Deciphering the miR-200a-3p/RUNX1 Axis: A Novel Oncogene Signature in Colorectal Cancer

Deciphering the miR-200a-3p/RUNX1 Axis: A Novel Oncogene Signature in Colorectal 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 Research Article Deciphering the miR-200a-3p/RUNX1 Axis: A Novel Oncogene Signature in Colorectal Cancer Xingkai Su, Xia Jiang, FangJian Shang, Yingchao Gao, JianWei Ma, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4844859/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 The dual role of carcinogenic or tumor suppressor makes Runt related transcription factor 1 (RUNX1) a new diagnostic markers or therapeutic target for colorectal cancer (CRC). In CRC, the relationship between RUNX1 and prognosis, biological function, and potential microRNA directly involved in the regulation of RUNX1 are unclear. Methods Gene expression of RUNX1 in colorectal cancer (CRC) was comprehensively analyzed using data from The Cancer Genome Atlas (TCGA) and Oncomine databases. Kaplan-Meier survival curves were constructed to assess the clinical and prognostic status associated with RUNX1 expression in CRC patients. The correlation between clinical features and RUNX1 expression was analyzed in the GSE17536 dataset using the Chi-square test. The relationship between RUNX1 expression and overall survival (OS) in CRC was investigated through both univariate and multivariate Cox regression analyses. Genes co-expressed with RUNX1 were identified using Spearman correlation analysis. The potential functions of RUNX1 in CRC were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. MiRNAs that negatively regulate RUNX1 expression were identified using TargetScan, ENCORI, and miRDB databases. The relationship between miR-200a-3p expression levels and clinicopathologic characteristics, as well as the prognosis of CRC patients, was analyzed using the Chi-square test. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was employed to determine the expression levels of RUNX1 and miR-200a-3p in CRC cell lines (HCT-116, HT-29, SW480, and SW620). The interaction between RUNX1 and miR-200a-3p was confirmed through a luciferase reporter assay. Results Compared with normal tissues, RUNX1 mRNA expression was up-regulated in most cancer tissues, including CRC. RUNX1 expression was closely correlated with TNM stage in CRC patients (P < 0.05). The high expression level of RUNX1 mRNA (HR: 2.198, 95%CI: [1.200, 4.027]) could be used as an independent risk factor for overall survival (OS) in CRC patients. The mRNA level of RUNX1 in CRC patients was significantly correlated with OS (P < 0.01), disease-free survival (DFS) (P < 0.01), and disease-specific survival (DSS) (P < 0.001). RUNX1 co-expressed genes are mainly involved in GO entries such as development and growth, differentiated cell morphogenesis, and KEGG signaling pathways such as adhesion plaques and adhesion junctions. miR-200a-3p may be the miRNAs with direct regulatory role of RUNX1. The expression of miR-200a-3p was significantly correlated with T stage (P = 0.03) and M stage (P = 0.026). Low expression of miR-200a-3p was significantly associated with poor prognosis in CRC patients (P = 0.02). The expression levels of RUNX1 and miR-200a-3p in CRC cell lines were negatively correlated. RUNX1 has specific binding sites with miR-200a-3p. The results of dual luciferase reporter gene detection showed that compared with three groups, Luc-3'UTR + mimic-NC, Luc-NC + miR-200a-3p mimic and Luc-NC + mimic-NC, luciferase activity of Luc-3'UTR + miR-200a-3p mimic group was significantly decreased (P < 0.05), suggesting that miR-200a-3p may be a direct negative regulator of RUNX1. Conclusion High expression of RUNX1 might function as an oncogene in CRC. The up-regulated expression of RUNX1 is associated with poor prognosis after CRC, which can be used as a biomarker of prognosis in CRC patients. This study is the first to report that RUNX1 is a direct negative regulatory target of miR-200a-3p in CRC and can be used as a potential therapeutic target for CRC patients. Colorectal cancer RUNX1 miR-200a-3p Prognosis Oncogene signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Colorectal cancer (CRC) is one of the common malignant tumors with a high incidence and a sharp increase with age [ 1 ]. According to authoritative data statistics, CRC is the tumor with the third highest number of new cases and the second highest number of death cases worldwide [ 2 ]. The pathogenesis of CRC is complex, and a large number of risk factors are known to contribute to the development of it, such as age > 60, family history of CRC, inflammatory bowel disease, obesity, and poor dietary habits (processed meat, low dietary fiber diet), etc. [ 1 , 3 , 4 ]. Currently, the genes known to cause CRC recurrence include human epidermal growth factor receptor 2 (HER-2), MET proto-oncogene (MET), KRAS proto-oncogene (KRAS) and epidermal growth factor receptor (EGFR) mutations [ 5 ]. At present, it is one of the directions of most cancer treatments to study the tumor from the gene level and find out the targeted therapeutic targets. In recent years, with the in-depth study of RUNX family, more and more relevant studies have been conducted in malignant tumors, and it is expected to become a new therapeutic target. RUNX family genes are a class of nuclear transcription factors with conserved protein sequences, which play an important regulatory function in cell lineage-specific genes, cell differentiation and development [ 6 ]. In the human genome, the RUNX family consists of three members: RUNX1, RUNX2, and RUNX3. RUNX2 is highly expressed in pancreatic cancer cells and is also highly expressed in tumor-associated fibroblasts, while it is less expressed in normal pancreatic tissues. Studies have shown that RUNX2 can be involved in regulating the expression of extracellular matrix regulatory factors such as matrix metalloproteinases 1 (MMP1), thus affecting the tumor microenvironment [ 7 ]. RUNX3 plays a dual role in promoting tumor migration but inhibiting tumor cell proliferation in pancreatic cancer [ 8 ]. In recent years, studies on the relationship between RUNX1 gene and cell apoptosis and chemotherapy resistance have gradually attracted attention [ 9 , 10 ]. RUNX1 plays a dual role of carcinogenic or tumor suppressor in hematologic tumors. In addition, RUNX1 activates or inhibits related genes or signaling pathways in different solid tumors, thus playing a role in carcinogenesis or tumor suppression. The role of RUNX1 varies with different tumor types. MicroRNA (miRNA) has attracted more and more attention due to its inherent timing and spatial characteristics and high regulatory accuracy. miRNA can be involved in the occurrence and development of almost all tumors including colorectal cancer, and they are highly closely related to the initiation of tumor inhibition, tumor proliferation, tumor invasion and metastasis, tumor colonization and heterotopic, tumor angiogenesis [ 11 , 12 ]. Although there have been many studies on miRNA molecules and colorectal cancer, due to the "one-to-many" characteristics of molecular regulation of miRNA, many different miRNA molecules play roles in the same type of tumor. Even the same miRNA in the same type of tumor may play different roles by targeting and regulating different genes. Therefore, there are still broad research prospects in the field of miRNA. In this study, the expression level of RUNX1 in colorectal cancer tissues was analyzed based on bioinformatic methods. The effect of RUNX1 RNA expression on the prognosis of colorectal cancer patients was further investigated, and its correlation with the clinical data of CRC patients and its biological function were analyzed. Finally, miRNA that may be directly involved in the negative regulation of RUNX1 were discussed and studied to provide a new therapeutic target for the early intervention and targeted treatment of CRC. 2 Materials and methods 2.1 Profiling of RUNX1 expression data mRNA expression profiles across various tissues were obtained from the TCGA database and the Genotype-Tissue Expression (GTEx) project, which serve as valuable resources for studying gene expression. The Gene Expression Profiling Interactive Analysis (GEPIA) web server http://gepia.cancer-pku.cn/ , a tool for analyzing RNA-Seq data derived from TCGA and GTEx, was utilized to explore the expression patterns and correlations of RUNX1 across different tissues and cancer types.The expression levels of RUNX1 in distinct stages and subtypes of liver cancer were assessed using both GEPIA and the UCSC Xena project, which has recalculated all raw expression data from TCGA. Additionally, GEPIA was employed to analyze the prognostic significance of RUNX1 expression in colorectal cancer (CRC) tissues [ 13 ].The relationship between RUNX1 mRNA levels and DNA copy number alterations in liver cancer cell lines was investigated using data from the Cancer Cell Line Encyclopedia (CCLE) project. Information regarding RUNX1 gene alterations was accessed through cBioPortal OncoPrint http://www.cBioPortal.org/index.do , a platform that provides comprehensive genomic data on cancer cell lines[ 14 ]. 2.2 Prognostic nomogram constructed using RUNX1 expression analysis in patients with CRC Expression levels of RUNX1 in colorectal cancer (CRC) and adjacent non-cancerous tissues were analyzed using patient data from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) dataset GSE17536 [ 15 ]. Variables were examined and transformed for evaluation in a Cox proportional hazards regression model, and survival analysis was conducted to assess clinical outcomes in CRC patients.Overall survival (OS) and relapse-free survival (RFS) were defined consistent with a previously published study [ 16 ]. To determine the prognostic role of RUNX1 in CRC, 177 tumor specimens were collected from consecutive CRC tumor resections performed between August 6, 2009, and the final follow-up day on August 3, 2020. Tumor stages were assessed using the 2010 International Union Against Cancer Tumor-Node-Metastasis (TNM) classification system [ 17 ], with curative resection defined as previously described [ 18 ].Clinicopathological data for the CRC patients were retrieved from hospital medical records. Survival data were obtained from the Social Security Death Index, telephone interviews, and medical records [ 19 ]. 2.3 GO and KEGG analysis For the analysis of RUNX1 gene co-expression in colorectal cancer (CRC), the TCGA Provisional dataset was selected and further analyzed using the Co-Expression functions of cBioPortal [ 14 , 20 ] and LinkedOmics [ 21 ]. Genes exhibiting a Spearman correlation coefficient greater than 0.5 with RUNX1 were identified as co-expressed genes.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the identified co-expressed genes using DAVID Bioinformatics Resources v6.7 [ 22 ]. A P-value cut-off of less than 0.05 was applied to filter for significant functional and pathway enrichment.Protein-protein interaction (PPI) networks for the targets were constructed using the STRING database [ 23 ]. The resulting PPI network was visualized using Cytoscape software. 