System biological and experimental validation of miRNAs target genes involved in colorectal cancer radiation response

Journal Pre-proof System biological and experimental validation of miRNAs target genes involved in colorectal cancer radiation response

Hamed Manoochehri, Mohsen Sheykhhasan, Pouria Samadi, Mona Pourjafar, Massoud Saidijam PII:

S2452-0144(19)30182-7

DOI:

https://doi.org/10.1016/j.genrep.2019.100540

Reference:

GENREP 100540

To appear in:

Gene Reports

Received date:

14 September 2019

Revised date:

8 October 2019

Accepted date:

14 October 2019

Please cite this article as: H. Manoochehri, M. Sheykhhasan, P. Samadi, et al., System biological and experimental validation of miRNAs target genes involved in colorectal cancer radiation response, Gene Reports (2018), https://doi.org/10.1016/ j.genrep.2019.100540

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© 2018 Published by Elsevier.

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System biological and experimental validation of miRNAs target genes involved in colorectal cancer radiation response Hamed Manoochehri1,2, Mohsen Sheykhhasan1,2, Pouria Samadi1,2, Mona Pourjafar1,2, Massoud Saidijam1* 1. Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran 2. Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran

*Corresponding author: Massoud Saidijam, Research Center for Molecular Medicine, Hamadan University of

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Medical Sciences, Hamadan, Iran Email: [email protected]

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Phone Number: +98-9121324616

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Fax: +98-8138380464

Abstract

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Background and objectives: Prediction of response to radiotherapy in colorectal cancer (CRC) patients is of great interest that protects radiation-resistant patients from imposing to high doses of X radiation. miRNAs and their target genes are potential candidates as biomarkers of cancer. Microarray provides a high-throughput analysis of cell transcriptome, however, this very complex data needs to be interpreted via network-based approaches. In this study, miRNAs target genes involved in the CRC radiation response were validated through a system biological and experimental manner.

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Material and methods: The microarray expression data was obtained from NCBI GEO datasets. The miRNAs target genes were identified using miRNA-mRNA interaction prediction databases. Gene enrichment analysis performed using DAVID database to identify genes involved in the tumor radiation response. For selected miRNAs and targets, the miRNA-regulated protein–protein interactions network (PPIN) was constructed and analyzed using cytoscape software. Survival analysis was done by Xena Browse. Radioresistant cell lines were established using fractional X irradiations and confirmed by clonogenic assay. After RNA isolation and cDNA synthesis, by Real-Time PCR, gene expression measurements were performed. SPSS V.22 was used for data analysis. Results: Finally, the selected genes and miRNAs based on network analysis were comprises: mTOR, MAPK1, CDC42, EGFR, MAPK8, CCND1, BCL2, PIK3CG, hsa-miR-7-2, hsamiR-17, hsa-miR-106a and hsa-miR-32. Among them, MAPK8 and CDC42 genes were significantly correlated with CRC patient’s survival. Established radioresistant cell lines showed a morphological change and a significantly higher survival fraction compared to parental cell line. Real-Time PCR showed higher expression of MAPK8, CDC42 and CCND1 in radioresistant cell lines compared to parental cell line.

Journal Pre-proof Conclusion: In this study, via several enrichment stages, 2,069,706 target genes were highly enriched to 3 genes which have crucial roles in tumor radiation response and can be used as potential predictive biomarkers. Keywords: Neoplasm, Gene, Protein Interaction Network, miRNA, Radiation Therapy

1. Introduction

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MicroRNAs (miRNAs) as a large member of the small non-coding RNAs with approximately 22 nucleotides in length regulate gene expression at the post-transcriptional level via binding to the 3’UTR region of their target mRNA(s). They inhibit the expression of target gene(s) via mRNA degradation or inhibition of translation. Currently, There are over 2,200 mature miRNA sequence in the miRbase [1].

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miRNAs play important roles in various biological processes such as cellular growth, differentiation, proliferation, metabolism, stem cell maintenance, and apoptosis. They could act as oncomirs or tumor suppressor miRNAs with crucial roles in development of variety of cancers, including colorectal cancer (CRC). So, miRNAs have a great potential for use as tumor marker in screening, diagnosis, prognosis, and tumor response prediction of various cancers [2]. Patients with advanced local CRC received neoadjuvant radiotherapy for reduction of tumor recurrence. However, not all patients benefit from radiotherapy, and those with radioresistant tumors does not respond to radiotherapy. Therefore, prediction of patient’s treatment response to radiotherapy seems important and prevents administration of high dosage radiation to the patients with not-responsive tumor. In this regard, microRNAs play valuable roles[3, 4].

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Detecting miRNAs that differentially expressed between the cancerous and normal tissues and also their target genes is an attractive strategy in order to identify involved miRNAs in the tumor radiation response and their role in carcinogenesis[1]. Nowadays, high-throughput technologies are capable of examining changes in the expression of miRNAs and mRNAs on a large-scale manner (Omics data). But these data have to be interpreted and analyzed[5], because the size of this kind of data is very large and complex. Simultaneous(co) expression of miRNAs and mRNAs at a specific time or location, cannot also definitely determine the relationship between them. Furthermore, there is a large number of false positives in predictive software for identifying target genes of miRNAs, even software's with powerful algorithms and finally, a miRNA has several targets and a target mRNA can be identified by several miRNAs. Therefore, the relationship between miRNAs and target genes is not one-toone and should be investigated in multiple-to-multiple cases [5, 6]. Therefore, it is necessary which miRNA targets prediction methods be more completed via biological information to reduce false positive results. Different pathway-based approaches and network-based approaches have been developed for this purpose [5]. Studies have shown the role of miRNA in the regulation of protein-protein-interactions networks (PPINs). miRNAs preferentially regulate proteins whose number of interactions with other proteins in the network is greater than the mean number of interactions [7]. Node degree in human PPIN

Journal Pre-proof is positively correlated with the number of target sites of miRNAs. Also, proteins with more interactions are regulated with a number of transcription factors and miRNAs [8]. Accordingly, in this study miRNA’s target genes involved in CRC radiation response were identified via system biological interpretation of omics data alongside with the experimental validation.

