RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation

RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation | 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 RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation Dorna Dayani, Simin Sharifi, Seyedeh Solmaz Mohammadi, Masoumeh Ghafourzadeh, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4271471/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 Breast cancer is the most commonly diagnosed cancer in women and ranks as the second most prevalent cancer globally. The prevalence of gastric cancer (GC) varies substantially between men and women, as well as in different countries. Male rates are two to three times higher than female rates. Colorectal cancer ranks as the third most common cancer globally and is the second leading cause of death related to cancer. In this study, our objective was to identify new non-coding biomarkers for breast cancer, gastric cancer, and colorectal cancer. Method Microarray analysis was performed to find the central protein-coding gene with the dysregulation in BC, GC, and CRC. Using ENCORI, validation of microarray analysis and survival analysis was utilized. RNA and protein interaction was performed by miRWalk, lncRRIsearch, and STRING. Signaling pathways were identified using Enrichr and Reactome databases. To validate the expression analysis and confirm the biomarker potential of RNAs, a qRT-PCR experiment was conducted. Results Based on microarray analysis, UHRF1 has a significant high expression in BC, CC, and GC. UHRF1 modulates DNA methylation and gene expression signaling pathways. lncRNAs EMX2OS and ZNF213-AS1 have interaction with UHRF1 mRNA. miR-4479 suppresses the expression of UHRF1 with interaction to 3’UTR region. qRT-PCR validates bioinformatics expression analysis. Furthermore, ROC analysis suggested that UHRF1, EMX2OS, and ZNF213-AS1 could potentially be used as diagnostic biomarkers for breast cancer, gastric cancer, and colorectal cancer. Conclusion miR-4479, lncRNAs EMX2OS, and ZNF213-AS1 regulate the DNA methylation signaling pathway via interaction with UHRF1. UHRF1, EMX2OS, and ZNF213-AS1 may be regarded as potential diagnostic biomarkers for breast cancer, gastric cancer, and colorectal cancer. Bioinformatics Cancer Biology microRNA lncRNA Systems Biology RNA Interaction Microarray UHRF1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Breast cancer is the most common cancer among women worldwide and ranks second among all newly diagnosed cancers. Extensive research suggests that lifestyle and environmental factors, such as a high-fat diet, alcohol consumption, and insufficient physical activity, contribute significantly to the development of breast cancer. By addressing these factors through primary prevention, it is possible to reduce both morbidity and mortality. It is estimated that environmental influences are responsible for approximately 70% of all cancer cases, with breast cancer constituting 90–95% of these instances ( 1 ). The incidence of gastric cancer (GC) varies greatly between men and women and between nations. Men have rates that are two to three times greater than women ( 2 ). When comparing countries, East Asia, East Europe, and South America have the greatest incidence rates, whereas North America and most of Africa have the lowest rates ( 3 ). Colorectal cancer ranks as the third most common disease worldwide and the second leading cause of cancer-related deaths. In 2018, it was estimated that there were 1.8 million new cases and 881,000 deaths from colorectal cancer. The epidemiology of CRC varies greatly throughout parts of the world, as well as with age, gender, and racial groups ( 4 ). Cancer biomarkers are widely utilized in cancer treatment and monitoring of disease progression, therapeutic response, relapses, and drug resistance. The majority of biomarkers investigated are in the field of cancer. Extensive research is being conducted utilizing various methods/tissues to uncover biomarkers for early detection, which has been generally fruitless ( 5 ). Different studies revealed coding or non-coding cancer biomarkers using different approaches. MicroRNAs (miRNAs) are non-coding RNA molecules with a single strand that influence gene expression post-transcriptionally by binding to messenger RNAs. MiRNAs are key gene expression regulators, and their dysregulation has been linked to a variety of human and canine disorders. MiRNA-based medicines have also been explored in several malignancies, with substantial therapeutic advantages for patients. Furthermore, studying miRNA production and regulatory mechanisms in cancer might give vital information regarding chemotherapeutic resistance, leading to more customized cancer treatment ( 6 ). Long noncoding RNAs (lncRNAs) have been shown in recent research to have essential roles in cancer spread. Nonprotein coding RNAs with a length of more than 200 nucleotides are referred to as lncRNAs. More and more research has found that lncRNAs are involved in a wide range of biological processes and are linked to a variety of disorders, including cancer ( 7 ). In this investigation, we performed an integrated systems biology investigation to find novel coding and non-coding biomarkers of GC, BC, and CRC. Using a high-throughput data analysis, a significant potential coding gene has been selected. Furthermore, through RNA and protein interaction analyses, potential novel non-coding RNAs that might regulate selected genes had been selected, and the expression level of them had been evaluated through bioinformatics and experimental evaluations. 2. Materials and methods 2.1. Microarray analysis High-throughput microarray data analysis was performed to find novel coding potential biomarkers of GC, BC, and CRC. For selecting the differentially expressed genes (DEGs) in BC, GC, and CRC, the following microarray datasets were selected: GSE10810, GSE54129, and GSE81558. Table 1 shows the characteristics of the mentioned datasets. Using the affy package, raw microarray datasets were analyzed. Raw data normalization was conducted using the Robust Multi-array Average (RMA) method from the affy package. The quality of the raw dataset was assessed using principal component analysis (PCA), sample correlation analysis, and boxplots of expression data for each normal and tumor sample. The limma package was utilized for statistical analysis of microarray expression data. Both the affy and limma packages were obtained from Bioconductor.org. Visualization of microarray analysis plots was achieved using the ggplot2 and pheatmap packages, which were downloaded from https://cran.r-project.org/ . Gene expression analysis was validated through the online databases GEPIA2 ( http://gepia2.cancer-pku.cn/ ) and ENCORI ( https://rnasysu.com/encori/ ). Table 1 Characteristics of microarray datasets in this study. Three datasets were used in this study. dataset tumor samples control samples platform disease reference GSE10810 31 27 GPL570 breast cancer ( 8 ) GSE54129 111 21 GPL570 gastric cancer - GSE81558 42 9 GPL15207 colorectal cancer ( 9 ) 2.2. RNA interaction and enrichment analyses In this study, different online software was used for selecting novel non-coding regulatory biomarkers in BC, GC, and CRC samples. Using lncRRIsearch online software ( http://rtools.cbrc.jp/LncRRIsearch ) ( 10 ), novel regulatory lncRNAs had been selected. For understanding the protein interaction network of selected mRNA, the STRING online database ( https://string-db.org/ ) ( 11 ) was used. For finding novel regulatory miRNAs, miRWalk online software was used ( http://mirwalk.umm.uni-heidelberg.de/ ) ( 12 – 14 ). Using Cytoscape software, the RNA interaction network was visualized ( 15 , 16 ). Pathway enrichment analysis was performed by enrichr ( https://maayanlab.cloud/Enrichr/ ) ( 17 , 18 ) and Reactome ( https://reactome.org/ ) ( 19 – 21 ) online databases. To understand the correlation between the expression level of targets with the survival rate of cancer patients, survival analysis was performed by ENCORI online database. 