Bioinformatics analysis of miR-2861 and miR-5011-5p that function as potential tumor suppressors in colorectal carcinogenesis.

Individuals who were operated on at Gaziantep University Faculty of Medicine, Department of General Surgery, and diagnosed with CRC were included in the current study. Ethics committee approval of the study that was conducted in accordance with the Helsinki Declaration was obtained from Gaziantep University Medical Ethics Committee (Approval number: 2021/179). The patients taking part in the investigation provided informed consent. The sample of the study, which included tumor, and non-tumor tissues, consisted of 80 CRC patients who participated in the study between 2017 and 2022. The inclusion criteria required patients to have a confirmed diagnosis of colon and rectum adenocarcinoma by histology and have no history of past chemotherapy or radiotherapy treatments (Fig.  1 ). In contrast, the persons who had autoimmune or inflammatory disorders, had undergone treatments that could affect miRNA expression, had various malignancies, were experiencing active infections, or had experienced cardiovascular disease within the last six months were excluded from the study. Before moving on to the next phase, RNA isolation, the acquired tumor and non-tumor tissues were kept in RNAlatter solution at -80 ℃. Fig. 1 Patient flow diagram. Flow diagram of included patients with CRC Patient flow diagram. Flow diagram of included patients with CRC Tissue samples were homogeneously lysed prior to miRNA isolation. miRNA was extracted using the mirVanaTM miRNA isolation kit and phenol (AM1560, Invitrogen, USA) with folllowing the producer instructions. A spectrophotometer (NanoDrop, Maestrogen) was used to measure the quantity of miRNAs, which were stored at -80 ℃. TaqMan™ Advanced miRNA cDNA Synthesis Kit ( A28007 , Applied Biosystems, USA) was utilized for cDNA synthesis. Prior to use in the quantitative Real-Time Polymerase Chain Reaction (RT-qPCR), cDNAs were stored at -20 ℃. RT-qPCR was carried out using Applied Biosystems (StepOne& StepOnePlus Real-Time PCR Systems) to assess the expression levels of miR-2861 and miR-5011-5p expression in the tumor and non-tumor tissues of CRC patients. TaqMan Primer Probe (Thermo Fisher Scientific), TaqManTM Advanced miRNA Assay (A25576, Applied Biosystems, USA), and TaqManTM Fast Advanced Master Mix (Thermo Fisher Scientific, 4444557, Applied Biosystems, USA) were used for RT-qPCR. RNU6B was used as an endogenous [ 20 ]. The sequences of primers are: miR-2861: Forward 5’- GGGCCTGGCGGT-3’, Revers 5’- GTTTTTTTTTTTTTTTCCGCCCA-3’; miR-5011-5p:5’-CGCAGTATATATACAGCCATGC-3’,Revers 5’- GGTCCAGTTTTTTTTTTTTTTTGAGT − 3’; RNU6B :Forward 5’-GCTTCGGCAGCACATATACTAAAAT-3’; Revers 5’-CGCTTCACGAATTTGCGTGTCAT-3’. All reactions were performed in triplicate and analysis of the expression levels of miR-5011-5p and miR-2861 were carried out by applying the 2 −∆∆Ct technique [ 21 ]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of databases that can be used to clarify the key characteristics and outcomes of biological systems [ 22 ]. The Gene Ontology (GO) is a high-quality database that exhibited the functional gene annotation and cell localization of genes [ 23 ]. DIANA-miRPath v3.0 that is provide accurate statistics is a miRNA pathway analysis online tool [ 24 ]. The GO and KEGG pathway enrichment of these miRNAs was examined using the online tool DIANA-miRPath v3.0 in order to further clarify the biological roles of target genes controlled by downregulated miR-2861 and miR-5011-5p ( http://www.microrna.gr/miRPathv3 ). The GO enrichment analysis consisted of three categories. These were the biological process, the cellular component, and the molecular function. The online tool Venny 2.1.0 was used to create the Venn diagram by overlapping the target genes of miRNAs ( https://bioinfogp.cnb.csic.es/tools/venny ). Results were analyzed using SPSS software (version 22.0; SPSS, Inc.). In calculating the difference in miRNAs expression levels in tumor and non-tumor adjacent tissues, ΔCt tumor and ΔCt non−tumor values were determined according to the 2 −∆∆Ct method [ 21 ]. The results obtained from the Kolmogorov–Smirnov and Shapiro–Wilk normality tests show that the ∆Ct values obtained from tumor and non-tumor tissues in this study conform to a normal distribution. As a result, parametric Paired-t test was used to determine the changes in the expression levels of miRNAs between tumor and non-tumor tissues. The experimental data are presented as the mean ± standard deviation. Mann Whitney U test was applied to determine the relationship between clinical and pathological parameters of patients with CRC and fold change data showing the expression values of miRNAs (Non-parametric test was used because fold change data did not comply with normal distribution). p  < 0.05 was considered to indicate a statistically significant difference.