2. Material and methods 2.1. MicroRNAs expression data

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2.2. MicroRNAs target prediction

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miRNA expression data was obtained from gene expression omnibus (GEO) datasets of NCBI (Accession number: GSE35982). This omics data was analyzed and adjusted using the online tool GEO2R. MicroRNAs were selected based on their expression fold change in tumor tissues compare to healthy adjacent tissues (logFC≥3, P≤0.001).

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miRNA target prediction was performed using RNA22, miRwalk, miRMap, microt. V4, miRanda, miRNAMap, mirbridge, miRDB, PITA, Pictar2, Targetscan and RNAhybrid databases. These databases use different algorithms (structural and sequence complementarity, low binding energy, conservation, number of MREs in target mRNA, etc.). This could help us to increase the accuracy of prediction. Finally, targets were selected based on identification by maximum number of databases (≥8). 2.3. Functional/enrichment analysis

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Functional analysis was done in DAVID Database (Database for annotation, visualization, and integrated discovery) based on gene ontology (GO) and KEGG pathways (Kyoto Encyclopedia of Genes and Genomes). GO categorize genes based on biological process, molecular function and cellular component. Target genes were selected based on involving in cellular DNA damage response (DDR) including cell cycle genes, apoptosis genes, DNA repair genes, and those involved in related signal transduction pathways. P<0.05 was considered as statistically significant.

2.4. Network construction PPIN was designed via search tool for the retrieval of interacting genes/proteins (STRING). This database contains confirmed and predicted physical and functional PPIs. PPIN was constructed with minimum required interaction score of 0.400. Interactions of miRNAs with their targets were merged with PPIN in cystoscope network software.

2.5. Network analyzing

Journal Pre-proof This step was performed to determine the topological properties of the constructed network. The nodes in the network were analyzed and scored according to their interaction with other nodes in the network. This was done using the network analyzer in cystoscope. Nodes were descripted by four parameters of degree, clustering coefficient, betweenness centrality, and closeness centrality. Finally, high score targets were selected based on degree, betweenness centrality and closeness centrality parameters in interactive Venn Diagram online software. 2.6. Survival analysis

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The Xena browser database was used to plot the Kaplan Mayer survival curve for the final selected genes. Gene expression analysis was performed by high-throughput RNA sequencing data of GDC TCGA Rectal Cancer (READ) and TCGA Colon and Rectal Cancer (COADREAD) cohorts. Survival time was the last time any patients be alive. Xena browser reports the log-rank test statistics and P-Value. P<0.05 was considered as statistically significant.

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2.7. Cell line and cell culture

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HCT-116 cell line (purchased from Iran Pasteur institute) was cultured in DMEM (high glucose) medium contains 10% Fetal bovine serum (FBS) and 1% penicillin-streptomycin (Gipco) at 37 ℃ in a 5% CO2 with suitable humidity. Cells was cultured in 25 cm2 T-flasks and were detached using 500µL trypsin/EDTA (0.25%), when cells reached 90% confluency. After that 3 ml of complete medium was added to the flask in order to inactivate trypsin.

2.8. Establishing of radioresistant cell lines

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Establishing of radioresistant cell lines has been explained in previous studies[9]. Two radioresistant cell line was established based on our previous published work[10]. Briefly, cells (parental cell line) contained T-flasks were irradiated repeatedly by high energy X-ray linear accelerator (Shimva, China) with incremental doses at different time intervals. These radiation doses were repeated until a cumulative dose of 21 Gy gained. This established cell line named radioresistant cell line 1 (RR1). RR1 cell line was irradiated with higher incremental doses until a cumulative dose of 45 Gy gained. This established cell line named radioresistant cell line 2 (RR2). The parental cell line as control experienced same condition of culture without irradiation. All assays on cell lines were performed 21 days after last irradiation.

2.9. Clonogenic Assay The radiosensitivity of established (RR1, RR2) and parental cell lines was determined by Clonogenic assay. Certain number (≈1100 cells) of each cell line was seeded in 6-well culture plate and then plates were incubated at 37 ̊C for 24h. Afterward, plates were exposed with different X-ray doses of 2, 4, 6 and 8 Gy. Control plate was not received any radiation dose

Journal Pre-proof (Dose=0). The irradiated plates were incubated at 37 ̊C to form cell colonies (10 day). After incubation, the plate media was drained and plates were washed twice with phosphate buffered saline (PBS). Colonies were stained with 0.5% crystal violet. After washing of excess stain, visible colonies were counted (≥50 cell/colony). Survival fractions (SF) were calculated for 2, 4, 6 and 8 radiation doses by following formula[10]: Colony number in irradiated plates Number of seeded cells in irradiated plates 𝐒𝐮𝐫𝐯𝐢𝐯𝐚𝐥 𝐅𝐫𝐚𝐜𝐭𝐢𝐨𝐧(𝐒𝐅) = × 100 Colony number in control plates Number of seeded cells in control plates

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2.10. RNA Extraction and Complementary DNA (cDNA) synthesis

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Total RNA was isolated from cell lines (parental, RR1 and RR2) using RNX+ reagent (Cinnagen, Iran) according to the manufacturer’s protocol. Resulted pellet was dissolved in 50 μl DEPC-treated water. RNA samples were treated with DNase I (Sigma) to remove any genomic contaminations. The quality (integrity) and quantity (concentration and purity) of extracted RNA were evaluated by electrophoresis on agarose gel (1%) and measuring the absorbance ratio at 260/280 nm and 260/230 nm. PrimeScript RT reagent Kit (TaKaRa, RR037A) was used for cDNA synthesis via 2 μg RNA, oligo-dT and random hexamer primers in final volume of 10 μl according to the manufacturer’s instructions.

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2.11. Primer designing and Real-Time PCR Human gene-specific forward and reverse primers for MAPK8, CDC42, CCND1 and 18s rRNA (internal control) were designed using Allele ID software V.6. Intrinsic properties of primers (Tm, GC content, hairpin, dimers,) were validated in oligoanalyzer tool (IDT). The specificity of primers was validated via NCBI primer blast tool. Designed primers are shown in Table 1. Table 1. Forward and reverse designed primers for real-time PCR

Gene symbol

Forward primer sequence (5’ to 3’)

Reverse primer sequence (5’ to 3’)

MAPK8 CDC42 CCND1 18s rRNA

AAGAATGGTGCTGCTCCTGAC GCAAGAGGATTATGACAGATTAC CCGTAGGTAGATGTGTAAC GTAACCCGTTGAACCCCATT

ACCTTGTTGCTTATGTGAGTATGC TTGGACAGTGGTGAGTTATC TTATAGTAGCGTATCGTAGG CCATCCAATCGGTAGTAGCG

Real-Time PCR reactions were performed on Roche LightCycler® 96 system. All reactions were conducted using SinaSYBR Blue HS-qPCR Mix Kit (Sinaclon, Cat. No MM2171) and

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gene specific primers to a final volume of 25 µl, containing 1 μl of each primer (10 pmol), 12.5 μl SYBR green, 1 μl cDNA (20 ng) and 9.5 µl water. The thermal cycling steps was as follows: reaction temperature increased to 95 °C and incubated for 5 min, followed by 40 cycles of amplification (15 seconds denaturation at 95°C, 30 seconds annealing at 54-60°C, 45 seconds extension at 72°C). Melting analysis was performed by heating at 65°C to 97°C. Specificity of Real-Time PCR products was confirmed by melting peak analysis and running on 1% agarose gel electrophoresis. ΔCt was calculated by subtracting Ct (cycle threshold) values of the target gene from the Ct values of internal control gene. ΔΔCt was calculated by subtracting ΔCt of the test sample (RR1 and RR2 cell lines) from ΔCt of the control sample (parental cell line). Fold change of gene expression was measured using the 2−ΔΔCt formula[11].

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2.12. Statistical Analysis

3. Results

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3.1. miRNA selection

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Clonogenic and Real Time PCR data are described as mean ± standard deviation (SD). Data were analyzed via SPSS V.22 by one-way ANOVA, Tukey post hoc and Pearson tests. P<0.05 was considered statistically significant.

Based on the mentioned criteria (logFC>3, P<0.001), 28 miRNAs were selected from GEO data set. Selected miRNAs are shown in Table 2.

Table 2. List of selected miRNAs. Selection criteria: logFC>3 and P<0.001. A total 28 miRNA were differentially expressed. logFC: Logarithm of Fold change. No. 1 2 3 4 5 6 7 8 9 10 11 12 13

miRNA hsa-miR-183

hsa-miR-1290 hsa-miR-335 hsa-miR-18a hsa-miR-521

hsa-miR-182 hsa-miR-135b hsa-miR-106b hsa-miR-7-2 hsa-miR-608 hsa-miR-637 hsa-miR-17 hsa-miR-32

Log FC -5.19 -3.36 -3.01 -3.17 -3.74 -3.25 -3.53 -3.40 -12.44 -9.61 -7.23 -6.59 -3.38

P-Value 0.00001 0.00007 0.0002 0.0002 0.0002 0.0003 0.0003 0.0003 0.0004 0.00007 0.0002 0.0004 0.0005

Journal Pre-proof hsa-miR-542-3p hsa-miR-106a

-3.05 -4.95 -11.38 -5.11 -5.24 -5.15 -3.82 -3.51 -11.00 -8.95 -10.04 -7.13 -8.35 -9.77 -9.91

hsa-miR-589 hsa-miR-1284 hsa-miR-1538 hsa-miR-1276 hsa-miR-1287 hsa-miR-592

hsa-miR-548p hsa-miR-525-5p hsa-miR-138-2 hsa-miR-512-3p hsa-let-7i hsa-miR-933 hsa-miR-569

0.0006 0.0007 0.0002 0.0005 0.0005 0.0006 0.0008 0.0008 0.0001 0.0004 0.0002 0.0007 0.0006 0.0005 0.0006

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3.2. miRNA target selection

3.3. Enrichment analysis

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The number of all predicted targets for selected miRNAs are shown in Table 3. Final targets were selected based on identification by at least 8 databases (cut off=8). Totally, 2,069,706 genes predicted as targets of selected miRNA. The number of potential targets reduced to 15,532 genes after considering cut off=8.

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MicroRNA target genes were undergo enrichment step based on involving in cellular DNA damage response (DDR) comprises of cell cycle genes, apoptosis genes, DNA repair genes, and those who involve in important signal transduction pathways such as PI3/Akt, MAPK, TGFβ, NFkB, Wnt, Notch, AMPK and mTOR pathways (significance level=0.05). After considering DDR involved genes and removing of redundant targets, the number of microRNAs were reduced to 1091 (Table 3).

Table 3. The number of target genes for selected miRNAs. Number of total targets reduced to 15,532 from 2,069,706 after considering cut off=8. Only 1,091 target genes were involved in DDR. No.

miRNA

1 2 3 4 5 6 7

hsa-miR-183 hsa-miR-1290 hsa-miR-335 hsa-miR-18a hsa-miR-521 hsa-miR-182 hsa-miR-135b

Total predicted targets 52923 60394 109830 100048 27316 116346 113159

Number of targets with cut off=8 100 442 729 528 20 957 445

Number of targets involved in DDR 13 27 71 50 2 69 28

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1146 719 819 693 1468 726 317 1222 839 242 5 399 221 242 529 524 702 714 600 8 176 15532

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89875 95620 63343 61702 107924 98264 47592 100094 89228 36948 41760 51314 100992 55276 44295 50823 105184 54706 102270 42955 49525 2069706

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hsa-miR-106b hsa-miR-7-2 hsa-miR-608 hsa-miR-637 hsa-miR-17 hsa-miR-32 hsa-miR-542-3p hsa-miR-106a hsa-miR-589 hsa-miR-1284 hsa-miR-1538 hsa-miR-1276 hsa-miR-1287 hsa-miR-592 hsa-miR-548p hsa-miR-525-5p hsa-miR-138-2 hsa-miR-512-3p hsa-let-7i hsa-miR-933 hsa-miR-569 Total

75 82 36 40 115 58 31 98 44 32 1 25 10 27 46 6 47 25 11 4 18 1091

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8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

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Two Type of networks were constructed, a Protein-Protein interaction (targets) network (Figure 1) and a miRNA-target interaction network (Figure 2). These networks were merged to construct a final network (Figure 3).

