A prognostic biomarker NRG1 promotes U-87 MG glioblastoma cell malignancy by inhibiting autophagy via ERBB2/AKT/mTOR pathway.

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Studies have shown that autophagy-related (ATG) genes play important roles in regulating GBM malignancy. However, the mechanism still needs to be fully elucidated. Based on clinical and gene expression information of GBM patients downloaded from The The Cancer Genome Atlas database, Kaplan-Meier, univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression were applied to construct a risk signature for GBM prognosis, followed by validation using receiver operating characteristic analysis. Next, Cell Counting Kit-8, wound healing assay, flow cytometry, monodansyl cadaverine autophagy staining assay, immunofluorescence staining and western blot, either in the absence or presence of ERBB2/AKT/mTOR inhibitors, were carried out in GBM U87 cell line to explore molecular pathway underlying GBM malignancy. A three-ATG-gene signature (HIF1A, ITGA3, and NGR1) was constructed for GBM prognosis with the greatest contribution from NRG1. In vitro experiments showed that NRG1 promoted U87 cell migration and proliferation by inhibiting autophagy, and ERBB2/AKT/mTOR is a downstream pathway that mediates the autophagy-inhibitory effects of NRG1. We constructed an ATG gene prognostic model for GBM and demonstrated that NRG1 inhibited autophagy by activating ERBB2/AKT/mTOR, promoting GBM malignancy, thus providing new insights into the molecular contribution of autophagy in GBM malignancy.

An Oxidative Stress-Related Gene Signature in Granulosa Cells Is Associated with Ovarian Aging.

Ovarian aging is associated with a decrease in fecundity. Increased oxidative stress of granulosa cells (GCs) is an important contributor. We thus asked whether there is an oxidative stress-related gene signature in GCs associated with ovarian aging. Public nonhuman primate (NHP) single-cell transcriptome was processed to identify GC cluster. Then, a GC signature for ovarian aging was established based on six oxidative stress-related differentially expressed genes (MAPK1, STK24, AREG, ATG7, ANXA1, and PON2). Receiver operating characteristic (ROC) analysis confirmed good discriminating capacity in both NHP single-cell and human bulk transcriptome datasets. Gene expression levels were investigated using qPCR in the human ovarian granulosa-like tumor cell line (KGN) and mouse GCs. In an oxidative stress model, KGN cells were treated with menadione (7.5 µM, 24 h) to induce oxidative stress, after which upregulation of MAPK1, STK24, ATG7, ANXA1, and PON2 and downregulation of AREG were observed (p < 0.05). In an aging model, KGN cells were continuously cultured for 3 months, leading to increased expressions of all genes (p < 0.05). In GCs of reproductively aged (8-month-old) Kunming mice, upregulated expression of Mapk1, Stk24, Atg7, and Pon2 and downregulated expression of Anxa1 and Areg were observed (p < 0.01). We therefore here identify a six-gene GC signature associated with oxidative stress and ovarian aging.

Three Oxidative Stress-Related Genes That Associate Endometrial Immune Cells Are Considered as Potential Biomarkers for the Prediction of Unexplained Recurrent Implantation Failure.

Recurrent implantation failure (RIF) represents a new challenge in the field of assisted reproductive technology (ART). Considering the known effects of immune cell regulation on embryo implantation process, as well as our gene set variation analysis (GSVA) results that suggested the association between RIF and pathways of oxidative stress and immune responses, we hypothesized that oxidative stress- related genes (OSGs) associated with aberrant immunological factor may represent novel biomarkers for unexplained RIF. We therefore screened out the immune cell coexpressed OSGs by performing CIBERSORT, LM22 matrix and Pearson correlation, followed by constructing an OSG signature by least absolute shrinkage and selection operator (LASSO) regression. Three OSGs (AXL, SLC7A11 and UBQLN1) were then identified to establish a RIF risk signature, which showed high ability to discriminating RIF from fertile control. A nomogram was established, with a free online calculator for easier clinical application. Finally, Chilibot, protein-protein interaction analysis and BioGPS were sequentially applied for the investigation of functional relationships of these three genes with RIF and other OSGs, as well as their expression abundance across different human tissues. In conclusion, we identified an OSG signature that are relevant novel markers for the occurrence of unexplained RIF.

Identification and validation of a five-lncRNA signature for predicting survival with targeted drug candidates in ovarian cancer.

The dysregulation of long non-coding RNAs (lncRNAs) plays a crucial role in ovarian cancer (OC). In this study, we screened out five differentially expressed lncRNAs (AC092718.4, AC138035.1, BMPR1B-DT, RNF157-AS1, and TPT1-AS1) between OC and normal ovarian based on TCGA and GTEx RNA-seq databases by using Kaplan-Meier analysis and univariate Cox, LASSO, and multivariate Cox regression. Then, a risk signature was constructed, with 1, 3, 5-year survival prediction accuracy confirmed by ROC curves, and an online survival calculator for easier clinical use. With lncRNA-microRNA-mRNA regulatory networks established, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, suggesting the involvement of a variety of cancer-related functions and pathways. Finally, five candidate small-molecule drugs (thioridazine, trifluoperazine, loperamide, LY294002, and puromycin) were predicted by Connectivity Map. In conclusion, we identified a 5-lncRNA signature of prognostic value with its ceRNA networks, and five candidate drugs against OC.[Figure: see text].

A risk signature of three autophagy-related genes for predicting lower grade glioma survival is associated with tumor immune microenvironment.

Treatment for lower-grade gliomas (LGG) has been challenging. Though emerging approaches such as immunotherapy is promising, it is still faced with immune tolerance, an obstacle that may be overcome by targeting autophagy-related (ATG) genes. After identifying three differentially expressed ATG genes (RIPK2, MUL1 and CXCR4), we constructed an ATG gene risk signature by Kaplan-Meier, univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression, followed by internal and external validation using K-M and ROC analysis. Since gene set enrichment analysis (GSEA) suggested that the signature was strongly associated with immune cell functions, CIBERSORT, LM22 matrix and Pearson correlation were further performed, showing that the risk signature was significantly correlated with immune cell infiltration and immune checkpoint genes. In conclusion, we identified and independently validated an ATG gene risk signature for LGG patients, as well as discovering its significant association with LGG immune microenvironment.

Identification and validation of a six-lncRNA prognostic signature with its ceRNA networks and candidate drugs in lower-grade gliomas.

Gliomas account for 75% of the primary malignant brain tumors and a majority of lower-grade gliomas (LGG) inevitably develop into glioblastoma. The dysregulation of lncRNAs play a crucial role in LGG. In the present study, we first screened out six differentially expressed lncRNAs (AC021739.2, AL031722.1, AL354740.1, FGD5-AS1, LINC00844, and NEAT1) based on TCGA and GTEx RNA-seq databases. LncRNA prognostic signature was then established by Kaplan-Meier and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. After lncRNA-miRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, with results enriched in various malignancy-related functions and pathways. Finally, six putative drugs (irinotecan, camptothecin, mitoxantrone, azacitidine, mestranol, and enilconazole) were predicted by Connectivity Map. In conclusion, we identified a 6-lncRNA prognostic signature with its ceRNA networks, and six candidate drugs against LGG.