Clinical and pathological features of a total of 80 patients with CRC are summarized in Table 1 . The patient’s age range was 29–87 (mean 58.425 ± 14.79). According to the tissue type, 55 cancer cases were located in the colon and 25 in the rectum. Lymphovascular invasion was known in 27.5% of the cases, of these, 47.5% had lymph node metastasis. Histologically, 77.5% of the tumors were adenocarcinoma. Table 1 Clinical and pathological characteristics of CRC patients Characteristics Percentage of CRC patients Tissue type Colon 68.75% Rectum 31.25% Gender Female 38.75% Male 61.25% Lymphovascular invasion Positive 27.5% Negative 72.5% Neural invasion Positive 20% Negative 80% Lymph node metastasis N0 52.5% N1 28.75% N2 18.75% Invasion T1 1.25% T2 18.75% T3 58.75% T4 21.25% Metastasis M0 78.75% M1 21.25% Stage I 13.75% II 35% III 33.75% IV 17.5% Tumor histology Adenocarcinoma 77.5% Mucinous adenocarcinoma 22.5% N0: No regional lymph node metastasis, N1: 1–3 pericolic lymph involvement N2:2–4 pericolic lymph involvement or perirectal involvement; M0: No distant metastasis, M1: Metastases in more than one organ/site or the peritoneum; T1: Tumor invades submucosa, T2: Tumor invades muscularis propria, T3: Tumor invades through the muscularis propria into pericolorectal tissues, T4: Tumor penetrates to the surface of the visceral peritoneum or tumor directly invades or is adherent to other organs or structures Clinical and pathological characteristics of CRC patients N0: No regional lymph node metastasis, N1: 1–3 pericolic lymph involvement N2:2–4 pericolic lymph involvement or perirectal involvement; M0: No distant metastasis, M1: Metastases in more than one organ/site or the peritoneum; T1: Tumor invades submucosa, T2: Tumor invades muscularis propria, T3: Tumor invades through the muscularis propria into pericolorectal tissues, T4: Tumor penetrates to the surface of the visceral peritoneum or tumor directly invades or is adherent to other organs or structures In the current study, to evaluate the expression level of miR-2861 and miR-5011-5p in CRC tissues, we performed RT-qPCR. As shown in Fig.  2 , the results of RT-qPCR revealed in the CRC tissue samples, the expression levels of both miR-2861 and miR-5011-5p were significantly decreased compared with the non-tumor tissue samples (Both p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:<$$\end{document} 0.001). Fig. 2 Expression of miR-2861 ( A ) and miR-5011-5p ( B ) in tumor and non-tumor tissue samples of patients with CRC Expression of miR-2861 ( A ) and miR-5011-5p ( B ) in tumor and non-tumor tissue samples of patients with CRC In this study, no significant relationship was found between the expression levels of both miR-2861 and miR-5011-5p and the clinical and pathological features of the patients ( p  > 0.05) (Table 2 ). Table 2 The association between miRNAs expressions and clinical-pathological characteristics of CRC patients Characteristics miR-2861 Fold Change miR-5011-5p Fold Change Mean ± SD Median (Q1-Q3) Mean ± SD Median (Q1-Q3) Tissue type Colon 0.76 ± 1.29 0.35 (0.14–0.57) 0.00012 ± 0.00012 0.00007 (0.00003-0.00019) Rectum 0.62 ± 0.92 0.36 (0.14–0.68) 0.00014 ± 0.00010 0.00013 (0.00008-0.00019) p-value 0.775 0.185 Gender Female 1.13 ± 1.65 0.36 (0.14–1.62) 0.00012 ± 0.00012 0.00009 (0.00004-0.00017) Male 0.46 ± 0.66 0.34 (0.14–0.49) 0.00013 ± 0.00011 0.00010 (0.00004-0.00019) p-value 0.275 0.726 Age ≥ 55 0.73 ± 1.18 0.33 (0.15–0.68) 0.00012 ± 0.00011 0.00009 (0.00004-0.00017) < 55 0.68 ± 1.23 0.41(0.09–0.49) 0.00014 ± 0.00012 0.00010 (0.00004-0.00019) p-value 0.983 0.444 Neural invasion Positive 0.89 ± 1.33 0.48 (0.21–0.75) 0.00011 ± 0.00011 0.00008 (0.00003-0.00018) Negative 0.67 ± 1.15 0.31 (0.14–0.57) 0.00013 ± 0.00012 0.00010 (0.00004-0.00019) p-value 0.248 0.455 Lymphovascular invasion Positive 0.77 ± 1.19 0.41 (0.14–0.68) 0.00011 ± 0.00010 0.00008 (0.00003-0.00017) Negative 0.70 ± 1.20 0.34 (0.15–0.57) 0.00014 ± 0.00012 0.00010 (0.00004-0.00019) p-value 0.690 0.264 Stage I-II 0.88 ± 1.38 0.40 (0.15–0.90) 