2.3. Clinical features of human samples for qRT-PCR experiment The Al-Zahra Hospital Ethics Committee of the Isfahan University of Medical Science authorized all research methodologies involving human samples in this investigation, and all patients completed written consent forms. In a case-control study, the expression levels of specific mRNA and lncRNAs in 20 BC, CRC, and GC tissue samples were compared to 20 nearby healthy tissue samples in each group. None of the patients had ever had chemotherapy or radiation. Tissue samples were thoroughly cleansed in distilled water before being frozen in liquid nitrogen for pathologist evaluation in the RNA Later solution (Invitrogen, USA). Clinicopathological characteristic of human samples is provided in Table 2 – 4 . Table 2 Clinical characteristics of BC samples. Variable Status Number % Stage I 0 0 II 6 30 III 12 60 IV 0 0 Unknown 2 10 Age 45 8 40 Unknown 2 10 Tumor size (TS) 5cm 6 30 Unknown 2 10 Menopausal status Yes 18 90 No 2 10 Unknown 0 0 Lymph node Yes 16 80 No 2 10 Unknown 2 10 ER receptor Positive 8 40 Negative 7 35 Unknown 5 25 PR receptor Positive 6 30 Negative 9 45 Unknown 5 25 HER2/neu receptor Positive 10 50 Negative 5 25 Unknown 5 25 Table 2 Clinicopathological characteristic of GC samples. Variable Status Number % Age 50 12 60 Sex Male 18 90 Female 2 10 Tumor Size 5 cm 10 50 Histology Adenocarcinoma 18 90 Mucinous Adenocarcinoma 1 5 Singet Ring Carcinoma 1 5 Perineural Invasion No 6 30 Yes 14 70 Nodal Extension No 16 80 Yes 4 20 TNM Staging I 1 5 II 6 30 IIIA 2 10 IIIB 4 20 IV 7 35 Family History No 14 70 Yes 6 30 Smoking DX-Smoker at Diagnosis but Discontinued 2 10 Ex-Smoker 2 10 Non-Smoker 15 75 smoker 1 5 Table 3 Clinicopathological table of colorectal cancer patients. Variable Status Number % Stage I 2 10 II 3 15 III 7 35 IV 8 40 Unknown 0 0 Age 50 15 75 Unknown 0 0 Tumor size (TS) 5cm 11 55 Unknown 0 0 Lymphatic Invasion Yes 8 40 No 11 55 Unknown 1 5 Perineural Invasion Yes 12 60 No 8 40 Unknown 0 0 Smoking Non-smoker 17 85 Smoker 3 15 Unknown 0 0 Sex Female 11 55 Male 9 45 Unknown 0 0 2.4. Statistical analyses Statistical analysis of the qRT-PCR experiment was performed by Graph Pad Prism 8. Paired t-test and unpaired t-test were performed to find the significance level. Receiver operating characteristic (ROC) analysis was performed to find the diagnostic capability of tumor samples. In the ROC results, the area under the curve (AUC) is evaluated. An AUC between 0.7 and 0.8 shows an acceptable biomarker, an AUC between 0.8 and 0.9 demonstrates a good biomarker, and an AUC between 0.9 and 1 shows an excellent diagnostic biomarker. 3. Results 3.1. Microarray analysis Microarray analysis was performed on three high-throughput datasets to find novel diagnostic biomarkers of BC, GC, and CRC. Principal component analysis (PCA) was performed to evaluate the quality of microarray samples. Based on PCA analysis for BC (Fig. 1 A), GC (Fig. 1 B), and CRC (Fig. 1 C), all microarray samples have suitable quality and are ready for further analysis. Differential expression analysis (DEA) was performed to find novel shared potential diagnostic biomarkers of BC, GC, and CRC. Based on the mentioned analysis, UHRF1 has a significant up-regulation in BC, GC, and CRC (Fig. 2 ). Figure 3 shows the top differentially expressed genes (DEGs) in tumor samples. 3.2. Protein interaction and pathway analysis Protein interaction analysis revealed the protein interactome of UHRF1 (Fig. 4 ). Pathway enrichment analysis was performed on the mentioned interactome to find a more accurate regulatory mechanism of UHRF1. Based on pathway enrichment analysis, UHRF1 regulates DNA methylation and epigenetics regulation of gene expression signaling pathways (Fig. 5 ). A list of related biological processes, molecular functions, and cellular components of UHRF1 is provided in Table 4 . Table 4 Gene ontology analysis of UHRF1 and its protein interactome. Pathway enrichment (Reactome) Term Adjusted P-value Odds Ratio Genes Epigenetic Regulation Of Gene Expression R-HSA-212165 1.44E-09 271.0909091 DNMT1;HDAC1;UHRF1;EHMT2;DNMT3A;EP300 Gene Expression (Transcription) R-HSA-74160 3.07E-06 51.48924358 RB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300 Chromatin Modifying Enzymes R-HSA-3247509 3.83E-06 84.79399142 KAT5;HDAC1;EHMT2;DNMT3A;EP300 DNA Methylation R-HSA-5334118 2.79E-05 275.9308756 DNMT1;UHRF1;DNMT3A Regulation Of TP53 Activity R-HSA-5633007 3.09E-05 86.43572985 KAT5;HDAC1;EHMT2;EP300 GO (Biological Process) Term Adjusted P-value Odds Ratio Genes Negative Regulation Of Transcription By RNA Polymerase II (GO:0000122) 3.75E-06 59.36419753 RB1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300 Negative Regulation Of DNA-templated Transcription (GO:0045892) 1.44E-05 43.48526523 RB1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300 Negative Regulation Of Gene Expression, Epigenetic (GO:0045814) 1.86E-04 178.0535714 RB1;DNMT1;UHRF1 Internal Protein Amino Acid Acetylation (GO:0006475) 4.07E-04 832.6666667 KAT5;EP300 Histone Modification (GO:0016570) 4.07E-04 109.4065934 KAT5;HDAC1;EP300 GO (Molecular function) Term Adjusted P-value Odds Ratio Genes Nucleus (GO:0005634) 0.001683855 13.85219915 RB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300 Euchromatin (GO:0000791) 0.001683855 118.7380952 UHRF1;DNMT3A Intracellular Membrane-Bounded Organelle (GO:0043231) 0.002852689 11.47513064 RB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300 Swr1 Complex (GO:0000812) 0.020958323 201.8080808 KAT5 Sin3 Complex (GO:0016580) 0.020958323 110.9444444 HDAC1 GO (Cellular component) Term Adjusted P-value Odds Ratio Genes DNA-binding Transcription Factor Binding (GO:0140297) 7.68E-06 71.16606498 RB1;KAT5;HDAC1;DNMT3A;EP300 Transcription Coregulator Binding (GO:0001221) 4.13E-04 88.8125 HDAC1;EHMT2;EP300 Histone H4 Acetyltransferase Activity (GO:0010485) 7.85E-04 277.3888889 KAT5;EP300 NF-kappaB Binding (GO:0051059) 7.85E-04 207.9791667 HDAC1;EP300 Histone Acetyltransferase Activity (GO:0004402) 7.85E-04 199.65 KAT5;EP300 3.3. Non-coding RNA interaction MiRNA-mRNA interaction analysis was performed to find novel regulatory RNA for UHRF1. miRNAs with binding probability (score) of 1, interaction in the seed region, and lower interaction energy were selected. Based on miRNA interaction, miR-4479 (score: 1, energy: -30.7) has a significant miRNA interaction with the 3’UTR of UHRF1 mRNA (Fig. 6 ). A list of the top 20 regulatory miRNAs for UHRF1 is provided in Table 5 . lncRNA interaction with lncRRIsearch revealed that UHRF1 has a significant interaction with EMX2OS and ZNF213-AS1 lncRNAs. Table 5 miRNA interaction analysis of UHRF1 using miRWalk. miRNA symbol score energy seed position miR-4479 UHRF1 1 -30.7 1 3UTR miR-3648 UHRF1 1 -30.6 1 3UTR miR-4740-5p UHRF1 1 -30.6 1 3UTR miR-1292-3p UHRF1 1 -30.5 1 3UTR miR-6769a-3p UHRF1 1 -30.4 1 3UTR miR-181d-3p UHRF1 1 -29.5 1 3UTR miR-5010-5p UHRF1 1 -28.9 1 3UTR miR-6735-3p UHRF1 1 -28.8 1 3UTR miR-7843-5p UHRF1 1 -28.8 1 3UTR miR-4739 UHRF1 1 -28.7 1 3UTR miR-10396a-3p UHRF1 1 -28.5 1 3UTR miR-7846-3p UHRF1 1 -28.3 1 3UTR miR-3132 UHRF1 1 -28.1 1 3UTR miR-6798-5p UHRF1 1 -28 1 3UTR miR-7160-5p UHRF1 1 -27.9 1 3UTR miR-1199-5p UHRF1 1 -27.8 1 3UTR miR-4663 UHRF1 1 -27.7 1 3UTR miR-6735-5p UHRF1 1 -27.7 1 3UTR miR-6822-3p UHRF1 1 -27.3 1 3UTR miR-7109-5p UHRF1 1 -27.1 1 3UTR 3.4. Non-coding expression analysis Based on expression analysis by ENCORI, UHRF1 and ZNF213-AS1 exhibit significantly elevated expression in BC, GC, and CRC. EMX2OS also shows significantly low expression in GC, BC, and CRC (Fig. 7 ). Survival analysis indicated a significant correlation between low expression of UHRF1 and improved survival rates in GC patients (p-value: 0.016, HR: 0.67). Conversely, there was a non-significant correlation between high expression of ZNF213-AS1 and lower survival rates in BC, GC, and CRC. Additionally, a significant positive correlation was found between the expression level of EMX2OS and survival rates in GC patients. 3.5. Validation of expression analysis by qRT-PCR qRT-PCR experiment revealed that UHRF1 and ZNF213-AS1 have significantly high expression in BC, CRC, and GC. Based on the experiment, EMX2OS was found to be significantly underexpressed in GC, CRC, and BC (Fig. 9 ). Obtained experimental results validate bioinformatics investigations. ROC analysis also indicated that UHRF1, ZNF213-AS1, and EMX2OS could potentially serve as effective diagnostic biomarkers of BC, GC, and CRC (Fig. 10 ). Table 6 provides the statistical information on expression and ROC analysis. Table 6 Statistical information of gene expression and ROC analyses, based on qRT-PCR data. expression ROC gene disease logFC p- value AUC p- value UHRF1 BC 3.0700 < 0.0001 0.8900 < 0.0001 GC 2.9920 0.0027 0.7975 0.0013 CC 2.7470 0.0009 0.7450 0.0080 EMX2OS BC -2.6090 0.0070 0.7775 0.0027 GC -3.1230 0.0009 0.7875 0.0019 CC -3.3610 < 0.0001 0.8725 < 0.0001 ZNF213-AS1 BC 2.7240 0.0034 0.8175 0.0006 GC 3.0050 0.0099 0.7350 0.1100 CC 2.0640 0.0164 0.7700 0.0035 4. Discussion UHRF1, an epigenetic regulator present in proliferating cancer cells, interacts with AMPK, inhibiting its activity in both normal and stress conditions. As a nuclear protein, UHRF1 promotes the retention of AMPK in the nucleus and significantly reduces its activity against substrates such as H2B and EZH2. Additionally, UHRF1 effectively decreases AMPK activity in the cytoplasm, likely as a result of the nucleocytoplasmic shuttling of AMPK ( 22 ). Previous studies revealed possible effects of UHRF1 protein in the different cancer types. For example, Q et al. in 2019 revealed that UHRF1 knockdown drastically reduced aerobic glycolysis in pancreatic cancer cells. Furthermore, they found that UHRF1 knockdown also reduced hypoxia-inducible factor (HIF)1 levels and HIF1 targeting glycolytic genes ( 23 ). Based on the study of Yin et al. in 2018, UHRF1/BRCA1 complex is one of the main targets of poly ADP ribose polymerase (PARP) inhibitor and histone deacetylase (HDAC) inhibitor ( 24 ). Previous studies also mentioned the possible roles of UHRF1 in the regulation of BC, GC, and CRC. In mammalian cells, UHRF1 aids in the creation and maintenance of DNA methylation patterns. The establishment domains, including E3 ligase activity, are well described, whereas the maintenance domains are less understood. In human CRC