2.4 Analysis of miRNAs associated with RUNX1 expression in CRC The LinkedOmics platform [ 21 ] was utilized for the discovery, comparison, and understanding of associations within and across omics datasets related to RUNX1 expression in colorectal cancer (CRC). miRNAs associated with RUNX1 expression were identified using databases such as LinkedOmics and TargetScan. Overlapping miRNAs related to RUNX1 expression across different datasets were determined using Venny 2.1.0 software [ 25 ] http://bioinfogp.cnb.csic.es/tools/venny/.Kaplan-Meier plotter analysis [ 24 ] http://kmplot.com/ was conducted to assess the correlation between miRNA expression and the overall survival (OS) of CRC patients. The optimal cutoff for miRNA expression was automatically selected based on the analysis, which included 379 CRC patients. The relationship between miRNA expression and the clinicopathological features of these patients was analyzed using the LinkedOmics database. The false discovery rate (FDR) was calculated using the Benjamini-Hochberg method. 2.5 Cell culture and transfection Human colorectal cancer (CRC) cell lines HCT-116, HT-29, SW480, SW620, and SW1116 were obtained from the American Type Culture Collection (ATCC, USA). HCT-116 and HT-29 cells were cultured in Dulbecco's modified Eagle's medium (DMEM, Invitrogen, Carlsbad, CA, USA), supplemented with 10% fetal bovine serum (FBS, Invitrogen). The remaining cell lines were cultured in McCoy's 5A medium (Invitrogen), also supplemented with 10% FBS. All cells were maintained in a humidified incubator with 5% CO2 at 37°C. Transfection of the cells was carried out using Lipofectamine 2000 (Invitrogen) following the manufacturer's protocols. The vectors used for transfection were synthesized by GenePharma (Shanghai, China) and included the miR-200a-3p mimic and a mimic negative control (mimic NC). Cells transfected solely with the transfection reagent were designated as the mock group. 2.6 Quantitative real-time polymerase chain reaction (qRT-PCR) Total RNA from the cells was extracted using Trizol Reagent (Invitrogen, Carlsbad, CA, USA) and subsequently used to synthesize complementary DNA (cDNA) with the PrimeScript RT reagent Kit (TaKaRa), following the manufacturer's instructions. Quantitative polymerase chain reaction (qPCR) was performed to quantify the expression levels of miR-200a-3p and RUNX1 mRNA using the SYBR Green I Master Mix kit (Invitrogen) and the 7500 Real-Time PCR System (Applied Biosystems, USA). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and U6 small nuclear RNA (snRNA) served as the internal controls for RUNX1 and miR-200a-3p expression, respectively. The relative expression levels were determined using the 2^ −ΔΔ Ct method and normalized to the respective endogenous control genes. 2.7 Luciferase reporter assay A luciferase reporter assay was employed to validate the interaction between RUNX1 and miR-200a-3p. The 3'-untranslated regions (3'-UTRs) of the wild type (WT) and mutant type (MT) of RUNX1 were cloned into firefly luciferase reporter vectors containing Renilla luciferase (Promega, Madison, WI, USA). Subsequently, these vectors were co-transfected with either the miR-200a-3p mimic or mimic negative control (mimic NC) into tumor cells using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). Luciferase activity was measured 48 hours post-transfection using the SecrePair Dual-Luciferase Reporter System (Promega Corporation, Mannheim, Germany). 2.8 Statistical analysis Differences in mRNA expression levels of genes between colorectal cancer (CRC) tissues and adjacent normal tissues were assessed using Student's t-test. The correlation between gene expressions was evaluated with Spearman's correlation coefficient. Kaplan-Meier survival analysis was conducted to estimate survival percentages, and differences in these percentages were compared using the generalized log-rank test. The diagnostic significance of RUNX1 was determined by constructing a receiver operating characteristic (ROC) curve. Stratified analyses of RUNX1 expression levels were performed to assess the relative hazard ratios for the prognosis of CRC patients.All statistical analyses were conducted using two-tailed tests and were executed with SPSS version 21.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 6.0 (GraphPad Software, Inc., San Diego, CA, USA). A p-value of less than 0.05 was considered to indicate statistical significance. 3 Results 3.1 Expression and distribution of RUNX1 in human tumor tissues By mining the Oncomine data, we found that RUNX1 mRNA expression was up-regulated in multiple types of cancer (blue represents low RUNX1 expression in the corresponding tumor, red represents high RUNX1 expression, Fig. 1 A). In addition, based on The Cancer Genome Atlas (TCGA), we found that RUNX1 expression was significantly upregulated in colorectal cancer tissues (n = 286) compared with normal tissues (n = 41) (red for tumor tissue, blue for normal tissue, Fig. 1 B). 3.2 Prognostic analysis of RUNX1 gene expression in colorectal cancer In this study, GEPIA database was used to assess the association between RUNX1 mRNA levels and overall survival (OS) and disease-free survival (DFS) in CRC patients. According to RUNX1 mRNA levels, CRC samples were divided into high expression group and low expression group (> 50% for high expression group and < 50% for low expression group). The results showed that RUNX1 mRNA levels were significantly associated with overall survival (P = 0.029, Fig. 2 A) and disease-free survival (P = 0.0082, Fig. 2 B) in CRC patients. 3.3 Relationship between RUNX1 expression and clinical pathological characteristics of CRC patients We searched GEO database and comprehensively analyzed the basic information and clinical features of 177 primary CRC patients with GSE17536 data. The group was grouped according to the median RUNX1 expression value ( 9.52 for high expression group). Among 177 CRC patients, 92 cases had high RUNX1 expression and 85 cases had low RUNX1 expression. Among 177 CRC patients, 92 cases had high RUNX1 expression and 85 cases had low RUNX1 expression. Correlation analysis (Chi-square test) was conducted between RUNX1 expression value and corresponding clinicopathological data of CRC patients (including age, gender, degree of differentiation and TNM stage, etc.). The results showed that RUNX1 expression was closely correlated with TNM pathological stage of CRC patients (P 0.05) (Table 1 ). Univariate analysis showed that the degree of tumor differentiation, TNM stage and RUNX1 expression level were correlated with the prognosis of patients with colorectal cancer (Table 2 ). Multivariate Cox regression model uses HR value as risk assessment parameter. The results of multivariate analysis showed that TNM staging and RUNX1 expression were correlated with the prognosis of colorectal cancer patients (Table 3 ). Cox regression analysis showed that high expression of RUNX1 mRNA (HR: 2.198, 95%CI: [1.200, 4.027]) (Table 3 ) could be an independent risk factor for survival in patients with colorectal cancer. Kaplan-Meier curve analysis showed that OS, DFS and DSS of colorectal cancer patients in the high RUNX1 RNA expression group were significantly lower than those in the low expression group, with statistical significance (P < 0.05) (Fig. 3 ). Table 1 Analysis of the relationship between RUNX1 expression and clinical characteristics of CRC patients (GSE17536) Characteristics RUNX1 expression P -value Low (n) High (n) Age (y) ≤ 55 14 22 0.26 >55 71 70 Gender Male 45 51 0.76 Female 40 41 Grade 1/2 74 75 0.53 3 11 16 TNM stage I/II 46 35 0.03 III/IV 39 57 Chi-square test analysis (P < 0.05). Table 2 Univariate Cox regression analysis of the relationship between RUNX1 expression and overall survival of colorectal cancer Characteristics Univariate Analysis HR (95% CI) P -value Age ≤ 55 vs.>55 0.758(0.412, 1.395) 0.373 Gender Male vs. female 0.843(0.491, 1.446) 0.534 RUNX1 Low vs. high 2.654(1.466, 4.806) 0.001 Grade 1/2 vs. 3 2.389(1.277, 4.468) 0.006 TNM stage I/II vs. III/IV 5.473(2.673, 11.206) 0.001 Cox proportional hazard regression analysis (P < 0.05); HR: hazard ratio; CI: confidence interval. Table 3 Multivariate Cox regression analysis of the relationship between RUNX1 expression and overall survival of colorectal cancer Characteristics Multivariate Analysis HR (95% CI) P -value Age (y) ≤ 55 vs.>55 1.096(0.578, 2.080) 0.779 Gender Male vs. female 1.053(0.601 1.844) 0.857 RUNX1 Low vs. high 2.198(1.200, 4.027) 0.011 Grade 1/2 vs. 3 1.787(0.941, 3.392) 0.076 TNM stage I/II vs. III/IV 4.628(2.226, 9.623) 0.001 Cox proportional hazard regression analysis (P < 0.05); HR: hazard ratio; CI: confidence interval. 3.4 Cluster analysis of RUNX1 co-expressed genes and construction of protein interaction network By mining the LinkedOmics database, 379 samples of correlation omics from CRC were queried and the differential genes were analyzed (Fig. 4 A). We obtained the top 50 genes that are significantly positively correlated with RUNX1 expression (Fig. 4 B) and the top 50 genes that are negatively correlated with RUNX1 expression (Fig. 4 C). Then, we mined the cBioPortal and LinkedOmics database, and selected the overlapped genes with Spearman correlation coefficient greater than 0.5 as the RUNX1 co-expressed genes. The results of Venn diagram showed that 55 co-expressed genes were obtained (Fig. 4 D). The genes, which always have similar expression changes in a physiological process or different tissues, can be considered that these genes are functionally related. In order to preliminarily explore the potential molecular mechanism of RUNX1 in CRC, we performed functional cluster analysis of 55 genes co-expressed with RUNX1 to predict the function of RUNX1. The Metascape database was used for GO and KEGG enrichment analysis (Fig. 4 F, Table 4 ). Through GO analysis, it was found that RUNX1 co-expressed genes were mainly involved in a variety of biological processes, including development and growth, differentiated cell morphogenesis, extracellular matrix tissue construction, and wound healing response. KEGG analysis showed that RUNX1 co-expressed genes were mainly involved in adhesion plaques and adhesion junctions signaling pathways. Meanwhile, the protein-protein interaction network (PPI network) was constructed by analyzing the 55 protein genes in STRING database. The results showed that RUNX1 directly interacted with TRPS1, ELK3, FGFR1, BICC1, and ZNF521 proteins (Fig. 4 E). Table 4 GO and KEGG pathways enrichment analysis of RUNX1 co-expressed genes GO Category Description Count Log10(P) Log10(q) GO:0048589 GO BP developmental growth 15 -10.76 -6.44 GO:0000904 GO BP cell morphogenesis involved in differentiation 15 -10.12 -6.1 GO:0030198 GO BP extracellular matrix organization 11 -9.22 -5.38 GO:0001501 GO BP skeletal system development 12 -8.75 -5.13 GO:0007423 GO BP sensory organ development 11 -7.44 -4.16 GO:0048729 GO BP tissue morphogenesis 11 -6.54 -3.34 GO:0001568 GO BP blood vessel development 11 -5.97 -2.93 GO:0034329 GO BP cell junction assembly 7 -5.87 -2.89 GO:0009611 GO BP response to wounding 10 -5.54 -2.73 GO:0001503 GO BP ossification 8 -5.46 -2.68 hsa04510 KEGG Pathway Focal adhesion 6 -5.25 -2.5 hsa04520 KEGG Pathway Adherens junction 4 -4.67 -2.01 GO:0097435 GO BP supramolecular fiber organization 9 -4.62 -1.98 GO:1905114 GO BP cell surface receptor signaling pathway involved in cell-cell signaling 8 -4.1 -1.62 3.5 Prediction of miRNA targeting negative regulation of RUNX1 By mining 379 CRC-related RNAseq omics samples from LinkedOmics database that were positively and negatively correlated with RUNX1 (P < 0.001) (Fig. 5 A), 193 miRNAs were negatively correlated with RUNX1 expression (Spearman correlation analysis). The gene heat map showed the top 50 miRNAs that were significantly positively correlated with RUNX1 expression (Fig. 5 B) and the top 50 miRNA that were negatively correlated with RUNX1 expression for subsequent analysis (Fig. 5 C). Then, the upstream miRNA with RUNX1 as the regulatory target genes were predicted by retrieving "TargetScan" databases. Overlapped miRNAs in "TargetScan", "ENCORI" and "miRDB" databases were analyzed by intersection, and 30 overlapped miRNAs were finally obtained (Fig. 5 D). Then, the intersection of 30 overlapping miRNAs and the top 50 miRNAs that were significantly negatively correlated with RUNX1 expression was selected to obtain 6 miRNAs, which were miR-200a, miR-18b, miR-192, miR-141, miR-215, and miR-18a (Fig. 5 E). By analyzing the information of binding sites and scores in the database, we speculated that miR-200a-3p might be the miRNA gene upstream regulated by RUNX1, which needs further experimental verification. 