Identification of colorectal cancer associated biomarkers: an integrated analysis of miRNA expression

Results

MicroRNAs are promising candidates for CRC screening and management. Since one biomarker is not enough to diagnose disease, a group of markers should increase sensitivity and specificity [59–61]. In that sense, here we aim to discover miRNAs associated with CRC development and/or progression and evaluate their combined diagnostic and prognostic values as panels of miRNAs.

MiRNA expression dataset selection

In order to perform an integrated analysis, we combined data from 4 datasets (discovery cohorts), obtained through Illumina platforms: the TCGA colon and cancer cohorts (TCGA-COAD/READ, from now on here referred as the TCGA cohort or dataset) and 3 GEO datasets that fitted our inclusion criteria (Table 1). TCGA is the most complete dataset used in our study, with numerous multi-omics data and patient clinical history and therefore was the dataset used for the prognostic analysis. The TCGA also comprised the largest number of miRNAs with a total number of 2164, followed by GSE33125 and GSE30454 with 878 each and GSE18392 with 510 miRNAs (Table 1 and Figure 1). In total, our bioinformatics analyses were carried out on 588 samples of which 69 were normal tissue and 519 tumour samples of colorectal carcinoma. Table 1 summarizes a detailed description of the discovery 4 datasets.

Table 1. Detailed patient information for colorectal cancer datasets used in miRNA expression analysis.

Datasets	TCGA	GSE33125	GSE18392	GSE30454
Number of miRNAs available	2164	878	510	878
Patient nationality	USA	Italy	USA	Spain
Platform	Illumina HiSeq miRNA Seq	Illumina Human v2 MicroRNA expression beadchip	Illumina Human v1 MicroRNA expression beadchip	Illumina Human v2 MicroRNA expression beadchip
Tissue type	Tumour and normal tissue	Paired tumour and normal tissue	Tumour and healthy tissue	Tumour and healthy tissue
Number of samples
Tumour	340 (97%)	9 (50%)	116 (80%)	20 (27%)
Normal	11 (3%)	9 (50%)	29 (20%)	54 (73%)
Age (mean ± SD1 years)
Tumour sample patients	65 ± 13	-	-	59 ± 16
Normal sample patients	68 ± 19	-	-	64 ± 16
Gender
Female	163 (46%)	-	-	43 (58%)
Male	185 (53%)	-	-	31 (42%)
Unknown	3 (1%)	18 (100%)	145 (100%)	-
Tumour Site
Colon	259 (74%)	18 (100%)	145 (100%)	74 (100%)
Rectum	92 (26%)	-	-	-
1 – standard deviation.
“-“– inexistent or unspecified.

Figure 1. Number of miRNAs throughout data processing and statistical analysis. The number of miRNAs after each major event (Listwise case deletion and Statistical analysis) is shown below each dataset. Different colours represent: yellow TCGA, green GSE33215, orange GSE18392 and reddish-purple GSE30454. From the top, the numbers of miRNAs represent: the initially number of miRNAs present in each dataset before any processing step; the number of miRNAs after removing the ones with more than 50% information missing (modified listwise case deletion); and the number of miRNAs after performing all the statistics and used to perform the identification of differently expressed miRNAs across datasets.

Identification of differently expressed miRNAs in CRC

To normalize our analysis, the 3 datasets from GEO were transformed and expressed as log₂ values to exhibit the same scale value as the TCGA dataset. From there all datasets were processed equally. We started by applying listwise deletion and outlier removal followed by a process of statistical analysis. All steps of this analysis resulted in the elimination of miRNAs as resumed in Figure 1. Proportionally, the GSE33125 dataset reported the biggest miRNA loss, with almost 86% of all miRNAs being excluded, and a total of 125 miRNAs remaining for further analysis. GSE30454, GSE18392 and TCGA exhibited losses close to 37%, 12% and 9% respectively, totalizing 459, 451 and 449 miRNAs remaining in each dataset (Figure 1). We then proceeded to identify which miRNAs were present in at least 2 of the 3 GEO datasets while simultaneously being present in the TCGA. A total of 42 miRNAs fulfilled our criteria. From these, 31 miRNAs were simultaneously found in 3 datasets: 6 in TCGA, GSE33125 and GSE18392, 23 in TCGA, GSE30454 and GSE18392 and 2 in TCGA, GSE30454 and GSE33125 (Figure 2). Notably, 11 miRNAs were found in all 4 datasets at the same time (Figure 2).

Figure 2. Venn diagram of the differently expressed miRNAs between datasets. Allocation of the 1043 differently expressed miRNAs found between the 4 datasets used in this work. Each dataset is represented by a colour, TCGA (yellow), GSE30545 (reddish purple), GSE33215 (green) and GSE18392 (orange). The number in each overlay of datasets represents the common miRNAs between those datasets.

Finally, we calculated the log₂FC between tumour and normal tissue samples for the identified 42 miRNAs. Since this study only involves human samples, the name of each miRNA from now on will not include the prefix related to the species (hsa-).

From these, 26 miRNAs presented lower expression levels in colorectal cancer tissues when compared to normal tissues and exhibited the same expression pattern in all the datasets where they were present: miR-139-5p, miR-125a-5p, miR-193a-5p, miR-484, miR-486-5p, miR-28-3p, miR-342-3p, miR-129-5p, miR-299-5p, miR-296-5p, miR-326, miR-324-5p, miR-339-5p, miR-133b, miR-127-3p, miR-331-3p, miR-324-3p, miR-423-3p, miR-490-3p, miR-502-3p, miR-574-3p, miR-330-3p, miR-320d, miR-320b, miR-375 and miR-320a (Figure 3). The remaining 16 miRNAs (miR-491-5p, miR-654-5p, miR-485-3p, miR-632, miR-76, miR-769-3p, miR-28-5p, miR-140-5p, miR-338-3p, miR-107, miR-199b-5p, miR-429, miR-501-5p, miR-582-5p, miR-142-3p and miR-1), exhibited opposite expression values in at least one dataset in which they were present and thus no regulation pattern could be perceived (Figure 3). Therefore, these miRNAs were excluded from the subsequent analyses.

Figure 3. Differentially expressed miRNAs between colorectal cancer and normal tissue samples. The log₂(FC) values calculated for each dataset are reported with red scale boxes for upregulated miRNAs and blue scale boxes for the downregulated miRNAs. White boxes represent the inexistence of the miRNA on the dataset. Only the miRNAs differentially expressed in at least 2 datasets while simultaneously being present in the TCGA dataset are displayed.

Our results also point out different magnitudes of the log₂(FC) values across datasets. While the TCGA dataset log₂(FC) values ranged from less than -2 to almost 2, for downregulated and upregulated miRNAs respectively, in the remaining datasets the log₂(FC) values were never below -1 or higher than 1 (Figure 3). Interestingly, the miRNA with the highest deregulation pattern overall (miR-129-5p) and the most downregulated in TCGA with a log₂(FC) value of -2, is reported as a frequently downregulated miRNA in CRC, supporting our findings [62, 63].

Gene target analysis of selected miRNAs

To better understand the role of the 26 downregulated miRNAs in CRC, we used the MirTarBase to identify functional miRNA-target interactions (MTIs). Our analysis identified a total of 309 different human target genes previously experimentally validated (either by report assay, western blot or qPCR) in a total of 384 papers (Supplementary Table 1). Among the target genes identified, ERBB2 was the most prevalent, described in 12 different papers as regulated by miR-125a-5p, miR-331-3p, miR-375 and miR-193a-5p. MCL1 was the second most frequent gene being reported in 9 papers as modulated by miR-133b, miR-320a, miR-139-5p and miR-125a-5p. IGF1R and SP1 genes followed, both cited in 8 occasions (Supplementary Table 1).