0.00015 ± 0.00012 0.00011 (0.00005–0.00020) III-IV 0.56 ± 0.96 0.26 (0.12–0.49) 0.00011 ± 0.00011 0.00007 (0.00003-0.00015) p-value 0.120 0.104 Tumor histology Adenocarcinoma 0.76 ± 1.31 0.34 (0.14–0.49) 0.00014 ± 0.00012 0.00010 (0.00004–0.00020) Mucinous adenocarcinoma 0.57 ± 0.56 0.47 (0.26–0.75) 0.00011 ± 0.00008 0.00010 (0.00004-0.00014) p-value 0.357 0.644 The association between miRNAs expressions and clinical-pathological characteristics of CRC patients GO analysis results showed that in terms of biological process, downregulated miRNAs were principally enriched in Cell junction assembly, Mitotic cell cycle, Regulation of small GTPase mediated signal transduction, Transcription from RNA polymerase II promoter, Protein complex assembly, Cell death, and Phospholipid metabolic process, etc.; that cellular component was enriched in Organelle, Protein complex, Cytosol, Nucleoplasm, Integral component of plasma membrane, Endosome; that molecular functions were mainly enriched in Ion binding, Nucleic acid binding transcription factor activity, Enzyme binding, Cytoskeletal protein binding, and Small conjugating protein binding, etc. As presented in Table 3 , it was discovered that 1919, 2011, and 2021 genes were linked to biological processes, cellular components, and molecular functions, respectively. Table 3 GO analysis of the miR-2861 and miR-5011-5p genes of focus in the biological process, cellular component, and molecular function subcategories Biological Process GO ID GO term name p-value Target gene counts GO:0034641 Cellular nitrogen compound metabolic process 3.56201920752e-30 606 GO:0009058 Biosynthetic process 4.38635914641e-27 535 GO:0048011 Neurotrophin TRK receptor signaling pathway 4.15709921254e-18 58 GO:0006464 Cellular protein modification process 6.40303847813e-17 314 GO:0007173 Epidermal growth factor receptor signaling pathway 1.05142339045e-10 47 GO:0016032 Viral process 4.57205248195e-10 72 GO:0007596 Blood coagulation 4.57205248195e-10 74 GO:0044403 Symbiosis, encompassing mutualism through parasitism 1.59183373991e-09 78 GO:0008543 Fibroblast growth factor receptor signaling pathway 2.24897369187e-07 40 GO:0007268 Synaptic transmission 2.31243557033e-07 69 GO:0010467 Gene expression 4.52592568861e-07 74 GO:0007267 Cell-cell signaling 6.8546866156e-07 98 GO:0048015 Phosphatidylinositol-mediated signaling 8.59465645713e-07 31 GO:0044267 Cellular protein metabolic process 5.47606994982e-06 61 GO:0022607 Cellular component assembly 2.36309493467e-05 159 GO:0044281 Small molecule metabolic process 8.60125604128e-05 255 GO:0008286 Insulin receptor signaling pathway 9.02071975233e-05 33 GO:0007411 Axon guidance 0.000103367354412 72 GO:0061024 Membrane organization 0.000138257413067 76 GO:0042475 Odontogenesis of dentin-containing tooth 0.0018141276907 22 GO:0001501 Skeletal system development 0.00181412806854 38 GO:0006351 Transcription, DNA-templated 0.00210511146199 304 GO:0065003 Macromolecular complex assembly 0.00498485716002 102 GO:0034330 Cell junction organization 0.00540947148139 25 GO:0045944 Positive regulation of transcription from RNA polymerase II promoter 0.00540947148139 193 GO:0050900 Leukocyte migration 0.00703996455424 20 GO:0030168 Platelet activation 0.00937256710515 28 GO:0010881 Regulation of cardiac muscle contraction by regulation of the release of sequestered calcium ion 0.00939908203927 8 GO:0034329 Cell junction assembly 0.00939908203927 12 GO:0006367 Transcription initiation from RNA polymerase II promoter 0.00939908203927 33 GO:0000278 Mitotic cell cycle 0.0100352893559 44 GO:0048870 Cell motility 0.0100352893559 73 GO:0009653 Anatomical structure morphogenesis 0.0116805437846 19 GO:0007202 Activation of phospholipase C activity 0.012977106784 13 GO:0051056 Regulation of small GTPase mediated signal transduction 0.0142452266974 33 GO:0043647 Inositol phosphate metabolic process 0.0177736871037 9 GO:0030198 Extracellular matrix organization 0.0177736871037 50 GO:0006366 Transcription from RNA polymerase II promoter 0.0177736871037 85 