RWR-algorithm-based dissection of microRNA-506-3p and microRNA-140-5p as radiosensitive biomarkers in colorectal cancer

CRCR-related microRNAs identified by the RWR algorithm

Eighteen microRNAs were collected by literature review, which has been experimentally validated in association with CRCR (Table 1). By integrating three microRNA databases, a microRNA-mRNA network was constructed consisting of 2275 microRNAs and 7045 target genes. Then, we selecting those 18 microRNAs as seed nodes, and conducted the RWR algorithm analysis on the network (Figure 1). After permutation test, 263 novel CRCR-related microRNAs were identified (Supplementary Table 1). There were 1386 common targets between the 18 microRNAs and 263 microRNAs (Figure 2).

Figure 1. Flowchart of the network-based method to identify radiosensitivity-related microRNAs in colorectal cancer.

Figure 2. Distribution of the targets of 18 experimentally validated microRNAs and 263 novel microRNAs predicted by the random walk with restart (RWR) algorithm. Red dot in the inner ring, experimentally validated microRNAs; green dot in the inner ring, novel microRNAs; red dot in the outer ring, common targets of the experimentally validated microRNAs and novel microRNAs; green dot in the outer ring, unique targets of the experimentally validated microRNAs or novel microRNAs; blue line, the interaction between microRNAs and the targets.

Functional enrichment analyses

Functional enrichment analyses were conducted on the targets of the 18 microRNAs and 263 novel microRNAs respectively. In biological processes, there was a high overlapped rate of 70% between the targets of the two groups (Figure 3). Most of these biological processes focused on the biosynthesis and of RNA and protein, and cell proliferation. In pathways, there was a relatively high overlapped rate of 44% between the targets of the two groups. Most of these pathways focused on the tumor related signaling pathways.

Figure 3. Functional enrichment analyses of the targets of 18 experimentally validated microRNAs and 263 novel microRNAs. Multiple biological processes and pathways were enriched in the targets of 18 experimentally validated microRNAs and 263 novel microRNAs, and there existed a high proportion of overlap.

Topological analysis of the sub-network of 263 microRNAs and their targets

The topological features of the sub-network of 263 microRNAs and their targets were evaluated by the degree, betweenness and closeness centrality. We listed the overlap of top 10 microRNAs with topological features in each dimension (Figure 4). Two microRNAs (hsa-mir-506-3p and hsa-mir-140-5p) were identified as hub nodes in the network (Table 2).

Figure 4. Distribution of the sub-network topological parameters. Topological analysis was conducted in the sub-network of 263 novel microRNAs and their targets, and the Venn diagram showed the overlap in top 10 of each dimension.

Hsa-mir-506-3p and hsa-mir-140-5p over-expression and CRCR

In the quantitative RT-PCR of 18 plasma samples, radiosensitive patients had a significant higher expression level of hsa-mir-506-3p (P=0.0004) and hsa-mir-140-5p (P=0.0009) than radioresistant patients (Figure 5A). Moreover, microRNA-506-3p and microRNA-140-5p showed a good diagnostic performance in CRCR (area under ROC curve (AUC) = 0.925; 0.900).

Figure 5. Validation of the association between hsa-mir-506-3p and hsa-mir-140-5p and the radiosensitivity in colorectal cancer. (A) Plasma expression levels of hsa-mir-506-3p and hsa-mir-140-5p between radiosensitive and radioresistant patients. (B) Transfection efficiency of hsa-mir-506-3p mimic and hsa-mir-140-5p mimic. (C) Survival curves of transfected cells with hsa-mir-506-3p mimic and hsa-mir-140-5p mimic, negative control and untransfected cells at 0, 2, 4 and 6Gy radiation. (D) Cell proliferation assay analysis of transfected cells with hsa-mir-506-3p mimic and hsa-mir-140-5p mimic, negative control and untransfected cells after 4Gy radiation. (E) Clone forming assay analysis of transfected cells with hsa-mir-506-3p mimic and hsa-mir-140-5p mimic, negative control and untransfected cells after 4Gy radiation. Compared with the control or negative control, *P<0.05, **P<0.01, ***P<0.001.

After microRNA mimic transfection into HT29 cells for 48h, the expression levels of hsa-mir-506-3p (P<0.001) and hsa-mir-140-5p (P<0.001) increased significantly (Figure 5B). After 0, 2, 4 and 6Gy radiation, the microRNA mimic group had a lower survival rate than the control group and the negative control group for hsa-mir-506-3p (P<0.01) and hsa-mir-140-5p (P<0.001) (Figure 5C). After 4Gy radiation, the microRNA mimic group had a lower growth rate than the control group and the negative control group for hsa-mir-506-3p (P<0.01) and hsa-mir-140-5p (P<0.001) (Figure 5D). After 4Gy radiation, the microRNA mimic group had a less number of colonies than the control group and the negative control group for hsa-mir-506-3p (P=0.0105) and hsa-mir-140-5p (P=0.0018) (Figure 5E).

Random walk with restart (RWR) algorithm analysis

As a classic ranking algorithm, the RWR algorithm always simulates a random walker starting from a seed node or several seed nodes and walking on a certain network [18]. Finally, possible novel nodes are identified and ranked from high to low probabilities. The algorithm has been adopted to search novel disease genes or other related problems [19–21].