Then, to understand which of the 26 miRNAs could be playing a role in the development of CRC, the 309 targets identified in the MTIs were screened in the CoReCG database which highlighted 173 genes previously associated to CRC (Figure 4 and Supplementary Figure 1 and Supplementary Table 2). From our set of 26 miRNAs, all were found to interact with at least one gene involved in CRC except for miR-502-3p. Among the remaining 25 miRNAs, miR-125a-5p a well-known deregulated miRNA in CRC [37] presented the highest number of interactions with CRC associated genes, interacting with a total of 44 genes among which were AKT1, EGFR, TP53, VEGFA, SMAD2 and SMAD4 (Figure 4 and Supplementary Table 2). MiR-133b which has also been reported as showing an altered expression in CRC [57], interacted with 38 genes including AKT1, EGFR, MET, FGFR1 among other frequently altered genes in CRC. MiR-320a showed a total of 35 interactions, proceeded by miR-375 with 34, miR-139-5p with 24 and miR-129-5p with 22 interactions. Altogether these miRNAs were the ones with the highest number of interactions with commonly altered genes in CRC suggesting their potential contribution to the development of this disease (Figure 4, Supplementary Figure 1 and Supplementary Table 2). Besides the genes above mentioned other genes such as APC, PIK3CA, PTEN, RUNX3 and CTNNB1, are some additional examples of the well-known altered genes in CRC targeted by some of the 25 miRNAs identified [25, 64–69] (Figure 4 and Supplementary Table 2). MiR-125a-5p, miR-375 and miR-28-3p were found to interact with TP53. Simultaneously, miR-125a-5p was also found to regulate both SMAD2 and SMAD4. SMAD4 was also a target gene of miR-574-3p. PTEN was a target of miR-486-5p and miR-320a, which also regulates CTNNB1. MiR-375, besides interacting with TP53 also regulates CTNBB1, RUNX3 and PIK3CA. PIK3CA was also found to be targeted by miR-139-5p. Finally, our results demonstrate that APC is a target of miR-129-5p (Figure 4, Supplementary Figure 1 and Supplementary Table 2). These results suggest that miRNAs may play a role in the development or progression of CRC through the regulation of key genes found frequently altered or mutated in CRC.

Figure 4. Chord-dendrogram of the interactions between differently expressed miRNAs and altered genes in CRC. The interactions between the 25 differently expressed miRNAs identified and the 173 experimentally validated target genes were obtained through MirTarBase. Target genes’ role in CRC is according to CoReCG database. All targets are represented in grey, while each miRNA is represented by one colour. MiRNA:target interactions are represented by a line of the same colour of the respective miRNA. The size of rectangle next to the name of the miRNAs and target genes is proportional to the number of interactions they perform.

To get more insights into the miRNA regulation in the specific context of CRC, we investigated the 173 genes expression patterns and its association with the 25 miRNAs expression profile, in the TCGA dataset. We analysed 172 genes since information for PKM was missing. We found 76 upregulated genes and 96 downregulated genes in tumour samples (Figure 5 and Supplementary Table 3). NTRK3, SFRP1, ABCG2 and WNT1 with log₂(FC) values of approximately -1.28, -1.22, -1.05 and -1.03 respectively (Figure 5) were the genes with the lowest expression values. Oppositely MMP13 (log₂(FC) ≈ 2.80) and WT1 (log₂(FC) ≈ 2.16) were the most upregulated genes followed by ABCC2 (log₂(FC) ≈ 0.85) and TFAP2A (log₂(FC) ≈ 0.71) (Figure 5 and Supplementary Table 3). Correlation analysis between the expression of the 25 miRNA expression levels and their targets revealed that 104 miRNA-target genes interactions were statistically significant. From the 42 negative correlations, 10 had a p-value < 0.0001 while for the 62 positive interactions a p-value < 0.0001 was found in 25 associations. These 35 associations also presented the highest correlation coefficients, ranging from 0.215 to 0.478 in absolute value (Supplementary Table 4). The strongest negative interaction identified was miR-375:YAP1 (correlation coefficient = -0.478; p-value = 4.99x10^-20), followed by miR-484:ZEB1 (correlation coefficient = -0.381; p-value = 1.17x10^-12) and by miR-375:MAP3K8 (correlation coefficient = -0.307; p-value = 1.17x10^-8). On the other hand, the strongest positive interaction was identified between miR-375:KLF4 (correlation coefficient = 0.427; p-value = 7.62x10^-16), followed by the interactions miR-133b:GLI3 (correlation coefficient = 0,417; p-value = 5.22x10^-14) and miR-133b:FGFR1 (correlation coefficient = 0,386; p-value = 4.42x10^-12). MiR-133 also interacted with ERG (correlation coefficient = 0.366; p-value = 6.89x10^-11) and GLI1 (correlation coefficient = 0.311; p-value = 4.14x10^-8). MiR-125a-5p which we found to target the highest number of CRC altered genes, displayed interactions with MEG3 (correlation coefficient = 0.373; p-value = 5.86x10^-12) and HDAC5 (correlation coefficient = 0.302; p-value = 3.87x10^-8). MiR-129-5p presented an interaction with IGF1 (correlation coefficient = 0.312; p-value = 1.70x10^-8) (Supplementary Table 4).

Figure 5. Circular barplot evidencing the expression of the 172 experimentally validated miRNA target genes. The expression values of the 172 genes were obtained from the TCGA colon and rectal cohorts and the log₂(FC) values between the primary tumour and normal tissue samples were calculated. The log₂(FC) value of each gene is given by the length and tone of each coloured bar in accordance with each gene regulation status (red colours – upregulated; blue colours- downregulated).

Altogether, these results suggest that miR-125a-5p, miR-133b, miR-375 and miR-129-5p, which were among the miRNAs with highest number of CRC-related targets, may play a key role in the altered gene expression observed in CRC.

Pathway analysis of the miRNAs target genes

To further understand the role of the 25 differentially expressed miRNAs in CRC development we investigated the molecular mechanisms of their 173 target genes. An enrichment pathway analysis using KEEG at DAVID database revealed a total of 104 pathways (p-value ≤ 0.05, Supplementary Table 5). The results pointed to the key role of miRNAs in cancer since pathways such as “Pathways in Cancer” came up on the top of our list (p-value = 6.44x10^-49; FDR of 7.98x10^-46), followed by “Prostate cancer” in the 3^rd position (p-value = 1.53x10^-28; FDR = 1.9x10^-25), “Pancreatic cancer” in the 4^th position (p-value = 3.2x10^-28; FDR = 4.0x10^-25) and “MicroRNAs in Cancer” in the 8^th position (p-value = 9.3x10^-23; FDR 1.2x10^-19) (Supplementary Table 5), evidenced the enrolment of these miRNAs in cancer. The “Colorectal cancer” pathway (p-value = 1.4x10^-22; FDR =1.7x10^-19, Supplementary Table 5) was in the 10^th position, stressing that the 25 miRNAs might play a role in CRC.

Furthermore, several pathways usually found altered in CRC came up in our analysis such as “PI3K-Akt signalling pathway” (p-value = 1.2x10^-21; FDR = 1.9x10^-20), “RAS signalling pathway” (p-value = 8.1x10^-11; FDR = 4.5x10^-10), “TP53 signalling pathway” (p-value = 7.1x10^-7; FDR = 2.3x10^-6), “TGF-beta signalling pathway” (p-value = 7.6x10^-7; FDR = 2.4X10^-6), “WNT signalling pathway” (p-value = 8.5x10^-8 and FDR = 3.0x10^-7 and “MAPK signalling pathway” (p-value = 2.39x10^-08; FDR = 2.96x10^-05). Additionally, cellular functions often aberrant in colorectal cancer such as “Apoptosis” (p-value = 3.4x10^-7; FDR = 1.2x10^-6) and “Cell cycle” (p-value = 9.9x10^-7; FDR = 3.0x10^-6) were found in our analysis (Supplementary Table 5). Together these results support the association between the 25 deregulated miRNAs and several alterations often reported in CRC.

We then aimed to understand in which frequently altered pathways in CRC (TP53, PI3K-AKT, RAS, TGF-Beta, WNT and MAPK signalling pathways) each miRNA was intervening [70–75]. For that, we crossed the miRNA-target genes found to be involved in CRC with the genes intervening in each of the signalling pathways. Our results show that most of the miRNAs were involved in at least one of these pathways, except for miR-320d (Figure 6). More interestingly, we found that several miRNAs seem to intervene in more than one pathway, and 5 miRNAs (miR-125a-5p, miR-320a, miR-375, miR-133b and miR-490-3p) interact with all the six pathways. Not surprisingly, apart from miR-490-3p these miRNAs represent the ones targeting the highest number of genes involved in CRC. Therefore, by targeting multiple genes that are constituents in these pathways it’s plausible that altered expression of these miRNAs may contribute to the aberration of frequently altered pathways in CRC (Figures 4–6). These results support the hypothesis that aberrant miRNA expression may contribute for the occurrence of several alteration events observed during CRC.

Figure 6. KEGG pathway analysis – interaction between the 25 differently expressed miRNAs and the frequently altered pathways in colorectal cancer. The 173 miRNA-target genes were used to perform the enrichment pathway analysis using KEEG at DAVID database. The miRNA-target genes found to be involved in CRC were crossed with the genes intervening in each signalling pathway in order to identify the miRNAs affecting each pathway. The miRNAs interaction with each KEGG Pathway is reported in pink.

Identification of potential diagnostic biomarkers for colorectal cancer

Given the potential key role in CRC of the 25 miRNAs identified in this study, we then investigated their diagnostic value. For that, Receiver Operating Characteristic (ROC) curve analysis was performed in all the datasets for the 25 differently expressed miRNAs and the Area Under the Curve (AUC) values were obtained in order to determine their ability to distinguish between colorectal cancer tissue and normal tissue. The diagnostic value of each miRNA was here characterized according to the classification by Greiner and colleagues as: less accurate (0.5 < AUC ≤ 0.7), moderately accurate (0.7 < AUC ≤ 0.9), highly accurate (0.9 < AUC < 1) and perfect diagnostic biomarker (AUC =1) [76]. Our analysis revealed that 23 out of the 25 miRNAs could be considered highly accurate (0.9 < AUC < 1) or better (AUC =1) in at least one of the cohorts they were present, except for miR-296-5p and miR-127-3p which were found to be moderately accurate in all the datasets they were present (Figure 7 and Supplementary Table 6).

Figure 7. Areas under the ROC curve (AUC) of the 25 differentially expressed miRNAs between colorectal cancer and normal tissues samples. The miRNAs AUC values in each of the datasets TCGA (Yellow), GSE18392 (orange), GSE33125 (Green) and GSE30454 (reddish purple) are reported as blue scale boxes. MiRNAs with AUC = 1 were considered perfect diagnostic biomarkers, 0.9 < AUC < 1 highly accurate, 0.7 < AUC ≤ 0.9 moderately accurate and 0.5 < AUC ≤ 0.7 less accurate [76].

Besides, the miRNAs miR-28-3p, miR-193a-5p, miR-299-5p, miR-320a, miR-320b, miR-324-3p, miR-326, miR-339-5p, miR-486-5p and miR-574-3p had perfect AUC values of diagnostic biomarker (AUC =1) in the TCGA dataset, and miR-125a-5p was considered perfect diagnostic tool (AUC =1) in the GEO GSE33125 dataset. Our results indicate that these 11 miRNAs could help to differentiate tumour from normal tissue with 100% sensibility and 100% specificity (Figure 7 and Supplementary Table 6). Thus, these deregulated miRNAs can be powerful diagnostic tools for an early CRC detection, an observation also corroborated by other groups [36, 56, 57, 77–79].

Identification of potential prognostic biomarkers for colorectal cancer

The current TNM staging system lacks the ability to accurately predict patient outcome, leading to suboptimal clinical management [17]. In this sense, we proceeded to investigate if the 25 differentially expressed miRNAs could differentiate patients within each stage of disease. To perform these analyses, Kaplan-Meier curves for Overall Survival (OS) and Recurrence Free Survival (RFS) were plotted using the median expression of each miRNA to form two distinct groups (“higher expression” group and “lower expression” group) with a minimum of 30 patients. Our analysis revealed that only miR-133b (p-value = 0.047) and miR-129-5p (p-value = 0.021), for stages III and IV respectively, could be potential solo OS biomarkers (Figure 8A, 8B). While a lower expression of miR-133b was indicative of a better outcome for patients in stage III, higher levels of miR-129-5p in stage IV patients seems to relate to extended overall survival (Figure 8A, 8B). This highlights the potential of miR-133b and miR-129-5p as individual OS biomarkers in CRC stages III and IV, respectively. On the other hand, none of the selected 25 miRNAs was able to perform as a good predictive biomarker for RFS. Nevertheless, it is highly unlikely that a single miRNA could provide a strong and precise prognostic tool. Instead, the combination of a group of miRNAs should be a more powerful clinical tool for patient management.

Figure 8. Individual miRNA overall survival (OS) prognosis for stage III and stage IV patients. (A) Stage III OS Kaplan-Meier curve for miR-133b (p-value = 0.047, Log rank; HR = 2.28) (B) Stage IV OS Kaplan-Meier curve for miR-129-5p (p-value = 0.021, Log rank; HR = 4.23). Time is represented in years. Higher (in red) and Lower (in blue) expression groups represent the patients with miRNA expression above and below miRNAs’ median expression, respectively. Censored data is represented by small plus signs in each group. The number of patients at risk for each group and per time point is shown in the table below each graph. HR, hazard ratio.

In this context, we analysed the potential of the 25 miRNAs to distinguish patients with different outcomes in combinations of up to 5 miRNAs. A maximum of 5 miRNAs was set in order to assure a minimum of 30 patients when performing the analysis, as panels with more miRNAs reduced the number of patients to less than 30. These analyses were performed across all stages of CRC, however the results from stages I and IV were excluded due to the small number of patients (below 30). Nevertheless, with this approach we could identify one panel of 5 miRNAs able to predict the outcome of patients in stage II regarding OS. In this panel, composed by miR-320b - miR-326 - miR-331-3p - miR-339-5p - miR-484 (p-value = 0.032; hazard ratio (HR) = 5.23), the group of patients with lower miRNA expression had a better outcome with ~5 times more probability to be alive than the patients in the group with higher miRNAs expression (Figure 9A and Supplementary Table 7). Moreover, by combining less than 5 miRNAs we could also identify 27 panels of 4 miRNAs, 54 panels of 3 miRNAs and 6 panels of 2 miRNAs capable of predicting the overall survival of stage II patients in CRC (Supplementary Table 7).

Figure 9. Panels of 5 miRNAs for overall survival (OS) and recurrence free survival (RFS) prognosis of stage II patients. (A) Stage II OS Kaplan-Meier curve based on miR-320b - miR-326 - miR-331-3p - miR-339-5p - miR-484 (p-value = 0.032, Log rank test; HR= 5.23) (B) Stage II recurrence free survival RFS Kaplan-Meier curve based on miR-320a - miR-324-5p - miR-324-3p - miR-423-3p - miR-484 (p-value = 0.043, Log rank test). Time is represented in years. Higher (in red) and Lower (in blue) expression groups represent the group of patients with miRNA expression above and below miRNAs median expression, respectively. Censored data is represented by small plus signs in each group. The number of patients at risk for each group and per time point is shown in the table below each graph. HR, hazard ratio.

Regarding RFS analysis, we could identify two panels of 5 miRNAs with prognostic value. The best panel was the one composed by miR-320a - miR-324-5p - miR-324-3p - miR-423-3p - miR-484 in which the group of patients with higher combined expression had a better outcome than the lower expression group (p-value = 0.043) (Figure 9B and Supplementary Table 8). Likewise, for the panel miR-320a - miR-320b - miR-324-3p - miR-423-3p - miR-484 the group of patients with higher miRNAs expression had a better outcome than the lower expression group (p-value = 0.049) (Supplementary Table 8). Additionally, we identified 1 panel of 4 miRNAs, 3 panels of 3 miRNAs and 3 panels of 2 miRNAs capable of predicting RFS for stage II patients in CRC (Supplementary Table 8).

Regarding Stage III patients, we have identified one panel with potential OS value: miR-324-5p - miR-324-3p - miR-331-3p - miR-484 - miR-486-5p (p-value = 0.020; HR = 8.150), indicating that at any given time, the patients with higher miRNAs expression had approximately 8 times more probability to be alive than the patients with lower expression levels (Figure 10A and Supplementary Table 9). Regarding RFS, one panel of 5 miRNAs showed potential prognostic value: miR-299-5p - miR-324-5p - miR-324-3p - miR-331-3p - miR-484 with a p-value = 0.030. With this panel of miRNAs, the patients presenting higher miRNAs expression have a better outcome than the patients with lower expression values (Figure 10B and Supplementary Table 10).

Figure 10. Panels of 5 miRNAs for overall survival (OS) and recurrence free survival (RFS) prognosis of stage III patients. (A) Stage III OS Kaplan-Meier curve based on miR-324-5p - miR-324-3p - miR-331-3p - miR-484 - miR-486-5p (p-value = 0.020, Log rank test; HR= 8.15). (B) Stage III recurrence free survival RFS Kaplan-Meier curve based on miR-299-5p - miR-324-5p - miR-324-3p - miR-331-3p - miR-484 (p = 0.030, Log rank test). Time is represented in years. Higher (in red) and Lower (in blue) expression groups represent the group of patients with miRNA expression above and below miRNAs median expression, respectively. Censored data is represented by small plus signs in each group. The number of patients at risk for each group and per time point is shown in the table below each graph. HR, hazard ratio.

Moreover, for stage III patients we could identify a total of 65 remaining panels for OS (19 panels of 4 miRNAs, 31 panels of 3 miRNAs and 15 panels of 2 miRNAs; Supplementary Table 9) and 27 panels for RFS (10 panels of 4 miRNAs, 14 panels of 3 miRNAs and 3 panels of 2 miRNAs; Supplementary Table 10). Altogether these results further evidence the power of panels of miRNAs to predict patient outcome regarding both overall and recurrence free survivals.

