptional target of p53, which cooperates with E2F1 and p53 to form feedback to induce programmed cell death (PCD) in neuroblastoma (NB) cells using DNA dam-age signals, and high-level expression of UNC5D correlates with the better outcomes of NB \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. At present, there are surprisingly few studies of the role and potential molecular mechanism of UNC5D in CRC.\u003c/p\u003e\u003cp\u003eWe further analyzed the function and pathway of DEmRNA UNC5D in the ceRNA network by Using GO and KEGG. Based on the ceRNA hypothesis, specific lncRNAs may also function or focus on potential pathways in an mRNA-like manner. The findings of the GO biological process revealed that the top 5 processes in which UNC5D was mainly enriched include \u0026ldquo;regulation of neuron migration\u0026rdquo;, \u0026ldquo;netrin receptor activity\u0026rdquo;, \u0026ldquo;netrin activated signaling pathway\u0026rdquo;, \u0026ldquo;cell surface\u0026rdquo;, \u0026ldquo;cell-cell adhesion via plasma-membrane adhesion molecules\u0026rdquo;. KEGG pathway analysis is mainly enriched in axon guidance. The axon guidance molecule Netrin protein has been reported to be strongly correlated with tumorigenesis, metastasis, and apoptosis \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. More importantly, UNC-5s are the receptor for netrins \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo determine the possible important role of this ceRNA network in CRC tumorigenesis and prognosis, a combination of correlation analysis, survival analysis, and univariate and multivariate cox regression analysis was carried out for further exploration. The result proved that there was a positive relevance between the expression of LINC00482 and UNC5D in CC samples. Moreover, patients with LINC00482high/mir-22-3plow/UNC5Dhigh had the best prognosis than patients with other combinations. In addition, the target gene was validated by IHC analysis experiments based on clinical samples.\u003c/p\u003e\u003cp\u003eFinally, the mechanism of the ceRNA network affecting the occurrence and development of CC was explored through immune cell infiltration and DNA methylation analysis. According to the analysis of TIMER online database, the UNC5D gene copy number in CC appeared to be obviously related to the expression levels of B cells, CD8\u0026thinsp;+\u0026thinsp;T cells and dendritic cells. Next, the expression level of UNC5D was positively correlated with the infiltration level of CD4\u0026thinsp;+\u0026thinsp;T cells and dendritic cells in CC. Moreover, low levels of CD8\u0026thinsp;+\u0026thinsp;T cells and neutrophils were related to poor outcome in patients with COAD (survival time within 30 months). More importantly, the analysis of three databases probed that UNC5D expression was relevant to B cells, Th17 cells, and Treg cells immune marker genes. Th17 and Treg cells are CD4\u0026thinsp;+\u0026thinsp;T cells subsets with significant immunosuppressive effect. It has been claimed that CD4\u0026thinsp;+\u0026thinsp;T cells exert a crucial impact in coordinating and improving the immune response against colon cancer cells \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The above analysis suggested that UNC5D may affect the immune filtration of the ceRNA regulatory network by influencing the expression of CD4\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e\u003cp\u003eAbnormal methylation of gene promoter is strongly relevant to the progression and outcome of tumor, and can be used as a marker for early diagnosis and prognosis evaluation of CC. A growing list of research revealed that aberrant DNA methylation exerts a crucial impact in the induction and development of CC \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In this paper, the analysis of various bioinformatics tools based on the TCGA database yielded consistent results indicating that UNC5D is hypermethylated in CC tissues. Next, the UNC5Dhigh group was obviously positively correlated with the high expression of DNMT1, DNMT3A and DMNT3B. These can well explain the down-regulated of UNC5D in CC. Then, we determined that the methylation levels of 9 out of 28 CpG sites were negatively correlated with expression. Moreover, there was a strong correlation between UNC5D methylation and clinical features as well as between different methylation sites and prognosis of CC patients. These results suggested that aberrant DNA methylation is a cause of colon cancer-related mortality.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we have successfully established ceRNA regulatory network that LINC00482 could modulate the expression of UNC5D by sponging mir-22-3p, and the target gene was validated in clinical samples. In addition, we also preliminarily explored the mechanism of LINC00482/mir-22-3p/UNC5D axis ceRNA network on colon cancer. The present research may offer a novel idea on the underlying mechanisms of gene regulation in CC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTCGA dataset retrieval and clinical sample collection\u003c/h2\u003e\u003cp\u003eThe RNA-sequencing profiles and miRNA-sequencing profiles and clinical information were downloaded from TCGA database (up to November 7, 2021) based on Il-lumina HiSeqRNASeq and Illumina HiSeqmiRNASeq platforms by using the Genomic Data Commons (GDC) Data Transfer Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/).Th\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee TCGA-COAD dataset contains mRNA and lncRNA expression profiles (RNA-seq data) of 456 CC samples and 41 normal samples, and miRNA expression profiles (miRNA-seq data) of 439 CC samples and 8 normal samples. Cancer tissue type was 01A and duplicate tissue samples were excluded. The above data followed the TCGA publication guide-lines.