cells, UHRF1's ability to bind histone- and hemimethylated DNA, but not its E3 ligase activity, maintains cancer-specific DNA methylation patterns. Disruption of these chromatin reader activities leads to the restoration of DNA hypermethylation, reactivation of epigenetically silenced tumor suppressor genes (TSGs), and a reduction in the oncogenic properties of CRC ( 25 ). Y et al. 2012 reported that UHRF1 has a significant role in the cellular proliferation biological process and might affect CRC risk through this pathway ( 26 ). Based on the study of Y et al. in 2019, miR-506 could have a significant regulatory effect on the KISS1/PI3K/NF-kB signaling pathway through silencing UHRF1 in CRC patients ( 27 ). The report of J et al. in 2021 revealed the possible effect of UHRF1 silencing on the STAT1 and DNMT1 regulation and inhibition of CRC growth ( 28 ). Our results are consistent with the mentioned previous studies. Based on our results, up-regulation of UHRF1 has a significant correlation with the higher risk of CRC. Based on the study of Luo G. et al. in 2022, UHRF1 regulates the estrogen signaling pathway and could regulate cell growth in BC patients ( 29 ). UHRF1 could enhance BC development through KLF17 suppression. This suppression happens using promoter hypermethylation ( 30 ). In GC patients, miR-146a/b could regulate GC invasion and metastasis by targeting UHRF1 ( 31 ). No prior research has explored the potential roles of ZNF213-AS1 in the development of breast cancer, gastric cancer, and colorectal cancer. Based on our study, EMX2OS and ZNF213-AS1 are the two potential regulatory diagnostic biomarkers of BC, GC, and CRC. The mentioned lncRNAs might affect the DNA methylation and gene expression signaling pathways through the regulation of UHRF1 expression level. Furthermore, we found that miR-4479 could modulate DNA methylation and gene expression signaling pathways via negative regulation of UHRF1. No previous research has investigated the potential role of miR-4479 in BC, CRC, and GC. However, previous studies revealed the potential roles of this microRNA in ovarian cancer and lung cancer. Based on the study of Wang et al. in 2022, miR-4479 has a significantly low expression in epithelial ovarian cancer (EOC) patients. Furthermore, miR-4479 could be considered as a potential diagnostic biomarker of EOC ( 32 ). According to the 2022 study by Chakraborty et al., miR-4479 targets genes that are up-regulated and overexpressed in lung cancer ( 33 ). Our previous studies also revealed potential coding and non-coding biomarkers of BC ( 34 – 37 ) and GC ( 38 ). Based on previous studies, lncRNA EMX2OS regulates the invasion and regulation of ovarian cancer cells through the regulation of PD-L1/AKT3/miR-654-3p ( 39 ). In The GC samples, EMX2OS could be an enhancer RNA and regulate the prognosis of EMX2OS ( 40 ) In 2021, Molaei Ramshe et al. conducted a study on the expression level of EMX2OS in breast cancer patients and found no significant change in the expression levels of EMX2OS in breast cancer samples ( 41 ). However, more studies are needed for the validation of the obtained results in this experiment. 5. Conclusion EMX2OS and ZNF213-AS1 lncRNAs are the two novel diagnostic biomarkers of BC, GC, and CRC as the two dysregulated non-coding RNA. These two lncRNAs modulate DNA methylation and gene expression signaling pathways through the regulation of UHRF1. UHRF1 is a potential diagnostic biomarker and oncogene of BC, GC, and CRC. miR-4479 also regulates UHRF1 and could affect the DNA methylation signaling pathway. 6. Declarations 6.1 Ethics approval: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences. 6.2 Consent for publication: Informed consent was obtained from all individual participants included in the study. 6.3 Availability of data and materials: The datasets generated or analyzed during the current study are available in the GEO repository, GSE10810, GSE54129, and GSE81558. 6.4 Conflicts of interest: The authors declare that they have no competing interests. 6.5 Financial support and sponsorship: Not applicable. 6.6 Authors’ contribution: Dorna Dayani, Simin Sharifi, Seyedeh Solmaz Mohammadi, Masoumeh Ghafourzadeh , Sheida Bahrami, Melika Azaripour, Nasim Karimi, and Ali Ghaneh : Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; Mohammad Rezaei and Seyedeh Zahra Shirdeli: Writing – Review & Editing, Conceptualization, Methodology, Validation, Supervision; Mansoureh Azadeh: Writing – Review & Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. 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Available from: https://pubmed.ncbi.nlm.nih.gov/30012171/ Kong X, Chen J, Xie W, Brown SM, Cai Y, Wu K, et al. Defining UHRF1 Domains that Support Maintenance of Human Colon Cancer DNA Methylation and Oncogenic Properties. Cancer Cell [Internet]. 2019 Apr 15 [cited 2023 Sep 20];35(4):633-648.e7. Available from: https://pubmed.ncbi.nlm.nih.gov/30956060/ Kofunato Y, Kumamoto K, Saitou K, Hayase S, Okayama H, Miyamoto K, et al. UHRF1 expression is upregulated and associated with cellular proliferation in colorectal cancer. Oncol Rep [Internet]. 2012 Dec [cited 2023 Sep 20];28(6):1997–2002. Available from: https://pubmed.ncbi.nlm.nih.gov/23023523/ Lin Y, Chen Z, Zheng Y, Liu Y, Gao J, Lin S, et al. MiR-506 Targets UHRF1 to Inhibit Colorectal Cancer Proliferation and Invasion via the KISS1/PI3K/NF- κ B Signaling Axis. Front Cell Dev Biol [Internet]. 2019 Nov 15 [cited 2023 Sep 20];7. Available from: https://pubmed.ncbi.nlm.nih.gov/31803739/ Han J, Chen X, Xu J, Chu L, Li R, Sun N, et al. Simultaneous silencing Aurora-A and UHRF1 inhibits colorectal cancer cell growth through regulating expression of DNMT1 and STAT1. Int J Med Sci [Internet]. 2021 [cited 2023 Sep 20];18(15):3437–51. Available from: https://pubmed.ncbi.nlm.nih.gov/34522170/ Luo G, Li Q, Yu M, Wang T, Zang Y, Liu Z, et al. UHRF1 modulates breast cancer cell growth via estrogen signaling. Med Oncol [Internet]. 2022 Aug 1 [cited 2023 Sep 20];39(8). Available from: https://pubmed.ncbi.nlm.nih.gov/35666346/ Gao SP, Sun HF, Li LD, Fu WY, Jin W. UHRF1 promotes breast cancer progression by suppressing KLF17 expression by hypermethylating its promoter. Am J Cancer Res [Internet]. 2017 [cited 2023 Sep 20];7(7):1554. Available from: /pmc/articles/PMC5523035/ Zhou L, Zhao X, Han Y, Lu Y, Shang Y, Liu C, et al. Regulation of UHRF1 by miR-146a/b modulates gastric cancer invasion and metastasis. FASEB J [Internet]. 2013 Dec [cited 2023 Sep 20];27(12):4929–39. Available from: https://pubmed.ncbi.nlm.nih.gov/23982143/ Wang S, Song X, Wang K, Zheng B, Lin Q, Yu M, et al. Plasma exosomal miR-320d, miR-4479, and miR-6763-5p as diagnostic biomarkers in epithelial ovarian cancer. Front Oncol [Internet]. 2022 Dec 14 [cited 2023 Sep 20];12. Available from: https://pubmed.ncbi.nlm.nih.gov/36591520/ Chakraborty S, Nath D. A Study on microRNAs Targeting the Genes Overexpressed in Lung Cancer and their Codon Usage Patterns. Mol Biotechnol [Internet]. 2022 Oct 1 [cited 2023 Sep 20];64(10):1095–119. Available from: https://pubmed.ncbi.nlm.nih.gov/35435592/ Ezzati E, Mosadeshi S, Akbarinia A, Horriat S, Rezaei M, Azadeh M. LINC00520 promotes breast cancer development by low expression as a tumor suppressor and prognostic biomarker by regulating the ESR2 expression level: integrated systems biology bioinformatics and experimental analyses. 2022 Aug 10 [cited 2022 Aug 18]; Available from: https://www.researchsquare.com Rezvani Sichani A, Dadkhah P, Tabandeh T, Kaviani Dehkordi N, Rezaei M, Rahimirad S, et al. Molecular insight into the expression level of an immunosuppression gene, VTCN1, and its regulatory factors in breast cancer patients and non-cancerous samples with a higher level of IgE. 2023 Feb 8 [cited 2024 Mar 2]; Available from: https://www.researchsquare.com Shirdeli SZ, Hashemi SA, Hashemi GS, Khalilian L, Ferdowsian S, Mostaghimi Y, et al. LINC1521 and miR-3679-5p modulate cellular response to chemical stress in breast cancer patients through regulation of XBP1 expression as a potential diagnostic biomarker. Res Sq [Internet]. 2023 Aug 11 [cited 2023 Nov 20]; Available from: https://www.researchsquare.com Tavousi N, Taqizadeh Q, Nasiriyan E, Tabaeian P, Rezaei M, Azadeh M. ADAMTS5 modulates breast cancer development as a diagnostic biomarker and potential tumor suppressor, regulating by BAIAP2-AS1, VTI1B, CRNDE, and hsa-miR-135b-3p: integrated systems biology and experimental approach. 2022 Jul 27 [cited 2022 Aug 6]; Available from: https://www.researchsquare.com Barani A, Beikverdi K, Mashhadi B, Parsapour N, Rezaei M, Javid P, et al. Transcription analysis of the THBS2 gene through regulation by potential non-coding diagnostic biomarkers and oncogenes of gastric cancer in the ECM receptor interaction signaling pathway: integrated systems biology and experimental investigation. 2022 Nov 23 [cited 2023 May 13]; Available from: https://www.researchsquare.com Duan M, Fang M, Wang C, Wang H, Li M. LncRNA EMX2OS Induces Proliferation, Invasion and Sphere Formation of Ovarian Cancer Cells via Regulating the miR-654-3p/AKT3/PD-L1 Axis. Cancer Manag Res [Internet]. 