Validation analysis

In order to validate our findings and further confirm the utility of the selected 25 miRNAs as potential non-invasive biomarkers for colorectal cancer we have inspected three additional and independent cohorts of CRC patients (validation cohorts). These included two cohorts with CRC tissue samples (GSE115513 and GSE41655 datasets) and one cohort with CRC plasma samples (GSE71008 dataset) plus the respective normal samples (Table 2). From these, the GSE41655 dataset showed the highest degree of similarity with the results described so far, with 23 out of the 25 miRNAs being downregulated in this cohort as well (Figure 11A). Although, since the total number of samples was relatively low (48 total samples) we have also analysed a larger cohort with a more significant number of cases (750 carcinomas and 649 normal mucosa samples). This analysis revealed that 12 out of the initial 25 miRNAs were also downregulated in the GSE115513 dataset (Figure 11A). Since miR-326 had no expression values available in this dataset for the normal mucosa samples we were unable to analyse its fold-change (Figure 11A).

Table 2. Detailed patient information for the three colorectal cancer datasets used to corroborate our analysis.

Datasets	GSE115513	GSE41655	GSE71008
Number of miRNAs available	2006	851	421
Patient nationality	USA	China	USA
Platform	Agilent-046064 Unrestricted Human miRNA V19.0 Microarray	Agilent-021827 Human miRNA Microarray	Illumina Genome Analyzer
Tissue type	Paired tumour and normal tissue	Tumour and normal tissue	Plasma microvesicles
Number of samples
Tumour	750 (54%)	33 (69%)	100 (67%)*
Normal	649 (46%)	15 (31%)	50 (33%)*
Age (mean ± SD1 years)
Tumour sample patients	65 ± 11	63 ± 11	55 ± 18
Normal sample patients	65 ± 11	48 ±14	54 ± 14
Gender
Female	667 (48%)	16 (33%)	74 (49%)
Male	732 (52%)	32 (67%)	76 (51%)
Tumour Site
Colon	792 (57%)	-	-
Rectum	607 (43%)	-	-
1 – standard deviation.
“-“– unspecified.
“*”– Tumour = cancerous patient samples; Normal = healthy individual samples.

Figure 11. Validation analysis for the 25 downregulated miRNAs in CRC. (A) The log₂(FC) values calculated for each dataset are reported with red scale boxes for upregulated miRNAs and blue scale boxes for the downregulated miRNAs. White boxes represent the inexistence of the miRNA on the dataset. (B) The miRNAs AUC values in each of the datasets GSE115513, GSE41655 and GSE71008 are reported as blue scale boxes. MiRNAs with AUC = 1 were considered perfect diagnostic biomarkers, 0.9 < AUC < 1 highly accurate, 0.7 < AUC ≤ 0.9 moderately accurate and 0.5 < AUC ≤ 0.7 less accurate [76]. (C) Stage III OS Kaplan-Meier curve based on miR-486-5p - miR-330-3p - miR-375 (p-value = 0.025, Log rank test; HR= 4.01). Time is represented in years. Higher (in red) and Lower (in blue) expression groups represent the group of patients with miRNA expression above and below miRNAs median expression, respectively. Censored data is represented by small plus signs in each group. The number of patients at risk for each group and per time point is shown in the table below each graph. HR, hazard ratio.

Furthermore, and toward identifying miRNAs that could be used as non-invasive diagnostic and prognostic tools, we have investigated if our results could be extrapolated to a plasma context. Therefore, the expression profile of the 25 downregulated miRNAs was assessed in a cohort with plasma samples from CRC patients (GSE71008 dataset; Figure 11A). MiR-133b, miR-296-5p and miR-299-5p were not present in the GSE71008 dataset and therefore their regulation status was not evaluated. For the remaining 22 miRNAs, 12 were found to be downregulated and thus presenting the same expression profile as in the tumour tissue (Figure 11A).

Interestingly, comparing the outcomes from the three validation datasets we were able to identify 8 miRNAs commonly downregulated: miR-139-5p, miR-28-3, miR-330-3p, miR-331-3p, miR-375, miR-423-3p, miR-486-5p and miR-574-3p, further corroborating part of our results from the set of ‘discovery’ cohorts (Figure 11A).

Finally, we proceed to validate the diagnostic and prognostic ability of the 8 miRNAs commonly downregulated in all three validation cohorts. Regarding diagnostic ability, the 8 miRNAs did not present accuracy values as high as the ones obtained in the set of ‘discovery’ cohorts, with most of the miRNAs showing AUCs in the range of 0.5 and 0.7 in all three datasets and only miR-375 reaching an AUC above 0.9 in the GSE41655 dataset (Figure 11B and Supplementary Table 11).

When considering prognostic ability, none of the validation datasets provided information regarding the OS or RFS of patients. Therefore, to infer the prognostic potential of these 8 miRNAs we had to use the patient information available in the TCGA dataset. Importantly, we found that the panel of 2 miRNAs containing miR-486-5p - miR-375 (p-value = 0.032; HR = 2.890) and the panels of 3 miRNAs containing miR-486-5p - miR-330-3p - miR-375 (p-value = 0.025; HR = 4.010) or miR-486-5p - miR-331-3p - miR-375 (p-value = 0.031; HR = 3.911) could predict survival outcomes of stage III patients (Supplementary Table 12 and Figure 11C).

Author Contributions

Conceptualization, VR, PCB and AM; Methodology, VR, AM and AF; Data Curation, AF, AM, AM and SR; Writing – Original Draft Preparation, IF and VR; Writing – Review and Editing, VR, PCB and RPN; Supervision, VR, PCB and AM; Funding Acquisition, VR and PCB.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This work was supported by the FCT Research Center Grant UID/BIM/04773/2013 CBMR 1334, Câmara Municipal de Loulé and by the Liga Portuguesa Contra o Cancro (Portuguese League against Cancer) through the Grant LPCC-PT Foundation 2016 to VPR.

