Assessment of Significant Pathway Signaling and Prognostic Value of GNG11 in Ovarian Serous Cystadenocarcinoma

Therapeutic antibody targeting natriuretic peptide receptor 1 inhibits gastric cancer growth via BCL-2-mediated intrinsic apoptosis

Despite recent advances in the development of therapeutic antibodies, the prognosis of unresectable or metastatic gastric cancer (GC) remains poor. Here, we searched for genes involved in the malignant phenotype of GC and investigated the potential of one candidate gene to serve as a novel therapeutic target. Analysis of transcriptome datasets of GC identified natriuretic peptide receptor 1 (NPR1), a plasma membrane protein, as a potential target. We employed a panel of human GC cell lines and gene-specific small interfering RNA-mediated NPR1 silencing to investigate the roles of NPR1 in malignancy-associated functions and intracellular signaling pathways. We generated an anti-NPR1 polyclonal antibody and examined its efficacy in a mouse xenograft model of GC peritoneal dissemination. Associations between NPR1 expression in GC tissue and clinicopathological factors were also evaluated. NPR1 mRNA was significantly upregulated in several GC cell lines compared with normal epithelial cells. NPR1 silencing attenuated GC cell proliferation, invasion, and migration, and additionally induced the intrinsic apoptosis pathway associated with mitochondrial dysfunction and caspase activation via downregulation of BCL-2. Administration of anti-NPR1 antibody significantly reduced the number and volume of GC peritoneal tumors in xenografted mice. High expression of NPR1 mRNA in clinical GC specimens was associated with a significantly higher rate of postoperative recurrence and poorer prognosis. NPR1 regulates the intrinsic apoptosis pathway and plays an important role in promoting the GC malignant phenotype. Inhibition of NPR1 with antibodies may have potential as a novel therapeutic modality for unresectable or metastatic GC.

Alfalfa xenomiR-162 targets G protein subunit gamma 11 to regulate milk protein synthesis in bovine mammary epithelial cells

OBJECTIVE

It was shown that microRNAs (miRNAs) play an important role in milk protein synthesis. However, the post-transcriptional regulation of casein expression by exogenous miRNA (xeno-miRNAs) in ruminants remains unclear. This study explores the regulatory roles of alfalfa xeno-miR162 on casein synthesis in bovine mammary epithelial cells (bMECs).

METHODS

The effects of alfalfa xenomiR-162 and G protein subunit gamma 11 (GNG11) on proliferation and milk protein metabolism of bMECs were detected by 5-Ethynyl-2'-Deoxyuridine (EdU) staining, flow cytometry, cell counting kit-8 (CCK-8), enzyme-linked immunosorbent assay, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blot. Dual-luciferase reporter assay was used to verify the targeting relationship between GNG11 and xenomiR-162.

RESULTS

Results showed that over-expression of xenomiR-162 inhibited cell proliferation but promoted apoptosis, which also up-regulated the expression of several casein coding genes, including CSN1S1, CSN1S2, and CSN3, while decreasing the expression of CSN2. Furthermore, the targeting relationship between GNG11 and xenomiR-162 was determined, and it was confirmed that GNG11 silencing also inhibited cell proliferation but promoted apoptosis and reduced the expression of casein coding genes and genes related to the mammalian target of rapamycin (mTOR) pathway.

CONCLUSION

Alfalfa xenomiR-162 appears to regulate bMECs proliferation and milk protein synthesis via GNG11 in the mTOR pathway, suggesting that this xeno-miRNA could be harnessed to modulate CSN3 expression in dairy cows, and increase κ-casein contents in milk.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

High GNG4 predicts adverse prognosis for osteosarcoma: Bioinformatics prediction and experimental verification

Background

Guanine nucleotide binding (G) protein subunit γ 4 (GNG4) is closely related to the malignant progression and poor prognosis of various tumours. However, its role and mechanism in osteosarcoma remain unclear. This study aimed to elucidate the biological role and prognostic value of GNG4 in osteosarcoma.

Methods

Osteosarcoma samples in the GSE12865, GSE14359, GSE162454 and TARGET datasets were selected as the test cohorts. The expression level of GNG4 between normal and osteosarcoma was identified in GSE12865 and GSE14359. Based on the osteosarcoma single-cell RNA sequencing (scRNA-seq) dataset GSE162454, differential expression of GNG4 among cell subsets was identified at the single-cell level. As the external validation cohort, 58 osteosarcoma specimens from the First Affiliated Hospital of Guangxi Medical University were collected. Patients with osteosarcoma were divided into high- and low-GNG4 groups. The biological function of GNG4 was annotated using Gene Ontology, gene set enrichment analysis, gene expression correlation analysis and immune infiltration analysis. Kaplan-Meier survival analysis was conducted and receiver operating characteristic (ROC) curves were calculated to determine the reliability of GNG4 in predicting prognostic significance and diagnostic value. Functional in vitro experiments were performed to explore the function of GNG4 in osteosarcoma cells.

Results

GNG4 was generally highly expressed in osteosarcoma. As an independent risk factor, high GNG4 was negatively correlated with both overall survival and event-free survival. Furthermore, GNG4 was a good diagnostic marker for osteosarcoma, with an area under the receiver operating characteristic curve (AUC) of more than 0.9. Functional analysis suggested that GNG4 may promote the occurrence of osteosarcoma by regulating ossification, B-cell activation, the cell cycle and the proportion of memory B cells. In in vitro experiments, silencing of GNG4 inhibited the viability, proliferation and invasion of osteosarcoma cells.

Conclusion

Through bioinformatics analysis and experimental verification, high expression of GNG4 in osteosarcoma was identified as an oncogene and reliable biomarker for poor prognosis. This study helps to elucidate the significant potential of GNG4 in carcinogenesis and molecular targeted therapy for osteosarcoma.

Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

Background

Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the main cause of this disease. The purpose of this research was to discover genes involved in PD development.

Methods

In this study, the microarray dataset (GSE22491) provided by GEO was used for further analysis. The Limma package under R software was used to examine and assess gene expression and identify DEGs. The DAVID online tool was used to accomplish GO enrichment analysis and KEGG pathway for DEGs. Furthermore, the PPI network of these DEGs was depicted using the STRING database and analyzed through the Cytoscape to identify hub genes. Support vector machine (SVM) classifier was subsequently employed to predict the accuracy of genes.

Result

PPI network consisted of 264 nodes as well as 502 edges was generated using the DEGs recognized from the Limma package under the R software. Moreover, three genes were identified as hubs: GNB5, GNG11, and ELANE. By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved.

Conclusion

According to the findings of the study, the 3 hub genes GNB5, GNG11, and ELANE may be used as PD detection biomarkers. Moreover, the results obtained from SVM with high accuracy can be considered as PD biomarkers in further investigations.