After the seed nodes are selected, the initial probability P₀ for each seed node is set as 1/N (where N was the number of seed nodes), while zero for non-seed nodes. Then, the RWR algorithm simulates a random walker that moved on certain network starting from seed nodes. For formulation, let P_i denote a vector representing the probability of each node after the i_th moving procedure is complete. After each moving procedure, P_i is updated as follows:

$P_{i+1}=(1-r)\text{M}{P}_i+\text{r}{P}_0$

where M is the column-wise normalized adjacency matrix of the network; r is the restart probability of returning to the seed nodes at every step (r was set to 0.8 in this study to indicate the importance of the known microRNAs). When the L1-norm of the difference between two successive vectors is less than 1×10⁶, the vector becomes stable, the RWR algorithm stops and output P_i+1 as the final vector. Each component in this vector indicates the probability of a node being trait-related. To remove the false positives, we also conducted a permutation test (1000 iterations). A false discovery rate (FDR) of less than 0.05 is considered significant.

Identification of candidate microRNAs

In this study, we adopted the RWR algorithm in the microRNA-mRNA network to identify candidate CRCR-related microRNAs. First, we reviewed the databases of PubMed and Embase, and chose certain microRNAs as the seed nodes. The microRNAs were included if meeting the following criteria: (i) reported in association with CRCR; (ii) conducted an experimental validation; (iii) availability of the experiment details. We excluded the microRNAs without experimental validation, or only detected by bioinformatics analysis.

The microRNA-mRNA network was constructed according to the interaction data from two computational prediction databases of miRDB (http://mirdb.org/) and TargetScan (http://www.targetscan.org/), and the experimental validation database of miRTarBase (http://http://mirtarbase.cuhk.edu.cn/php/index.php). To obtain more reliable results, we only included the interactions overlapped across the databases.

Then, the sub-network of candidate microRNAs and their targets were extracted from the microRNA-mRNA network. The network degree, betweenness and closeness centrality were calculated respectively to analyze topological features of the sub-network [22]. The overlapped microRNAs with top 10 topological features in each dimension were regarded as novel CRCR biomarkers for experimental validation.

Functional enrichment analyses

To investigate the potential function of novel microRNAs, gene ontology (GO) analyses of biological processes and pathways are performed using the online tool ToppGene (https://toppgene.cchmc.org/). A FDR of less than 0.05 is chosen as the cut-off criteria.

Plasma samples and cell culture

The plasma samples were obtained from 18 CRC patients who subsequently underwent radiotherapy at Renmin Hospital of Wuhan University, from September to October in 2018. All patients received no other therapies before radiotherapy, and were provided written informed consent before sample collection. The present study protocol was approved by the ethics committee of Renmin Hospital of Wuhan University. The radiotherapy efficacy was classified into sensitive (n=10) and resistant (n=8) groups according to response evaluation criteria in solid tumors (RECIST).

Human CRC cell line HT29 was cultured in Dulbecco’s Modified Eagle medium (DMEM, Thermo Fisher, USA) supplemented with 10% fetal bovine serum (FBS, Thermo Fisher, USA) in a humidified incubator with 5% CO₂ at 37°C.

RNA extraction and quantitative RT-PCR

Total RNA was extracted using TRIzol reagent (Invitrogen, USA). The RNA concentration and purity were measured by the NanoDrop spectrophotometer (Thermo Fisher, USA). Total RNA was synthesized into first-strand cRNA using a synthesis kit (Thermo Fisher, USA). Gene expression levels were subsequently detected by the ABI Sequence Detection System (ABI, USA) using the SYBR Green Premix Ex Taq II (Takara, Japan). The relative expression levels were analyzed using the 2^−ΔΔCt method. Each sample was performed in triplicate. The specific primers were as follows: hsa-mir-506-3p: 5’-ACACTCATAAGGCACCCTTC-3’ (forward): 5’-TCTACTCAGAAGGGGAGTAC-3’ (reverse); hsa-mir-140-5p: 5’-CAGTGGTTTTACCCTATGGTAG-3’ (forward): 5’-ACCATAGGGTAAAACCACTGTT-3’ (reverse); GAPDH: 5’-TATAAATTGAGCCCGCAGCC-3’ (forward): 5’-TACGACCAAATCCGTTGACTC-3’ (reverse).

MicroRNA mimic transfection

Hsa-mir-506-3p mimic, hsa-mir-140-5p mimic and their negative controls were purchased from RiboBio (Guangzhou, China). Cells were transfected with Lipofectamine 2000 (Invitrogen, USA) according to the manufacturer's protocol for 48h, and then harvested for further experiments.

Colony forming assay

After transfection, cells were irradiated at the doses of 2, 4, and 6Gy using 250kV X-rays (Philips, the Netherlands). Then, 1000 cells were incubated in 6-well plate for 2 weeks to form visualized colonies of survived cells. Finally, cells were fixed with formaldehyde, stained with 0.1% crystal violet and air-dried. The number of clones was quantified. Plating efficiency was calculated as the ratio of the visible colonies from all seeded cells. The survival fraction was determined by the percentage of irradiated cells compared with the control.

Cell proliferation assay

After transfection and irradiation, cells were seeded in 96-well plate with 1×10⁵/well and cultured for 24, 48 and 72h. Then, 10μl CCK8 solution (Beyotime, China) was added into each well, and the absorbance at 450nm was measured after incubating for 2h.

Statistical analysis

All statistical analyses are performed using SPSS 20.0 (IBM, USA) and GraphPad Prism 7.0 (GraphPad, UAS). Data are presented as mean ± SD, and t test and one-way ANOVA are used to compare two or multiple groups. Receiver operating characteristic (ROC) curve analysis is conducted to evaluate the diagnostic ability of microRNAs. P<0.05 is considered statistically significant.