3.6 Relationship between miR-200a-3p expression and clinical characteristics and prognosis of CRC patients In this study, 286 CRC-related RNAseq samples from the TCGA database were used to analyze the relationship between miR-200a-3p expression and clinical characteristics and prognosis of CRC patients (Table 5 ). The results showed that the expression of miR-200a-3p was significantly correlated with T stage (P = 0.03) and M stage (P = 0.026). Meanwhile, prognostic analysis of miR-200a-3p expression in CRC patients suggested that low expression of miR-200a-3p was significantly associated with poor prognosis in CRC patients (P = 0.02) (Fig. 6 ). Table 5 Relationship between miR-200a-3p expression and clinical characteristics of patients with CRC Characteristics N miR-200a-3p level Low High P -value Age(y) ≤ 55 66 30 36 0.4 >55 219 113 106 T stage T0-2 51 18 33 0.03 T3-4 234 124 110 N stage N0 156 71 85 0.3 N1 78 41 37 N2 49 28 21 M stage M0 201 94 107 0.026 M1 34 23 11 Stage I-II 146 66 80 0.14 III-IV 124 68 56 Tumor location Proximal 210 103 107 0.58 Distal 71 38 33 Chi-square test analysis (P < 0.05). 3.7 Relationship between miR-200a-3p and RUNX1 in CRC cell lines In addition, the effect of miR-200a-3p on the expression of RUNX1 was further confirmed in CRC cells. HCT116, HT29, SW480 and SW620 CRC cell lines were used in the current study. As shown in Fig. 7 A and 7 B, we observed that the expression of RUNX1 was significantly upregulated, while the expression of miR-200a-3p was remarkably downregulated. These results were consistent with the data obtained from bioinformatics analyses. To verify that the RUNX1 was a directly target gene of miR-200a-3p, luciferase reporter assay was performed in the SW1116, which had the significant differential expression of RUNX1 and miR-200a-3p. The overexpression of miR-200a-3p by miR-200a-3p mimic could decreased the luciferase activity of WT 3'-UTR of RUNX1 (P < 0.01), but no effect was observed in the RUNX1-MT group (Fig. 7 C-D), indicating that there was a direct interaction between miR-200a-3p and RUNX1. Moreover, the expression of RUNX1 was suppressed in the cells with overexpression of miR-200a-3p (Fig. 7 E), which further implied that miR-200a-3p targeted negative regulation of RUNX1. 4 Discussion The existing body of literature has consistently demonstrated that the RUNX1 gene exhibits elevated levels of expression across a spectrum of malignancies, correlating with an unfavorable prognosis in various human cancers [ 25 – 27 ]. Recent scholarly inquiries have discovered that RUNX1 has the capacity to enhance the proliferation of leukemia cells, underscoring its multifaceted roles in diverse hematological malignancies. As the depth of research progresses, it has become increasingly evident that RUNX1's influence extends beyond hematological neoplasms, with significant implications for solid tumors as well. A plethora of contemporary studies have illuminated the dualistic nature of RUNX1, suggesting that it may either stimulate or suppress tumor cell proliferation, survival, and differentiation within the context of various solid tumors, through the modulation of genes pertinent to tumorigenesis [ 9 , 10 ]. Nonetheless, the extant literature is somewhat sparse in terms of elucidating the diagnostic and prognostic implications of RUNX1 in colorectal cancer (CRC), as well as the underlying molecular mechanisms through which RUNX1 contributes to the etiology of CRC. This investigation marks the pioneering effort to systematically assess the clinical relevance of RUNX1 in CRC, utilizing a comprehensive analysis of the GEPIA database, the GSE17536 dataset, and RNA-seq data sourced from The Cancer Genome Atlas (TCGA). The findings reveal a notably elevated expression of RUNX1 in CRC samples within the aforementioned repositories. These observations collectively imply that RUNX1 could be a pivotal factor in the pathogenesis of CRC. Emerging research has identified that the RUNX1 transcription factor is capable of binding to coactivators of transcription, which are integral regulatory proteins within signaling pathways, thereby exerting control over the transcriptional regulation of a spectrum of genes [ 28 ]. The induction of RUNX1 expression by IL-1β is hypothesized to be orchestrated by the P38 mitogen-activated protein kinase (MAPK) signaling molecule, which in turn orchestrates the expression of a cadre of molecules pivotal for invasion and angiogenesis, including matrix metalloproteinases (MMP-1, MMP-2, MMP-9, MMP-19) and vascular endothelial growth factor A (VEGFA) [ 29 ]. To elucidate the functional role of RUNX1 in colorectal cancer (CRC), we undertook a comprehensive bioinformatics analysis, employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methodologies, to scrutinize genes co-expressed with RUNX1. Our findings underscore the multifaceted involvement of RUNX1 in a plethora of biological processes, such as cell morphogenesis integral to differentiation, the orchestration of the extracellular matrix, the development of the skeletal system, the assembly of cell junctions, and the formation of focal adhesions. Furthermore, RUNX1 has been implicated in the modulation of several signaling pathways, with a notable role in the regulation of adherens junctions. To delve deeper into the clinical ramifications of RUNX1 in colorectal cancer (CRC), our analysis of datasets from GSE17536 and The Cancer Genome Atlas (TCGA) has uncovered a correlation between RUNX1 expression and the malignancy of CRC phenotypes, encompassing variables such as age, gender, tumor grade, TNM stage, and patient survival. Our findings indicate that elevated RUNX1 expression is significantly and positively associated with the advanced TNM pathological stage of CRC (P < 0.05). Prior research has intimated that RUNX1 could be instrumental in the facilitation of CRC cell metastasis and recurrence. In alignment with our preceding analysis, subsequent Kaplan-Meier survival analysis has illuminated that CRC patients exhibiting RUNX1 expression levels above the median are significantly linked to a diminished overall survival (OS) and relapse-free survival (RFS). Consequently, our data collectively suggest that RUNX1 could function as an oncoprotein, exerting a pivotal influence in the neoplastic transformation of CRC. MicroRNAs (miRNAs) have emerged as pivotal biomarkers and therapeutic targets in the pathology of various conditions, notably in the realm of oncology [ 30 ]. This novel class of short noncoding RNAs exerts post-transcriptional regulatory effects and is intricately involved in a multitude of physiological and pathological processes. Consequently, the identification of miRNAs is deemed essential for the advancement of therapeutic strategies for colorectal cancer (CRC). Accumulating evidence has underscored the functional roles of several miRNAs in CRC, with miR-200a-3p being a prominent example [ 31 – 33 ]. Ubiquitously expressed across various tissues, miR-200a-3p plays a significant role in vital life processes and has been increasingly implicated in the pathogenesis of numerous cancers.Previous studies have reported a marked downregulation of miR-200a-3p in CRC cell lines and clinical tissues [ 32 ]. In the present investigation, we observed a diminished expression level of miR-200a-3p in human CRC tissues relative to adjacent non-CRC tissues, suggesting a functional role for miR-200a-3p in the progression of CRC. Furthermore, we discovered an inverse correlation between RUNX1 expression and miR-200a-3p levels. Computational predictions have identified potential binding sites for RUNX1 mRNA on miR-200a-3p, suggesting that RUNX1 may serve as a target of miR-200a-3p. Additionally, elevated miR-200a-3p expression was found to be significantly associated with age at diagnosis, pathological stage, T stage, and overall survival in CRC patients. Ultimately, Kaplan-Meier survival analysis revealed that diminished miR-200a-3p expression is significantly linked to a reduction in overall survival among CRC patients. miR-200a-3p, which is downregulated in CRC, has been shown to exert an anti-proliferative effect on CRC cells both in vitro and is correlated with TNM stage and differentiation grade.In summary, the findings of the current bioinformatics analysis study underscore a potential association between RUNX1 expression and miR-200a-3p, highlighting their interplay in the pathophysiology of CRC. In colorectal cancer (CRC) cells, the levels of RUNX1 and miR-200a-3p were assessed. Elevated miR-200a-3p was found to diminish RUNX1 expression, suggesting its role as a repressive miRNA in colorectal cancer (CRC). This finding corroborated with bioinformatics data. Furthermore, RUNX1 was validated as a direct target of miR-200a-3p, evidenced by diminished luciferase activity and reduced RUNX1 levels in miR-200a-3p-overexpressing cells. Analysis of colorectal cancer (CRC) tissues revealed higher RUNX1 expression compared to normal tissue, correlating with poor prognosis and offering potential for personalized CRC management. This study utilized qRT-PCR to measure RUNX1 and miRNA-200a-3p in CRC cell lines, identifying a negative correlation between their levels. The dual-luciferase assay confirmed RUNX1 as a direct target of miRNA-200a-3p's negative regulation. Clinical data analysis linked RUNX1 expression to CRC's TNM staging, with high RNA levels of RUNX1 emerging as an independent risk factor for patient survival. RUNX1 mRNA levels significantly predict overall, disease-free, and disease-specific survival in CRC, suggesting its utility in prognostic evaluation. Elevated RUNX1 expression may also serve as a novel biomarker for rectal cancer, guiding individualized CRC diagnosis and therapy. 5 Conclusions Elevated RUNX1 expression in colorectal cancer (CRC) tissues is oncogenic, with its upregulation linked to adverse patient outcomes and serving as a prognostic biomarker. RUNX1 is a direct target of miR-200a-3p's negative regulation, positioning it as a potential therapeutic target for CRC. Abbreviations CRC, colorectal cancer; mRNA, messenger RNA; RNA, ribonucleic acid; PBS, phosphate buffered saline; DMSO, dimethyl sulfoxide; DMEM, Dulbecco’s minimum essential medium; miRNA, microRNAs; FBS, fetal bovine serum; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; OS, overall survival; RFS, relapse free survival; RUNX1, Runt-related transcription factor 1; OD, optical density; kD, kilodalton; LB, lysogeny broth; DEPC, diethylpyrocarbonate; PCR, polymerase chain reaction; RT-PCR, reverse transcriptase PCR; UTR, untranslated Region. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are available within the article. Competing Interests The authors declare no competing interests. Funding This research was supported by the 2022 &2023 Hebei introduction of foreign expert intelligence projects [NO. YZ202201, YZ202305], Hebei Natural Science Foundation [NO. H2020206374, H2021206306], Hebei clinical medicine excellent talents project of Province [NO. LS202001] and Hebei Provincial Health Commission Medical Science Research Project [NO.20240925]. The funding sources were not involved in the study design, the collection, analysis, and interpretation of data; in the writing of the report, neither in the decision to submit the article for publication. Authors' contributions Xingkai Su, Xia Jiang and Fangjian Shang conducted and completed the data analysis and manuscript writing. Yingchao Gao, Jianwei Ma, Mei Wang and Haobo Wang completed the literature retrieval and data mining. Yuanyuan Wang and Zengren Zhao provided some good suggestions and supervision. All authors contributed to the article and approved the submitted version. Acknowledgements We wish to extend our thanks to any individual for their assistance in the conduction of this study. References Miller, K.D., et al., Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin, 2022. 72 (5): p. 409-436. Gunter, M.J., et al., Meeting report from the joint IARC-NCI international cancer seminar series: a focus on colorectal cancer. Ann Oncol, 2019. 30 (4): p. 510-519. Johnson, C.M., et al., Meta-analyses of colorectal cancer risk factors. Cancer Causes Control, 2013. 24 (6): p. 1207-22. Moore, S.C., et al., Association of Leisure-Time Physical Activity With Risk of 26 Types of Cancer in 1.44 Million Adults. JAMA Intern Med, 2016. 176 (6): p. 816-25. Labianca, R., et al., Primary colon cancer: ESMO Clinical Practice Guidelines for diagnosis, adjuvant treatment and follow-up. Ann Oncol, 2010. 21 Suppl 5 : p. v70-7. de Bruijn, M. and E. Dzierzak, Runx transcription factors in the development and function of the definitive hematopoietic system. Blood, 2017. 129 (15): p. 2061-2069. Kayed, H., et al., Regulation and functional role of the Runt-related transcription factor-2 in pancreatic cancer. Br J Cancer, 2007. 97 (8): p. 1106-15. Whittle, M.C., et al., RUNX3 Controls a Metastatic Switch in Pancreatic Ductal Adenocarcinoma. Cell, 2015. 161 (6): p. 1345-60. Li, P. and X.Y. Jia, MicroRNA-18-5p inhibits the oxidative stress and apoptosis of myocardium induced by hypoxia by targeting RUNX1. Eur Rev Med Pharmacol Sci, 2022. 26 (2): p. 432-439. Romanova, E.I., et al., RUNX1/CEBPA Mutation in Acute Myeloid Leukemia Promotes Hypermethylation and Indicates for Demethylation Therapy. Int J Mol Sci, 2022. 23 (19). 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Cerami, E., et al., The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov, 2012. 2 (5): p. 401-4. Vasaikar, S.V., et al., LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res, 2018. 46 (D1): p. D956-d963. Huang da, W., B.T. Sherman, and R.A. Lempicki, Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 2009. 37 (1): p. 1-13. Szklarczyk, D., et al., STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res, 2015. 43 (Database issue): p. D447-52. Lánczky, A., et al., miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat, 2016. 160 (3): p. 439-446. Akhade, V.S., et al., Control of focal adhesion kinase activation by RUNX1-regulated miRNAs in high-risk AML. Leukemia, 2023. 37 (4): p. 776-787. Gialesaki, S., et al., RUNX1 isoform disequilibrium promotes the development of trisomy 21-associated myeloid leukemia. Blood, 2023. 141 (10): p. 1105-1118. Halperin, C., et al., Global DNA Methylation Analysis of Cancer-Associated Fibroblasts Reveals Extensive Epigenetic Rewiring Linked with RUNX1 Upregulation in Breast Cancer Stroma. Cancer Res, 2022. 82 (22): p. 4139-4152. Appleford, P.J. and A. Woollard, RUNX genes find a niche in stem cell biology. J Cell Biochem, 2009. 108 (1): p. 14-21. Sangpairoj, K., et al., RUNX1 Regulates Migration, Invasion, and Angiogenesis via p38 MAPK Pathway in Human Glioblastoma. Cell Mol Neurobiol, 2017. 37 (7): p. 1243-1255. Han, S., X. Chen, and L. Huang, The tumor therapeutic potential of long non-coding RNA delivery and targeting. Acta Pharm Sin B, 2023. 13 (4): p. 1371-1382. Di, Z., et al., Integrated Analysis Identifies a Nine-microRNA Signature Biomarker for Diagnosis and Prognosis in Colorectal Cancer. Front Genet, 2020. 11 : p. 192. Li, Y., Y. Lu, and Y. Chen, Long non-coding RNA SNHG16 affects cell proliferation and predicts a poor prognosis in patients with colorectal cancer via sponging miR-200a-3p. Biosci Rep, 2019. 39 (5). Shadbad, M.A., et al., A scoping review on the potentiality of PD-L1-inhibiting microRNAs in treating colorectal cancer: Toward single-cell sequencing-guided biocompatible-based delivery. Biomed Pharmacother, 2021. 143 : p. 112213. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4844859","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":353763660,"identity":"55bf8fcf-d51a-4215-b121-f6d80c68deb0","order_by":0,"name":"Xingkai Su","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingkai","middleName":"","lastName":"Su","suffix":""},{"id":353763661,"identity":"5c2c7902-a1b8-4606-b6fc-41ab5f4cd170","order_by":1,"name":"Xia Jiang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Jiang","suffix":""},{"id":353763662,"identity":"4908b7ae-9b90-4a26-878b-cc4f43702ca6","order_by":2,"name":"FangJian Shang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"FangJian","middleName":"","lastName":"Shang","suffix":""},{"id":353763663,"identity":"1ff62f53-5111-4fb8-8569-bf50d6e278f8","order_by":3,"name":"Yingchao Gao","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingchao","middleName":"","lastName":"Gao","suffix":""},{"id":353763664,"identity":"ea3743b8-875d-4a4d-bd01-0f37ec4b0d97","order_by":4,"name":"JianWei Ma","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"JianWei","middleName":"","lastName":"Ma","suffix":""},{"id":353763665,"identity":"c4c2ca18-2654-4455-8a66-09aedef010be","order_by":5,"name":"Mei Wang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Wang","suffix":""},{"id":353763666,"identity":"b654f2b6-e314-405c-8b21-bb475b986ec6","order_by":6,"name":"Haobo Wang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haobo","middleName":"","lastName":"Wang","suffix":""},{"id":353763667,"identity":"2b970e9c-6614-4cb6-bd6f-21c670277285","order_by":7,"name":"Yuanyuan Wang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Wang","suffix":""},{"id":353763668,"identity":"63406674-fbcf-4b9b-8a90-683627c1fcdc","order_by":8,"name":"Zengren Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCQST8TEDwwF0QfxamI1J1sImTZQW+dnNDx/+qLhjt72991h1QcWdaIMDzAdv8+DRwjjnmLGBxJlnyXPOnEu7PePMs9wNB9iSrfFpYZZIMJMwbDucLCGRY3abt+0wUAuPmTQ+LWwS6d8kEv8Btci/MSvm/QfSwv8NrxYeoOESBxsO20lI8Jgx8zaAbWHDqwXonmLDhmOHEyR4coyleY49y515mM3Ycg4eLfIz0jc+/FFz2F6C/YzhZ56aO7l9x5sf3niDRwsMJDbAmcxEKAcBeyLVjYJRMApGwUgEADkYTv3n1Mm9AAAAAElFTkSuQmCC","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zengren","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-08-02 00:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4844859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4844859/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66650222,"identity":"2f4c5b59-d532-4270-bc08-518e7e64de44","added_by":"auto","created_at":"2024-10-15 07:33:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":342946,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of RUNX1 in human tumor tissue and colorectal cancer. (A) Expression of RUNX1 in various tumor data sets. Blue indicates low expression of RUNX1 in the corresponding tumor, red indicates high expression, and gray indicates no data. (B) The RUNX1 mRNA expression levels in CRC primary tumor and normal tissues. red for tumor tissue, blue for normal tissue.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/0997b32d5a2de891b3ca7317.png"},{"id":66650217,"identity":"24b6d3e7-5443-4786-ad4e-aae21fb0d4da","added_by":"auto","created_at":"2024-10-15 07:33:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":417900,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between RUNX1 and clinical prognosis of patients with CRC.\u003cstrong\u003e \u003c/strong\u003eAnalysis of RUNX1 expression by median was associated with overall survival (OS) (P=0.029) and disease-free survival (RFS) in CRC patients (P=0.0082).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/2728ba2728ee0cbc8aac7ce2.png"},{"id":66650219,"identity":"4069e1c1-f162-4f55-8a9c-4d3b1074a252","added_by":"auto","created_at":"2024-10-15 07:33:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":287388,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between RUNX1 and clinical prognosis of patients with CRC (GSE17536). Patients with above-(red) and below-(green) median RUNX1 abundance had significantly different survival rates in CRC, according to the GSEA17536. The CRC patients with high RUNX1 expression had poorer prognoses value as measured by OS (A) (P＜0.01), DFS (B) (P＜0.01) and DSS (C) (P＜0.001).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/eb48415a2b41f106f3de7af1.png"},{"id":66650223,"identity":"11dcba15-3583-4e36-899a-bd6f27b7f44f","added_by":"auto","created_at":"2024-10-15 07:33:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1707285,"visible":true,"origin":"","legend":"\u003cp\u003eExpression cluster analysis of RUNX1 co-expressed genes and construction of protein interaction network. (A) Funnel chart display RUNX1 positively/negatively correlated significant target genes (P \u0026lt;0.0001). (B) RUNX1 positively correlated significant genes (top 50). (C) RUNX1 negatively correlated significant genes (top 50). (D) 55 genes with a spearman’s correlation greater than 0.5 were selected as RUNX1 co-expressed genes overlapping in \"cBioPortal\" and \"LinkedOmics\". (E) Construction of protein-protein interaction (PPI) network for RUNX1 is visualized by Cytoscape. (F) Expression cluster analysis of RUNX1 co-expressed genes and construction of protein interaction network by Metascape.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/54c8388fca3832e6a8a5cea8.png"},{"id":66650218,"identity":"e80aa4a3-302f-45dd-b338-33ed801ce512","added_by":"auto","created_at":"2024-10-15 07:33:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1341423,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of miRNA genes involved in regulating RUNX1 expression. (A) Funnel plot showing miRNAs positively and negatively correlated with RUNX1 (p\u0026lt;0.0001). (B) RUNX1 positively correlated significant miRNAs (top 50). (C) RUNX1 negatively correlated significant miRNAs (top 50). (D) The venny plot shows that 30 overlapping miRNAs in \"TargetScan\", \"ENCORI\" and \"miRDB\" interact with RUNX1, different color areas represented different datasets. (E) The venny plot shows that 6 overlapping miRNAs in 30 \"Common miRNAs\" and 50 RUNX1 negatively correlated significant miRNAs (top 50) in \"LinkedOmics\".\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/93a330d1862d31b1b59b746b.png"},{"id":66651769,"identity":"f272d084-d4a8-4a25-b505-a26d74f86c34","added_by":"auto","created_at":"2024-10-15 07:41:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":370804,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between miR-200a-3p and clinical prognosis of patients with CRC. Patients with above-(red) and below-(green) median hsa-miR-200a-3p abundance had significantly different survival rates in CRC, according to the TCGA.