Immunoregulatory framework and the role of miRNA in the pathogenesis of NSCLC - A systematic review

Analysis and identification of mRNAsi‑related expression signatures via RNA sequencing in lung cancer

High stemness index scores are associated with poor survival in patients with lung cancer. Studies on the mRNA expression-based stemness index (mRNAsi) are typically conducted using tumor tissues; however, mRNAsi-related expression signatures based on cell-free RNA (cfRNA) are yet to be comprehensively investigated. The present study aimed to elucidate the gene expression profiles of tumor stemness in lung cancer tissues and corresponding cfRNAs in blood, and to assess their links with immune infiltration. Tumor tissue, paracancerous tissue, peripheral blood and lymph node samples were collected from patients with stage I-III non-small cell lung cancer and RNA sequencing was performed. The TCGAbiolinks package was used to calculate the mRNAsi for each of these four types of sample. Weighted gene co-expression network analysis and differentially expressed gene analyses were performed to investigate mRNAsi-related genes, and pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology-based annotation system. In addition, the STAR-Fusion tool was used to detect fusion variants, and CIBERSORT was used to analyze the correlations of stemness signatures in tissues and blood with immune cell infiltration. The mRNAsi values in peripheral blood and lymph nodes were found to be higher than those in cancer tissues. 'Hematopoietic cell lineage' was the only KEGG pathway enriched in mRNAsi-related genes in both lung cancer tissues and peripheral blood. In addition, the protein tyrosine phosphatase receptor type C associated protein gene was the only gene commonly associated with the mRNAsi in these two types of sample. The expression of mRNAsi-related genes was increased in the dendritic and Treg cells in tumor tissues, but was elevated in Treg and CD8 cells in the blood. In conclusion, cfRNAs in the blood exhibit unique stemness signatures that have potential for use in the diagnosis of lung cancer.

Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion

miRNAs are a class of small non-coding RNA molecules that play important roles in gene regulation. They are crucial for maintaining normal cellular functions, and dysregulation or dysfunction of miRNAs which are linked to the onset and advancement of multiple human diseases. Research on miRNAs has unveiled novel avenues in the realm of the diagnosis, treatment, and prevention of human diseases. However, clinical trials pose challenges and drawbacks, such as complexity and time-consuming processes, which create obstacles for many researchers. Graph Attention Network (GAT) has shown excellent performance in handling graph-structured data for tasks such as link prediction. Some studies have successfully applied GAT to miRNA-disease association prediction. However, there are several drawbacks to existing methods. Firstly, most of the previous models rely solely on concatenation operations to merge features of miRNAs and diseases, which results in the deprivation of significant modality-specific information and even the inclusion of redundant information. Secondly, as the number of layers in GAT increases, there is a possibility of excessive smoothing in the feature extraction process, which significantly affects the prediction accuracy. To address these issues and effectively complete miRNA disease prediction tasks, we propose an innovative model called Multiplex Adaptive Modality Fusion Graph Attention Network (MAMFGAT). MAMFGAT utilizes GAT as the main structure for feature aggregation and incorporates a multi-modal adaptive fusion module to extract features from three interconnected networks: the miRNA-disease association network, the miRNA similarity network, and the disease similarity network. It employs adaptive learning and cross-modality contrastive learning to fuse more effective miRNA and disease feature embeddings as well as incorporates multi-modal residual feature fusion to tackle the problem of excessive feature smoothing in GATs. Finally, we employ a Multi-Layer Perceptron (MLP) model that takes the embeddings of miRNA and disease features as input to anticipate the presence of potential miRNA-disease associations. Extensive experimental results provide evidence of the superior performance of MAMFGAT in comparison to other state-of-the-art methods. To validate the significance of various modalities and assess the efficacy of the designed modules, we performed an ablation analysis. Furthermore, MAMFGAT shows outstanding performance in three cancer case studies, indicating that it is a reliable method for studying the association between miRNA and diseases. The implementation of MAMFGAT can be accessed at the following GitHub repository: https://github.com/zixiaojin66/MAMFGAT-master.

Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.