On the use of Networks in Biomedicine

Structural and Pharmacological Network Focused on MiRNAs Involved in Rheumatoid Arthritis: A Systematic Review

BACKGROUND

Rheumatoid Arthritis (RA) is a chronic autoimmune disease that has a prevalence of over one percent of the world population, causing substantial pain, joint deformity, and functional disability in patients. The identification and measurement of miRNAs are relatively easy to perform. Future studies will corroborate if miRNAs can fulfill their roles as biomarkers with either predictive or diagnostic evaluation of treatment potential and provide actual clinical utility.

METHODS

In the last decade, various advances have been made regarding the identification of the origin and exact functions of miRNAs, allowing us to have a potential use both in the research and clinical fields.

OBJECTIVE

This systematic review aimed to collect, analyze, and improve the current understanding of RA-related miRNAs and their applicability in therapeutics. A bibliographic search of the miRNAs involved in RA was carried out, and through the use of databases, their target genes and small molecules that had some relationship with their expression were searched. The analysis of these data was done through structural network analysis.

RESULTS

During the network analysis, miR-30a, miR-30c, let-7a, miR-144, miR-17-5p, miR-124, miR -23b, miR-23, miR-15a, miR-16 were the most connected, which could be used as possible biomarkers or be candidates for further analysis due to their interaction with other miRNAs and genes.

CONCLUSION

Additionally, this is the first systematic review, in which we proposed that small compounds like toxicants and drugs could have a potential role within RA because they regulate the expression of miRNAs involved in this pathology. Some of these compounds are commonly found as environmental contaminants, and others as drugs. These ideas open a new panorama of understanding RA, proposing possible causes or treatments against this pathology. Therefore, these small molecules would give us some indication of a relationship with RA, thereby helping in seeking causes, treatment, or prevention of this disease.

CONCLUSION

This is the first time it is intended to use structural network analysis to determine possible biomarkers of AR for diagnosis and prognosis through the expression of these miRNAs and their relationship with compounds of daily life.

Structural and Pharmacological Network Analysis of miRNAs Involved in Acute Ischemic Stroke: A Systematic Review

Acute ischemic stroke (AIS) is among the main causes of mortality worldwide. A rapid and opportune diagnosis is crucial to improve a patient’s outcomes; despite the current advanced image technologies for diagnosis, their implementation is challenging. MicroRNAs have been recognized as useful as biomarkers since they are specific and stable for characterization of AIS. However, there is still a lack of consensus over the primary miRNAs implicated in AIS. Here, we performed a systematic review of the literature covering from 2015–2021 regarding miRNAs expression during AIS and built structural networks to analyze and identify the most common miRNAs expressed during AIS and shared pathways, genes, and compounds that seem to influence their expression. We identified two sets of miRNAs: on one side, a set that was independent of geographical location and tissue (miR-124, miR-107, miR-221, miR-223, miR-140, miR-151a, miR-181a, miR-320b, and miR-484); and on the other side, a set that was connected (hubs) in biological networks (miR-27b-3p, miR-26b-5p, miR-124-3p, miR-570-3p, miR-19a-3p, miR-101-3p and miR-25-3p), which altered FOXO3, FOXO4, and EP300 genes. Interestingly, such genes are involved in cell death, FOXO-mediated transcription, and brain-derived neurotrophic factor signaling pathways. Finally, our pharmacological network analysis depicted a set of toxicants and drugs related to AIS for the first time.

Melanoma Detection by Means of Multiple Instance Learning

We present an application to melanoma detection of a multiple instance learning (MIL) approach, whose objective, in the binary case, is to discriminate between positive and negative sets of items. In the MIL terminology these sets are called bags and the items inside the bags are called instances. Under the hypothesis that a bag is positive if at least one of its instances is positive and it is negative if all its instances are negative, the MIL paradigm fits very well with images classification, since an image (bag) is in general classified on the basis of some its subregions (instances). In this work we have applied a MIL algorithm on some clinical data constituted by color dermoscopic images, with the aim to discriminate between melanomas (positive images) and common nevi (negative images). In comparison with standard classification approaches, such as the well known support vector machine, our method performs very well in terms both of accuracy and sensitivity. In particular, using a leave-one-out validation on a data set constituted by 80 melanomas and 80 common nevi, we have obtained the following results: accuracy = 92.50%, sensitivity = 97.50% and specificity = 87.50%. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to physicians in melanoma detection.