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/5a3b58210f0dcb73a8e52937.png"},{"id":66651770,"identity":"3a800ee9-2375-4403-aa25-0cecf52130a7","added_by":"auto","created_at":"2024-10-15 07:41:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1519952,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between miR-200a-3p and RUNX1 expression in CRC cells. (A) Expression of RUNX1 in CRC cells was higher than that in HT-29 cell (P\u0026lt;0.001). (B) Expression of miR-200a-3p was decreased in CRC cells compared with the HT-29 cell (P\u0026lt; 0.0001). (C) Complementary sequences for miR-200a-3p in 3'UTR of RUNX1. (D) Luciferase activity of Luc-3'UTR was significantly decreased by the over-expression of miR-200a-3p (P\u0026lt; 0.01). (E) The upregulation of miR-200a-3p in tumor cells resulted in reduced expression of RUNX1 (P\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/0997a39208c4e13700dbbd1f.png"},{"id":73422757,"identity":"8c9ee07c-e5b0-45a6-8668-eb84502db239","added_by":"auto","created_at":"2025-01-09 19:16:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7465632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4844859/v1/0dcfbcfa-7eac-4b51-bb87-4f4ccf678790.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering the miR-200a-3p/RUNX1 Axis: A Novel Oncogene Signature in Colorectal Cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the common malignant tumors with a high incidence and a sharp increase with age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to authoritative data statistics, CRC is the tumor with the third highest number of new cases and the second highest number of death cases worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The pathogenesis of CRC is complex, and a large number of risk factors are known to contribute to the development of it, such as age\u0026thinsp;\u0026gt;\u0026thinsp;60, family history of CRC, inflammatory bowel disease, obesity, and poor dietary habits (processed meat, low dietary fiber diet), etc. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, the genes known to cause CRC recurrence include human epidermal growth factor receptor 2 (HER-2), MET proto-oncogene (MET), KRAS proto-oncogene (KRAS) and epidermal growth factor receptor (EGFR) mutations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. At present, it is one of the directions of most cancer treatments to study the tumor from the gene level and find out the targeted therapeutic targets. In recent years, with the in-depth study of RUNX family, more and more relevant studies have been conducted in malignant tumors, and it is expected to become a new therapeutic target.\u003c/p\u003e \u003cp\u003eRUNX family genes are a class of nuclear transcription factors with conserved protein sequences, which play an important regulatory function in cell lineage-specific genes, cell differentiation and development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the human genome, the RUNX family consists of three members: RUNX1, RUNX2, and RUNX3. RUNX2 is highly expressed in pancreatic cancer cells and is also highly expressed in tumor-associated fibroblasts, while it is less expressed in normal pancreatic tissues. Studies have shown that RUNX2 can be involved in regulating the expression of extracellular matrix regulatory factors such as matrix metalloproteinases 1 (MMP1), thus affecting the tumor microenvironment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. RUNX3 plays a dual role in promoting tumor migration but inhibiting tumor cell proliferation in pancreatic cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, studies on the relationship between RUNX1 gene and cell apoptosis and chemotherapy resistance have gradually attracted attention [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. RUNX1 plays a dual role of carcinogenic or tumor suppressor in hematologic tumors. In addition, RUNX1 activates or inhibits related genes or signaling pathways in different solid tumors, thus playing a role in carcinogenesis or tumor suppression. The role of RUNX1 varies with different tumor types.\u003c/p\u003e \u003cp\u003eMicroRNA (miRNA) has attracted more and more attention due to its inherent timing and spatial characteristics and high regulatory accuracy. miRNA can be involved in the occurrence and development of almost all tumors including colorectal cancer, and they are highly closely related to the initiation of tumor inhibition, tumor proliferation, tumor invasion and metastasis, tumor colonization and heterotopic, tumor angiogenesis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although there have been many studies on miRNA molecules and colorectal cancer, due to the \"one-to-many\" characteristics of molecular regulation of miRNA, many different miRNA molecules play roles in the same type of tumor. Even the same miRNA in the same type of tumor may play different roles by targeting and regulating different genes. Therefore, there are still broad research prospects in the field of miRNA.\u003c/p\u003e \u003cp\u003eIn this study, the expression level of RUNX1 in colorectal cancer tissues was analyzed based on bioinformatic methods. The effect of RUNX1 RNA expression on the prognosis of colorectal cancer patients was further investigated, and its correlation with the clinical data of CRC patients and its biological function were analyzed. Finally, miRNA that may be directly involved in the negative regulation of RUNX1 were discussed and studied to provide a new therapeutic target for the early intervention and targeted treatment of CRC.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Profiling of RUNX1 expression data\u003c/h2\u003e \u003cp\u003emRNA expression profiles across various tissues were obtained from the TCGA database and the Genotype-Tissue Expression (GTEx) project, which serve as valuable resources for studying gene expression. The Gene Expression Profiling Interactive Analysis (GEPIA) web server \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, a tool for analyzing RNA-Seq data derived from TCGA and GTEx, was utilized to explore the expression patterns and correlations of RUNX1 across different tissues and cancer types.The expression levels of RUNX1 in distinct stages and subtypes of liver cancer were assessed using both GEPIA and the UCSC Xena project, which has recalculated all raw expression data from TCGA. Additionally, GEPIA was employed to analyze the prognostic significance of RUNX1 expression in colorectal cancer (CRC) tissues [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].The relationship between RUNX1 mRNA levels and DNA copy number alterations in liver cancer cell lines was investigated using data from the Cancer Cell Line Encyclopedia (CCLE) project. Information regarding RUNX1 gene alterations was accessed through cBioPortal OncoPrint \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cBioPortal.org/index.do\u003c/span\u003e\u003cspan address=\"http://www.cBioPortal.org/index.do\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, a platform that provides comprehensive genomic data on cancer cell lines[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Prognostic nomogram constructed using RUNX1 expression analysis in patients with CRC\u003c/h2\u003e \u003cp\u003eExpression levels of RUNX1 in colorectal cancer (CRC) and adjacent non-cancerous tissues were analyzed using patient data from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) dataset GSE17536 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Variables were examined and transformed for evaluation in a Cox proportional hazards regression model, and survival analysis was conducted to assess clinical outcomes in CRC patients.Overall survival (OS) and relapse-free survival (RFS) were defined consistent with a previously published study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To determine the prognostic role of RUNX1 in CRC, 177 tumor specimens were collected from consecutive CRC tumor resections performed between August 6, 2009, and the final follow-up day on August 3, 2020. Tumor stages were assessed using the 2010 International Union Against Cancer Tumor-Node-Metastasis (TNM) classification system [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], with curative resection defined as previously described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Clinicopathological data for the CRC patients were retrieved from hospital medical records. Survival data were obtained from the Social Security Death Index, telephone interviews, and medical records [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GO and KEGG analysis\u003c/h2\u003e \u003cp\u003eFor the analysis of RUNX1 gene co-expression in colorectal cancer (CRC), the TCGA Provisional dataset was selected and further analyzed using the Co-Expression functions of cBioPortal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and LinkedOmics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Genes exhibiting a Spearman correlation coefficient greater than 0.5 with RUNX1 were identified as co-expressed genes.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the identified co-expressed genes using DAVID Bioinformatics Resources v6.7 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A P-value cut-off of less than 0.05 was applied to filter for significant functional and pathway enrichment.Protein-protein interaction (PPI) networks for the targets were constructed using the STRING database [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The resulting PPI network was visualized using Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of miRNAs associated with RUNX1 expression in CRC\u003c/h2\u003e \u003cp\u003eThe LinkedOmics platform [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] was utilized for the discovery, comparison, and understanding of associations within and across omics datasets related to RUNX1 expression in colorectal cancer (CRC). miRNAs associated with RUNX1 expression were identified using databases such as LinkedOmics and TargetScan. Overlapping miRNAs related to RUNX1 expression across different datasets were determined using Venny 2.1.0 software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfogp.cnb.csic.es/tools/venny/.Kaplan-Meier\u003c/span\u003e\u003cspan address=\"http://bioinfogp.cnb.csic.es/tools/venny/.Kaplan-Meier\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e plotter analysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e was conducted to assess the correlation between miRNA expression and the overall survival (OS) of CRC patients. The optimal cutoff for miRNA expression was automatically selected based on the analysis, which included 379 CRC patients. The relationship between miRNA expression and the clinicopathological features of these patients was analyzed using the LinkedOmics database. The false discovery rate (FDR) was calculated using the Benjamini-Hochberg method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cell culture and transfection\u003c/h2\u003e \u003cp\u003e Human colorectal cancer (CRC) cell lines HCT-116, HT-29, SW480, SW620, and SW1116 were obtained from the American Type Culture Collection (ATCC, USA). HCT-116 and HT-29 cells were cultured in Dulbecco's modified Eagle's medium (DMEM, Invitrogen, Carlsbad, CA, USA), supplemented with 10% fetal bovine serum (FBS, Invitrogen). The remaining cell lines were cultured in McCoy's 5A medium (Invitrogen), also supplemented with 10% FBS. All cells were maintained in a humidified incubator with 5% CO2 at 37\u0026deg;C. Transfection of the cells was carried out using Lipofectamine 2000 (Invitrogen) following the manufacturer's protocols. The vectors used for transfection were synthesized by GenePharma (Shanghai, China) and included the miR-200a-3p mimic and a mimic negative control (mimic NC). Cells transfected solely with the transfection reagent were designated as the mock group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quantitative real-time polymerase chain reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA from the cells was extracted using Trizol Reagent (Invitrogen, Carlsbad, CA, USA) and subsequently used to synthesize complementary DNA (cDNA) with the PrimeScript RT reagent Kit (TaKaRa), following the manufacturer's instructions. Quantitative polymerase chain reaction (qPCR) was performed to quantify the expression levels of miR-200a-3p and RUNX1 mRNA using the SYBR Green I Master Mix kit (Invitrogen) and the 7500 Real-Time PCR System (Applied Biosystems, USA). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and U6 small nuclear RNA (snRNA) served as the internal controls for RUNX1 and miR-200a-3p expression, respectively. The relative expression levels were determined using the 2^\u003csup\u003e\u0026minus;ΔΔ\u003c/sup\u003eCt method and normalized to the respective endogenous control genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Luciferase reporter assay\u003c/h2\u003e \u003cp\u003eA luciferase reporter assay was employed to validate the interaction between RUNX1 and miR-200a-3p. The 3'-untranslated regions (3'-UTRs) of the wild type (WT) and mutant type (MT) of RUNX1 were cloned into firefly luciferase reporter vectors containing Renilla luciferase (Promega, Madison, WI, USA). Subsequently, these vectors were co-transfected with either the miR-200a-3p mimic or mimic negative control (mimic NC) into tumor cells using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). Luciferase activity was measured 48 hours post-transfection using the SecrePair Dual-Luciferase Reporter System (Promega Corporation, Mannheim, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eDifferences in mRNA expression levels of genes between colorectal cancer (CRC) tissues and adjacent normal tissues were assessed using Student's t-test. The correlation between gene expressions was evaluated with Spearman's correlation coefficient. Kaplan-Meier survival analysis was conducted to estimate survival percentages, and differences in these percentages were compared using the generalized log-rank test. The diagnostic significance of RUNX1 was determined by constructing a receiver operating characteristic (ROC) curve. Stratified analyses of RUNX1 expression levels were performed to assess the relative hazard ratios for the prognosis of CRC patients.All statistical analyses were conducted using two-tailed tests and were executed with SPSS version 21.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 6.0 (GraphPad Software, Inc., San Diego, CA, USA). A p-value of less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Expression and distribution of RUNX1 in human tumor tissues\u003c/h2\u003e \u003cp\u003eBy mining the Oncomine data, we found that RUNX1 mRNA expression was up-regulated in multiple types of cancer (blue represents low RUNX1 expression in the corresponding tumor, red represents high RUNX1 expression, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In addition, based on The Cancer Genome Atlas (TCGA), we found that RUNX1 expression was significantly upregulated in colorectal cancer tissues (n\u0026thinsp;=\u0026thinsp;286) compared with normal tissues (n\u0026thinsp;=\u0026thinsp;41) (red for tumor tissue, blue for normal tissue, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prognostic analysis of RUNX1 gene expression in colorectal cancer\u003c/h2\u003e \u003cp\u003eIn this study, GEPIA database was used to assess the association between RUNX1 mRNA levels and overall survival (OS) and disease-free survival (DFS) in CRC patients. According to RUNX1 mRNA levels, CRC samples were divided into high expression group and low expression group (\u0026gt;\u0026thinsp;50% for high expression group and \u0026lt;\u0026thinsp;50% for low expression group). The results showed that RUNX1 mRNA levels were significantly associated with overall survival (P\u0026thinsp;=\u0026thinsp;0.029, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and disease-free survival (P\u0026thinsp;=\u0026thinsp;0.0082, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) in CRC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Relationship between RUNX1 expression and clinical pathological characteristics of CRC patients\u003c/h2\u003e \u003cp\u003eWe searched GEO database and comprehensively analyzed the basic information and clinical features of 177 primary CRC patients with GSE17536 data. The group was grouped according to the median RUNX1 expression value (\u0026lt;\u0026thinsp;9.52 for low expression group, \u0026gt; 9.52 for high expression group). Among 177 CRC patients, 92 cases had high RUNX1 expression and 85 cases had low RUNX1 expression. Among 177 CRC patients, 92 cases had high RUNX1 expression and 85 cases had low RUNX1 expression. Correlation analysis (Chi-square test) was conducted between RUNX1 expression value and corresponding clinicopathological data of CRC patients (including age, gender, degree of differentiation and TNM stage, etc.). The results showed that RUNX1 expression was closely correlated with TNM pathological stage of CRC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no significant correlation with the gender and age of CRC patients (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnivariate analysis showed that the degree of tumor differentiation, TNM stage and RUNX1 expression level were correlated with the prognosis of patients with colorectal cancer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariate Cox regression model uses HR value as risk assessment parameter. The results of multivariate analysis showed that TNM staging and RUNX1 expression were correlated with the prognosis of colorectal cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cox regression analysis showed that high expression of RUNX1 mRNA (HR: 2.198, 95%CI: [1.200, 4.027]) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) could be an independent risk factor for survival in patients with colorectal cancer. Kaplan-Meier curve analysis showed that OS, DFS and DSS of colorectal cancer patients in the high RUNX1 RNA expression group were significantly lower than those in the low expression group, with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the relationship between RUNX1 expression and clinical characteristics of CRC patients (GSE17536)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRUNX1 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh (n)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026gt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIII/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChi-square test analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eUnivariate Cox regression analysis of the relationship between RUNX1 expression and overall survival of colorectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;55 vs.\u0026gt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.758(0.412, 1.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale vs. female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843(0.491, 1.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow vs. high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.654(1.466, 4.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/2 vs. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.389(1.277, 4.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI/II vs. III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.473(2.673, 11.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCox proportional hazard regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); HR: hazard ratio; CI: confidence interval.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analysis of the relationship between RUNX1 expression and overall survival of colorectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;55 vs.\u0026gt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.096(0.578, 2.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale vs. female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.053(0.601 1.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow vs. high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.198(1.200, 4.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/2 vs. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.787(0.941, 3.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI/II vs. III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.628(2.226, 9.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCox proportional hazard regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); HR: hazard ratio; CI: confidence interval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cluster analysis of RUNX1 co-expressed genes and construction of protein interaction network\u003c/h2\u003e \u003cp\u003eBy mining the LinkedOmics database, 379 samples of correlation omics from CRC were queried and the differential genes were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We obtained the top 50 genes that are significantly positively correlated with RUNX1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) and the top 50 genes that are negatively correlated with RUNX1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Then, we mined the cBioPortal and LinkedOmics database, and selected the overlapped genes with Spearman correlation coefficient greater than 0.5 as the RUNX1 co-expressed genes. The results of Venn diagram showed that 55 co-expressed genes were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The genes, which always have similar expression changes in a physiological process or different tissues, can be considered that these genes are functionally related.\u003c/p\u003e \u003cp\u003eIn order to preliminarily explore the potential molecular mechanism of RUNX1 in CRC, we performed functional cluster analysis of 55 genes co-expressed with RUNX1 to predict the function of RUNX1. The Metascape database was used for GO and KEGG enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Through GO analysis, it was found that RUNX1 co-expressed genes were mainly involved in a variety of biological processes, including development and growth, differentiated cell morphogenesis, extracellular matrix tissue construction, and wound healing response. KEGG analysis showed that RUNX1 co-expressed genes were mainly involved in adhesion plaques and adhesion junctions signaling pathways.