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Figure 1. Protein-Protein (Targets) interaction constructed in STRING database. Colored nodes: query proteins and first shell of interactors. White nodes: second shell of interactors. Empty nodes: proteins with unknown 3D structure. filled nodes: 3D structure is known or predicted.

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Figure 2. miRNA-target interaction constructed in cystoscope software. The interaction between targets is not shown in this figure. The main network is much larger and only a small part of the network is shown here.

Figure 3. miRNA-protein-protein(miRNA-target-target) interaction network. This network was constructed by merging two pervious networks. The main network is much larger and only a small part of the network is shown here.

Journal Pre-proof 3.5. Network analysis

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Twenty first target with the highest score in degree or betweenness centrality or closeness centrality in final network were selected (60 targets). In next step, 12 targets with the highest score of all parameters were selected by Interactive Venn Diagram (Figure 4). Similarly, four miRNAs with the highest score of degree, betweenness centrality and closeness centrality in final network were selected by Interactive Venn Diagram (Figure 5). The genes that were not directly interact with these four miRNAs in the final network were excluded (CTNNB1, TP53, GSK3, EP300). In Table 4, the list of final selected miRNAs and their final targets are shown. The interaction network for these final four miRNAs and final 8 targets are shown in Figure 6. The role of these miRNAs and their targets in regular cell signaling of DDR is shown in Figure 7.

Figure 4. Venn diagram of high score targets. A: betweenness centrality. B: closeness centrality. C: degree. [A] and [B] and [c]: BCL2, PIK3CG, CCN1D, CTNB1, mTOR, TP53, EGFR, MAPK8, GSK3B, EP300, CDC42, MAPK1.

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Figure 5. Venn diagram of high score miRNAs. A: Betweenness centrality. B: closeness centrality. C: degree. [A] and [B] and [c]: has-miR-17, has-miR-106a, has-miR-7-2, has-miR-32.

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Table 4. Selected miRNAs and their target genes based on network analysis. Totally, 4 miRNAs and 8 target genes were selected after network analysis. Betweenness centrality is the sum of times which a node acts as a bridge between two other nodes. Closeness centrality is the average distance of a node to other nodes in the network. The degree is the number of connections in a node with other nodes in the network. The clustering coefficient is different from centrality parameters. The clustering coefficient is cluster tendency of nodes in the network.

Betweenness Centrality

Clustering Coefficient

0.458685

0.025089

0.09441

0.472632

0.048125

0.062088

0.4618

0.037594

0.060521

hsa-miR-7-2

82

hsa-miR-17 hsa-miR-106a

115

hsa-miR-32 mTOR

58

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miRNAs/Targets degree Closeness Centrality

0.441558

0.023838

0.069182

111

0.492308

0.013361

0.295004

MAPK1

163

0.533857

0.067317

0.202594

CDC42

102

0.49009

0.019825

0.231433

EGFR

107

0.4932

0.015372

0.262564

MAPK8

109

0.498169

0.023334

0.253993

CCND1

108

0.492754

0.016049

0.308687

BCL2

94

0.488769

0.012671

0.334477

PIK3CG

133

0.506518

0.01781

0.265951

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Figure 6. The interaction network for final four miRNAs and their final 8 targets. Directed arrows indicate the direction of effect of a node on other nodes in the network. MicroRNAs regulate the expression of genes (one-way relationship). The relationship between targets is reciprocal (twoway relationship).

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Figure 7. Role of final miRNAs and their targets in regulate cell signaling of DDR. The red ellipses represent the final miRNAs and the yellow boxes represent the final targets. Stared boxes have more interaction with other in pathway. JNK=MAPK

3.6. Survival analysis

Kaplan-Meier survival curves of final selected genes were drawn using the Xena Browser database. These curves are shown in Figure 8-15. Based on the significance level of 0.05, only CCND1 and MAPK-8 genes could significantly predict the patients’ survival.

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Figure 8. Kaplan-Meier survival curve for mTOR. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from TCGA Colon and Rectal Cancer (COADREAD) cohort study (n=418). Expression level normalization method was using FPKM-UQ:

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Fragments per kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.3943, indicates no significant difference between

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two populations (red, blue) survival curves.

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Figure 9. Kaplan-Meier survival curve for MAPK1. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from TCGA Colon and Rectal Cancer (COADREAD) cohort study (n=418). Expression level normalization method was using FPKM-UQ:

Fragments per kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.4239, indicates no significant difference between

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two populations (red, blue) survival curves.

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Figure 10. Kaplan-Meier survival curve for CDC42. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from GDC TCGA Rectal Cancer (READ) cohort study (n=167). Expression level normalization method was using FPKM-UQ: Fragments per

kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.0093, indicates a significant difference between three populations

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(red, pale blue, dark blue) survival curves.

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Figure 11. Kaplan-Meier survival curve for EGFR. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from TCGA Colon and Rectal Cancer (COADREAD) cohort study (n=418). Expression level normalization method was using FPKM-UQ:

Fragments per kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.8648, indicates no significant difference between

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three populations (red, blue) survival curves.

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Figure 12. Kaplan-Meier survival curve for MAPK8. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from TCGA Colon and Rectal Cancer (COADREAD) cohort study (n=418). Expression level normalization method was using FPKM-UQ:

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Fragments per kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.0108, indicates a significant difference between

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three populations (red, blue) survival curves.

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Figure 13. Kaplan-Meier survival curve for CCND1. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from GDC TCGA Rectal Cancer (READ) cohort study (n=83). Expression level normalization method was using FPKM-UQ: Fragments per kilobase

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survival curves.

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of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.2008, indicates no significant difference between three populations (red, blue)

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Figure 14. Kaplan-Meier survival curve for BCL2. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from TCGA Colon and Rectal Cancer (COADREAD) cohort study (n=418). Expression level normalization method was using FPKM-UQ:

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Fragments per kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.7955, indicates no significant difference between

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three populations (red, blue) survival curves.