References

• 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018; 68:394–424. https://doi.org/10.3322/caac.21492 [PubMed]
• 2. Van der Jeught K, Xu HC, Li YJ, Lu XB, Ji G. Drug resistance and new therapies in colorectal cancer. World J Gastroenterol. 2018; 24:3834–48. https://doi.org/10.3748/wjg.v24.i34.3834 [PubMed]
• 3. Levin B, Lieberman DA, McFarland B, Smith RA, Brooks D, Andrews KS, Dash C, Giardiello FM, Glick S, Levin TR, Pickhardt P, Rex DK, Thorson A, Winawer SJ, and American Cancer Society Colorectal Cancer Advisory Group, and US Multi-Society Task Force, and American College of Radiology Colon Cancer Committee. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. CA Cancer J Clin. 2008; 58:130–60. https://doi.org/10.3322/CA.2007.0018 [PubMed]
• 4. Falzone L, Scola L, Zanghì A, Biondi A, Di Cataldo A, Libra M, Candido S. Integrated analysis of colorectal cancer microRNA datasets: identification of microRNAs associated with tumor development. Aging (Albany NY). 2018; 10:1000–14. https://doi.org/10.18632/aging.101444 [PubMed]
• 5. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2014; 383:1490–502. https://doi.org/10.1016/S0140-6736(13)61649-9 [PubMed]
• 6. Strul H, Arber N. Screening techniques for prevention and early detection of colorectal cancer in the average-risk population. Gastrointest Cancer Res. 2007; 1:98–106. [PubMed]
• 7. Ciombor KK, Wu C, Goldberg RM. Recent therapeutic advances in the treatment of colorectal cancer. Annu Rev Med. 2015; 66:83–95. https://doi.org/10.1146/annurev-med-051513-102539 [PubMed]
• 8. Simon K. Colorectal cancer development and advances in screening. Clin Interv Aging. 2016; 11:967–76. https://doi.org/10.2147/CIA.S109285 [PubMed]
• 9. U.S. Preventive Services Task Force. Screening for colorectal cancer: recommendation and rationale. Ann Intern Med. 2002; 137:129–31. https://doi.org/10.7326/0003-4819-137-2-200207160-00014 [PubMed]
• 10. Levine JS, Ahnen DJ. Clinical practice. Adenomatous polyps of the colon. N Engl J Med. 2006; 355:2551–57. https://doi.org/10.1056/NEJMcp063038 [PubMed]
• 11. O’Connell JB, Maggard MA, Ko CY. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. J Natl Cancer Inst. 2004; 96:1420–25. https://doi.org/10.1093/jnci/djh275 [PubMed]
• 12. Haggar FA, Boushey RP. Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin Colon Rectal Surg. 2009; 22:191–97. https://doi.org/10.1055/s-0029-1242458 [PubMed]
• 13. Cappellani A, Di Vita M, Zanghi A, Veroux P, Cavallaro A, Lo Menzo E, Cacopardo B, Canzonieri V, Murabito P, Tirelli U, Berretta M. Biological and clinical markers in colorectal cancer: state of the art. Front Biosci (Schol Ed). 2010; 2:422–31. https://doi.org/10.2741/s75 [PubMed]
• 14. Vukobrat-Bijedic Z, Husic-Selimovic A, Sofic A, Bijedic N, Bjelogrlic I, Gogov B, Mehmedovic A. Cancer Antigens (CEA and CA 19-9) as Markers of Advanced Stage of Colorectal Carcinoma. Med Arch. 2013; 67:397–401. https://doi.org/10.5455/medarh.2013.67.397-401 [PubMed]
• 15. Swiderska M, Choromańska B, Dąbrowska E, Konarzewska-Duchnowska E, Choromańska K, Szczurko G, Myśliwiec P, Dadan J, Ladny JR, Zwierz K. The diagnostics of colorectal cancer. Contemp Oncol (Pozn). 2014; 18:1–6. https://doi.org/10.5114/wo.2013.39995 [PubMed]
• 16. Zamani M, Hosseini SV, Mokarram P. Epigenetic biomarkers in colorectal cancer: premises and prospects. Biomarkers. 2018; 23:105–14. https://doi.org/10.1080/1354750X.2016.1252961 [PubMed]
• 17. Li J, Yi CH, Hu YT, Li JS, Yuan Y, Zhang SZ, Zheng S, Ding KF. TNM Staging of Colorectal Cancer Should be Reconsidered According to Weighting of the T Stage: Verification Based on a 25-Year Follow-Up. Medicine (Baltimore). 2016; 95:e2711. https://doi.org/10.1097/MD.0000000000002711 [PubMed]
• 18. Brierley JD, Gospodarowicz MK, Wittekind C. Essential TNM. In: Sobin LH, Sobin LH, Gospodarowicz MK, Wittekind C, Brierley JD, editors. TNM Online. 2017. https://doi.org/10.1002/9780471420194.tnmc66
• 19. Bender U, Rho YS, Barrera I, Aghajanyan S, Acoba J, Kavan P. Adjuvant therapy for stages II and III colon cancer: risk stratification, treatment duration, and future directions. Curr Oncol. 2019 (Suppl 1); 26:S43–52. https://doi.org/10.3747/co.26.5605 [PubMed]
• 20. Bardhan K, Liu K. Epigenetics and colorectal cancer pathogenesis. Cancers (Basel). 2013; 5:676–713. https://doi.org/10.3390/cancers5020676 [PubMed]
• 21. Armaghany T, Wilson JD, Chu Q, Mills G. Genetic alterations in colorectal cancer. Gastrointest Cancer Res. 2012; 5:19–27. [PubMed]
• 22. Shen L, Toyota M, Kondo Y, Lin E, Zhang L, Guo Y, Hernandez NS, Chen X, Ahmed S, Konishi K, Hamilton SR, Issa JP. Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci USA. 2007; 104:18654–59. https://doi.org/10.1073/pnas.0704652104 [PubMed]
• 23. Khare S, Verma M. Epigenetics of colon cancer. Methods Mol Biol. 2012; 863:177–85. https://doi.org/10.1007/978-1-61779-612-8_10 [PubMed]
• 24. Kinzler KW, Vogelstein B. Lessons from hereditary colorectal cancer. Cell. 1996; 87:159–70. https://doi.org/10.1016/s0092-8674(00)81333-1 [PubMed]
• 25. Sharma S, Kelly TK, Jones PA. Epigenetics in cancer. Carcinogenesis. 2010; 31:27–36. https://doi.org/10.1093/carcin/bgp220 [PubMed]
• 26. Grønbaek K, Hother C, Jones PA. Epigenetic changes in cancer. APMIS. 2007; 115:1039–59. https://doi.org/10.1111/j.1600-0463.2007.apm_636.xml.x [PubMed]
• 27. Fedoriw A, Mugford J, Magnuson T. Genomic imprinting and epigenetic control of development. Cold Spring Harb Perspect Biol. 2012; 4:a008136. https://doi.org/10.1101/cshperspect.a008136 [PubMed]
• 28. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai K, Rassenti L, Kipps T, Negrini M, et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA. 2002; 99:15524–29. https://doi.org/10.1073/pnas.242606799 [PubMed]
• 29. Li M, Marin-Muller C, Bharadwaj U, Chow KH, Yao Q, Chen C. MicroRNAs: control and loss of control in human physiology and disease. World J Surg. 2009; 33:667–84. https://doi.org/10.1007/s00268-008-9836-x [PubMed]
• 30. Zhang P, Ji DB, Han HB, Shi YF, Du CZ, Gu J. Downregulation of miR-193a-5p correlates with lymph node metastasis and poor prognosis in colorectal cancer. World J Gastroenterol. 2014; 20:12241–48. https://doi.org/10.3748/wjg.v20.i34.12241 [PubMed]
• 31. Chen B, Xia Z, Deng YN, Yang Y, Zhang P, Zhu H, Xu N, Liang S. Emerging microRNA biomarkers for colorectal cancer diagnosis and prognosis. Open Biol. 2019; 9:180212. https://doi.org/10.1098/rsob.180212 [PubMed]
• 32. Fateh A, Feizi MA, Safaralizadeh R, Azarbarzin S. Importance of miR-299-5p in colorectal cancer. Ann Gastroenterol. 2017; 30:322–26. https://doi.org/10.20524/aog.2017.0139 [PubMed]
• 33. Moya L, Meijer J, Schubert S, Matin F, Batra J. Assessment of miR-98-5p, miR-152-3p, miR-326 and miR-4289 Expression as Biomarker for Prostate Cancer Diagnosis. Int J Mol Sci. 2019; 20:1154. https://doi.org/10.3390/ijms20051154 [PubMed]
• 34. Zhou C, Liu G, Wang L, Lu Y, Yuan L, Zheng L, Chen F, Peng F, Li X. MiR-339-5p regulates the growth, colony formation and metastasis of colorectal cancer cells by targeting PRL-1. PLoS One. 2013; 8:e63142. https://doi.org/10.1371/journal.pone.0063142 [PubMed]
• 35. Tian F, Wang J, Ouyang T, Lu N, Lu J, Shen Y, Bai Y, Xie X, Ge Q. MiR-486-5p Serves as a Good Biomarker in Nonsmall Cell Lung Cancer and Suppresses Cell Growth With the Involvement of a Target PIK3R1. Front Genet. 2019; 10:688. https://doi.org/10.3389/fgene.2019.00688 [PubMed]