\u003c/p\u003e \u003cp\u003eMeanwhile, the protein-protein interaction network (PPI network) was constructed by analyzing the 55 protein genes in STRING database. The results showed that RUNX1 directly interacted with TRPS1, ELK3, FGFR1, BICC1, and ZNF521 proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO and KEGG pathways enrichment analysis of RUNX1 co-expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog10(P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLog10(q)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0048589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edevelopmental growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0000904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecell morphogenesis involved in differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0030198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextracellular matrix organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0001501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eskeletal system development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0007423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esensory organ development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0048729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etissue morphogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0001568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eblood vessel development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0034329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecell junction assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0009611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eresponse to wounding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0001503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eossification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG Pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocal adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG Pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdherens junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0097435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esupramolecular fiber organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:1905114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecell surface receptor signaling pathway involved in cell-cell signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Prediction of miRNA targeting negative regulation of RUNX1\u003c/h2\u003e \u003cp\u003eBy mining 379 CRC-related RNAseq omics samples from LinkedOmics database that were positively and negatively correlated with RUNX1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), 193 miRNAs were negatively correlated with RUNX1 expression (Spearman correlation analysis). The gene heat map showed the top 50 miRNAs that were significantly positively correlated with RUNX1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and the top 50 miRNA that were negatively correlated with RUNX1 expression for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Then, the upstream miRNA with RUNX1 as the regulatory target genes were predicted by retrieving \"TargetScan\" databases. Overlapped miRNAs in \"TargetScan\", \"ENCORI\" and \"miRDB\" databases were analyzed by intersection, and 30 overlapped miRNAs were finally obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Then, the intersection of 30 overlapping miRNAs and the top 50 miRNAs that were significantly negatively correlated with RUNX1 expression was selected to obtain 6 miRNAs, which were miR-200a, miR-18b, miR-192, miR-141, miR-215, and miR-18a (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). By analyzing the information of binding sites and scores in the database, we speculated that miR-200a-3p might be the miRNA gene upstream regulated by RUNX1, which needs further experimental verification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Relationship between miR-200a-3p expression and clinical characteristics and prognosis of CRC patients\u003c/h2\u003e \u003cp\u003eIn this study, 286 CRC-related RNAseq samples from the TCGA database were used to analyze the relationship between miR-200a-3p expression and clinical characteristics and prognosis of CRC patients (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results showed that the expression of miR-200a-3p was significantly correlated with T stage (P\u0026thinsp;=\u0026thinsp;0.03) and M stage (P\u0026thinsp;=\u0026thinsp;0.026). Meanwhile, prognostic analysis of miR-200a-3p expression in CRC patients suggested that low expression of miR-200a-3p was significantly associated with poor prognosis in CRC patients (P\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between miR-200a-3p expression and clinical characteristics of patients with CRC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003emiR-200a-3p level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT0-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProximal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChi-square test analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Relationship between miR-200a-3p and RUNX1 in CRC cell lines\u003c/h2\u003e \u003cp\u003eIn addition, the effect of miR-200a-3p on the expression of RUNX1 was further confirmed in CRC cells. HCT116, HT29, SW480 and SW620 CRC cell lines were used in the current study. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, we observed that the expression of RUNX1 was significantly upregulated, while the expression of miR-200a-3p was remarkably downregulated. These results were consistent with the data obtained from bioinformatics analyses. To verify that the RUNX1 was a directly target gene of miR-200a-3p, luciferase reporter assay was performed in the SW1116, which had the significant differential expression of RUNX1 and miR-200a-3p. The overexpression of miR-200a-3p by miR-200a-3p mimic could decreased the luciferase activity of WT 3'-UTR of RUNX1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but no effect was observed in the RUNX1-MT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D), indicating that there was a direct interaction between miR-200a-3p and RUNX1. Moreover, the expression of RUNX1 was suppressed in the cells with overexpression of miR-200a-3p (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE), which further implied that miR-200a-3p targeted negative regulation of RUNX1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe existing body of literature has consistently demonstrated that the RUNX1 gene exhibits elevated levels of expression across a spectrum of malignancies, correlating with an unfavorable prognosis in various human cancers [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recent scholarly inquiries have discovered that RUNX1 has the capacity to enhance the proliferation of leukemia cells, underscoring its multifaceted roles in diverse hematological malignancies. As the depth of research progresses, it has become increasingly evident that RUNX1's influence extends beyond hematological neoplasms, with significant implications for solid tumors as well. A plethora of contemporary studies have illuminated the dualistic nature of RUNX1, suggesting that it may either stimulate or suppress tumor cell proliferation, survival, and differentiation within the context of various solid tumors, through the modulation of genes pertinent to tumorigenesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nonetheless, the extant literature is somewhat sparse in terms of elucidating the diagnostic and prognostic implications of RUNX1 in colorectal cancer (CRC), as well as the underlying molecular mechanisms through which RUNX1 contributes to the etiology of CRC. This investigation marks the pioneering effort to systematically assess the clinical relevance of RUNX1 in CRC, utilizing a comprehensive analysis of the GEPIA database, the GSE17536 dataset, and RNA-seq data sourced from The Cancer Genome Atlas (TCGA). The findings reveal a notably elevated expression of RUNX1 in CRC samples within the aforementioned repositories. These observations collectively imply that RUNX1 could be a pivotal factor in the pathogenesis of CRC.\u003c/p\u003e \u003cp\u003eEmerging research has identified that the RUNX1 transcription factor is capable of binding to coactivators of transcription, which are integral regulatory proteins within signaling pathways, thereby exerting control over the transcriptional regulation of a spectrum of genes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The induction of RUNX1 expression by IL-1β is hypothesized to be orchestrated by the P38 mitogen-activated protein kinase (MAPK) signaling molecule, which in turn orchestrates the expression of a cadre of molecules pivotal for invasion and angiogenesis, including matrix metalloproteinases (MMP-1, MMP-2, MMP-9, MMP-19) and vascular endothelial growth factor A (VEGFA) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To elucidate the functional role of RUNX1 in colorectal cancer (CRC), we undertook a comprehensive bioinformatics analysis, employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methodologies, to scrutinize genes co-expressed with RUNX1. Our findings underscore the multifaceted involvement of RUNX1 in a plethora of biological processes, such as cell morphogenesis integral to differentiation, the orchestration of the extracellular matrix, the development of the skeletal system, the assembly of cell junctions, and the formation of focal adhesions. Furthermore, RUNX1 has been implicated in the modulation of several signaling pathways, with a notable role in the regulation of adherens junctions.\u003c/p\u003e \u003cp\u003eTo delve deeper into the clinical ramifications of RUNX1 in colorectal cancer (CRC), our analysis of datasets from GSE17536 and The Cancer Genome Atlas (TCGA) has uncovered a correlation between RUNX1 expression and the malignancy of CRC phenotypes, encompassing variables such as age, gender, tumor grade, TNM stage, and patient survival. Our findings indicate that elevated RUNX1 expression is significantly and positively associated with the advanced TNM pathological stage of CRC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Prior research has intimated that RUNX1 could be instrumental in the facilitation of CRC cell metastasis and recurrence. In alignment with our preceding analysis, subsequent Kaplan-Meier survival analysis has illuminated that CRC patients exhibiting RUNX1 expression levels above the median are significantly linked to a diminished overall survival (OS) and relapse-free survival (RFS). Consequently, our data collectively suggest that RUNX1 could function as an oncoprotein, exerting a pivotal influence in the neoplastic transformation of CRC.\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) have emerged as pivotal biomarkers and therapeutic targets in the pathology of various conditions, notably in the realm of oncology [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This novel class of short noncoding RNAs exerts post-transcriptional regulatory effects and is intricately involved in a multitude of physiological and pathological processes. Consequently, the identification of miRNAs is deemed essential for the advancement of therapeutic strategies for colorectal cancer (CRC). Accumulating evidence has underscored the functional roles of several miRNAs in CRC, with miR-200a-3p being a prominent example [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Ubiquitously expressed across various tissues, miR-200a-3p plays a significant role in vital life processes and has been increasingly implicated in the pathogenesis of numerous cancers.Previous studies have reported a marked downregulation of miR-200a-3p in CRC cell lines and clinical tissues [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the present investigation, we observed a diminished expression level of miR-200a-3p in human CRC tissues relative to adjacent non-CRC tissues, suggesting a functional role for miR-200a-3p in the progression of CRC. Furthermore, we discovered an inverse correlation between RUNX1 expression and miR-200a-3p levels. Computational predictions have identified potential binding sites for RUNX1 mRNA on miR-200a-3p, suggesting that RUNX1 may serve as a target of miR-200a-3p. Additionally, elevated miR-200a-3p expression was found to be significantly associated with age at diagnosis, pathological stage, T stage, and overall survival in CRC patients. Ultimately, Kaplan-Meier survival analysis revealed that diminished miR-200a-3p expression is significantly linked to a reduction in overall survival among CRC patients. miR-200a-3p, which is downregulated in CRC, has been shown to exert an anti-proliferative effect on CRC cells both in vitro and is correlated with TNM stage and differentiation grade.In summary, the findings of the current bioinformatics analysis study underscore a potential association between RUNX1 expression and miR-200a-3p, highlighting their interplay in the pathophysiology of CRC.