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Figure 15. Kaplan-Meier survival curve for PIK3CG. The Kaplan-Meier curve is plotted according to high-throughput RNA sequencing data from TCGA Colon and Rectal Cancer (COADREAD) cohort study (n=418). Expression level normalization method was using FPKM-

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UQ: Fragments per kilobase of transcript per million mapped reads-upper quartiles. Overall survival time is measured by day. P-Value=0.7955, indicates no significant difference between

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three populations (red, blue) survival curves.

3.7. Morphological assessments of cell lines The morphology of RR1, RR2 and parental cell lines is compared in Figure 16. Parental HCT-116 cell line(a) has a typical epithelial-like morphology and cells are closely contacted. RR1 showed an irregular shape and considerable morphological change compared to parental cell line. They had stretched foots that enable them to adhere tightly to the plate surface. It seems that RR1 cells have less direct contact with each other, and the cell's ectoplasm is connected to each other through narrow redundancies. RR2 cell line has a high tendency to exhibit a mesenchymal cell morphology.

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Figure 16. The morphology of parental (a), RR1 (b) and RR2 (c) cell lines. Parental cell line shows a typical epithelial cell morphology, RR1 shows an irregular shape and RR2 has a mesenchymal-like morphology.

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3.8. Radiosensitivity of cell lines

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Survival fraction of RR1, RR2 and parental cell lines in 2, 4, 6 and 8 Gy doses are compared in Figure 17. A significant different between survival fraction of cell lines was observed in all applied doses. Pairwise comparisons showed a significant higher survival fraction of RR2 than parental cell line in 2 (P<0.001), 4 (P<0.01), 6 (P<0.05) and 8 Gy (P<0.01) radiation doses. The RR1 cell line had a significant higher survival fraction in 2 and 4 Gy radiation doses when compared with parental cell line (P<0.05). RR2 survival fraction was significantly higher than RR1 survival fraction in 2 (P<0.001), 4 (P<0.01) and 8 (P<0.05) Gy radiation doses.

Journal Pre-proof Figure 17. Comparison of radiosensitivity of parental, RR1 and RR2 cell lines. Data are expressed as mean ± SD. †(P<0.001) VS parental cell line. ††(P<0.01) VS parental cell line. †††(P<0.05) VS parental cell line. *P<0.05, **P<0.01, ***P<0.01. Statistical tests showed a significant different between cell lines in term of survival fraction.

3.9. Gene expression results

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Expression levels of CCND1, CDC42 and MAPK8 genes in parental, RR1 and RR2 cell lines were measured by Real Time PCR. These genes were differentially expressed in parental, RR1 and RR2 cell lines (Figure 18). Pairwise comparison of cell lines showed that expression of CCND1, CDC42 and MAPK8 genes were significantly increased in RR2 in relation to RR1(P<0.05) and parental cell lines(P<0.01), but in RR1 there were no significant change in expression compared to parental cell line(P>0.05). Expression of CCND1 was significantly correlated with CDC42 (correlation coefficient=0.940, P<0.01) and MAPK8(correlation coefficient=0.940, P<0.01) expression. Also, expression of CDC42 was significantly correlated with MAPK8 (correlation coefficient=0.933, P<0.01) expression.

Figure 18. Comparison of gene expression level of CCND1, CDC42 and MAPK8 genes in parental, RR1 and RR2 cell lines. Data are expressed as mean ± SD. ††(P<0.01) VS parental cell line. *P<0.05, **P<0.01. Difference in expression of all genes was significant between the cell lines.

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4. Discussion

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MiRNAs are gene expression regulators at posttranscriptional level. The target genes of miRNAs are usually important nodes in the PPIN. For this reason, the dysregulation of miRNAs causes a huge change in the PPIN. This function can activates/deactivates some cancer related signaling pathways[12]. It is believed that miRNAs and their targets involved in DDR might be suitable biomarkers for predicting response to radiotherapy[13]. In our study, 26 differentially expressed miRNAs (LogFC>3) between cancerous and normal adjacent tissue were selected with significant level of 0.001. According to Tian et al. study higher expression changes in cancer is most probably related to important genes involved in cancer pathogenesis[14]. Also, Daniels et al revealed that miRNAs with higher expression changes in cancerous tissues can have fewer false positive results when they used as biomarkers[15].

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In our study, a total of 2,069,706 target genes were identified for 26 miRNAs. In the another word, each miRNA has 79,604 target genes. In this regard, Yu et al. stated that approximately a miRNA in the human cell targets 500 genes [16]. In Gennarino et al. study, the average number of targets for a miRNA was reported to be about 100-200 genes [17]. However, it is obvious that findings of the predictive software of interactions between miRNA/mRNA have been associated with a large number of false positives [18, 19]. Because these databases predict interactions mainly based on thermodynamic calculations and do not consider localization of mRNAs and miRNAs in the cell [20]. Pinzon et al indicated that probability of false positive results through one miRNA-target prediction software is at least 50% which reduced with increasing the number of prediction software [18].

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So, in order to reduce false positive results, only predicted targets with at least 8 software were selected. By applying this filter, the number of targets dropped from 2,069,706 to 15,532. Therefore, the number of targets per miRNA was approximately 597. This result is consistent with the Yu et al study result[16]. Highest number of identified targets was for miR-17 (1468 Target) and the lowest number of targets was for miR-1538 (5 Target). In the next step, among the selected targets, those involved in DDR pathway were identified using DAVID database. The tool identified targets involved in DDR included 1091 genes. Thus, there were approximately 42 targets per miRNA. The highest number of targets was for miR-17 (115 targets) and lowest was related to miR-1538 (1 target). One of the hallmarks of cancer is deregulation of DNA repair genes which causes genetic instability (microsatellite stability). Upregulation of these genes results in tumor resistance to radiation and some chemotherapy drugs such as bleomycin[21]. According to previous studies, 2-4% of the human genome is involved in the DNA repair and cell cycle[22, 23]. Similarly, in our study among 15,532 primary targets, 1,091 (7.02%) played roles in cell response to radiation. Prediction of treatment response using several genes is much more reliable than the single biomarker approach[24, 25]. The microarray data provides high-throughput information on transcriptional genomic scale. However, microarray co-expression data could not establish an appropriate miRNA-target