GO:0071872 Cellular response to epinephrine stimulus 0.0224737242598 6 GO:0006461 Protein complex assembly 0.0224737242598 88 GO:0097105 Presynaptic membrane assembly 0.027933581536 6 GO:0006661 Phosphatidylinositol biosynthetic process 0.032189112552 12 GO:0022617 extracellular matrix disassembly 0.0385975565239 16 GO:0008219 Cell death 0.040802650832 102 GO:0006644 Phospholipid metabolic process 0.0421169733405 24 GO:0035115 Embryonic forelimb morphogenesis 0.0435596744425 14 GO:0006950 Response to stress 0.0435596744425 238 GO:0001701 In utero embryonic development 0.0462428421608 51 GO:0008150 Biological_process 8.82457811177e-07 1919 Cellular Component GO ID GO term name p-value Target gene counts GO:0043226 Organelle 6.03900159495e-77 1288 GO:0043234 Protein complex 2.61246399076e-06 244 GO:0005829 Cytosol 4.64509957821e-06 330 GO:0005654 Nucleoplasm 0.000187077528473 145 GO:0005887 Integral component of plasma membrane 0.00651335183428 157 GO:0005768 Endosome 0.0451369481673 87 GO:0005575 Cellular component 2.63877605369e-13 2011 Molecular Function GO ID GO term name p-value Target gene counts GO:0043167 Ion binding 2.65673519667e-29 753 GO:0001071 Nucleic acid binding transcription factor activity 1.66409389563e-21 177 GO:0019899 Enzyme binding 3.69126090702e-11 184 GO:0030234 Enzyme regulator activity 6.05984458377e-09 126 GO:0000988 Protein binding transcription factor activity 2.74550365868e-07 74 GO:0008092 Cytoskeletal protein binding 1.00759465352e-06 119 GO:0001077 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in positive regulation of transcription 0.00275430864634 49 GO:0008289 Lipid binding 0.0298316963194 82 GO:0032182 Small conjugating protein binding 0.0371915193051 17 GO:0003674 Molecular function 4.57628650454e-21 2021 GO analysis of the miR-2861 and miR-5011-5p genes of focus in the biological process, cellular component, and molecular function subcategories As a result of KEGG pathway analysis, it was determined that miR-2861 has 1 ( AKT2 ) and miR-5011-5p has 10 ( GSK3B , SMAD2 , TCF4 , PIK3CB , JUN , PIK3R1 , MAPK3 , AKT3 , PIK3CA and TGFBR2 ) target genes in the pathogenesis of CRC (Fig.  3 ). The 16 KEGG pathways significantly enriched the downregulated miR-2861 and miR-5011-5p. These were signaling mechanisms that controlled the pluripotency of stem cells, TGF-beta signaling pathway, proteoglycans in cancer, Rap1 signaling pathway, Ras signaling pathway, transcriptional misregulation in cancer, choline metabolism in cancer, pathways in cancer, cAMP signaling pathway, insulin signaling pathway, T cell receptor signaling pathway, Wnt signaling pathway, endocytosis, mTOR signaling pathway, PI3K-Akt signaling pathway, platelet activation (Table 4 ). Since both miRNAs included in a total of 16 KEGG pathways had target genes and the target genes showed significant overlap among the signaling pathways, it was confirmed by Venn diagram, which showed that both miRNAs were commonly found in the four top-regulated pathways (Signaling pathways regulating pluripotency of stem cells, TGF-beta signaling pathway, Proteoglycans in cancer, Rap1 signaling pathway) shown in Table 4 . Therefore, a Venn diagram was constructed to analyze the overlaps among the target genes of both miR-2861 and miR-5011-5p in Signaling pathways regulating pluripotency of stem cells, TGF-beta signaling pathway, Proteoglycans in cancer and Rap1 signaling pathways. The common target gene of miR-5011-5p in the first four pathways was found to be MAPK3 (Fig.  4 A and Supplementary Table 1 ). The common target gene of miR-2861 in Signaling pathways regulating pluripotency of stem cells, Proteoglycans in cancer and Rap1 signaling pathway was AKT2 (Fig.  4 B and Supplementary Table 2 ). Common signaling pathways involving target genes of both miRNAs were examined and target genes in these pathways were intersected with a Venn diagram. In these signaling pathways given in Table 4 , it was detected 21 target genes of miR-2861, 253 target genes of miR-5011-5p and 1 common overlapping target