Integration of gene profile to explore the hub genes of lung adenocarcinoma: A quasi-experimental study

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.

Tanshinone IIA suppresses the progression of lung adenocarcinoma through regulating CCNA2-CDK2 complex and AURKA/PLK1 pathway

Lung adenocarcinoma (LUAD) belongs to a subgroup of non-small cell lung cancer (NSCLC) with an increasing incidence all over the world. Tanshinone IIA (TSA), an active compound of Salvia miltiorrhiza Bunge., has been found to have anti-tumor effects on many tumors, but its anti-LUAD effect and its mechanism have not been reported yet. In this study, bio-information analysis was applied to characterize the potential mechanism of TSA on LUA, biological experiments were used to verify the mechanisms involved. TCGA, Pubchem, SwissTargetPrediction, Venny2.1.0, STRING, DAVID, Cytoscape 3.7.2, Omicshare, GEPIA, RSCBPDB, Chem Draw, AutoDockTools, and PyMOL were utilized for analysis in the bio-information analysis and network pharmacology. Our experiments in vitro focused on the anti-LUAD effects and mechanisms of TSA on LUAD cells (A549 and NCI-H1975 cells) via MTT, plate cloning, Annexin V-FITC and PI dual staining, flow cytometry, and western blot assays. A total of 64 differentially expressed genes (DEGs) of TSA for treatment of LUAD were screened out. Gene ontology and pathway analysis revealed characteristic of the DEGs network. After GEPIA-based DEGs confirmation, 46 genes were considered having significant differences. Further, 10 key DEGs (BTK, HSD11B1, ADAM33, TNNC1, THRA, CCNA2, AURKA, MIF, PLK1, and SORD) were identified as the most likely relevant genes from overall survival analysis. Molecular Docking results showed that CCNA2, CDK2 and PLK1 had the lowest docking energy. MTT and plate cloning assays results showed that TSA inhibited the proliferation of LUAD cells in a concentration-dependent manner. Annexin V-FITC and PI dual staining and flow cytometry assays results told that TSA promoted the apoptosis of the two LUAD cells in different degrees, and induced cycle arrest in the G1/S phase. Western blot results showed that TSA significantly down-regulated the expression of CCNA2, CDK2, AURKA, PLK1, and p-ERK. In summary, TSA could suppress the progression of LUAD by inducing cell apoptosis and arresting cell cycle, and these were done by regulating CCNA2-CDK2 complex and AURKA/PLK1 pathway. These findings are the first to demonstrate the molecular mechanism of TSA in treatment of LUAD combination of network bio-information analysis and biological experiments in vitro.

Subtype-dependent regulation of Gβγ signalling

G protein-coupled receptors (GPCRs) transmit information to the cell interior by transducing external signals to heterotrimeric G protein subunits, Gα and Gβγ subunits, localized on the inner leaflet of the plasma membrane. Though the initial focus was mainly on Gα-mediated events, Gβγ subunits were later identified as major contributors to GPCR-G protein signalling. A broad functional array of Gβγ signalling has recently been attributed to Gβ and Gγ subtype diversity, comprising 5 Gβ and 12 Gγ subtypes, respectively. In addition to displaying selectivity towards each other to form the Gβγ dimer, numerous studies have identified preferences of distinct Gβγ combinations for specific GPCRs, Gα subtypes and effector molecules. Importantly, Gβ and Gγ subtype-dependent regulation of downstream effectors, representing a diverse range of signalling pathways and physiological functions have been found. Here, we review the literature on the repercussions of Gβ and Gγ subtype diversity on direct and indirect regulation of GPCR/G protein signalling events and their physiological outcomes. Our discussion additionally provides perspective in understanding the intricacies underlying molecular regulation of subtype-specific roles of Gβγ signalling and associated diseases.

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

BACKGROUND

GNG11 (G protein subunit gamma 11) is a member of guanine nucleotide-binding protein (G protein) gamma family. Few studies elucidated the role of GNG11 in human disease, especially in tumors. The present study initially analyzed the function of GNG11 in ovarian serous cystadenocarcinoma.

METHODS

The differential expression of GNG11 mRNA in ovarian cancer and normal tissues was evaluated through Oncomine, CCLE, Gepia, UCSC Xena and UALCAN databases. The protein expression of GNG11 was assessed via HPA database. Prognosis analysis was performed by Kaplan-Meier Plotter. Restrict survival analysis to subtypes including tumor grade, cancer stage and TP53 mutation status was then carried out. GSEA enrichment analysis was performed to explore the significant pathways associated with GNG11 in ovarian cancer. Finally, the upstream miRNAs of GNG11 were predicted by DIANA, Target Scan, miRDB and miRWalk databases, and the potential key KEGG pathways were subsequently determined by DIANA.

RESULTS

The mRNA expression of GNG11 was down-regulated in ovarian cancer patients (P<0.05). The cancer stage of patients correlated with the expression of GNG11 (P<0.05). Survival analysis indicated that GNG11 high expression statistically shortened the overall survival time of patients (HR=1.26, P=0.0043) compared with low expression group, especially for the patients with earlier stage (HR=2.48, P=0.035) and lower grade (HR=1.72, P=0.0016). Subsequently, the consistent upstream miRNA of GNG11, hsa-miR-22-5p, was predicted from 4 databases. The differential expression profile of hsa-miR-22-5p in blood was observed in ovarian cancer patients. According to the GSEA analysis on GNG11 and KEGG analysis on hsa-miR-22-5p, the consistent pathway of ECM-receptor interaction was observed (all P<0.01). ECM-receptor interaction pathway and differential expression of hsa-miR-22-5p in blood suggested the migration risk of ovarian cancer.

CONCLUSION

High expression of GNG11 indicated the poor prognosis of ovarian cancer patients. GNG11 might play a crucial role in the biological process of ovarian serous cystadenocarcinoma by ECM-receptor interaction pathway, thus affecting the prognosis of patients.