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interaction. On the other hand, cancer is a very heterogeneous disease, so it is necessary to search for biomarkers of cancer in a systemic and dynamic approach[26, 27]. Therefore, in present study miRNA-target interaction data were merged with PPIN data and a miRNAregulated PPIN network was obtained. Degree and betweenness centrality are two important parameters in the network to check the importance of the nodes. Typically, nodes with higher degree called hubs and nodes with high centrality are called bottlenecks[12]. In our study, 20 first node (2%) with highest degree were considered as hubs. Also, 20 first nodes (2%) with highest closeness centrality and 20 first nodes (2%) with the highest betweenness centrality considered as bottlenecks[12]. In Wang et al. study, 10% of nodes with the highest degree and centrality were considered as hubs and bottlenecks, respectively[28]. According to Yu et al., study, the importance of centrality in determining important network nodes is greater than degree[29]. In general, nodes with higher degree, betweenness centrality, and closeness centrality in a network, is regulated by more miRNAs and also interacted with more genes. These nodes are potential candidates for drug targets. The present study showed that more interacted targets had lower clustering coefficiency than the network mean. So, these targets are intermodular hubs which are involved in various cellular processes.

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Final Selected genes and miRNAs based on network analysis included mTOR, MAPK1, CDC42, EGFR, MAPK8, CCND1, BCL2, PIK3CG, hsa-miR-7-2, hsa-miR-17, hsa-miR106a and hsa-miR-32. The mentioned genes are involved in cancer progression pathways which lead to carcinogenesis and more aggressive phenotype of tumors. So, their controlling miRNAs are expected to be act as tumors suppressor.

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EGFR, as a growth factor receptor, is overexpressed in many cancers, including CRC. In of Cengel et al. study, has been shown that inhibition of EGFR by Gefitinib lead to radiation sensitivity of colorectal cancer[30]. The activity of mTOR gene in the PI3K/AKt/mTOR pathway induces survival, apoptosis inhibition, cell proliferation, angiogenesis, cellular differentiation, and resistance to radiation of tumor cells[31]. Inhibition of mTOR has been suggested as a therapeutic target for treatment of radioresistant prostate cancer[32]. PIK3CG is the PI3K catalytic gamma subunit. PI3K converts PIP2 to PIP3 and activates AKT and PI3K/AKT pathway. Overexpression of PI3K in CRC causes malignancy and metastasis[33]. CCN1D (cyclin D1) is required to cell pass from G1 phase. Balcerczak et al showed that CCN1D gene expression levels significantly increased in CRC tissue compared to normal mucosa (33). CDC42 is a member of the Rho family GTPases. Its overexpression has been demonstrated in various cancers, including breast cancer[34], head and neck cancers[35], lung cancer[36], and CRC[34]. This gene is involved in re-arranging cellular skeletons, cell polarity, intracellular traffic, cell cycle regulation and cell fate[37]. The mitogen-activated protein kinases (MAPKs) regulate many cellular functions, including proliferation, differentiation, migration, and apoptosis. The main three MAPKs are ERK1/2 (MAPK1), JNK (MAPK8) and P38[38]. Studies have shown that MAPKs are very important gene markers that are associated with colorectal cancer[38].

Journal Pre-proof BCL-2 is an anti-apoptotic protein that inhibits leakage of cytochrome C from mitochondria and subsequently prevent activation of caspases[39]. Nix et al., reported that high expression level of BCL-2 in laryngeal cancer is predictor of resistance to radiotherapy[40]. Based on GEO dataset (GSE35982) used in our study, the expression of hsa-miR-7-2, hsamiR-17, hsa-miR-106a and hsa-miR-32 significantly decreased in cancerous tissues compared to normal adjacent tissue. Therefore, these miRNAs are tumor suppressor miRNA which suppress the expression of 8 selected oncogenes. MiR-7-2 has been suggested as an inhibitor of cell proliferation and apoptosis inducer in CRC[41]. MiR-17 inhibits cancer growth in some cancers[42] and miR-106a inhibits the proliferation and migration of cancer cells[43]. Also, the role of miR-32 as a suppressor tumor has been mentioned in studies[43].

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Survival analysis using the Kaplan-Meier curve showed that expression of the MAPK8 and CDC42 genes could significantly predicted the survival of CRC patients. Furthermore, significant level of CCND1 gene was lower than other five genes. Also, MAPK8, CDC42 and CCND1 genes had more interaction with others in network. For these reasons, expression changes of these genes in established radioresistant CRC cell lines and parental cell line were compared.

Conclusion

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First, we stablished radioresistant cell lines by irradiating HCT-116 colorectal cancer cell line via fractional repeated X-ray doses. HCT-116 is highly radiation sensitive cell line based on Yang et al., study[44]. Our RR1 and RR2 cell lines were significantly more resistant to radiation than parental cell lines. A morphological change was observed in RR1 and RR2 cell lines. In Voos et al., study radiation-treated cells had much larger foots than non-treated cells[45]. Also, development a mesenchymal-like morphology in radioresistant derivatives cells was previously reported by Gray et al.,[46]. The expression of all three genes were significantly increased in RR2 and cell line when compared with RR1 and parental cell line. Therefore, findings of experimentally gene expression analysis was consistent with network analysis result.

In this study, through different screening steps, primary miRNAs/targets were reduced from 26,206,9706 to 4/8 miRNAs/ target. These final miRNAs/targets are potentially good biomarkers for prediction of CRC patient’s radiation response. Survival analysis revealed that MAPK8 and CDC42 could significantly predict CRC patient’s survival. Expression changes of MAPK8, CDC42 and CCND1 in radioresistant colorectal cell lines and parental radiosensitive cell line were consistent with network analysis results. Based on our Results, we can propose that expression level of MAPK8, CDC42 and CCND1 genes be evaluated in clinical specimen of CRC patients undergone radiotherapy. Patients can be divided in two groups of radioresistant and radiosensitive patients and expressional differences of mentioned genes be compared between them. Also, mentioned genes can be used in gene therapy experimental and trials.

