Identifying the miRNA Signature Association with Aging-Related Senescence in Glioblastoma

An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction

Purpose

Emerging evidence demonstrates the vital role of aging and long non-coding RNAs (lncRNAs) in breast cancer (BC) progression. Our study intended to develop a prognostic risk model based on aging-related lncRNAs (AG-lncs) to foresee BC patients' outcomes.

Patients and Methods

307 aging-related genes (AGs) were sequenced from the TCGA project. Then, 697 AG-lncs were identified by the co-expression analysis with AGs. Using multivariate and univariate Cox regression analysis, and LASSO, 6 AG-lncs, including al136531.1, mapt-as1, al451085.2, otud6b-as1, tnfrsf14-as1, and linc01871, were validated to compute the risk score and establish a risk signature. Expression levels of al136531.1, mapt-as1, al451085.2, tnfrsf14-as1, and linc01871 were higher in low-risk BC patients, whereas otud6b-as1 expression was higher in high-risk BC patients. In the training and testing set, high-risk patients performed shorter PFI, OS, and DFS than low-risk patients.

Results

Our risk signature had the highest concordance index among other established prognostic signatures and displayed ideal predictive ability for 1-, 3- and 5-year patient OS in the nomogram. Additionally, BC patients with different risk score levels showed different immune statuses and responses to immunotherapy via GSEA, ssGSEA, ESTIMATE algorithm, and TIDE algorithm analysis. Of note, the qRT-PCR analysis validated that these 6 AG-lncs expressed quite differentially in BC tissues at various clinical stages.

Conclusion

The risk signature of 6 AG-lncs might offer a novel prognostic biomarker and promisingly enhance BC immunotherapy's effectiveness.

E3 ligases: a potential multi-drug target for different types of cancers and neurological disorders

Ubiquitylation is a posttranslational modification of proteins that is necessary for a variety of cellular processes. E1 ubiquitin activating enzyme, E2 ubiquitin conjugating enzyme, and E3 ubiquitin ligase are all involved in transferring ubiquitin to the target substrate to regulate cellular function. The objective of this review is to provide an overview of different aspects of E3 ubiquitin ligases that can lead to major biological system failure in several deadly diseases. The first part of this review covers the important characteristics of E3 ubiquitin ligases and their classification based on structural domains. Further, the authors provide some online resources that help researchers explore the data relevant to the enzyme. The following section delves into the involvement of E3 ubiquitin ligases in various diseases and biological processes, including different types of cancer and neurological disorders.

Identification of an Aging-Related Gene Signature in Predicting Prognosis and Indicating Tumor Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most commonly diagnosed malignancy accompanied by high invasion and metastasis features. Importantly, emerging studies have supported that aging is a key clue that participates in the immune state and development of BC. Nevertheless, there are no studies concerning the aging-related genes (AGs) in constructing the prognosis signature of BC. Here, to address this issue, we initially performed a systematic investigation of the associations between AGs and BC prognosis and accordingly constructed a prognosis risk model with 10 AGs including PLAU, JUND, IL2RG, PCMT1, PTK2, HSPA8, NFKBIA, GCLC, PIK3CA, and DGAT1 by using the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis. Meanwhile, our analysis further confirmed that the nomogram possessed a robust performance signature for predicting prognosis compared to clinical characteristics of BC patients, including age, clinical stage, and TNM staging. Moreover, the risk score was confirmed as an independent prognostic index of BC patients and was potentially correlated with immune scores, estimate score, immune cell infiltration level, tumor microenvironment, immunotherapy effect, and drug sensitivity. Furthermore, in the external clinical sample validation, AGs were expressed differentially in patients from different risk groups, and tumor-associated macrophage markers were elevated in high-risk BC tissues with more co-localization of AGs. In addition, the proliferation, transwell, and wound healing assays also confirmed the promoting effect of DGAT1 in BC cell proliferation and migration. Therefore, this well-established risk model could be used for predicting prognosis and immunotherapy in BC, thus providing a powerful instrument for combating BC.

Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools

Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.

MicroRNA Expression Profile Distinguishes Glioblastoma Stem Cells from Differentiated Tumor Cells

Glioblastoma (GBM) represents the most common and aggressive tumor of the brain. Despite the fact that several studies have recently addressed the molecular mechanisms underlying the disease, its etiology and pathogenesis are still poorly understood. GBM displays poor prognosis and its resistance to common therapeutic approaches makes it a highly recurrent tumor. Several studies have identified a subpopulation of tumor cells, known as GBM cancer stem cells (CSCs) characterized by the ability of self-renewal, tumor initiation and propagation. GBM CSCs have been shown to survive GBM chemotherapy and radiotherapy. Thus, targeting CSCs represents a promising approach to treat GBM. Recent evidence has shown that GBM is characterized by a dysregulated expression of microRNA (miRNAs). In this study we have investigated the difference between human GBM CSCs and their paired autologous differentiated tumor cells. Array-based profiling and quantitative Real-Time PCR (qRT-PCR) were performed to identify miRNAs differentially expressed in CSCs. The Cancer Genome Atlas (TCGA) data were also interrogated, and functional interpretation analysis was performed. We have identified 14 miRNAs significantly differentially expressed in GBM CSCs (p < 0.005). MiR-21 and miR-95 were among the most significantly deregulated miRNAs, and their expression was also associated to patient survival. We believe that the data provided here carry important implications for future studies aiming at elucidating the molecular mechanisms underlying GBM.