2020 [cited 2023 Sep 22];12:2141–54. Available from: https://pubmed.ncbi.nlm.nih.gov/32273754/ Liu GX, Tan YZ, He GC, Zhang QL, Liu P, Li CF. EMX2OS plays a prognosis-associated enhancer RNA role in gastric cancer. Medicine [Internet]. 2021 Oct 15 [cited 2023 Sep 22];100(41):E27535. Available from: https://pubmed.ncbi.nlm.nih.gov/34731149/ Ramshe SM, Ghaedi H, Omrani MD, Geranpayeh L, Alipour B, Ghafouri-Fard S. Up-regulation of FOXN3-AS1 in invasive ductal carcinoma of breast cancer patients. Heliyon [Internet]. 2021 Oct 1 [cited 2023 Sep 22];7(10). Available from: https://pubmed.ncbi.nlm.nih.gov/34703931/ Additional Declarations The authors declare no competing interests. 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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-4271471","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291542453,"identity":"f970a008-f0c7-4f70-889d-5b3f5b0293be","order_by":0,"name":"Dorna Dayani","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Dorna","middleName":"","lastName":"Dayani","suffix":""},{"id":291542552,"identity":"0c022fec-c61d-4ec7-945b-1aeb6a0020ed","order_by":1,"name":"Simin Sharifi","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Simin","middleName":"","lastName":"Sharifi","suffix":""},{"id":291542553,"identity":"79671293-e037-423c-8f19-0b55a351b9be","order_by":2,"name":"Seyedeh Solmaz Mohammadi","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Seyedeh","middleName":"Solmaz","lastName":"Mohammadi","suffix":""},{"id":291542588,"identity":"c14e116b-7772-4a6d-a177-0d7b2c0e41af","order_by":3,"name":"Masoumeh Ghafourzadeh","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Masoumeh","middleName":"","lastName":"Ghafourzadeh","suffix":""},{"id":291542613,"identity":"d0e795e1-8ee2-421c-bcbf-5158f767bc14","order_by":4,"name":"Sheida Bahrami","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sheida","middleName":"","lastName":"Bahrami","suffix":""},{"id":291542687,"identity":"1e87c8a4-05f7-44e9-b9d1-11628fdfca1d","order_by":5,"name":"Melika Azaripour","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Melika","middleName":"","lastName":"Azaripour","suffix":""},{"id":291542743,"identity":"cc4b9845-0358-433b-80ca-c025c9bb1135","order_by":6,"name":"Nasim Karimi","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Nasim","middleName":"","lastName":"Karimi","suffix":""},{"id":291542744,"identity":"fc79d5ef-2dd5-410f-b022-f934e796bb77","order_by":7,"name":"Ali Ghaneh","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Ghaneh","suffix":""},{"id":291542825,"identity":"acaa7e89-47eb-403c-b2dc-dedd89bcea9a","order_by":8,"name":"Sayedeh Zahra Shirdeli","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sayedeh","middleName":"Zahra","lastName":"Shirdeli","suffix":""},{"id":291542826,"identity":"e08b7274-8f3f-4769-bb12-628cd4f33537","order_by":9,"name":"Mohammad Rezaei","email":"","orcid":"https://orcid.org/0000-0003-3888-5839","institution":"Department of Biology and Biotechnology, University of Pavia, Pavia, Italy","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Rezaei","suffix":""},{"id":291542991,"identity":"5d7a2ba9-d606-4d7e-bc4c-829e3447cef5","order_by":10,"name":"Mansoureh Azadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3SMWvCQBTA8edyU2JXSyD5BIULBYsU/Cx3HDRLbB07ZDgQ4lLsmiEfwk7F7eAgLilZb+hgKDg5ZJJCIVSDBIdTO3a4//aGH7zHHYDJ9F+zQLjQAwzkMDahC+T2mHT4XwjlewLH5FQ302VZbZ4/g3dnModVJJ+uXj+WK4iG0HWElvRzwq7TfD1apNkYSCYHiXqkHDIGqEv0RBDh2LEczVW4uwVJDMryOSAByNIv1i/KyY9dywA3pJbYK/Idqc8QxZBjc0kaQmOJsQh93onPkTUapJn0F8nDWNBZgH0V+gmdMev0YsGX2kTSu+uxt/J7e4/dIsdVtR263ouetO0fRbQTaf/ABWIymUwmbb8scGMPe1qBDAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2031-4640","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":true,"prefix":"","firstName":"Mansoureh","middleName":"","lastName":"Azadeh","suffix":""}],"badges":[],"createdAt":"2024-04-15 18:46:00","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4271471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4271471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54864011,"identity":"95965dbe-fd7f-4480-bc74-9454876bca7a","added_by":"auto","created_at":"2024-04-17 20:31:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90351,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of BC (A), GC (B), and CRC (C) microarray datasets. Based on the mentioned analysis, microarray samples have suitable quality and no need for removing samples.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/2d0d3bc1d87067e699b80b51.png"},{"id":54864012,"identity":"f1f2f3e8-6f1a-4dba-adac-b876c9f32f2d","added_by":"auto","created_at":"2024-04-17 20:31:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227426,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of DEGs in BC (A), GC (B), and CRC (C) samples. The red color shows up-regulated genes, and the green shows low-expressed genes. UHRF1 is shown in the plot with a black point.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/f5e634012435c4bdc19d7e75.png"},{"id":54864014,"identity":"a9ecf8e5-7f7d-4025-89de-80ba5d8092cb","added_by":"auto","created_at":"2024-04-17 20:31:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":564194,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of top DEGs at BC (a), GC (b), and CRC (c).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/0467bf6302b5581b4522cc0d.png"},{"id":54864019,"identity":"6c661c1b-cc4d-484c-b6a6-c391e9e1f633","added_by":"auto","created_at":"2024-04-17 20:31:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1170556,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction of UHRF1.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/08e35b426432c537c03ff502.png"},{"id":54864021,"identity":"981a1d1d-76c7-47dd-bc6d-bf00b35848ce","added_by":"auto","created_at":"2024-04-17 20:31:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":908110,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic visualization of DNA methylation and gene expression signaling pathways.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/6d475eb56fc85dff30ee8fbe.png"},{"id":54864703,"identity":"befeb092-ef80-4025-9240-afd2048b768f","added_by":"auto","created_at":"2024-04-17 20:39:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":208570,"visible":true,"origin":"","legend":"\u003cp\u003enon-coding interaction analysis of UHRF1. Red nodes indicate miRNAs, green node indicates mRNA (protein coding), and yellow nodes indicate lncRNAs.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/8efba2ca1a050875018116db.png"},{"id":54864022,"identity":"1bf6f2a7-3f50-4b4b-97a6-e041b6625f45","added_by":"auto","created_at":"2024-04-17 20:31:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":422439,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression analysis of UHRF1, ZNF213-AS1, and EMX2OS in BC, GC, and CRC, based on ENCORI online database.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/9780aa0b619f527bb0c47341.png"},{"id":54864017,"identity":"7918d66b-1d5d-4754-a616-63d7c61975c6","added_by":"auto","created_at":"2024-04-17 20:31:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":249865,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis demonstrates possible correlation of UHRF1, ZNF213-AS1, and EMX2OS with the survival rate of BC, GC, and CRC.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/92c82d89fd834fae14c7095c.png"},{"id":54864015,"identity":"8a23e6eb-f39a-4db8-8767-0720a07bf508","added_by":"auto","created_at":"2024-04-17 20:31:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":194328,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR data analysis. Expression analysis of genes at BC (A), GC (B), and CRC (C).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/fb888cd2d46821c2cbe736d7.png"},{"id":54864704,"identity":"2afae033-44e0-452d-a3fd-cbc1b88f7363","added_by":"auto","created_at":"2024-04-17 20:39:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":225730,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of UHRF1, EMX2OS, and ZNF213-AS1 for finding possibility of being diagnostic biomarker for BC (A), GC (B), and CRC (C).\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/cefbf92513662e5276c47bb4.png"},{"id":54865673,"identity":"016f3983-8fcd-4c95-b3ba-f6d3c8199505","added_by":"auto","created_at":"2024-04-17 20:47:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4038869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4271471/v1/0f143818-f01d-4532-9f89-f4be8055597b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer is the most common cancer among women worldwide and ranks second among all newly diagnosed cancers. Extensive research suggests that lifestyle and environmental factors, such as a high-fat diet, alcohol consumption, and insufficient physical activity, contribute significantly to the development of breast cancer. By addressing these factors through primary prevention, it is possible to reduce both morbidity and mortality. It is estimated that environmental influences are responsible for approximately 70% of all cancer cases, with breast cancer constituting 90\u0026ndash;95% of these instances (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe incidence of gastric cancer (GC) varies greatly between men and women and between nations. Men have rates that are two to three times greater than women (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). When comparing countries, East Asia, East Europe, and South America have the greatest incidence rates, whereas North America and most of Africa have the lowest rates (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Colorectal cancer ranks as the third most common disease worldwide and the second leading cause of cancer-related deaths. In 2018, it was estimated that there were 1.8\u0026nbsp;million new cases and 881,000 deaths from colorectal cancer. The epidemiology of CRC varies greatly throughout parts of the world, as well as with age, gender, and racial groups (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCancer biomarkers are widely utilized in cancer treatment and monitoring of disease progression, therapeutic response, relapses, and drug resistance. The majority of biomarkers investigated are in the field of cancer. Extensive research is being conducted utilizing various methods/tissues to uncover biomarkers for early detection, which has been generally fruitless (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Different studies revealed coding or non-coding cancer biomarkers using different approaches. MicroRNAs (miRNAs) are non-coding RNA molecules with a single strand that influence gene expression post-transcriptionally by binding to messenger RNAs.\u003c/p\u003e \u003cp\u003eMiRNAs are key gene expression regulators, and their dysregulation has been linked to a variety of human and canine disorders. MiRNA-based medicines have also been explored in several malignancies, with substantial therapeutic advantages for patients. Furthermore, studying miRNA production and regulatory mechanisms in cancer might give vital information regarding chemotherapeutic resistance, leading to more customized cancer treatment (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Long noncoding RNAs (lncRNAs) have been shown in recent research to have essential roles in cancer spread. Nonprotein coding RNAs with a length of more than 200 nucleotides are referred to as lncRNAs. More and more research has found that lncRNAs are involved in a wide range of biological processes and are linked to a variety of disorders, including cancer (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this investigation, we performed an integrated systems biology investigation to find novel coding and non-coding biomarkers of GC, BC, and CRC. Using a high-throughput data analysis, a significant potential coding gene has been selected. Furthermore, through RNA and protein interaction analyses, potential novel non-coding RNAs that might regulate selected genes had been selected, and the expression level of them had been evaluated through bioinformatics and experimental evaluations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Microarray analysis\u003c/h2\u003e \u003cp\u003eHigh-throughput microarray data analysis was performed to find novel coding potential biomarkers of GC, BC, and CRC. For selecting the differentially expressed genes (DEGs) in BC, GC, and CRC, the following microarray datasets were selected: GSE10810, GSE54129, and GSE81558. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the mentioned datasets. Using the affy package, raw microarray datasets were analyzed. Raw data normalization was conducted using the Robust Multi-array Average (RMA) method from the affy package. The quality of the raw dataset was assessed using principal component analysis (PCA), sample correlation analysis, and boxplots of expression data for each normal and tumor sample. The limma package was utilized for statistical analysis of microarray expression data. Both the affy and limma packages were obtained from Bioconductor.org. Visualization of microarray analysis plots was achieved using the ggplot2 and pheatmap packages, which were downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Gene expression analysis was validated through the online databases GEPIA2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ENCORI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\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\u003eCharacteristics of microarray datasets in this study. Three datasets were used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003edataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etumor samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eplatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE10810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE54129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003egastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE81558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPL15207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecolorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\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=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. RNA interaction and enrichment analyses\u003c/h2\u003e \u003cp\u003eIn this study, different online software was used for selecting novel non-coding regulatory biomarkers in BC, GC, and CRC samples. Using lncRRIsearch online software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rtools.cbrc.jp/LncRRIsearch\u003c/span\u003e\u003cspan address=\"http://rtools.cbrc.jp/LncRRIsearch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), novel regulatory lncRNAs had been selected. For understanding the protein interaction network of selected mRNA, the STRING online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) was used. For finding novel regulatory miRNAs, miRWalk online software was used (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Using Cytoscape software, the RNA interaction network was visualized (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Pathway enrichment analysis was performed by enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and Reactome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) online databases. To understand the correlation between the expression level of targets with the survival rate of cancer patients, survival analysis was performed by ENCORI online database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Clinical features of human samples for qRT-PCR experiment\u003c/h2\u003e \u003cp\u003eThe Al-Zahra Hospital Ethics Committee of the Isfahan University of Medical Science authorized all research methodologies involving human samples in this investigation, and all patients completed written consent forms. In a case-control study, the expression levels of specific mRNA and lncRNAs in 20 BC, CRC, and GC tissue samples were compared to 20 nearby healthy tissue samples in each group. None of the patients had ever had chemotherapy or radiation. Tissue samples were thoroughly cleansed in distilled water before being frozen in liquid nitrogen for pathologist evaluation in the RNA Later solution (Invitrogen, USA). Clinicopathological characteristic of human samples is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of BC samples.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size (TS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eER receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePR receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHER2/neu receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristic of GC samples.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMucinous Adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSinget Ring Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerineural Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eNodal Extension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTNM Staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDX-Smoker at Diagnosis but Discontinued\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx-Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological table of colorectal cancer patients.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size (TS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLymphatic Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePerineural Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analysis of the qRT-PCR experiment was performed by Graph Pad Prism 8. Paired t-test and unpaired t-test were performed to find the significance level. Receiver operating characteristic (ROC) analysis was performed to find the diagnostic capability of tumor samples. In the ROC results, the area under the curve (AUC) is evaluated. An AUC between 0.7 and 0.8 shows an acceptable biomarker, an AUC between 0.8 and 0.9 demonstrates a good biomarker, and an AUC between 0.9 and 1 shows an excellent diagnostic biomarker.