• 36. Tang Y, Zhao Y, Song X, Song X, Niu L, Xie L. Tumor-derived exosomal miRNA-320d as a biomarker for metastatic colorectal cancer. J Clin Lab Anal. 2019; 33:e23004. https://doi.org/10.1002/jcla.23004 [PubMed]
• 37. Tang L, Zhou L, Wu S, Shi X, Jiang G, Niu S, Ding D. miR-125a-5p inhibits colorectal cancer cell epithelial-mesenchymal transition, invasion and migration by targeting TAZ. Onco Targets Ther. 2019; 12:3481–89. https://doi.org/10.2147/OTT.S191247 [PubMed]
• 38. Treiber T, Treiber N, Meister G. Regulation of microRNA biogenesis and its crosstalk with other cellular pathways. Nat Rev Mol Cell Biol. 2019; 20:5–20. https://doi.org/10.1038/s41580-018-0059-1 [PubMed]
• 39. Bartel DP. Metazoan MicroRNAs. Cell. 2018; 173:20–51. https://doi.org/10.1016/j.cell.2018.03.006 [PubMed]
• 40. Hanna J, Hossain GS, Kocerha J. The Potential for microRNA Therapeutics and Clinical Research. Front Genet. 2019; 10:478. https://doi.org/10.3389/fgene.2019.00478 [PubMed]
• 41. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov. 2017; 16:203–22. https://doi.org/10.1038/nrd.2016.246 [PubMed]
• 42. Condrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cretoiu D, Suciu N, Cretoiu SM, Voinea SC. miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells. 2020; 9:276. https://doi.org/10.3390/cells9020276 [PubMed]
• 43. Hur K. MicroRNAs: promising biomarkers for diagnosis and therapeutic targets in human colorectal cancer metastasis. BMB Rep. 2015; 48:217–22. https://doi.org/10.5483/bmbrep.2015.48.4.007 [PubMed]
• 44. Cortez MA, Bueso-Ramos C, Ferdin J, Lopez-Berestein G, Sood AK, Calin GA. MicroRNAs in body fluids--the mix of hormones and biomarkers. Nat Rev Clin Oncol. 2011; 8:467–77. https://doi.org/10.1038/nrclinonc.2011.76 [PubMed]
• 45. Carter JV, Galbraith NJ, Yang D, Burton JF, Walker SP, Galandiuk S. Blood-based microRNAs as biomarkers for the diagnosis of colorectal cancer: a systematic review and meta-analysis. Br J Cancer. 2017; 116:762–74. https://doi.org/10.1038/bjc.2017.12 [PubMed]
• 46. Yan S, Han B, Gao S, Wang X, Wang Z, Wang F, Zhang J, Xu D, Sun B. Exosome-encapsulated microRNAs as circulating biomarkers for colorectal cancer. Oncotarget. 2017; 8:60149–58. https://doi.org/10.18632/oncotarget.18557 [PubMed]
• 47. Dumache R. Early Diagnosis of Oral Squamous Cell Carcinoma by Salivary microRNAs. Clin Lab. 2017; 63:1771–76. https://doi.org/10.7754/Clin.Lab.2017.170607 [PubMed]
• 48. Buermans HP, den Dunnen JT. Next generation sequencing technology: Advances and applications. Biochim Biophys Acta. 2014; 1842:1932–41. https://doi.org/10.1016/j.bbadis.2014.06.015 [PubMed]
• 49. Zhang J, Chiodini R, Badr A, Zhang G. The impact of next-generation sequencing on genomics. J Genet Genomics. 2011; 38:95–109. https://doi.org/10.1016/j.jgg.2011.02.003 [PubMed]
• 50. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009; 10:57–63. https://doi.org/10.1038/nrg2484 [PubMed]
• 51. Beg MS, Brenner AJ, Sachdev J, Borad M, Kang YK, Stoudemire J, Smith S, Bader AG, Kim S, Hong DS. Phase I study of MRX34, a liposomal miR-34a mimic, administered twice weekly in patients with advanced solid tumors. Invest New Drugs. 2017; 35:180–88. https://doi.org/10.1007/s10637-016-0407-y [PubMed]
• 52. Kanaan Z, Rai SN, Eichenberger MR, Roberts H, Keskey B, Pan J, Galandiuk S. Plasma miR-21: a potential diagnostic marker of colorectal cancer. Ann Surg. 2012; 256:544–51. https://doi.org/10.1097/SLA.0b013e318265bd6f [PubMed]
• 53. Kanaan Z, Roberts H, Eichenberger MR, Billeter A, Ocheretner G, Pan J, Rai SN, Jorden J, Williford A, Galandiuk S. A plasma microRNA panel for detection of colorectal adenomas: a step toward more precise screening for colorectal cancer. Ann Surg. 2013; 258:400–08. https://doi.org/10.1097/SLA.0b013e3182a15bcc [PubMed]
• 54. Masuda T, Hayashi N, Kuroda Y, Ito S, Eguchi H, Mimori K. MicroRNAs as Biomarkers in Colorectal Cancer. Cancers (Basel). 2017; 9:124. https://doi.org/10.3390/cancers9090124 [PubMed]
• 55. Naidu S, Magee P, Garofalo M. MiRNA-based therapeutic intervention of cancer. J Hematol Oncol. 2015; 8:68. https://doi.org/10.1186/s13045-015-0162-0 [PubMed]
• 56. Liu X, Xu X, Pan B, He B, Chen X, Zeng K, Xu M, Pan Y, Sun H, Xu T, Hu X, Wang S. Circulating miR-1290 and miR-320d as Novel Diagnostic Biomarkers of Human Colorectal Cancer. J Cancer. 2019; 10:43–50. https://doi.org/10.7150/jca.26723 [PubMed]
• 57. Yu J, Xu J, Zhao J, Zhang R. Serum miR-133b is a potential novel prognostic biomarker for colorectal cancer. Int J Clin Exp Pathol. 2017; 10:11673–78. [PubMed]
• 58. Okugawa Y, Grady WM, Goel A. Epigenetic Alterations in Colorectal Cancer: Emerging Biomarkers. Gastroenterology. 2015; 149:1204–25.e12. https://doi.org/10.1053/j.gastro.2015.07.011 [PubMed]
• 59. Hong HC, Chuang CH, Huang WC, Weng SL, Chen CH, Chang KH, Liao KW, Huang HD. A panel of eight microRNAs is a good predictive parameter for triple-negative breast cancer relapse. Theranostics. 2020; 10:8771–89. https://doi.org/10.7150/thno.46142 [PubMed]
• 60. O’Brien SJ, Netz U, Hallion J, Bishop C, Stephen V, Burton J, Paas M, Feagins K, Pan J, Rai SN, Galandiuk S. Circulating plasma microRNAs in colorectal neoplasia: A pilot study in assessing response to therapy. Transl Oncol. 2021; 14:100962. https://doi.org/10.1016/j.tranon.2020.100962 [PubMed]
• 61. Fredsøe J, Rasmussen AK, Mouritzen P, Borre M, Ørntoft T, Sørensen KD. A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine. Int J Cancer. 2019; 145:2558–67. https://doi.org/10.1002/ijc.32296 [PubMed]
• 62. Li M, Tian L, Wang L, Yao H, Zhang J, Lu J, Sun Y, Gao X, Xiao H, Liu M. Down-regulation of miR-129-5p inhibits growth and induces apoptosis in laryngeal squamous cell carcinoma by targeting APC. PLoS One. 2013; 8:e77829. https://doi.org/10.1371/journal.pone.0077829 [PubMed]
• 63. Zhang Y, An J, Lv W, Lou T, Liu Y, Kang W. miRNA-129-5p suppresses cell proliferation and invasion in lung cancer by targeting microspherule protein 1, E-cadherin and vimentin. Oncol Lett. 2016; 12:5163–69. https://doi.org/10.3892/ol.2016.5372 [PubMed]
• 64. Ashton-Rickardt PG, Dunlop MG, Nakamura Y, Morris RG, Purdie CA, Steel CM, Evans HJ, Bird CC, Wyllie AH. High frequency of APC loss in sporadic colorectal carcinoma due to breaks clustered in 5q21-22. Oncogene. 1989; 4:1169–74. [PubMed]
• 65. Chang PY, Chen JS, Chang SC, Wang MC, Chang NC, Wen YH, Tsai WS, Liu WH, Liu HL, Lu JJ. Acquired somatic TP53 or PIK3CA mutations are potential predictors of when polyps evolve into colorectal cancer. Oncotarget. 2017; 8:72352–62. https://doi.org/10.18632/oncotarget.20376 [PubMed]
• 66. Jiang BH, Liu LZ. PI3K/PTEN signaling in angiogenesis and tumorigenesis. Adv Cancer Res. 2009; 102:19–65. https://doi.org/10.1016/S0065-230X(09)02002-8 [PubMed]
• 67. Binefa G, Rodríguez-Moranta F, Teule A, Medina-Hayas M. Colorectal cancer: from prevention to personalized medicine. World J Gastroenterol. 2014; 20:6786–808. https://doi.org/10.3748/wjg.v20.i22.6786 [PubMed]
• 68. Stoffel EM, Koeppe E, Everett J, Ulintz P, Kiel M, Osborne J, Williams L, Hanson K, Gruber SB, Rozek LS. Germline Genetic Features of Young Individuals With Colorectal Cancer. Gastroenterology. 2018; 154:897–905.e1. https://doi.org/10.1053/j.gastro.2017.11.004 [PubMed]
