## 3 Results
### Accuracy of miRNA Panels in Diagnosing Colorectal Cancer  
A comprehensive assessment of the current state of miRNA-based colorectal cancer diagnosis was conducted by a meta-analysis of seven studies. These results of the analysis show that groups of miRNAs have the potential as the diagnostic tool in the recognition of individuals who have colorectal cancer with high precision.  
Figure 3 illustrates the pooled estimate of all miRNAs, which showed a high degree of accuracy in diagnosing individuals with colorectal cancer. The sensitivity was 1.84 (95% CI: 1.48, 2.19) which means that the panels of miRNA have a high power to distinguish CRC positives. Yet significant heterogeneity was encountered (\(\Omega^{2}=89.16\%\); \(p<0.0001\)).  
The specificity pooled odd ratio (OR) in Figure 4 is 1.43 (95% CI: 1.01, 1.85), with heterogeneity (\(\Omega^{2}=94.32\%\); \(p<0.0001\)) evident. The combined OR implies that miRNAs perform well in identifying individuals who do not have colorectal cancer, despite high heterogeneity suggesting differences in study parameters such as sample type.  
Figure 4: Forest plot for specificity on overall miRNA. The pooled specificity (1.43, 95% CI: 1.01, 1.85) indicates that miRNA biomarkers are proficient at separating CRC patients from non-CRC controls, albeit with significant heterogeneity (\(\Omega^{2}=94.32\%\)).  
Figure 3: Forest plot for sensitivity on overall miRNA. The pooled sensitivity estimates from the meta-analysis (1.84, 95% CI: 1.48, 2.19) suggest a high ability of miRNA panels to detect CRC cases (substantial heterogeneity exists across studies as \(\Omega^{2}=89.16\%\)).  
The diagnostic odds ratio (DOR) assessment is shown by the forest plot in Figure 5, where significant heterogeneity was identified (I\({}^{2}\) = 89.51%; \(p\) < 0.0001) and the pooled OR was 0.38 (95% CI: \(-\)0.12, 0.88). Consequently, the cumulative OR demonstrated that miRNA, a generic diagnostic performance parameter, could be used to accurately diagnose colorectal cancer. However, the variability between studies also implies that further refinement of sample characteristics will be necessary to increase the outcome.  
Furthermore, the SROC curve, which is depicted in Figure 6, was created in this investigation to evaluate diagnostic accuracy. With an AUC of 0.907 (95% CI: 0.075-0.095), the combined miRNA panels demonstrated remarkable diagnostic accuracy. This result is especially promising since the AUC value approaches 1 for the more accurate diagnosis performance.  
Figure 5: Forest plot for DOR on overall miRNA. The overall diagnostic accuracy for miRNA biomarkers was shown by pooled DOR (0.38, 95% CI: \(-\)0.12, 0.88), and substantial heterogeneity among studies (I\({}^{2}\) = 89.51%).  
Figure 6: SROC curves constructed using overall miRNA data. The area under the curve (AUC = 0.907, 95% CI: 0.075, 0.095) emphasizes the high diagnostic yield potential of miRNA-based screening approaches. \(\times\) = Observed Data; o = Summary Estimate.  
All of these results together show that miRNA panels as a highly potential non-invasive diagnostic tool in CRC. The high sensitivity is indicative that they may be useful in early detection, and the relatively high specificity indicates a low risk of false positives. The heterogeneity observed indicates that differences between the samples, such as patient demographics and sample type, are likely to affect variability in diagnostic performance in humans.