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Microarray analysis\u003c/h2\u003e\n\u003cp\u003eMicroarray analysis was performed on three high-throughput datasets to find novel diagnostic biomarkers of BC, GC, and CRC. Principal component analysis (PCA) was performed to evaluate the quality of microarray samples. Based on PCA analysis for BC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), GC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), and CRC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC), all microarray samples have suitable quality and are ready for further analysis. Differential expression analysis (DEA) was performed to find novel shared potential diagnostic biomarkers of BC, GC, and CRC. Based on the mentioned analysis, UHRF1 has a significant up-regulation in BC, GC, and CRC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the top differentially expressed genes (DEGs) in tumor samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Protein interaction and pathway analysis\u003c/h2\u003e\n\u003cp\u003eProtein interaction analysis revealed the protein interactome of UHRF1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Pathway enrichment analysis was performed on the mentioned interactome to find a more accurate regulatory mechanism of UHRF1. Based on pathway enrichment analysis, UHRF1 regulates DNA methylation and epigenetics regulation of gene expression signaling pathways (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). A list of related biological processes, molecular functions, and cellular components of UHRF1 is provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"624\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 4\u003c/p\u003e\n\u003cp\u003eGene ontology analysis of UHRF1 and its protein interactome.\u003c/p\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003e\u003cstrong\u003ePathway enrichment (Reactome)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"402\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eAdjusted P-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eOdds Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGenes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eEpigenetic Regulation Of Gene Expression R-HSA-212165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e1.44E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e271.0909091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eDNMT1;HDAC1;UHRF1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eGene Expression (Transcription) R-HSA-74160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e3.07E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e51.48924358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eChromatin Modifying Enzymes R-HSA-3247509\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e3.83E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e84.79399142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5;HDAC1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eDNA Methylation R-HSA-5334118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e2.79E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e275.9308756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eDNMT1;UHRF1;DNMT3A\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eRegulation Of TP53 Activity R-HSA-5633007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e3.09E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e86.43572985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5;HDAC1;EHMT2;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"402\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO (Biological Process)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eAdjusted P-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eOdds Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGenes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eNegative Regulation Of Transcription By RNA Polymerase II (GO:0000122)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e3.75E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e59.36419753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eNegative Regulation Of DNA-templated Transcription (GO:0045892)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e1.44E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e43.48526523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eNegative Regulation Of Gene Expression, Epigenetic (GO:0045814)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e1.86E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e178.0535714\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;DNMT1;UHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eInternal Protein Amino Acid Acetylation (GO:0006475)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e4.07E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e832.6666667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eHistone Modification (GO:0016570)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e4.07E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e109.4065934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5;HDAC1;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"402\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO (Molecular function)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eAdjusted P-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eOdds Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGenes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eNucleus (GO:0005634)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e0.001683855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e13.85219915\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eEuchromatin (GO:0000791)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e0.001683855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e118.7380952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eUHRF1;DNMT3A\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eIntracellular Membrane-Bounded Organelle (GO:0043231)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e0.002852689\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e11.47513064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eSwr1 Complex (GO:0000812)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e0.020958323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e201.8080808\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eSin3 Complex (GO:0016580)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e0.020958323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e110.9444444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eHDAC1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"402\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO (Cellular component)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eAdjusted P-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eOdds Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGenes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eDNA-binding Transcription Factor Binding (GO:0140297)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e7.68E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e71.16606498\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eRB1;KAT5;HDAC1;DNMT3A;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eTranscription Coregulator Binding (GO:0001221)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e4.13E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e88.8125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eHDAC1;EHMT2;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eHistone H4 Acetyltransferase Activity (GO:0010485)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e7.85E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e277.3888889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eNF-kappaB Binding (GO:0051059)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e7.85E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e207.9791667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eHDAC1;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eHistone Acetyltransferase Activity (GO:0004402)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e7.85E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e199.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eKAT5;EP300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Non-coding RNA interaction\u003c/h2\u003e\n\u003cp\u003eMiRNA-mRNA interaction analysis was performed to find novel regulatory RNA for UHRF1. miRNAs with binding probability (score) of 1, interaction in the seed region, and lower interaction energy were selected. Based on miRNA interaction, miR-4479 (score: 1, energy: -30.7) has a significant miRNA interaction with the 3\u0026rsquo;UTR of UHRF1 mRNA (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). A list of the top 20 regulatory miRNAs for UHRF1 is provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. lncRNA interaction with lncRRIsearch revealed that UHRF1 has a significant interaction with EMX2OS and ZNF213-AS1 lncRNAs.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003emiRNA interaction analysis of UHRF1 using miRWalk.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emiRNA\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esymbol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003escore\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eenergy\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eseed\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eposition\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emiR-4479\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e-30.7\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-3648\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-30.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-4740-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-30.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-1292-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-30.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-6769a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-30.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-181d-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-29.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-5010-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-6735-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-7843-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-4739\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-10396a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-7846-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-3132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-6798-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-7160-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-1199-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-4663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-6735-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-6822-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emiR-7109-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Non-coding expression analysis\u003c/h2\u003e\n\u003cp\u003eBased on expression analysis by ENCORI, UHRF1 and ZNF213-AS1 exhibit significantly elevated expression in BC, GC, and CRC. EMX2OS also shows significantly low expression in GC, BC, and CRC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Survival analysis indicated a significant correlation between low expression of UHRF1 and improved survival rates in GC patients (p-value: 0.016, HR: 0.67). Conversely, there was a non-significant correlation between high expression of ZNF213-AS1 and lower survival rates in BC, GC, and CRC. Additionally, a significant positive correlation was found between the expression level of EMX2OS and survival rates in GC patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5. Validation of expression analysis by qRT-PCR\u003c/h2\u003e\n\u003cp\u003eqRT-PCR experiment revealed that UHRF1 and ZNF213-AS1 have significantly high expression in BC, CRC, and GC. Based on the experiment, EMX2OS was found to be significantly underexpressed in GC, CRC, and BC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Obtained experimental results validate bioinformatics investigations. ROC analysis also indicated that UHRF1, ZNF213-AS1, and EMX2OS could potentially serve as effective diagnostic biomarkers of BC, GC, and CRC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e provides the statistical information on expression and ROC analysis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatistical information of gene expression and ROC analyses, based on qRT-PCR data.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eexpression\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eROC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003edisease\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elogFC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eUHRF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.0700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.9920\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.7470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0080\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eEMX2OS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.6090\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.1230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.3610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eZNF213-AS1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.7240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.0050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.0640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0035\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUHRF1, an epigenetic regulator present in proliferating cancer cells, interacts with AMPK, inhibiting its activity in both normal and stress conditions. As a nuclear protein, UHRF1 promotes the retention of AMPK in the nucleus and significantly reduces its activity against substrates such as H2B and EZH2. Additionally, UHRF1 effectively decreases AMPK activity in the cytoplasm, likely as a result of the nucleocytoplasmic shuttling of AMPK (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Previous studies revealed possible effects of UHRF1 protein in the different cancer types. For example, Q et al. in 2019 revealed that UHRF1 knockdown drastically reduced aerobic glycolysis in pancreatic cancer cells. Furthermore, they found that UHRF1 knockdown also reduced hypoxia-inducible factor (HIF)1 levels and HIF1 targeting glycolytic genes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Based on the study of Yin et al. in 2018, UHRF1/BRCA1 complex is one of the main targets of poly ADP ribose polymerase (PARP) inhibitor and histone deacetylase (HDAC) inhibitor (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies also mentioned the possible roles of UHRF1 in the regulation of BC, GC, and CRC. In mammalian cells, UHRF1 aids in the creation and maintenance of DNA methylation patterns. The establishment domains, including E3 ligase activity, are well described, whereas the maintenance domains are less understood. In human CRC cells, UHRF1's ability to bind histone- and hemimethylated DNA, but not its E3 ligase activity, maintains cancer-specific DNA methylation patterns. Disruption of these chromatin reader activities leads to the restoration of DNA hypermethylation, reactivation of epigenetically silenced tumor suppressor genes (TSGs), and a reduction in the oncogenic properties of CRC (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Y et al. 2012 reported that UHRF1 has a significant role in the cellular proliferation biological process and might affect CRC risk through this pathway (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Based on the study of Y et al. in 2019, miR-506 could have a significant regulatory effect on the KISS1/PI3K/NF-kB signaling pathway through silencing UHRF1 in CRC patients (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The report of J et al. in 2021 revealed the possible effect of UHRF1 silencing on the STAT1 and DNMT1 regulation and inhibition of CRC growth (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our results are consistent with the mentioned previous studies. Based on our results, up-regulation of UHRF1 has a significant correlation with the higher risk of CRC.\u003c/p\u003e \u003cp\u003eBased on the study of Luo G. et al. in 2022, UHRF1 regulates the estrogen signaling pathway and could regulate cell growth in BC patients (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). UHRF1 could enhance BC development through KLF17 suppression. This suppression happens using promoter hypermethylation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In GC patients, miR-146a/b could regulate GC invasion and metastasis by targeting UHRF1 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo prior research has explored the potential roles of ZNF213-AS1 in the development of breast cancer, gastric cancer, and colorectal cancer. Based on our study, EMX2OS and ZNF213-AS1 are the two potential regulatory diagnostic biomarkers of BC, GC, and CRC. The mentioned lncRNAs might affect the DNA methylation and gene expression signaling pathways through the regulation of UHRF1 expression level. Furthermore, we found that miR-4479 could modulate DNA methylation and gene expression signaling pathways via negative regulation of UHRF1. No previous research has investigated the potential role of miR-4479 in BC, CRC, and GC. However, previous studies revealed the potential roles of this microRNA in ovarian cancer and lung cancer. Based on the study of Wang et al. in 2022, miR-4479 has a significantly low expression in epithelial ovarian cancer (EOC) patients. Furthermore, miR-4479 could be considered as a potential diagnostic biomarker of EOC (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). According to the 2022 study by Chakraborty et al., miR-4479 targets genes that are up-regulated and overexpressed in lung cancer (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Our previous studies also revealed potential coding and non-coding biomarkers of BC (\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and GC (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on previous studies, lncRNA EMX2OS regulates the invasion and regulation of ovarian cancer cells through the regulation of PD-L1/AKT3/miR-654-3p (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In The GC samples, EMX2OS could be an enhancer RNA and regulate the prognosis of EMX2OS (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) In 2021, Molaei Ramshe et al. conducted a study on the expression level of EMX2OS in breast cancer patients and found no significant change in the expression levels of EMX2OS in breast cancer samples (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, more studies are needed for the validation of the obtained results in this experiment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eEMX2OS and ZNF213-AS1 lncRNAs are the two novel diagnostic biomarkers of BC, GC, and CRC as the two dysregulated non-coding RNA. These two lncRNAs modulate DNA methylation and gene expression signaling pathways through the regulation of UHRF1. UHRF1 is a potential diagnostic biomarker and oncogene of BC, GC, and CRC. miR-4479 also regulates UHRF1 and could affect the DNA methylation signaling pathway.