Sex-specific SNP-SNP interaction analyses within topologically associated domains reveals ANGPT1 as a novel tumor suppressor gene for lung cancer

Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

Background

Lung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms.

Methods

Gene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database.

Results

First, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis.

Conclusions

The low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08308-3.

The Relationship between Long Noncoding RNA H19 Polymorphism and the Epidermal Growth Factor Receptor Phenotypes on the Clinicopathological Characteristics of Lung Adenocarcinoma

The aim of the current study is to investigate potential associations among Long Noncoding RNA (LncRNA) H19 single nucleotide polymorphism (SNP) and epidermal growth factor receptor (EGFR) phenotypes on the clinicopathological characteristics of lung adenocarcinoma (LADC). Five loci of LncRNA H19 SNPs (rs217727, rs2107425, rs2839698, rs3024270, and rs3741219) were genotyped by using TaqMan allelic discrimination in 223 LADC patients with wild-type EGFR phenotype and 323 LADC individuals with EGFR mutations. After the statistical analyses, patients with the EGFR mutation were related to a higher distribution frequency of rs217727 SNP CT heterozygote (p = 0.030), and the female population with EGFR mutation demonstrated a higher distribution frequency of rs217727 SNP CT heterozygote (p < 0.001) and rs2107425 CT heterozygote (p = 0.002). In addition, the presence of LncRNA H19 SNP rs217727 T allele (CT + TT) in patients with EGFR wild-type was associated to higher tumor T status (stage III or IV, p = 0.037) and poorer cell differentiation status (poor differentiation, p = 0.012) compared to those EGFR wild-type individuals with LncRNA H19 SNP rs217727 CC allele. Besides, a prominently higher tumor T status was found in subjects with LncRNA H19 SNP rs2107425 T allele (CT + TT) (stage III or IV, p = 0.007) compared to EGFR wild-type LADC individuals with LncRNA CC allele in EGFR wild-type patients. Our findings suggest that the presence of LncRNA H19 SNP rs217727 is related to the EGFR mutation in LADC patients, and the LncRNA H19 SNP rs217727 and rs2107425 are associated with progressed tumor status for LADC patients with EGFR wild-type.

Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA-mRNA regulatory network involved in LUAD has not been fully elucidated.

METHODS

Differentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA-mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein-protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA).

RESULTS

The miRNA-mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD.

CONCLUSION

This study investigated a miRNA-mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.

Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

BACKGROUND In recent years, the morbidity and mortality rates of lung adenocarcinoma in non-smoking females have been increasing dramatically. Although much research has been done with some progress, the molecular mechanism remains unclear. In this study we aimed to estimate hub genes and infiltrating immune cells in non-smoking females with lung adenocarcinoma. MATERIAL AND METHODS Firstly, we obtained differentially expressed genes (DEGs) by GEO2R analysis based on 3 independent mRNA microarray datasets of GSE10072, GSE31547, and GSE32863. The DAVID database was utilized for functional enrichment analysis of DEGs. Moreover, we identified hub genes with prognostic value by STRING, Cytoscape, and Kaplan Meier plotter. Subsequently, these genes were further analyzed by Gene Expression Profiling Interactive Analysis, Oncomine, Tumor Immune Estimation Resource, and Human Protein Atlas. Finally, the immune infiltration analysis was performed by CIBERSORT and The Cancer Genome Atlas with R packages. RESULTS We found 315 DEGs enriching in the extracellular matrix organization, cell adhesion, integrin binding, angiogenesis, and hypoxic response. And among these DEGs, we identified 10 hub genes (SPP1, ENG, ATF3, TOP2A, COL1A1, PAICS, CAV1, CAT, TGFBR2, and ANGPT1) of significant prognostic value. Simultaneously, we illustrated the distribution and differential expressions of 22 immune cell subtypes. and dendritic cells resting and macrophages M1 were identified with prognostic significance. CONCLUSIONS The results indicated that 10 hub genes and 2 immune cell subtypes might be promising biomarkers for lung adenocarcinoma in non-smoking females. This finding needs to be further evaluated.

PPARG Could Work as a Valid Therapeutic Strategy for the Treatment of Lung Squamous Cell Carcinoma

Previous studies showed that PPAR-gamma (PPARG) ligands might serve as potential therapeutic agents for nonsmall cell lung cancer (NSCLC). However, a few studies reported the specific relationship between PPARG and lung squamous cell carcinoma (LSCC). Here, we made an effort to explore the relationship between PPARG and LSCC. First, we used mega-analysis and partial mega-analysis to analyze the effects of PPARG on LSCC by using 12 independent LSCC expression datasets (285 healthy controls and 375 LSCC cases). Then, literature-based molecular pathways between PPARG and LSCC were established. After that, a gene set enrichment analysis (GSEA) was conducted to study the functionalities of PPARG and PPARG-driven triggers within the molecular pathways. Finally, another mega-analysis was constructed to test the expression changes of PPARG and its driven targets. The partial mega-analysis showed a significant downregulated expression of PPARG in LSCC (LFC = -1.08, p value = 0.00073). Twelve diagnostic markers and four prognostic markers were identified within multiple PPARG-LSCC regulatory pathways. Our results suggested that the activation of PPARG expression may inhibit the development and progression of LSCC through the regulation of LSCC upstream regulators and downstream marker genes, which were involved in tumor cell proliferation and protein polyubiquitination/ubiquitination.