gene ( SP1 ) of both miRNAs (Fig.  5 ). Fig. 3 Signaling pathways and genes targeted by miR-2861 and miR-5011-p in CRC carcinogenesis Signaling pathways and genes targeted by miR-2861 and miR-5011-p in CRC carcinogenesis Fig. 4 Venn diagram showing the overlaps between target genes of miR-2861 ( A ) and miR-5011-5p ( B ), which regulate the first four KEGG pathways in Table 4 . (Diagrams were drawn using Venny 2.1.0 website ( https://bioinfogp.cnb.csic.es/tools/venny/ , accessed 10 November 2024). The list of miRNAs and genes is reported in Supplementary Tables 1 and 2 , respectively Venn diagram showing the overlaps between target genes of miR-2861 ( A ) and miR-5011-5p ( B ), which regulate the first four KEGG pathways in Table 4 . (Diagrams were drawn using Venny 2.1.0 website ( https://bioinfogp.cnb.csic.es/tools/venny/ , accessed 10 November 2024). The list of miRNAs and genes is reported in Supplementary Tables 1 and 2 , respectively Fig. 5 Target genes of miR-2861 and miR-5011-5p in common signaling pathways Target genes of miR-2861 and miR-5011-5p in common signaling pathways Table 4 KEGG analysis of downregulated miR-2861 and miR-5011-5p in tissues with CRC patients Pathways Pathways ID miRNAs Target Genes p -values Signaling pathways regulating pluripotency of stem cells hsa04550 miR-2861 AKT2 3.45847352172e-10 miR-5011-5p BMI1 , FZD7 , JARID2 , GSK3B , FZD5 , OTX1 , ID2 , KAT6A , SMAD2 , FGFR3 , SMAD9 , PIK3CB , WNT5A , BMPR1B , TBX3 , HAND1 , SMARCAD1 , WNT3 , ZFHX3 , ID4 , HESX1 , FZD3 , ACVR2B , RIF1 , LIFR , SKIL , ESRRB , ZIC3 , PIK3R1 , MAPK3 , BMP2 , WNT11 , FZD1 , IGF1 , AKT3 , BMPR1A , PIK3CA , IL6ST , MYF5 , FGFR2 , FGFR1 , SOX2 , GRB2 , BMPR2. TGF-beta signaling pathway hsa04350 miR-2861 SP1 1.53415737369e-07 miR-5011-5p ID2 , ROCK1 , SMAD2 , SMAD6 , SMAD9 , BMPR1B , BMP5 , PITX2 , ID4 , ACVR2B , ZFYVE16 , RBL1 , GDF6 , MAPK3 , BMP2 , SP1 , EP300 , BMPR1A , BAMBI , IFNG , LTBP1 , SMAD7 , TGFBR2 , BMPR2 , RPS6KB1 Proteoglycans in cancer hsa05205 miR-2861 AKT2 , PRKACA , FLNA , HSPG2 2.75436696556e-05 miR-5011-5p FZD7 , CAMK2D , FZD5 , PDCD4 , ROCK1 , SMAD2 , PIK3CB , WNT5A , ARHGEF12 , ROCK2 , TIAM1 , WNT3 , TLR4 , PPP1R12B , FZD3 , ARAF , ANK2 , PPP1R12A , HIF1A , ITGA2 , PIK3R1 , SOS1 , MAPK3 , WNT11 , FZD1 , IGF1 , AKT3 , PIK3CA , HOXD10 , VEGFA , FGFR1 , KDR , GRB2 , MDM2 , ELK1 , RPS6KB1. Rap1 signaling pathway hsa04015 miR-2861 AKT2 , CSF1R , ADCY4 , NGFR 0.00027364149203 miR-5011-5p MAGI2 , FGF12 , PDGFRA , SIPA1L3 , PARD6G , FGFR3 , CALM3 , CALM1 , LPAR3 , PIK3CB , RAP1A , MAGI3 , FGF4 , TIAM1 , EFNA3 , EFNA2 , FGF20 , LPAR1 , F2R , FLT1 , KIT , PIK3R1 , DOCK4 , EPHA2 , MAPK3 , FGF18 , FYB , IGF1 , AKT3 , MAGI1 , PDGFD , PIK3CA , VASP , ADCY8 , PRKD3 , RASGRP3 , FGFR2 VEGFA , FGFR1 , KDR , CSF1 , ADCY9 , PARD6B , TEK , RAPGEF5 , GRIN2B Ras signaling pathway hsa04014 miR-2861 AKT2 , JMJD7-PLA2G4B , PRKACA , CSF1R , NGFR , PLA2G2C 0.000626751886551 miR-5011-5p FGF12 , KSR2 , PDGFRA , FGFR3 , STK4 , CALM3 , CALM1 , PIK3CB , RAP1A , ETS2 , ETS1 , GNG12 , FGF4 , TIAM1 , EFNA3 , EFNA2 , FGF20 , REL , FLT1 , KIT , PIK3R1 , SOS1 , PLA2G4A , EPHA2 , MAPK3 , FGF18 , IGF1 , AKT3 , PDGFD , PIK3CA , RASAL2 , RASGRP3 , RGL1 , RAB5C , FGFR2 , VEGFA , HTR7 , FGFR1 , KDR , CSF1 , GRB2 , TEK , ELK1 , RAPGEF5 , GRIN2B Transcriptional misregulation in cancer hsa05202 miR-2861 BAIAP3 , PAX8 , SP1 , TFE3 , CSF1R , NGFR 0.00147120191369 miR-5011-5p BMI1 , ID2 , ETV6 , RUNX1 , HMGA2 , FLI1 , HOXA9 , MLLT3 , RUNX2 , UTY , CEBPB , RUNX1T1 , NR4A3 , KLF3 , BMP2K , REL , FLT1 , ETV1 , HDAC2 , ATF1 , KDM6A , SP1 , BCL6 , IGF1 , WHSC1 , CSF2 , KMT2A , MYCN , MEF2C , HOXA11 , SIX4 , TGFBR2 , MDM2 Choline metabolism in cancer hsa05231 miR-2861 AKT2 , JMJD7-PLA2G4B , SP1 miR-5011-5p PDGFRA , WASF1 , PIK3CB , DGKG , JUN , HIF1A , PPAP2B , PIK3R1 , SOS1 , PLA2G4A , MAPK3 , SP1 , DGKB , SLC44A1 , AKT3 , PDGFD , PIK3CA , CHKA , GRB2 , DGKI , RPS6KB1 , DGKH Pathways in cancer hsa05200 miR-2861 E2F2 , PAX8 , AKT2 , PRKACA , CSF1R , ADCY4 0.0107771560553 miR-5011-5p FZD7 , FGF12 , GSK3B , PDGFRA , FZD5 , ROCK1 , SMAD2 , FGFR3 , STK4 , AGTR1 , LPAR3 , RUNX1 , PIK3CB , CUL2 , TPR , TCF7L2 , WNT5A , HHIP , COL4A5 , ARHGEF12 , ROCK2 , ARNT , ETS1 , GNG12 , RARB , FGF4 , WNT3 , FGF20 , TRAF5 , FZD3 , ARAF , PTGER3 , ARHGEF11 , RUNX1T1 , AR , JUN , LPAR1 , HIF1A , F2R , KIT , ITGA2 , PIK3R1 , COL4A4 , SOS1 , HDAC2 , MAPK3 , FGF18 , BMP2 , WNT11 , FZD1 , IGF1 , EP300 , CASP8 , AKT3 , CTNNA3 , PIK3CA , ADCY8 , RASGRP3 , FGFR2 , SHH , VEGFA , PTEN , FGFR1 , GRB2 , ADCY9 , TGFBR2 , MDM2 , COL4A1 cAMP signaling pathway hsa04024 miR-2861 AKT2 , PRKACA , ADCY4 0.0140031341194 miR-5011-5p SSTR1 , CAMK2D , PDE4B , ATP1B2 , ROCK1 , CAMK4 , ATP2B2 , CALM3 , CALM1 , PIK3CB , HHIP , RAP1A , ROCK2 , PDE3A , DRD1 , HTR1F , SUCNR1 , TIAM1 , BDNF , PTGER3 , PDE4D , CREB1 , GRIA2 , PPP1R12A , JUN , SOX9 , F2R , PIK3R1 , MAPK3 , CHRM2 , HTR4 , EP300 , AKT3 , PIK3CA , ADCY8 , ATP2B4 , ADCY9 , NPY , GRIN2B Insulin signaling pathway hsa04910 miR-2861 G6PC3 , AKT2 , PRKACA 0.022598936178 miR-5011-5p IRS2 , GSK3B , SOCS4 , CALM3 , CALM1 , PIK3CB , PPP1R3F , PDE3A , PTPN1 , SORBS1 , ARAF , G6PC , PPP1R3A , EIF4E , PIK3R1 , SOS1 , IRS1 , MAPK3 , PRKAA1 , AKT3 , PPARGC1A , ACACB , PRKAG2 , PIK3CA , PRKAR2B , GRB2 , ELK1 , RPS6KB1 T cell receptor signaling pathway hsa04660 miR-2861 PTPRC , AKT2 0.0240218130101 miR-5011-5p GSK3B , PIK3CB , TEC , DLG1 , PPP3R1 , PPP3CC , FYN , NCK1 , IL4 , JUN , NCK2 , PIK3R1 , SOS1 , MAPK3 , AKT3 , CSF2 , PIK3CA , MALT1 , IFNG , MAP3K8 , CTLA4 , GRB2 Wnt signaling pathway hsa04310 miR-2861 PRKACA 0.0317650097424 miR-5011-5p FZD7 , CAMK2D , GSK3B , FZD5 , LRP6 , VANGL1 , TCF7L2 , WNT5A , ROCK2 , PPP3R1 , WNT3 , PPP3CC , FZD3 , NLK , SENP2 , JUN , VANGL2 , CSNK2A1 , WNT11 , FZD1 , EP300 , BAMBI , DAAM1 , TBL1XR1 Endocytosis hsa04144 miR-2861 CHMP1B , RAB11FIP4 , ASAP3 , GIT1 , CSF1R 0.0445627541529 miR-5011-5p ARAP2 , PDGFRA , PARD6G , SMAD2 , SMAD6 , STAM , ADRBK2 , FGFR3 , VTA1 , WWP1 , NEDD4L , VPS36 , TSG101 , ASAP1 , VPS4B , PSD3 , ZFYVE16 , RAB11FIP2 , VPS37A , F2R , EPS15 , FLT1 , KIT , RAB11FIP3 , STAM2 , USP8 , NEDD4 , LDLR , DNM3 , SMAD7 , RAB5C , FGFR2 , IQSEC2 , KDR , PARD6B , TGFBR2 , MDM2 , ASAP2 mTOR signaling pathway hsa04150 miR-2861 AKT2 0.0445627541529 miR-5011-5p PIK3CB , RICTOR , EIF4E , HIF1A , PIK3R1 , IRS1 , MAPK3 , PRKAA1 , RPS6KA3 , IGF1 , AKT3 , PIK3CA , VEGFA , PTEN , RPS6KB1 PI3K-Akt signaling pathway hsa04151 miR-2861 G6PC3 , AKT2 , CSF1R , CSF3 , NGFR 0.0450929718348 miR-5011-5p FGF12 , GSK3B , PDGFRA , PPP2R5E , MYB , ITGB8 , FGFR3 , THBS4 , LPAR3 , PIK3CB , COL4A5 , GNG12 , FGF4 , EFNA3 , EFNA2 , FGF20 , TLR4 , IFNAR1 , CREB1 , IL4 , G6PC , EIF4E , LPAR1 , F2R , COL5A1 , FLT1 , KIT , ITGA2 , PIK3R1 , COL4A4 , SOS1 , EPHA2 , IRS1 , MAPK3 , PRKAA1 , CHRM2 , FGF18 , IGF1 , AKT3 , COL11A1 , PDGFD , PIK3CA , PKN2 , FGFR2 , VEGFA , PTEN , FGFR1 , KDR , CSF1 , GRB2 , COL5A2 , TEK , MDM2 , RPS6KB1 , COL4A1 Platelet activation hsa04611 miR-2861 AKT2 , JMJD7-PLA2G4B , PRKACA , ADCY4 0.0452427810114 miR-5011-5p GUCY1B3 , ROCK1 , PIK3CB , ARHGEF12 , RAP1A , ROCK2 , FYN , PPP1R12A , F2R , COL5A1 , ITGA2 , PIK3R1 , PLA2G4A , MAPK3 , AKT3 , COL11A1 , P2RY12 , GUCY1A2 , PIK3CA , VASP , ADCY8 , ADCY9 , COL5A2 KEGG analysis of downregulated miR-2861 and miR-5011-5p in tissues with CRC patients MAGI2 , FGF12 , PDGFRA , SIPA1L3 , PARD6G , FGFR3 , CALM3 , CALM1 , LPAR3 , PIK3CB , RAP1A , MAGI3 , FGF4 , TIAM1 , EFNA3 , EFNA2 , FGF20 , LPAR1 , F2R , FLT1 , KIT , PIK3R1 , DOCK4 , EPHA2 , MAPK3 , FGF18 , FYB , IGF1 , AKT3 , MAGI1 , PDGFD , PIK3CA , VASP , ADCY8 , PRKD3 , RASGRP3 , FGFR2 VEGFA , FGFR1 , KDR , CSF1 , ADCY9 , PARD6B , TEK , RAPGEF5 , GRIN2B

In conclusion, the findings of this study showed that the expression of both miR-2861 and miR-5011-5p was downregulated in CRC tissues. As a result, both miR-2861 and miR-5011-5p may play a tumor suppressor role in colorectal carcinoma, suggesting that both miRNAs could be used as candidate biomarkers for the detection of CRC. However, functional studies are still needed to reveal the roles of these miRNAs in CRC.

There are many reasons for the high incidence of CRC worldwide, but the most important are genetic and environmental factors. Excessive body weight, smoking, drinking too much alcohol, eating a lot of red or processed meat, and not getting enough exercise are additional risk factors for CRC [ 25 ]. Although the incidence and mortality rate of CRC have decreased thanks to novel surveillance and treatment methods, identifying the molecular mechanisms causing the formation of CRC is crucial for the early detection of patients [ 26 , 27 ]. Especially in recent years, researchers are paying a growing amount of attention to microRNAs as a result of the critical functions that they play in cell differentiation, the development of cancer [ 28 ]. However, considering the powerful role of miRNAs in various signaling pathways that are effective in cancer pathogenesis, it cannot be excluded that these small molecules will constitute an important area of study in future cancer management strategies. As a result, many miRNAs have been discovered to act as oncogenes or tumor-suppressor miRNAs and to be up-or down-regulated in human cancers [ 29 ]. miRNAs expressed at abnormal levels in CRC may contribute to cancer formation by disrupting cell signaling and cellular survival pathways such as the Wnt signaling pathway, EGFR, and p53 [ 30 ]. Patient survival, tumor stage, the existence of lymph node metastases, and response to CRC treatment can all be determined using the miRNA expression profile [ 31 ]. Different expression levels have been reported in several studies of CRC. It was shown that the expression levels of miR-485-3p, miR-4728-5p, miR-143 downregulated [ 20 , 32 ], while the expression levels of miR-17-5p, miR-20a-5p, miR-592, miR-584-5p, and miR-625-3p upregulated [ 33 , 34 ]. In this study, we evaluated the expression levels of miR-2861 and miR-5011-5p in tumor and non-tumor tissue samples collected from CRC patients. As a result of the study, expression levels of both miR-5011-5p and miR-2861 were reduced in CRC tissues compared with non-tumor subjects. Vastard et al. (2017) reported that miR-5011-5p showed lower expression in glioma and glioblastoma, which is consistent with our findings [ 35 ]. In another study, the downregulation of miR-5011-5p was observed in gastric cancer tissues and cell lines, and an inverse association was found between miR-5011-5p and its target gene, PAK2 [ 18 ]. Decreased expression of miR-5011-5p was also detected in blood samples of cervical dystonia patients compared to healthy controls [ 36 ]. In this research, KEGG pathway analysis showed that the common target gene of miR-5011-5p in Signaling pathways regulating pluripotency of stem cells, TGF-beta signaling pathway, Proteoglycans in cancer and Rap1 signaling pathway was MAPK3 gene, and Slattery et al. exhibited that MAPK3 was associated with colon cancer [ 37 ]. These results suggest that MAPK3 should be investigated as a target gene of miR-5011-5p in CRC. Another miRNA, miR-2861 has been found to be downregulated in endometriotic tissues, and its lower expression may be connected to endometriosis’s ectopic endometrial cell growth and death. Additionally, it was discovered in this study that miR-2861 targets STAT3 and MMP2 in cell growth and death [ 38 ]. Shi et al. found that miR-2861 showed low expression in diabetic retinopathy compared to the control group. In the same study, they showed that miR-2861 can inhibit the proliferation and promote apoptosis of human retinal vascular endothelial cells by targeting NDUFB7 [ 39 ]. In the another study, miR-2861 expression was decreased in cervical cancer patients [ 40 ]. Also, a different study in cervical cancer revealed that miR-2861 targets the CCND1, AKT2, and EGFR pathways [ 15 ]. In our study, KEGG pathway analysis revealed that the common target gene of miR-2861 in Signaling pathways regulating pluripotency of stem cells, Proteoglycans in cancer and Rap1 signaling pathway was AKT2 . PI3K/AKT signaling pathway plays an important role in cell proliferation, survival, migration and invasion in various cancers including colorectal cancer [ 41 ]. Previous studies have shown that AKT2 is the predominant isoform involved in CRC carcinogenesis and plays an important role in CRC metastasis [ 42 ]. Considering this information, AKT2 may be a target gene for miR-2861 in the formation of colorectal carcinogenesis. Contrary to our study findings, lung cancer stem cells (LCSCs), it has been shown that miR-2861 expression level was increased and HDAC expression was positively related with to miR-2861 [ 43 ]. Based on the RT-qPCR results, it can be thought that both miR-2861 and miR-5011-5p play a role as tumor suppressor in CRC. In the current study, GO and the KEGG Pathway databases were consulted in order to investigate target genes that are predictive of probable pathways in relation to downregulated miRNAs. According to GO analysis, both miR-2861 and miR-5011-5p are associated with 1919 genes with biological process, 2011 genes with cellular components, and 2021 genes with molecular functions. Numerous cancer-related pathways were found by KEGG Pathway analysis (Table 4 ). These pathways include TGF-beta, Rap1, Ras, cAMP, Wnt, mTOR, and PI3K-Akt signaling pathways. An important pathway for controlling cellular growth, differentiation, extracellular matrix remodeling, angiogenesis, and