Expression and network analysis of YBX1 interactors for identification of new drug targets in lung adenocarcinoma

YBX-1 alleviates sepsis-stimulated lung epithelial cell injury

OBJECTIVE

To explore the role of Y-box binding protein 1 (YBX-1) in the lipopolysaccharide (LPS)-stimulated inflammation and oxidative stress of BEAS-2B cell line and clarify the underlying mechanism.

METHODS

LPS-stimulated BEAS-2B cells were used as a cell model of sepsis-stimulated acute lung injury (ALI). Immunoblot and quantitative polymerase chain reaction assays were used to detect the expression of YBX-1 in LPS-stimulated BEAS-2B cells. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide, TdT-mediated dUTP nick end labeling, and immunoblot assays were conducted to determine the effects of YBX-1 on cell survival. JC-1 staining and adenosine triphosphate production were used to detect the effects of YBX-1 on mitochondrial function. Immunostaining and enzyme-linked immunosorbent serologic assay were performed to examine the effects of YBX-1 on the inflammation and oxidative stress of cells. Immunoblot assay was conducted to confirm the mechanism.

RESULTS

YBX-1 was lowly expressed in LPS-stimulated BEAS-2B cells and enhanced the survival of LPS-stimulated lung epithelial cells. In addition, YBX-1 improved mitochondrial function of LPS-stimulated BEAS-2B cells. YBX-1 inhibited the inflammation and oxidative stress of LPS-stimulated BEAS-2B cells. Mechanically, YBX-1 inhibited mitogen-activated protein kinase (MAPK) axis, thereby alleviating sepsis-stimulated ALI.

CONCLUSION

YBX-1 alleviated inflammation and oxidative stress of LPS-stimulated BEAS-2B cells via MAPK axis.

Newly discovered roles of triosephosphate isomerase including functions within the nucleus

Triosephosphate isomerase (TPI) is best known as a glycolytic enzyme that interconverts the 3-carbon sugars dihydroxyacetone phosphate (DHAP) and glyceraldehyde-3-phosphate (G3P). TPI is an essential enzyme that is required for the catabolism of DHAP and a net yield of ATP from anaerobic glucose metabolism. Loss of TPI function results in the recessive disease TPI Deficiency (TPI Df). Recently, numerous lines of evidence suggest the TPI protein has other functions beyond glycolysis, a phenomenon known as moonlighting or gene sharing. Here we review the numerous functions ascribed to TPI, including recent findings of a nuclear role of TPI implicated in cancer pathogenesis and chemotherapy resistance.

RNA-binding proteins in diabetic microangiopathy

BACKGROUND

As the most common complication of diabetes, the diabetic microangiopathy characterizes diabetic retinopathy (DR) and nephropathy (DN). Diabetic microangiopathy has always been a serious clinical problem. A wide variety of nucleic acid interacting factors called the RNA binding proteins (RBPS) take part in several crucial cellular processes.

METHODS

Over the past decade, studies have shown that RBPs have crucial part in both malignant tumors and diabetes, especially in diabetic microangiopathy. This review examined the research history of RBPS in DR and DN.

RESULTS

We reviewed the literature and found that RBPS is potentially useful as therapeutic targets, diagnostic markers, or predict disease progression.

CONCLUSION

HuR acts as a vital therapeutic targeting protein in diabetic microangiopathy. IGF2BP2, P311, TTP, YBX1, and MBNL1 have a potential role in the treatment of DN.

Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Simple Summary

Here we introduce a new feature selection algorithm DTA, which selects important, non-redundant, and relevant features from diverse omics data. DTA selects non-redundant features by maximizing the similarity between each patient pair by an approximate k-cover algorithm. We successfully applied this algorithm to three different biological problems: (a) disease to healthy sample classification, (b) multiclass classification of different disease samples, and (c) disease subtypes detection. DTA outperformed other feature selection techniques in the binary classification of healthy and disease samples and multiclass classification of various diseases. It also improved the performance of a subtype detection algorithm by selecting the important features for few cancer types.

Abstract

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

Elevated GAS2L3 Expression Correlates With Poor Prognosis in Patients With Glioma: A Study Based on Bioinformatics and Immunohistochemical Analysis

Background

Growth arrest–specific 2 like 3 (GAS2L3) is a cytoskeleton-associated protein that interacts with actin filaments and tubulin. Abnormal GAS2L3 expression has been reported to be associated with carcinogenesis. However, the biological role of GAS2L3 in glioma remains to be determined.

Methods

The transcriptome level of GAS2L3 and its relationship with clinicopathological characteristics were analyzed among multiple public databases and clinical specimens. Bioinformatics analyses were conducted to explore biological functions and prognostic value of GAS2L3 in glioma.

Results

GAS2L3 was substantially expressed in glioma, and high GAS2L3 expression correlated with shorter overall survival time and poor clinical variables. Gene set enrichment analysis (GSEA), single-sample gene-set enrichment analysis, and CIBERSORT algorithm analyses showed that GAS2L3 expression was closely linked to immune-related pathways, inflammatory activities, and immune cell infiltration. Moreover, GAS2L3 was synergistic with T cell–inflamed gene signature, immune checkpoints, T-cell receptor diversities, and neoantigen numbers.

Conclusion

This study suggests that GAS2L3 is a prognostic biomarker for glioma, providing a reference for further study of the potential role of GAS2L3 in the immunomodulation of glioma.