• 69. Garraway LA, Lander ES. Lessons from the cancer genome. Cell. 2013; 153:17–37. https://doi.org/10.1016/j.cell.2013.03.002 [PubMed]
• 70. Li XL, Zhou J, Chen ZR, Chng WJ. P53 mutations in colorectal cancer - molecular pathogenesis and pharmacological reactivation. World J Gastroenterol. 2015; 21:84–93. https://doi.org/10.3748/wjg.v21.i1.84 [PubMed]
• 71. Slattery ML, Mullany LE, Wolff RK, Sakoda LC, Samowitz WS, Herrick JS. The p53-signaling pathway and colorectal cancer: Interactions between downstream p53 target genes and miRNAs. Genomics. 2019; 111:762–71. https://doi.org/10.1016/j.ygeno.2018.05.006 [PubMed]
• 72. Zenonos K, Kyprianou K. RAS signaling pathways, mutations and their role in colorectal cancer. World J Gastrointest Oncol. 2013; 5:97–101. https://doi.org/10.4251/wjgo.v5.i5.97 [PubMed]
• 73. Hamada T, Nowak JA, Ogino S. PIK3CA mutation and colorectal cancer precision medicine. Oncotarget. 2017; 8:22305–06. https://doi.org/10.18632/oncotarget.15724 [PubMed]
• 74. Zhao M, Mishra L, Deng CX. The role of TGF-β/SMAD4 signaling in cancer. Int J Biol Sci. 2018; 14:111–23. https://doi.org/10.7150/ijbs.23230 [PubMed]
• 75. Xu Y, Pasche B. TGF-beta signaling alterations and susceptibility to colorectal cancer. Hum Mol Genet. 2007; 16:R14–20. https://doi.org/10.1093/hmg/ddl486 [PubMed]
• 76. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med. 2000; 45:23–41. https://doi.org/10.1016/s0167-5877(00)00115-x [PubMed]
• 77. Wang Q, Huang Z, Ni S, Xiao X, Xu Q, Wang L, Huang D, Tan C, Sheng W, Du X. Plasma miR-601 and miR-760 are novel biomarkers for the early detection of colorectal cancer. PLoS One. 2012; 7:e44398. https://doi.org/10.1371/journal.pone.0044398 [PubMed]
• 78. Liu X, He B, Xu T, Pan Y, Hu X, Chen X, Wang S. MiR-490-3p Functions As a Tumor Suppressor by Inhibiting Oncogene VDAC1 Expression in Colorectal Cancer. J Cancer. 2018; 9:1218–30. https://doi.org/10.7150/jca.23662 [PubMed]
• 79. Tao K, Yang J, Guo Z, Hu Y, Sheng H, Gao H, Yu H. Prognostic value of miR-221-3p, miR-342-3p and miR-491-5p expression in colon cancer. Am J Transl Res. 2014; 6:391–401. [PubMed]
• 80. Xuan Y, Yang H, Zhao L, Lau WB, Lau B, Ren N, Hu Y, Yi T, Zhao X, Zhou S, Wei Y. MicroRNAs in colorectal cancer: small molecules with big functions. Cancer Lett. 2015; 360:89–105. https://doi.org/10.1016/j.canlet.2014.11.051 [PubMed]
• 81. Wang YN, Chen ZH, Chen WC. Novel circulating microRNAs expression profile in colon cancer: a pilot study. Eur J Med Res. 2017; 22:51. https://doi.org/10.1186/s40001-017-0294-5 [PubMed]
• 82. Huang Z, Huang D, Ni S, Peng Z, Sheng W, Du X. Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. Int J Cancer. 2010; 127:118–26. https://doi.org/10.1002/ijc.25007 [PubMed]
• 83. Fesler A, Jiang J, Zhai H, Ju J. Circulating microRNA testing for the early diagnosis and follow-up of colorectal cancer patients. Mol Diagn Ther. 2014; 18:303–08. https://doi.org/10.1007/s40291-014-0089-0 [PubMed]
• 84. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR. MicroRNA expression profiles classify human cancers. Nature. 2005; 435:834–38. https://doi.org/10.1038/nature03702 [PubMed]
• 85. Sun HL, Cui R, Zhou J, Teng KY, Hsiao YH, Nakanishi K, Fassan M, Luo Z, Shi G, Tili E, Kutay H, Lovat F, Vicentini C, et al. ERK Activation Globally Downregulates miRNAs through Phosphorylating Exportin-5. Cancer Cell. 2016; 30:723–36. https://doi.org/10.1016/j.ccell.2016.10.001 [PubMed]
• 86. Lee EJ, Baek M, Gusev Y, Brackett DJ, Nuovo GJ, Schmittgen TD. Systematic evaluation of microRNA processing patterns in tissues, cell lines, and tumors. RNA. 2008; 14:35–42. https://doi.org/10.1261/rna.804508 [PubMed]
• 87. Qu Y, Shi B, Hou P. Activated ERK: An Emerging Player in miRNA Downregulation. Trends Cancer. 2017; 3:163–65. https://doi.org/10.1016/j.trecan.2017.01.002 [PubMed]
• 88. Peng Y, Croce CM. The role of MicroRNAs in human cancer. Signal Transduct Target Ther. 2016; 1:15004. https://doi.org/10.1038/sigtrans.2015.4 [PubMed]
• 89. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, Chiew MY, Tai CS, Wei TY, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018; 46:D296–302. https://doi.org/10.1093/nar/gkx1067 [PubMed]
• 90. Shen Y, Han X, Wang J, Wang S, Yang H, Lu SH, Shi Y. Prognostic impact of mutation profiling in patients with stage II and III colon cancer. Sci Rep. 2016; 6:24310. https://doi.org/10.1038/srep24310 [PubMed]
• 91. Gao ZJ, Yuan WD, Yuan JQ, Yuan K, Wang Y. miR-486-5p functions as an oncogene by targeting PTEN in non-small cell lung cancer. Pathol Res Pract. 2018; 214:700–05. https://doi.org/10.1016/j.prp.2018.03.013 [PubMed]
• 92. Bronisz A, Godlewski J, Wallace JA, Merchant AS, Nowicki MO, Mathsyaraja H, Srinivasan R, Trimboli AJ, Martin CK, Li F, Yu L, Fernandez SA, Pécot T, et al. Reprogramming of the tumour microenvironment by stromal PTEN-regulated miR-320. Nat Cell Biol. 2011; 14:159–67. https://doi.org/10.1038/ncb2396 [PubMed]
• 93. Slattery ML, Mullany LE, Sakoda LC, Wolff RK, Stevens JR, Samowitz WS, Herrick JS. The PI3K/AKT signaling pathway: Associations of miRNAs with dysregulated gene expression in colorectal cancer. Mol Carcinog. 2018; 57:243–61. https://doi.org/10.1002/mc.22752 [PubMed]
• 94. Balacescu O, Sur D, Cainap C, Visan S, Cruceriu D, Manzat-Saplacan R, Muresan MS, Balacescu L, Lisencu C, Irimie A. The Impact of miRNA in Colorectal Cancer Progression and Its Liver Metastases. Int J Mol Sci. 2018; 19:3711. https://doi.org/10.3390/ijms19123711 [PubMed]
• 95. Rokavec M, Li H, Jiang L, Hermeking H. The p53/microRNA connection in gastrointestinal cancer. Clin Exp Gastroenterol. 2014; 7:395–413. https://doi.org/10.2147/CEG.S43738 [PubMed]
• 96. Reid JF, Sokolova V, Zoni E, Lampis A, Pizzamiglio S, Bertan C, Zanutto S, Perrone F, Camerini T, Gallino G, Verderio P, Leo E, Pilotti S, et al. miRNA profiling in colorectal cancer highlights miR-1 involvement in MET-dependent proliferation. Mol Cancer Res. 2012; 10:504–15. https://doi.org/10.1158/1541-7786.MCR-11-0342 [PubMed]
• 97. Pamudurthy V, Bissonnette M, Konda V. Biomarkers in Colorectal Cancer Screening. J Gastrointest Dig Syst. 2016; 06:2–9. https://doi.org/10.4172/2161-069X.1000389
• 98. Chakraborty C, Das S. Profiling cell-free and circulating miRNA: a clinical diagnostic tool for different cancers. Tumour Biol. 2016; 37:5705–14. https://doi.org/10.1007/s13277-016-4907-3 [PubMed]
• 99. Marcuello M, Vymetalkova V, Neves RPL, Duran-Sanchon S, Vedeld HM, Tham E, van Dalum G, Flügen G, Garcia-Barberan V, Fijneman RJ, Castells A, Vodicka P, Lind GE, et al. Circulating biomarkers for early detection and clinical management of colorectal cancer. Mol Aspects Med. 2019; 69:107–22. https://doi.org/10.1016/j.mam.2019.06.002
• 100. Mansueto G, Benincasa G, Della Mura N, Nicoletti GF, Napoli C. Epigenetic-sensitive liquid biomarkers and personalised therapy in advanced heart failure: a focus on cell-free DNA and microRNAs. J Clin Pathol. 2020; 73:535–43. https://doi.org/10.1136/jclinpath-2019-206404 [PubMed]
• 101. Hayes CN, Chayama K. MicroRNAs as Biomarkers for Liver Disease and Hepatocellular Carcinoma. Int J Mol Sci. 2016; 17:280. https://doi.org/10.3390/ijms17030280 [PubMed]