inflammation is the TGF- signaling system. Slattery et al. (2017) showed that the TGF-β signaling pathway is involved in the formation of colon and rectal tumors through the effects of various miRNAs, including miR-2861 [ 44 ]. In a recent study of CRC and TGF-β/Smad signaling pathway, it was reported that Smad3 expression is quite high in CRC, indicating that the TGF-/Smad signaling pathway is abnormally active during the formation and growth of tumors [ 45 ]. The RAS signaling pathway is highly important in mediating cellular growth and malignant transformation [ 46 ]. Jeon et al. (2012) exhibited that APC (WNT pathway) mutations can increase Ras stability in CRC, which can up-regulate the Ras pathway [ 47 ]. Ras-associated protein-1 (Rap1) plays a crucial role in controlling several important cancerous cell movements, spread, and metastasis-related activities [ 48 ]. In a previous study, downregulation of SIPA1 , exhibiting GAP activities on Ras-related Protein 1 (RAP1) and Ras-related Protein 2 (RAP2), has been reported to increase the invasive ability of cells in colon cancer [ 49 ]. Any change in the Wingless/Integrated (Wnt) signaling pathway can lead to cancer progression because it plays a role in the change in the shape or structure of an organism through growth, differentiation, and regeneration of stem cells in different parts of the organism. Many CRC patients have damage to important Wnt signaling pathway components [ 50 ]. WNT5a regulates WNT/β-catenin-dependent single pathway. WNT5a regulates the single WNT/β-catenin-dependent pathway. Sun et al. reported that WNT5a expression in CRC cells was inversely associated with tumor grade in CRC patients [ 51 ]. However, increased expression of the PI3K/AKT/mTOR signaling pathway has been exhibited in a variety of malignancies, including CRC [ 52 ]. At the same time, 21 target genes of miR-2861, 253 target genes of miR-5011-5p and 1 common overlapping target gene ( SP1 ) of both miRNAs were detected in the signaling pathways given in Table 4 . SP1 , which plays an important role in tumor survival, progression and metastasis, showed increased expression in various cancers including CRC [ 53 ]. Considering this important role of SP1 in colorectal cancer, it can be said that it can be considered as a target gene for both miRNAs investigated in this study.

One of the most prevalent gastrointestinal tract malignancies, colorectal cancer (CRC), affects both men and women equally and has a significant mortality rate throughout the World [ 1 ]. CRC is a different disease in terms of clinical and biological features and shows changes in both disease progression and treatment response [ 2 ]. The incidence of CRC in the young population is increasing in many countries [ 3 ]. By 2030, it is estimated that annual CRC diagnoses will exceed 2.2 million and deaths will be greater than 1.1 million [ 4 ]. Thus, the discovery of potential molecular targets for the early detection and treatment of CRC is important to decrease CRC-induced mortality. The pathogenesis of some malignancies, including CRC, is heavily influenced by epigenetics, which are heritable alterations in gene expression that do not result in long-term changes in the DNA sequence [ 5 ]. Examination of epigenetic changes has contributed to the elucidation of the gene-CRC relationship by revealing differences in gene expression patterns and expression levels of some genes specific to CRC [ 6 ]. MicroRNAs (miRNAs) are one of the components of epigenetic mechanisms [ 7 ]. miRNAs are small, non-coding, single-stranded RNA molecules about 19–23 nucleotides long. miRNAs bind to the 3’ untranslated regions (UTR) of target mRNAs, causing mRNA degradation and thereby controlling gene expression [ 8 ]. Bioinformatics studies have shown that a single miRNA can target many different mRNAs and thus these miRNAs are involved in various cellular events such as cell proliferation, differentiation, apoptosis and, immune response [ 9 ]. Studies from the past to the present have shown that microRNAs may be associated with cancer progression and play a role in the pathogenesis of tumors by controlling oncogenes or tumor suppressor genes, which are cancer-related genes [ 10 ]. Because of their effects on gene expression, presence in body tissues and fluids, and potential utility as disease biomarkers, miRNAs are important molecules for translational research [ 11 ]. Recently studies were showed that dysregulation of miRNAs is responsible for the development of CRC [ 12 ]. miR-2861, which is located in the chromosome 2 region [ 13 ], has a negative and positive correlation to variety cancer types including chordomas, cervical cancer, papillary thyroid carcinoma and basal cell carcinoma [ 14 – 17 ]. There are also studies showing that miR-5011-5p is dysregulated in gastric cancer, glioma, and glioblastoma [ 18 , 19 ]. To the best of our knowledge, there are no research on the significance of miR-2861 and miR-5011-5p in CRC even though numerous lines of evidence highlight the essential function of miRNAs in various human malignancies. In the present study, we aimed to examine the expression levels of these miRNAs in tumor and healthy tissues of CRC patients. Additionally, enrichment pathway analyses utilizing Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed.

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