\u003c/p\u003e"},{"header":"6. Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Ethics approval:\u0026nbsp;\u003c/strong\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Consent for publication:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Availability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets generated or analyzed during the current study are available in the GEO repository, GSE10810, GSE54129, and GSE81558.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Conflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Financial support and sponsorship:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Authors\u0026rsquo; contribution:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDorna Dayani, Simin Sharifi, Seyedeh Solmaz Mohammadi, Masoumeh Ghafourzadeh\u003c/strong\u003e\u003cstrong\u003e, Sheida Bahrami, Melika Azaripour, Nasim Karimi, and Ali Ghaneh\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Software, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization; \u003cstrong\u003eMohammad Rezaei and Seyedeh Zahra Shirdeli:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Supervision; \u003cstrong\u003eMansoureh Azadeh:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Dorna Dayani, Simin Sharifi, Seyedeh Solmaz Mohammadi, and Masoumeh Ghafourzadeh equally contributed to this study as the first authors. Sheida Bahrami, Melika Azaripour, Nasim Karimi, and Ali Ghaneh equally contributed to this study as second authors. Mohammad Rezaei and Seyedeh Zahra Shirdeli equally contributed to this study as supervisors and third authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKolak A, Kamińska M, Sygit K, Budny A, Surdyka D, Kukiełka-Budny B, et al. Primary and secondary prevention of breast cancer. Ann Agric Environ Med [Internet]. 2017 [cited 2023 Sep 20];24(4):549\u0026ndash;53. Available from: https://pubmed.ncbi.nlm.nih.gov/29284222/\u003c/li\u003e\n\u003cli\u003eFerlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer [Internet]. 2010 Dec 15 [cited 2023 Sep 20];127(12):2893\u0026ndash;917. Available from: https://pubmed.ncbi.nlm.nih.gov/21351269/\u003c/li\u003e\n\u003cli\u003eForman D, Burley VJ. Gastric cancer: global pattern of the disease and an overview of environmental risk factors. Best Pract Res Clin Gastroenterol [Internet]. 2006 [cited 2023 Sep 20];20(4):633\u0026ndash;49. Available from: https://pubmed.ncbi.nlm.nih.gov/16997150/\u003c/li\u003e\n\u003cli\u003eBaidoun F, Elshiwy K, Elkeraie Y, Merjaneh Z, Khoudari G, Sarmini MT, et al. Colorectal Cancer Epidemiology: Recent Trends and Impact on Outcomes. Curr Drug Targets [Internet]. 2021 Nov 19 [cited 2023 Sep 20];22(9):998\u0026ndash;1009. Available from: https://pubmed.ncbi.nlm.nih.gov/33208072/\u003c/li\u003e\n\u003cli\u003eKarimi P, Islami F, Anandasabapathy S, Freedman ND, Kamangar F. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev [Internet]. 2014 [cited 2023 Sep 20];23(5):700\u0026ndash;13. Available from: https://pubmed.ncbi.nlm.nih.gov/24618998/\u003c/li\u003e\n\u003cli\u003eChakrabortty A, Patton DJ, Smith BF, Agarwal P. miRNAs: Potential as Biomarkers and Therapeutic Targets for Cancer. Genes (Basel) [Internet]. 2023 Jun 29 [cited 2023 Sep 20];14(7):1375. Available from: https://pubmed.ncbi.nlm.nih.gov/37510280/\u003c/li\u003e\n\u003cli\u003eLi J, Meng H, Bai Y, Wang K. Regulation of lncRNA and Its Role in Cancer Metastasis. Oncol Res [Internet]. 2016 [cited 2023 Sep 20];23(5):205\u0026ndash;17. Available from: https://pubmed.ncbi.nlm.nih.gov/27098144/\u003c/li\u003e\n\u003cli\u003ePedraza V, Gomez-Capilla JA, Escaramis G, Gomez C, Torn\u0026eacute; P, Rivera JM, et al. Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness. Cancer [Internet]. 2010 Jan 15 [cited 2023 Sep 20];116(2):486\u0026ndash;96. 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LINC1521 and miR-3679-5p modulate cellular response to chemical stress in breast cancer patients through regulation of XBP1 expression as a potential diagnostic biomarker. Res Sq [Internet]. 2023 Aug 11 [cited 2023 Nov 20]; Available from: https://www.researchsquare.com\u003c/li\u003e\n\u003cli\u003eTavousi N, Taqizadeh Q, Nasiriyan E, Tabaeian P, Rezaei M, Azadeh M. ADAMTS5 modulates breast cancer development as a diagnostic biomarker and potential tumor suppressor, regulating by BAIAP2-AS1, VTI1B, CRNDE, and hsa-miR-135b-3p: integrated systems biology and experimental approach. 2022 Jul 27 [cited 2022 Aug 6]; Available from: https://www.researchsquare.com\u003c/li\u003e\n\u003cli\u003eBarani A, Beikverdi K, Mashhadi B, Parsapour N, Rezaei M, Javid P, et al. Transcription analysis of the THBS2 gene through regulation by potential non-coding diagnostic biomarkers and oncogenes of gastric cancer in the ECM receptor interaction signaling pathway: integrated systems biology and experimental investigation. 2022 Nov 23 [cited 2023 May 13]; Available from: https://www.researchsquare.com\u003c/li\u003e\n\u003cli\u003eDuan M, Fang M, Wang C, Wang H, Li M. LncRNA EMX2OS Induces Proliferation, Invasion and Sphere Formation of Ovarian Cancer Cells via Regulating the miR-654-3p/AKT3/PD-L1 Axis. Cancer Manag Res [Internet]. 2020 [cited 2023 Sep 22];12:2141\u0026ndash;54. Available from: https://pubmed.ncbi.nlm.nih.gov/32273754/\u003c/li\u003e\n\u003cli\u003eLiu GX, Tan YZ, He GC, Zhang QL, Liu P, Li CF. EMX2OS plays a prognosis-associated enhancer RNA role in gastric cancer. Medicine [Internet]. 2021 Oct 15 [cited 2023 Sep 22];100(41):E27535. Available from: https://pubmed.ncbi.nlm.nih.gov/34731149/\u003c/li\u003e\n\u003cli\u003eRamshe SM, Ghaedi H, Omrani MD, Geranpayeh L, Alipour B, Ghafouri-Fard S. Up-regulation of FOXN3-AS1 in invasive ductal carcinoma of breast cancer patients. Heliyon [Internet]. 2021 Oct 1 [cited 2023 Sep 22];7(10). Available from: https://pubmed.ncbi.nlm.nih.gov/34703931/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","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":"microRNA, lncRNA, Systems Biology, RNA Interaction, Microarray, UHRF1","lastPublishedDoi":"10.21203/rs.3.rs-4271471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4271471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer is the most commonly diagnosed cancer in women and ranks as the second most prevalent cancer globally. The prevalence of gastric cancer (GC) varies substantially between men and women, as well as in different countries. Male rates are two to three times higher than female rates. Colorectal cancer ranks as the third most common cancer globally and is the second leading cause of death related to cancer. In this study, our objective was to identify new non-coding biomarkers for breast cancer, gastric cancer, and colorectal cancer.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eMicroarray analysis was performed to find the central protein-coding gene with the dysregulation in BC, GC, and CRC. Using ENCORI, validation of microarray analysis and survival analysis was utilized. RNA and protein interaction was performed by miRWalk, lncRRIsearch, and STRING. Signaling pathways were identified using Enrichr and Reactome databases. To validate the expression analysis and confirm the biomarker potential of RNAs, a qRT-PCR experiment was conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on microarray analysis, UHRF1 has a significant high expression in BC, CC, and GC. UHRF1 modulates DNA methylation and gene expression signaling pathways. lncRNAs EMX2OS and ZNF213-AS1 have interaction with UHRF1 mRNA. miR-4479 suppresses the expression of UHRF1 with interaction to 3\u0026rsquo;UTR region. qRT-PCR validates bioinformatics expression analysis. Furthermore, ROC analysis suggested that UHRF1, EMX2OS, and ZNF213-AS1 could potentially be used as diagnostic biomarkers for breast cancer, gastric cancer, and colorectal cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003emiR-4479, lncRNAs EMX2OS, and ZNF213-AS1 regulate the DNA methylation signaling pathway via interaction with UHRF1. UHRF1, EMX2OS, and ZNF213-AS1 may be regarded as potential diagnostic biomarkers for breast cancer, gastric cancer, and colorectal cancer.\u003c/p\u003e","manuscriptTitle":"RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 20:31:37","doi":"10.21203/rs.3.rs-4271471/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":"f6f54b9f-9278-4bf6-9873-29af9e229dae","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30712894,"name":"Bioinformatics"},{"id":30712895,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2024-04-17T20:31:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 20:31:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4271471","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4271471","identity":"rs-4271471","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}