\u003c/p\u003e \u003cp\u003eIn colorectal cancer (CRC) cells, the levels of RUNX1 and miR-200a-3p were assessed. Elevated miR-200a-3p was found to diminish RUNX1 expression, suggesting its role as a repressive miRNA in colorectal cancer (CRC). This finding corroborated with bioinformatics data. Furthermore, RUNX1 was validated as a direct target of miR-200a-3p, evidenced by diminished luciferase activity and reduced RUNX1 levels in miR-200a-3p-overexpressing cells.\u003c/p\u003e \u003cp\u003eAnalysis of colorectal cancer (CRC) tissues revealed higher RUNX1 expression compared to normal tissue, correlating with poor prognosis and offering potential for personalized CRC management. This study utilized qRT-PCR to measure RUNX1 and miRNA-200a-3p in CRC cell lines, identifying a negative correlation between their levels. The dual-luciferase assay confirmed RUNX1 as a direct target of miRNA-200a-3p's negative regulation. Clinical data analysis linked RUNX1 expression to CRC's TNM staging, with high RNA levels of RUNX1 emerging as an independent risk factor for patient survival. RUNX1 mRNA levels significantly predict overall, disease-free, and disease-specific survival in CRC, suggesting its utility in prognostic evaluation. Elevated RUNX1 expression may also serve as a novel biomarker for rectal cancer, guiding individualized CRC diagnosis and therapy.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eElevated RUNX1 expression in colorectal cancer (CRC) tissues is oncogenic, with its upregulation linked to adverse patient outcomes and serving as a prognostic biomarker. RUNX1 is a direct target of miR-200a-3p's negative regulation, positioning it as a potential therapeutic target for CRC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRC, colorectal cancer; mRNA, messenger RNA; RNA, ribonucleic acid; PBS, phosphate buffered saline; DMSO, dimethyl sulfoxide; DMEM, Dulbecco\u0026rsquo;s minimum essential medium; miRNA, microRNAs; FBS, fetal bovine serum; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; OS, overall survival; RFS, relapse free survival; RUNX1, Runt-related transcription factor 1; OD, optical density; kD, kilodalton; LB, lysogeny broth; DEPC, diethylpyrocarbonate; PCR, polymerase chain reaction; RT-PCR, reverse transcriptase PCR; UTR, untranslated Region.\u003c/p\u003e"},{"header":"Declarations","content":"\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\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the 2022 \u0026amp;2023 Hebei introduction of foreign expert intelligence projects [NO. YZ202201, YZ202305], Hebei Natural Science Foundation [NO. H2020206374, H2021206306], Hebei clinical medicine excellent talents project of Province [NO. LS202001] and Hebei Provincial Health Commission Medical Science Research Project [NO.20240925]. The funding sources were not involved in the study design, the collection, analysis, and interpretation of data; in the writing of the report, neither in the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXingkai Su, Xia Jiang and Fangjian Shang\u0026nbsp;conducted and completed the data analysis and manuscript writing. Yingchao Gao, Jianwei Ma, Mei Wang and Haobo Wang completed the literature retrieval and data mining. Yuanyuan Wang and Zengren Zhao provided some good suggestions and supervision. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to extend our thanks to any individual for their assistance in the conduction of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMiller, K.D., et al., \u003cem\u003eCancer treatment and survivorship statistics, 2022.\u003c/em\u003e CA Cancer J Clin, 2022. \u003cstrong\u003e72\u003c/strong\u003e(5): p. 409-436.\u003c/li\u003e\n\u003cli\u003eGunter, M.J., et al., \u003cem\u003eMeeting report from the joint IARC-NCI international cancer seminar series: a focus on colorectal cancer.\u003c/em\u003e Ann Oncol, 2019. \u003cstrong\u003e30\u003c/strong\u003e(4): p. 510-519.\u003c/li\u003e\n\u003cli\u003eJohnson, C.M., et al., \u003cem\u003eMeta-analyses of colorectal cancer risk factors.\u003c/em\u003e Cancer Causes Control, 2013. \u003cstrong\u003e24\u003c/strong\u003e(6): p. 1207-22.\u003c/li\u003e\n\u003cli\u003eMoore, S.C., et al., \u003cem\u003eAssociation of Leisure-Time Physical Activity With Risk of 26 Types of Cancer in 1.44 Million Adults.\u003c/em\u003e JAMA Intern Med, 2016. \u003cstrong\u003e176\u003c/strong\u003e(6): p. 816-25.\u003c/li\u003e\n\u003cli\u003eLabianca, R., et al., \u003cem\u003ePrimary colon cancer: ESMO Clinical Practice Guidelines for diagnosis, adjuvant treatment and follow-up.\u003c/em\u003e Ann Oncol, 2010. \u003cstrong\u003e21 Suppl 5\u003c/strong\u003e: p. v70-7.\u003c/li\u003e\n\u003cli\u003ede Bruijn, M. and E. 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Woollard, \u003cem\u003eRUNX genes find a niche in stem cell biology.\u003c/em\u003e J Cell Biochem, 2009. \u003cstrong\u003e108\u003c/strong\u003e(1): p. 14-21.\u003c/li\u003e\n\u003cli\u003eSangpairoj, K., et al., \u003cem\u003eRUNX1 Regulates Migration, Invasion, and Angiogenesis via p38 MAPK Pathway in Human Glioblastoma.\u003c/em\u003e Cell Mol Neurobiol, 2017. \u003cstrong\u003e37\u003c/strong\u003e(7): p. 1243-1255.\u003c/li\u003e\n\u003cli\u003eHan, S., X. Chen, and L. Huang, \u003cem\u003eThe tumor therapeutic potential of long non-coding RNA delivery and targeting.\u003c/em\u003e Acta Pharm Sin B, 2023. \u003cstrong\u003e13\u003c/strong\u003e(4): p. 1371-1382.\u003c/li\u003e\n\u003cli\u003eDi, Z., et al., \u003cem\u003eIntegrated Analysis Identifies a Nine-microRNA Signature Biomarker for Diagnosis and Prognosis in Colorectal Cancer.\u003c/em\u003e Front Genet, 2020. \u003cstrong\u003e11\u003c/strong\u003e: p. 192.\u003c/li\u003e\n\u003cli\u003eLi, Y., Y. Lu, and Y. Chen, \u003cem\u003eLong non-coding RNA SNHG16 affects cell proliferation and predicts a poor prognosis in patients with colorectal cancer via sponging miR-200a-3p.\u003c/em\u003e Biosci Rep, 2019. \u003cstrong\u003e39\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eShadbad, M.A., et al., \u003cem\u003eA scoping review on the potentiality of PD-L1-inhibiting microRNAs in treating colorectal cancer: Toward single-cell sequencing-guided biocompatible-based delivery.\u003c/em\u003e Biomed Pharmacother, 2021. \u003cstrong\u003e143\u003c/strong\u003e: p. 112213.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, RUNX1, miR-200a-3p, Prognosis, Oncogene signature","lastPublishedDoi":"10.21203/rs.3.rs-4844859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4844859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe dual role of carcinogenic or tumor suppressor makes Runt related transcription factor 1 (RUNX1) a new diagnostic markers or therapeutic target for colorectal cancer (CRC). In CRC, the relationship between RUNX1 and prognosis, biological function, and potential microRNA directly involved in the regulation of RUNX1 are unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGene expression of RUNX1 in colorectal cancer (CRC) was comprehensively analyzed using data from The Cancer Genome Atlas (TCGA) and Oncomine databases. Kaplan-Meier survival curves were constructed to assess the clinical and prognostic status associated with RUNX1 expression in CRC patients. The correlation between clinical features and RUNX1 expression was analyzed in the GSE17536 dataset using the Chi-square test. The relationship between RUNX1 expression and overall survival (OS) in CRC was investigated through both univariate and multivariate Cox regression analyses. Genes co-expressed with RUNX1 were identified using Spearman correlation analysis. The potential functions of RUNX1 in CRC were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. MiRNAs that negatively regulate RUNX1 expression were identified using TargetScan, ENCORI, and miRDB databases. The relationship between miR-200a-3p expression levels and clinicopathologic characteristics, as well as the prognosis of CRC patients, was analyzed using the Chi-square test. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was employed to determine the expression levels of RUNX1 and miR-200a-3p in CRC cell lines (HCT-116, HT-29, SW480, and SW620). The interaction between RUNX1 and miR-200a-3p was confirmed through a luciferase reporter assay.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with normal tissues, RUNX1 mRNA expression was up-regulated in most cancer tissues, including CRC. RUNX1 expression was closely correlated with TNM stage in CRC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The high expression level of RUNX1 mRNA (HR: 2.198, 95%CI: [1.200, 4.027]) could be used as an independent risk factor for overall survival (OS) in CRC patients. The mRNA level of RUNX1 in CRC patients was significantly correlated with OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), disease-free survival (DFS) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and disease-specific survival (DSS) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RUNX1 co-expressed genes are mainly involved in GO entries such as development and growth, differentiated cell morphogenesis, and KEGG signaling pathways such as adhesion plaques and adhesion junctions. miR-200a-3p may be the miRNAs with direct regulatory role of RUNX1. The expression of miR-200a-3p was significantly correlated with T stage (P\u0026thinsp;=\u0026thinsp;0.03) and M stage (P\u0026thinsp;=\u0026thinsp;0.026). Low expression of miR-200a-3p was significantly associated with poor prognosis in CRC patients (P\u0026thinsp;=\u0026thinsp;0.02). The expression levels of RUNX1 and miR-200a-3p in CRC cell lines were negatively correlated. RUNX1 has specific binding sites with miR-200a-3p. The results of dual luciferase reporter gene detection showed that compared with three groups, Luc-3'UTR\u0026thinsp;+\u0026thinsp;mimic-NC, Luc-NC\u0026thinsp;+\u0026thinsp;miR-200a-3p mimic and Luc-NC\u0026thinsp;+\u0026thinsp;mimic-NC, luciferase activity of Luc-3'UTR\u0026thinsp;+\u0026thinsp;miR-200a-3p mimic group was significantly decreased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that miR-200a-3p may be a direct negative regulator of RUNX1.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigh expression of RUNX1 might function as an oncogene in CRC. The up-regulated expression of RUNX1 is associated with poor prognosis after CRC, which can be used as a biomarker of prognosis in CRC patients. This study is the first to report that RUNX1 is a direct negative regulatory target of miR-200a-3p in CRC and can be used as a potential therapeutic target for CRC patients.\u003c/p\u003e","manuscriptTitle":"Deciphering the miR-200a-3p/RUNX1 Axis: A Novel Oncogene Signature in Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 07:33:04","doi":"10.21203/rs.3.rs-4844859/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":"f827fd97-5b64-4405-a733-c5d6d0661afc","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-09T19:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 07:33:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4844859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4844859","identity":"rs-4844859","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}