• 102. Szejniuk WM, Robles AI, McCulloch T, Falkmer UG, Røe OD. Epigenetic predictive biomarkers for response or outcome to platinum-based chemotherapy in non-small cell lung cancer, current state-of-art. Pharmacogenomics J. 2019; 19:5–14. https://doi.org/10.1038/s41397-018-0029-1 [PubMed]
• 103. Rapado-González Ó, Álvarez-Castro A, López-López R, Iglesias-Canle J, Suárez-Cunqueiro MM, Muinelo-Romay L. Circulating microRNAs as Promising Biomarkers in Colorectal Cancer. Cancers (Basel). 2019; 11:898. https://doi.org/10.3390/cancers11070898 [PubMed]
• 104. Schou JV, Johansen JS, Nielsen D, Rossi S. Circulating microRNAs as Prognostic and Predictive Biomarkers in Patients with Colorectal Cancer. Noncoding RNA. 2016; 2:5. https://doi.org/10.3390/ncrna2020005 [PubMed]
• 105. Wang H, Cao F, Li X, Miao H, E J, Xing J, Fu CG. miR-320b suppresses cell proliferation by targeting c-Myc in human colorectal cancer cells. BMC Cancer. 2015; 15:748. https://doi.org/10.1186/s12885-015-1728-5 [PubMed]
• 106. Zhao D, Sui Y, Zheng X. MiR-331-3p inhibits proliferation and promotes apoptosis by targeting HER2 through the PI3K/Akt and ERK1/2 pathways in colorectal cancer. Oncol Rep. 2016; 35:1075–82. https://doi.org/10.3892/or.2015.4450 [PubMed]
• 107. Fujii T, Shimada K, Asano A, Tatsumi Y, Yamaguchi N, Yamazaki M, Konishi N. MicroRNA-331-3p Suppresses Cervical Cancer Cell Proliferation and E6/E7 Expression by Targeting NRP2. Int J Mol Sci. 2016; 17:1351. https://doi.org/10.3390/ijms17081351 [PubMed]
• 108. Guo X, Guo L, Ji J, Zhang J, Zhang J, Chen X, Cai Q, Li J, Gu Q, Liu B, Zhu Z, Yu Y. miRNA-331-3p directly targets E2F1 and induces growth arrest in human gastric cancer. Biochem Biophys Res Commun. 2010; 398:1–6. https://doi.org/10.1016/j.bbrc.2010.05.082 [PubMed]
• 109. Ferretti E, De Smaele E, Miele E, Laneve P, Po A, Pelloni M, Paganelli A, Di Marcotullio L, Caffarelli E, Screpanti I, Bozzoni I, Gulino A. Concerted microRNA control of Hedgehog signalling in cerebellar neuronal progenitor and tumour cells. EMBO J. 2008; 27:2616–27. https://doi.org/10.1038/emboj.2008.172 [PubMed]
• 110. Liu C, Li G, Yang N, Su Z, Zhang S, Deng T, Ren S, Lu S, Tian Y, Liu Y, Qiu Y. miR-324-3p suppresses migration and invasion by targeting WNT2B in nasopharyngeal carcinoma. Cancer Cell Int. 2017; 17:2. https://doi.org/10.1186/s12935-016-0372-8 [PubMed]
• 111. Peng Y, Dai Y, Hitchcock C, Yang X, Kassis ES, Liu L, Luo Z, Sun HL, Cui R, Wei H, Kim T, Lee TJ, Jeon YJ, et al. Insulin growth factor signaling is regulated by microRNA-486, an underexpressed microRNA in lung cancer. Proc Natl Acad Sci USA. 2013; 110:15043–48. https://doi.org/10.1073/pnas.1307107110 [PubMed]
• 112. Christensen LL, Holm A, Rantala J, Kallioniemi O, Rasmussen MH, Ostenfeld MS, Dagnaes-Hansen F, Øster B, Schepeler T, Tobiasen H, Thorsen K, Sieber OM, Gibbs P, et al. Functional screening identifies miRNAs influencing apoptosis and proliferation in colorectal cancer. PLoS One. 2014; 9:e96767. https://doi.org/10.1371/journal.pone.0096767 [PubMed]
• 113. Hu Y, Xie H, Liu Y, Liu W, Liu M, Tang H. miR-484 suppresses proliferation and epithelial-mesenchymal transition by targeting ZEB1 and SMAD2 in cervical cancer cells. Cancer Cell Int. 2017; 17:36. https://doi.org/10.1186/s12935-017-0407-9 [PubMed]
• 114. Roberto VP, Gavaia P, Nunes MJ, Rodrigues E, Cancela ML, Tiago DM. Evidences for a New Role of miR-214 in Chondrogenesis. Sci Rep. 2018; 8:3704. https://doi.org/10.1038/s41598-018-21735-w [PubMed]
• 115. Berezikov E. Evolution of microRNA diversity and regulation in animals. Nat Rev Genet. 2011; 12:846–60. https://doi.org/10.1038/nrg3079 [PubMed]
• 116. Zhou K, Liu M, Cao Y. New Insight into microRNA Functions in Cancer: Oncogene-microRNA-Tumor Suppressor Gene Network. Front Mol Biosci. 2017; 4:46. https://doi.org/10.3389/fmolb.2017.00046 [PubMed]
• 117. Gu J, Zhang J, Zheng L, Ajani JA, Wu X, Ye Y. Serum miR-331-3p predicts tumor recurrence in esophageal adenocarcinoma. Sci Rep. 2018; 8:14006. https://doi.org/10.1038/s41598-018-32282-9 [PubMed]
• 118. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G, Noushmehr H. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016; 44:e71. https://doi.org/10.1093/nar/gkv1507 [PubMed]
• 119. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015; 19:A68–77. https://doi.org/10.5114/wo.2014.47136 [PubMed]
• 120. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014; 42:D68–73. https://doi.org/10.1093/nar/gkt1181 [PubMed]
• 121. Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013; 64:402–06. https://doi.org/10.4097/kjae.2013.64.5.402 [PubMed]
• 122. Aguinis H, Gottfredson RK, Joo H. Best-Practice Recommendations for Defining, Identifying, and Handling Outliers. Organ Res Methods. 2013; 16:270–301. https://doi.org/10.1177/1094428112470848
• 123. Rousseeuw PJ, Hubert M. Robust statistics for outlier detection. Wiley Interdiscip Rev Data Min Knowl Discov. 2011; 1:73–79. https://doi.org/10.1002/widm.2
• 124. Ghasemi A, Zahediasl S. Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab. 2012; 10:486–89. https://doi.org/10.5812/ijem.3505 [PubMed]
• 125. Davis RB, Mukamal KJ. Hypothesis testing: means. Circulation. 2006; 114:1078–82. https://doi.org/10.1161/CIRCULATIONAHA.105.586461 [PubMed]
• 126. Lamb TJ, Graham AL, Petrie A. T testing the immune system. Immunity. 2008; 28:288–92. https://doi.org/10.1016/j.immuni.2008.02.003 [PubMed]
• 127. Gastwirth JL, Gel YR, Miao W. The Impact of Levene’s Test of Equality of Variances on Statistical Theory and Practice. Stat Sci. 2009; 24:343–60. https://doi.org/10.1214/09-STS301
• 128. Hart A. Mann-Whitney test is not just a test of medians: differences in spread can be important. BMJ. 2001; 323:391. https://doi.org/10.1136/bmj.323.7309.391
• 129. Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 2003; 19:368–75. https://doi.org/10.1093/bioinformatics/btf877 [PubMed]
• 130. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc B. 1995; 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
• 131. Agarwal R, Kumar B, Jayadev M, Raghav D, Singh A. CoReCG: a comprehensive database of genes associated with colon-rectal cancer. Database (Oxford). 2016; 2016:baw059. https://doi.org/10.1093/database/baw059 [PubMed]
• 132. Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012; 24:69–71. [PubMed]
• 133. Hajian-Tilaki K. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian J Intern Med. 2013; 4:627–35. [PubMed]
• 134. In J, Lee DK. Survival analysis: part II - applied clinical data analysis. Korean J Anesthesiol. 2019; 72:441–57. https://doi.org/10.4097/kja.19183 [PubMed]
• 135. Bakhshi E, Ali Akbari Khoei R, Azarkeivan A, Kooshesh M, Biglarian A. Survival analysis of thalassemia major patients using Cox, Gompertz proportional hazard and Weibull accelerated failure time models. Med J Islam Repub Iran. 2017; 31:97. https://doi.org/10.14196/mjiri.31.97 [PubMed]
• 136. Goerdten J, Carrière I, Muniz-Terrera G. Comparison of Cox proportional hazards regression and generalized Cox regression models applied in dementia risk prediction. Alzheimers Dement (N Y). 2020; 6:e12041. https://doi.org/10.1002/trc2.12041 [PubMed]
• 137. Jager KJ, van Dijk PC, Zoccali C, Dekker FW. The analysis of survival data: the Kaplan-Meier method. Kidney Int. 2008; 74:560–65. https://doi.org/10.1038/ki.2008.217 [PubMed]
• 138. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2020. https://www.R-project.org/.