MaTAR25 lncRNA regulates the Tensin1 gene to impact breast cancer progression

Misregulation of long non-coding RNA (lncRNA) genes has been linked to a wide variety of cancer types. Here we report on Mammary Tumor Associated RNA 25 (MaTAR25), a nuclear enriched and chromatin associated lncRNA that plays a role in mammary tumor cell proliferation, migration, and invasion, both in vitro and in vivo. MaTAR25 functions by interacting with purine rich element binding protein B (PURB), and associating with a major downstream target gene Tensin1 (Tns1) to regulate its expression in trans. The Tns1 protein product is a critical component of focal adhesions linking signaling between the extracellular matrix and the actin cytoskeleton. Knockout of MaTAR25 results in down-regulation of Tns1 leading to a reorganization of the actin cytoskeleton, and a reduction of focal adhesions and microvilli. We identify LINC01271 as the human ortholog of MaTAR25, and importantly, increased expression of LINC01271 is associated with poor patient prognosis and metastasis. Our findings demonstrate that LINC01271 represents a potential therapeutic target to alter breast cancer progression.

Growth arrest-specific 2 protein family: Structure and function

Members of the growth arrest–specific 2 (GAS2) protein family consist of a putative actin‐binding (CH) domain and a microtubule‐binding (GAR) domain and are considered miniversions of spectraplakins. There are four members in the GAS2 family, viz. GAS2, GAS2L1, GAS2L2 and GAS2L3. Although GAS2 is defined as a family of growth arrest–specific proteins, the significant differences in the expression patterns, interaction characteristics and biological issues or diseases among the different GAS2 family members have not been systemically reviewed to date. Therefore, we summarized the available evidence on the structures and functions of GAS2 family members. This review facilitates a comprehensive molecular understanding of the involvement of the GAS2 family members in an array of biological processes, including cytoskeleton reorganization, cell cycle, apoptosis and cancer development.

Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer

A growing body of evidence has indicated that behaviors of cancers are defined by not only intrinsic activities of tumor cells but also tumor-infiltrating immune cells (TIICs) in the tumor microenvironment. However, it still lacks a well-structured and comprehensive analysis of TIICs and its therapeutic value in esophageal cancer (EC). The proportions of 22 TIICs were evaluated between 150 normal tissues and 141 tumor tissues of EC by the CIBERSORT algorithm. Besides, correlation analyses between proportions of TIICs and clinicopathological characters, including age, gender, histologic grade, tumor location, histologic type, LRP1B mutation, TP53 mutation, tumor stage, lymph node stage, and TNM stage, were conducted. We constructed a risk score model to improve prognostic capacity with 5 TIICs by least absolute shrinkage and selection operator (lasso) regression analysis. The risk score = −1.86∗plasma + 2.56∗T cell follicular helper − 1.37∗monocytes − 3.64∗activated dendritic cells − 2.24∗resting mast cells (immune cells in the risk model mean the proportions of immune cell infiltration in EC). Patients in the high-risk group had significantly worse overall survival than these in the low-risk group (HR: 2.146, 95% CI: 1.243-3.705, p = 0.0061). Finally, we identified Semustine and Sirolimus as two candidate compounds for the treatment of EC based on CMap analysis. In conclusion, the proportions of TIICs may be important to the progression, prognosis, and treatment of EC.

Why Be One Protein When You Can Affect Many? The Multiple Roles of YB-1 in Lung Cancer and Mesothelioma

Lung cancers and malignant pleural mesothelioma (MPM) have some of the worst 5-year survival rates of all cancer types, primarily due to a lack of effective treatment options for most patients. Targeted therapies have shown some promise in thoracic cancers, although efficacy is limited only to patients harboring specific mutations or target expression. Although a number of actionable mutations have now been identified, a large population of thoracic cancer patients have no therapeutic options outside of first-line chemotherapy. It is therefore crucial to identify alternative targets that might lead to the development of new ways of treating patients diagnosed with these diseases. The multifunctional oncoprotein Y-box binding protein-1 (YB-1) could serve as one such target. Recent studies also link this protein to many inherent behaviors of thoracic cancer cells such as proliferation, invasion, metastasis and involvement in cancer stem-like cells. Here, we review the regulation of YB-1 at the transcriptional, translational, post-translational and sub-cellular levels in thoracic cancer and discuss its potential use as a biomarker and therapeutic target.

Gene coexpression analysis offers important modules and pathway of human lung adenocarcinomas

Lung adenocarcinomas injured greatly on the people worldwide. Although clinic experiments and gene profiling analyses had been well performed, to our knowledge, systemic coexpression analysis of human genes for this cancer is still limited to date. Here, using the published data GSE75037, we built the coexpression modules of genes by Weighted Gene Co-Expression Network Analysis (WGCNA), and investigated function and protein-protein interaction network of coexpression genes by Database for Annotation, visualization, and Integrated Discovery (DAVID) and String database, respectively. First, 11 coexpression modules were conducted for 5,000 genes in the 83 samples recently. Number of genes for each module ranged from 90 to 1,260, with the mean of 454. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high scale independence of the modules. Third, functional enrichment of the coexpression modules showed great differences. We found that genes in modules 8 significantly enriched in the biological process and/or pathways of cell adhesion, extracellular matrix (ECM)-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway, and so forth. It was inferred as the key module underlying lung adenocarcinomas. Furthermore, PPI analysis revealed that the genes COL1A1, COL1A2, COL3A1, CTGF, and BGN owned the largest number of adjacency genes, unveiling that they may functioned importantly during the occurrence of lung adenocarcinomas. To summary, genes involved in cell adhesion, ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas.