Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology

Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis

Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. Many miRNAs have been detected in recent years. Consequently, a substantial amount of miRNA-related data comparing patients and healthy individuals is available, which contributes to a better understanding of the initiation, development, malignancy, and metastasis of cancer using non-invasive sampling procedures. However, a re-analysis of available ncRNA data is rare. This study used available data about miRNAs in exosomes comparing healthy individuals and cancer patients to identify possible global changes related to the presence of cancer. A robust transcriptomic analysis identified two common miRNAs (miR-495-3p and miR-543) deregulated in five cancer datasets. They had already been implicated in different cancers but not reported in exosomes circulating in blood. The study also examined their target genes and the implications of these genes for functional processes.

A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing's Sarcoma

Objective

In order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms.

Methods

Clinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application.

Results

LNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py).

Conclusion

With the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.

Drug Research Meets Network Science: Where Are We?

Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.