Small-world networks of prognostic genes associated with lung adenocarcinoma development

The Enrichment of miRNA-Targeted mRNAs in Translationally Less Active over More Active Polysomes

miRNAs moderately inhibit the translation and enhance the degradation of their target mRNAs via cognate binding sites located predominantly in the 3'-untranslated regions (UTR). Paradoxically, miRNA targets are also polysome-associated. We studied the polysome association by the comparative translationally less-active light- and more-active heavy-polysome profiling of a wild type (WT) human cell line and its isogenic mutant (MT) with a disrupted DICER1 gene and, thus, mature miRNA production. As expected, the open reading frame (ORF) length is a major determinant of light- to heavy-polysome mRNA abundance ratios, but is rendered less powerful in WT than in MT cells by miRNA-regulatory activities. We also observed that miRNAs tend to target mRNAs with longer ORFs, and that adjusting the mRNA abundance ratio with the ORF length improves its correlation with the 3'-UTR miRNA-binding-site count. In WT cells, miRNA-targeted mRNAs exhibit higher abundance in light relative to heavy polysomes, i.e., light-polysome enrichment. In MT cells, the DICER1 disruption not only significantly abrogated the light-polysome enrichment, but also narrowed the mRNA abundance ratio value range. Additionally, the abrogation of the enrichment due to the DICER1 gene disruption, i.e., the decreases of the ORF-length-adjusted mRNA abundance ratio from WT to MT cells, exhibits a nearly perfect linear correlation with the 3'-UTR binding-site count. Transcription factors and protein kinases are the top two most enriched mRNA groups. Taken together, the results provide evidence for the light-polysome enrichment of miRNA-targeted mRNAs to reconcile polysome association and moderate translation inhibition, and that ORF length is an important, though currently under-appreciated, transcriptome regulation parameter.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine

Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.

eEF-2K knockdown synergizes with STS treatment to inhibit cell proliferation, migration, and invasion via the TG2/ERK pathway in A549 cells

Emerging research has suggested the anticancer potential of tanshinone IIA, the bioactive ingredient isolated from the traditional Chinese herb Salvia miltiorrhiza. However, the molecular mechanism of sodium tanshinone IIA sulfonate (STS) antilung cancer effect is not very clear. In this study, our purpose is to investigate the roles of STS and elongation factor-2 kinase (eEF-2K) in regulating the proliferation, migration, and invasion of A549 cells and explore the implicated pathways. We found that STS suppressed A549 cell survival and proliferation in a time- and xdose-dependent manner. Knockdown of eEF-2K and treatment with STS synergistically exerted antiproliferative, -migratory, and -invasive effects on A549 cells. These effects were caused by attenuation of the extracellular signal-regulated kinase (ERK) pathway via inhibition of tissue transglutaminase (TG2). In summary, the inhibition of eEF-2K synergizes with STS treatment, exerting anticancer effects on lung adenocarcinoma cells through the TG2/ERK signaling pathway, which provides a potential therapeutic target for treating lung adenocarcinoma.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders

Different metabolic and hormonal disorders like type 2 diabetes mellitus (T2DM), obesity, and polycystic ovary syndrome (PCOS) have tangible socio-economic impact. Prevalence of these metabolic and hormonal disorders is steadily increasing among women. There are clinical evidences that these physiological conditions are related to the manifestation of different gynecological cancers and their poor prognosis. The relationship between metabolic and hormonal disorders with gynecological cancers is quite complex. The need for gene level association study is extremely important to find markers and predicting risk factors. In the current work, we have selected metabolic disorders like T2DM and obesity, hormonal disorder PCOS, and 4 different gynecological cancers like endometrial, uterine, cervical, and triple negative breast cancer (TNBC). The gene list was downloaded from DisGeNET database (v 6.0). The protein interaction network was constructed using HIPPIE (v 2.2) and shared proteins were identified. Molecular comorbidity index and Jaccard coefficient (degree of similarity) between the diseases were determined. Pathway enrichment analysis was done using ReactomePA and significant modules (clusters in a network) of the constructed network was analyzed by MCODE plugin of Cytoscape. The comorbid conditions like PCOS-obesity found to increase the risk factor of ovarian and triple negative breast cancers whereas PCOS alone has highest contribution to the endometrial cancer. Different gynecological cancers were found to be differentially related to the metabolic/hormonal disorders and comorbid condition.