Journal Pre-proof Funding The authors received no funding for this work.

Conflict of interest The authors declare no conflict of interests.

Acknowledgment

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Authors thank the personnel of molecular biology Lab in Molecular Medicine research center, Hamadan university of medical sciences, for friendly help to us in this project.

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Author Contributions

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Conceptualization, Data curation, Supervision.

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Hamed Manoochehri: Formal analysis; Investigation; Methodology; Project administration, Resources; Software, original draft; Writing. Mohsen Sheykhhasan: review & editing. Pouria Samadi: Validation; Visualization. Mona Pourjafar: Roles/Writing. Massoud Saidijam:

Journal Pre-proof References

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1. Fu J, Tang W, Du P, Wang G, Chen W, Li J, et al. Identifying microRNA-mRNA regulatory network in colorectal cancer by a combination of expression profile and bioinformatics analysis. BMC systems biology. 2012;6(1):68. 2. Esquela-Kerscher A, Slack FJ. Oncomirs—microRNAs with a role in cancer. Nature reviews cancer. 2006;6(4):259. 3. Svoboda M, Izakovicova Holla L, Sefr R, Vrtkova I, Kocakova I, Tichy B, et al. Micro-RNAs miR125b and miR137 are frequently upregulated in response to capecitabine chemoradiotherapy of rectal cancer. International journal of oncology. 2008;33(3):541-7. 4. Kheirelseid EA, Miller N, Chang KH, Curran C, Hennessey E, Sheehan M, et al. miRNA expressions in rectal cancer as predictors of response to neoadjuvant chemoradiation therapy. International journal of colorectal disease. 2013;28(2):247-60. 5. Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, et al. PathVisio 3: an extendable pathway analysis toolbox. PLoS computational biology. 2015;11(2):e1004085. 6. Linde J, Olsson B, Lubovac Z. Network properties for ranking predicted miRNA targets in breast cancer. Advances in bioinformatics. 2009;2009. 7. Liang H, Li W-H. MicroRNA regulation of human protein–protein interaction network. Rna. 2007;13(9):1402-8. 8. Jonsson PF, Bates PA. Global topological features of cancer proteins in the human interactome. Bioinformatics. 2006;22(18):2291-7. 9. Su H, Jin X, Zhang X, Xue S, Deng X, Shen L, et al. Identification of micro RNA s involved in the radioresistance of esophageal cancer cells. Cell biology international. 2014;38(3):318-25. 10. Khoshinani HM, Afshar S, Pashaki AS, Mahdavinezhad A, Nikzad S, Najafi R, et al. Involvement of miR-155/FOXO3a and miR-222/PTEN in acquired radioresistance of colorectal cancer cell line. Japanese journal of radiology. 2017;35(11):664-72. 11. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. methods. 2001;25(4):402-8. 12. Liu Z, Guo Y, Pu X, Li M. Dissecting the regulation rules of cancer-related miRNAs based on network analysis. Scientific reports. 2016;6:34172. 13. Peng Q, Zhu J, Shen P, Yao W, Lei Y, Zou L, et al. Screening candidate microRNAmRNA regulatory pairs for predicting the response to chemoradiotherapy in rectal cancer by a bioinformatics approach. Scientific Reports. 2017;7(1):11312. 14. Tian F, Li R, Chen Z, Shen Y, Lu J, Xie X, et al. Differentially expressed miRNAs in tumor, adjacent, and normal tissues of lung adenocarcinoma. BioMed research international. 2016;2016. 15. Daniels SI, Sillé FC, Goldbaum A, Yee B, Key EF, Zhang L, et al. Improving power to detect changes in blood miRNA expression by accounting for sources of variability in experimental designs. AACR; 2014. 16. Yu Z, Jian Z, Shen S-H, Purisima E, Wang E. Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos. Nucleic acids research. 2006;35(1):152-64.

Journal Pre-proof

Jo ur

na

lP

re

-p

ro

of

17. Gennarino VA, Sardiello M, Avellino R, Meola N, Maselli V, Anand S, et al. MicroRNA target prediction by expression analysis of host genes. Genome research. 2009;19(3):481-90. 18. Pinzón N, Li B, Martinez L, Sergeeva A, Presumey J, Apparailly F, et al. microRNA target prediction programs predict many false positives. Genome research. 2017;27(2):23445. 19. M Witkos T, Koscianska E, J Krzyzosiak W. Practical aspects of microRNA target prediction. Current molecular medicine. 2011;11(2):93-109. 20. Liu J, Valencia-Sanchez MA, Hannon GJ, Parker R. MicroRNA-dependent localization of targeted mRNAs to mammalian P-bodies. Nature cell biology. 2005;7(7):719. 21. Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, et al. Genomic and molecular landscape of DNA damage repair deficiency across The Cancer Genome Atlas. Cell reports. 2018;23(1):239. 22. Hu Y, Zhou W, Ren J, Dong L, Wang Y, Jin S, et al. Annotating the function of the human genome with gene ontology and disease ontology. BioMed Research International. 2016;2016. 23. Brohi RD, Wang L, Hassine NB, Cao J, Talpur HS, Wu D, et al. Expression, Localization of SUMO-1, and Analyses of Potential SUMOylated Proteins in Bubalus bubalis Spermatozoa. Frontiers in physiology. 2017;8:354. 24. Ren X, Wang Y, Chen L, Zhang X-S, Jin Q. ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions. Nucleic acids research. 2012;41(4):e53-e. 25. Peng Q, Zhang X, Min M, Zou L, Shen P, Zhu Y. The clinical role of microRNA-21 as a promising biomarker in the diagnosis and prognosis of colorectal cancer: a systematic review and meta-analysis. Oncotarget. 2017;8(27):44893. 26. Zeng T, Sun Sy, Wang Y, Zhu H, Chen L. Network biomarkers reveal dysfunctional gene regulations during disease progression. The FEBS journal. 2013;280(22):5682-95. 27. Chen X, Yan G-Y. Semi-supervised learning for potential human microRNA-disease associations inference. Scientific reports. 2014;4:5501. 28. Wang C, Jiang W, Li W, Lian B, Chen X, Hua L, et al. Topological properties of the drug targets regulated by microRNA in human protein–protein interaction network. Journal of drug targeting. 2011;19(5):354-64. 29. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS computational biology. 2007;3(4):e59. 30. Cengel KA, Voong KR, Chandrasekaran S, Maggiorella L, Brunner TB, Stanbridge E, et al. Oncogenic K-Ras signals through epidermal growth factor receptor and wild-type HRas to promote radiation survival in pancreatic and colorectal carcinoma cells. Neoplasia. 2007;9(4):341-8. 31. Jin H, Gao S, Guo H, Ren S, Ji F, Liu Z, et al. Re-sensitization of radiation resistant colorectal cancer cells to radiation through inhibition of AMPK pathway. Oncology letters. 2016;11(5):3197-201.

