## 3 Results  
An analysis of the selected studies can be found in Table 1, including the synthesis that follows the order that we have considered most pertinent to facilitate the understanding and integration of the results. [PERSON] et al. [18] designed a study that focused on triple-negative breast cancer (TNBC), a subtype of breast cancer with a poor clinical outcome for which no specific approved treatment exists. MicroRNAs (miRNAs) have been identified as promising biomarkers with an important role in human cancer tumorigenesis. Due to the growing dataset of TNBC miRNA profiles, their investigation requires proper analysis. The focal point of this exploration lies in the regulation of miRNAs, which involves their over- and down-regulation, determined by specific criteria like fold change and \(p\)-value. Different studies collect lists of up-regulated and down-regulated miRNAs, prioritizing them grounded on their expression change and \(p\)-value statistics. The significance of miRNA expression in TNBC is determined by the \(p\)-value or fold change of each miRNA. Several studies have linked a miRNA metasignature ( hsa-miR-135b-5p, hsa-miR-18a-5p, hsa-miR-9-5p, hsa-miR-522-3p, hsa-miR-190b, hsa-miR-9a) that exhibits significant differences and demonstrates high prophetic delicacy. These linked miRNAs are implicit as individual biomarkers for CMTN, warranting farther analysis of their pivotal part in TNBC. [PERSON] et al. [19] conducted exploration concentrated on exploring the mechanisms of gene relations specific to each subtype of breast cancer, as these mechanisms play a pivotal part in acclimatizing substantiated treatments. To achieve this, they incorporated the natural significance of genes deduced from gene nonsupervisory networks into the analysis of discriminatory gene expression. This integration allowed them to identify weighted differentially expressed genes (weighted DEGs) that retain natural significance deduced from the gene nonsupervisory network. By exercising weighted SDR computations, they developed double classifiers that displayed strong performance in terms of \"perceptivity\", \"particularly\", \"delicacy\", \"F1 score\", and \"AUC\" criteria. These classifiers were suitable to distinguish between control and experimental groups, furnishing new perceptivity through gene ontology (GO) enrichment analysis. The fortified GO terms uncovered specific natural functions associated with colorful subtypes of BRCA. [PERSON] [20] participated in the implementation of the study where the miRNA expression dataset of patients with breast cancer from the TCGA database was used to develop predictive models, with which they identified miRNA biomarkers for diagnosis and the molecular subtyping of BRCA. For the purposes of this article, to gain empirical negative control miRNAs, they used an in silico approach and calculated the \(p\)-value for all miRNAs. Three tree-grounded algorithms (Random Forest, Rpart, and treebag) were employed to model the breast cancer status grounded on the regularized expression of the filtered miRNAs in a balanced training dataset. The significance of each point was determined during the construction of the bracket models, and the results were compared to identify miRNAs that constantly showed significance across all models. Among them, hsa-miR-139 and has-miR-96 were set up to be constantly significant in all three models. Also, the top ten miRNAs for classifying breast cancer from a normal solid towel using tree-grounded machine literacy algorithms included hsa-miR-139 and has-miR-96, as well as miRNAs 15, 183, 592, 20,125 b, 2, 21, 11, and 125b.1. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Authors. Reference and Year** & **Type of Cancer** & **Feature** & **Data Origin** & **Value** & **M. Method** & **Performance** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} [PERSON], [PERSON] \\ and [PERSON] et al.