\u003c/p\u003e\u003cp\u003eFor immunohistochemical (IHC) analysis, we selected 10 pairs of COAD tissue samples and their paired adjacent non-tumor tissue samples were obtained from 10 primary COAD patients who underwent surgical resection at the People's Hospital of Hunan Province (Hunan, China). These patients (aged 46\u0026ndash;84 years) were diagnosed with COAD based on clinical history and histopathology. Prior to sample collection, written informed consent forms were acquired from COAD patients. The research was ratified by the Ethics Committee of the People's Hospital of Hunan Province (Hunan, China). All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of differentially expressed RNAs (DERNAs)\u003c/h2\u003e\u003cp\u003eFirst, the downloaded raw data of lncRNAs, miRNAs and mRNAs were normalized, and then the \"edge\" package in R software was applied to carry out differential expression analysis on the normalized values to obtain the DERNAs (including differentially expressed lncRNAs (DElncRNAs)、Differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs)) between CC samples and normal samples. DEmiRNAs and DEmRNAs were screened by thresholds of | logFC (fold change) |\u0026ge;2 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Besides, DElncRNAs with | logFC (fold change) |\u0026gt;0.7 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as statistically significant. The results were visualized by drawing volcano plots and heatmaps using TBtools (version 1.075) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and GraphPad Prism 8(version 8.3.0).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eBuild of lncRNA-miRNA-mRNA triple networks with prognostic significance\u003c/h2\u003e\u003cp\u003eFirst, DElncRNA-based lncRNA-miRNA regulatory interactions were predicted by miRcode (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mircode.org/\u003c/span\u003e\u003cspan address=\"http://www.mircode.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Then, DEmiRNAs were compared with the candidate target miRNAs to obtain intersections. Next, miRDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mirdb.org/\u003c/span\u003e\u003cspan address=\"http://www.mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and TargetScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_72/\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_72/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e were applied to predict miRNA-mRNA regulatory interactions based on DEmRNAs. Furthermore, the triple regulatory networks based on the lncRNA-miRNA-mRNA axis with prognostic significance were screened by combining the above interaction pairs and using survival analysis. Finally, co-expression networks were built and visualized by Cytoscape (v3.7.0) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEstablishment of the ceRNA regulatory network and functional enrichment analysis\u003c/h2\u003e\u003cp\u003eBy the TCGA database, we compared the expression and survival outcomes of miRNAs, mRNAs and lncRNAs among the triple network in CRC, only these RNAs with opposite expression and prognostic significance in colon cancer and adjacent cancer samples were included in this study. Next, the qualified lncRNA-miRNA interaction group and miRNA-mRNA interaction group were combined to build a ceRNA network. The prediction of biological targets is accomplished through TargetScan and StarBase online analysis tools.\u003c/p\u003e\u003cp\u003eSubsequently, to explore promising biological processes and signaling pathways, functional enrichment analysis of DEmRNA in ceRNA networks was carried out by the online analysis tool KOBAS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kobas.cbi.pku.edu.cn/genelist/\u003c/span\u003e\u003cspan address=\"http://kobas.cbi.pku.edu.cn/genelist/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analysis. The results of the enrichment analysis were displayed by the ggplot2 package of R software. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set as the critical value.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eImmunohistochemistry (IHC) analysis\u003c/h2\u003e\u003cp\u003e10 pairs of colon tumors and normal tissues from 10 CC patients were fixed with 10% formalin, dehydrated, paraffin embedded, and sectioned. IHC staining was applied using antibodies against UNC5D (1:100 dilution; bs-11494R; Bioss, Beijing, China). Two pathologists evaluated all pathological sections and obtained IHC analysis results under a microscope (AE41, Motic, Xiamen, China). The immunoreactive score of each sample was calculated by multiplying the tissue staining intensity score (Negative\u0026thinsp;=\u0026thinsp;0, Buff\u0026thinsp;=\u0026thinsp;1\u0026ndash;4, Yellow\u0026thinsp;=\u0026thinsp;5\u0026ndash;8 and Brown\u0026thinsp;=\u0026thinsp;9\u0026ndash;12) by the degree of tissue staining (0% = 0, 1%-24% = 1, 25%-49% = 2, 50%-74% = 3, and 75%-100% = 4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTumor Immune Cell Infiltration Analysis\u003c/h2\u003e\u003cp\u003eThe Tumor Immunity Estimation Resource (TIMER, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e database was utilized to explore the relevance in cancer be-tween the expression of target genes in the ceRNA network and six tumor-infiltrating immune cells (including B cells, neutrophils, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages and dendritic cells). The somatic copy number alteration (SCNA) module of the TIMER tool was applied to explore the relationship between genetic copy number variation (CNV) of target genes and the level of immune cell infiltration. Moreover, the survival module was used to study the effect of tumor immune infiltrating cells on the clinical outcome of CC patients. Finally, we used three databases (TIMER, Gene Expression Profile Interaction Analysis (GEPIA) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and TCGA) to jointly analyze the relevance between target gene and 16 marker genes of immune cells in CC samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDNA methylation analysis of target genes\u003c/h2\u003e\u003cp\u003eThe methylation level of target genes in CC was evaluated by different strategies. First, the expression of three DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) between UNC5DHigh and UNC5DLow was explored based on CC data from TCGA database. Next, the analytical tools DiseaseMeth 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.hrbmu.edu.cn/diseasemeth/\u003c/span\u003e\u003cspan address=\"http://bioinfo.hrbmu.edu.cn/diseasemeth/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e were used to detect the methylation levels of target genes in CC and para-cancer normal tissues. In addition, the MethSurv (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/methsurv/\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/methsurv/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e database was applied to visualize DNA methylation of target genes at CpG sites. MEXPRESS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mexpress.be\u003c/span\u003e\u003cspan address=\"http://mexpress.be\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e was performed to examine the relevance between the expression of target genes and their DNA methylation status by aggregating and visualizing gene expression, clinical data and methylation data from TCGA. Finally, UALCAN was also used to assess promoter methylation levels of target genes in clinicopathological features based on colon cancer staging, patient race, sex, age, body weight, lymph node metastasis, and tumor histology. MethSurv was used to evaluated the prognostic value of CpG methylation in target genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eR software (version 4.1.1), SPSS 23.0 (SPSS, Inc., Chicago, IL, USA) and GraphPad Prism 8 were utilized to statistically analyze the experimental data. Part of the statistical analysis was utilized by bioinformatics online tools, including GEPIA. Most graphics were drawn by GraphPad Prism 8 software. Correlations between the expression of RNAs in the ceRNA network were analyzed by Spearman's rank correlation test. Mann-whitney test, Wilcoxon test and T test were applied for comparison be-tween the two groups of data. The importance of various clinical features and RNAs expression for OS was estimated by univariate and multivariate Cox regression analysis. Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e This study involving human samples was approved by the Medical Ethics Committee of the First Affiliated Hospital of Hunan Normal University (protocol code: (2021) Scientific research ethics review NO: (17) and date of approval: 2021.12.10). Our study is based on public databases, so there are no ethical issues and other conflicts of interest. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAdditional information\u003c/h2\u003e\u003cp\u003eSupplementary Information The online version contains supplementary material available at\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis work was supported by Hunan Provincial Natural Science Foundation (2025JJ80767).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW. W.: Writing review \u0026amp; editing, Writing \u0026ndash; original draft, Data curation. Z.X.: Visualization, Methodology, Funding acquisition, Data curation. M.J. and Q.C.: Formal analysis, Investigation, Software. Y.S.: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Conceptualization. J.D.: Writing \u0026ndash; review \u0026amp; editing, Resources. W.L.: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Data curation.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Forevertek Biotechnology CO., LTD for providing antibody and Hunan Aifang Biotechnology CO., LTD for helping with immunohistochemistry. We are also sincerely thank the data providers of the various public databases.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J. 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First, we obtained DElncRNAs, DEmiRNAs and DEmRNAs by analyzing the RNA expression profiles of CRC retrieved from the TCGA database, respectively. Then, those differentially expressed genes with the same trend in expression analysis and survival analysis were used to construct the LINC00482/mir-22-3p/UNC5D axis ceRNA regulatory network by a comprehensive bioinformatics approach. In addition, the results were validated in both correlation analysis and immunohistochemical experiment. The univariate and multivariate cox regression analyses yielded that low expression of LINC00482/UNC5D may be an independent prognostic clinical factor affecting overall survival (OS) in CC patients. Finally, the regulatory mechanism of the ceRNA network affecting the occurrence and development of CC was explored through immune cell infiltration and DNA methylation analysis. In conclusion, the construction of a novel LINC00482/mir-22-3p/ UNC5D axis ceRNA regulatory network may provide new insights into CC pathogenesis and may serve as a promising target for CC therapy.\u003c/p\u003e","manuscriptTitle":"Identification of LINC00482/mir-22-3p/UNC5D regulatory axis associated with diagnostic and prognostic value of colon cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 12:18:24","doi":"10.21203/rs.3.rs-7221309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-15T13:43:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T11:48:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-11T13:10:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T12:49:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-08T12:45:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2acf37c9-ae79-43c4-b666-d82a64980cbc","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56868145,"name":"Health sciences/Biomarkers"},{"id":56868146,"name":"Biological sciences/Cancer"},{"id":56868147,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56868148,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-10-29T12:18:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 12:18:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7221309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7221309","identity":"rs-7221309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}