Journal Pre-proof

Jo ur

na

lP

re

-p

ro

of

32. Chang L, Graham PH, Ni J, Hao J, Bucci J, Cozzi PJ, et al. Targeting PI3K/Akt/mTOR signaling pathway in the treatment of prostate cancer radioresistance. Critical reviews in oncology/hematology. 2015;96(3):507-17. 33. Zhu Y-F, Yu B-H, Li D-L, Ke H-L, Guo X-Z, Xiao X-Y. PI3K expression and PIK3CA mutations are related to colorectal cancer metastases. World journal of gastroenterology: WJG. 2012;18(28):3745. 34. Kamai T, Yamanishi T, Shirataki H, Takagi K, Asami H, Ito Y, et al. Overexpression of RhoA, Rac1, and Cdc42 GTPases is associated with progression in testicular cancer. Clinical cancer research. 2004;10(14):4799-805. 35. Abraham MT, Kuriakose MA, Sacks PG, Yee H, Chiriboga L, Bearer EL, et al. Motility‐related proteins as markers for head and neck squamous cell cancer. The Laryngoscope. 2001;111(7):1285-9. 36. Liu Y, Wang Y, Zhang Y, Miao Y, Zhao Y, Zhang P-X, et al. Abnormal expression of p120-catenin, E-cadherin, and small GTPases is significantly associated with malignant phenotype of human lung cancer. Lung cancer. 2009;63(3):375-82. 37. Vega FM, Ridley AJ. Rho GTPases in cancer cell biology. FEBS letters. 2008;582(14):2093-101. 38. Slattery ML, Lundgreen A, Wolff RK. MAP kinase genes and colon and rectal cancer. Carcinogenesis. 2012;33(12):2398-408. 39. Huang Q, Li S, Cheng P, Deng M, He X, Wang Z, et al. High expression of antiapoptotic protein Bcl-2 is a good prognostic factor in colorectal cancer: Result of a metaanalysis. World journal of gastroenterology. 2017;23(27):5018. 40. Nix P, Cawkwell L, Patmore H, Greenman J, Stafford N. Bcl-2 expression predicts radiotherapy failure in laryngeal cancer. British journal of cancer. 2005;92(12):2185. 41. Xu K, Chen Z, Qin C, Song X. miR-7 inhibits colorectal cancer cell proliferation and induces apoptosis by targeting XRCC2. OncoTargets and therapy. 2014;7:325. 42. Dai H, Wang C, Yu Z, He D, Yu K, Liu Y, et al. MiR-17 Regulates Prostate Cancer Cell Proliferation and Apoptosis Through Inhibiting JAK-STAT3 Signaling Pathway. Cancer biotherapy & radiopharmaceuticals. 2018;33(3):103-9. 43. Zhang D, Ni Z, Xu X, Xiao J. MiR-32 Functions as a Tumor Suppressor and Directly Targets EZH2 in Human Oral Squamous Cell Carcinoma. Medical Science Monitor : International Medical Journal of Experimental and Clinical Research. 2014;20:2527-35. 44. Yang X-D, Xu X-H, Zhang S-Y, Wu Y, Xing C-G, Ru G, et al. Role of miR-100 in the radioresistance of colorectal cancer cells. Am J Cancer Res. 2015;5:545-59. 45. Voos P, Fuck S, Weipert F, Babel L, Tandl D, Meckel T, et al. Ionizing radiation induces morphological changes and immunological modulation of Jurkat cells. Frontiers in immunology. 2018;9:922. 46. Gray M, Turnbull AK, Ward C, Meehan J, Martínez-Pérez C, Bonello M, et al. Development and characterisation of acquired radioresistant breast cancer cell lines. Radiation Oncology. 2019;14(1):64.

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Gene Abbreviations: Akt: Ak strain transforming BAD: BCL2 Associated agonist of cell Death BCL2: B-Cell Lymphoma 2 CCN1D: Cyclin D1 CDC42: Cell division control protein 42 homolog CTNB1: Catenin beta-1

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EGFR: Epidermal Growth Factor Receptor

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EP300: E1A Binding Protein P300

GSK3B: Glycogen synthase kinase 3 beta

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JNK: c-Jun N-terminal kinases

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ERK: Extracellular-signal-Regulated Kinase

MAPK8: Mitogen-Activated Protein Kinase 8

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MEK: MAPK/ERK Kinase

mTOR: Mammalian target of rapamycin

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PIK3CG: Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Gamma

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P110: PIK3 catalytic subunit alpha P85: PIK3 regulatory subunit

RAF: Rapidly Accelerated Fibrosarcoma RAS: Retrovirus-Associated DNA Sequences RSK: Ribosomal S6 Kinase S6K: S6 Kinase TP53: Tumor Protein 53

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships with other people or organizations that could inappropriately influence (bias) the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Journal Pre-proof Highlights 

The Omics data is very large and complex and needed to be interpreted by suitable methods. Also, miRNA prediction databases have many false positive results.

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We screened microRNAs/Target genes involved in tumor radiation response using gene enrichment analysis, network analysis, and survival analysis.

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Computational findings were also experimentally assessed by gene expression analysis on established radioresistant cell lines. Finally, we introduced three key genes (MAPK8, CDC42 and CCND1) involved in radiotherapy response of CRC. These genes are good candidates for prediction of

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CRC patient’s radiation response to early treatment decision. Also, these genes could

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be appropriate targets in cancer gene therapy approaches.

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