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

MicroRNAs Associated with Keloids Identified by Microarray Analysis and In Vitro Experiments

MicroRNAs (miRNAs) play a crucial role in gene regulation and the development of keloid. This research aimed to identify and verify miRNAs associated with keloids by microarray analysis and in vitro experiments, shedding light on seeking for potential therapeutic molecular targets. In this study, the weighted gene co-expression network analysis was performed based on the GSE113620. The key miRNA module most relevant to the keloid was further screened to identify hub miRNAs, and then hub miRNAs was verified by the microarray analysis and qRT-PCR experiments. Additionally, targeted genes of hub miRNAs were predicted and verified. Gene ontology (GO) analysis and KEGG enrichment analysis were also conducted. Five miRNA modules were divided, and the blue module exhibited the highest correlation with keloids. Then, hsa-miR-127-3p, hsa-miR-214-3p, hsa-miR-155-5p, hsa-miR-409-5p, and hsa-miR-542-5p were identified as the hub miRNAs. Subsequently, the microarray analysis and qRT-PCR results demonstrated that the expression of five miRNAs were upregulated in keloid tissues. The GO analysis revealed that the target genes of these miRNAs were mainly enriched in biological processes including gene transcription, protein phosphorylation and the MAPK (mitogen-activated protein kinase) cascade, and the KEGG pathway enrichment analysis showed that the PI3K-AKT signaling pathway were significantly enriched. In conclusion, these five miRNAs (hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-214-3p, hsa-miR-409-5p, and hsa-miR-542-5p) play vital roles in the pathogenesis of keloid and might be potential therapeutic targets. These miRNAs might regulate genes enriched in gene transcription, protein phosphorylation, the MAPK cascade, and the PI3K-Akt signaling pathway.

The Landscape of Small Leucine-Rich Proteoglycan Impact on Cancer Pathogenesis with a Focus on Biglycan and Lumican

Cancer development is a multifactorial procedure that involves changes in the cell microenvironment and specific modulations in cell functions. A tumor microenvironment contains tumor cells, non-malignant cells, blood vessels, cells of the immune system, stromal cells, and the extracellular matrix (ECM). The small leucine-rich proteoglycans (SLRPs) are a family of nineteen proteoglycans, which are ubiquitously expressed among mammalian tissues and especially abundant in the ECM. SLRPs are divided into five canonical classes (classes I-III, containing fourteen members) and non-canonical classes (classes IV-V, including five members) based on their amino-acid structural sequence, chromosomal organization, and functional properties. Variations in both the protein core structure and glycosylation status lead to SLRP-specific interactions with cell membrane receptors, cytokines, growth factors, and structural ECM molecules. SLRPs have been implicated in the regulation of cancer growth, motility, and invasion, as well as in cancer-associated inflammation and autophagy, highlighting their crucial role in the processes of carcinogenesis. Except for the class I SLRP decorin, to which an anti-tumorigenic role has been attributed, other SLPRs' roles have not been fully clarified. This review will focus on the functions of the class I and II SLRP members biglycan and lumican, which are correlated to various aspects of cancer development.

Weighted Gene Co-expression Network Analysis Revealed That CircMARK3 Is a Potential CircRNA Affects Fat Deposition in Buffalo

Background

Buffalo meat is increasingly widely accepted for consumption as it shares several quality attributes with cattle meat (beef). Hence, there is a huge opportunity for growth in the buffalo meat industry. However, buffalo meat has relatively low intramuscular fat (IMF) content, affecting its flavor, tenderness and juiciness. As there is a dearth of information on factors that control fat deposition, this study was undertaken to provide new candidate factor associated with buffalo fat deposition. Circular RNA (circRNA) is a novel class of non-coding RNA with a closed-loop structure, and play an important role in fat deposition.

Methods

In this study, weighted gene co-expression network analysis (WGCNA) was used to construct a circRNA co-expression network and revealed a candidate circRNA that may affect the IMF deposition of buffalo as determined by RT-qPCR, semiquantitative PCR and gain-of-function experiments.

Results

Herein, WGCNA determined that one module (turquoise module) is significantly associated with the growth and development stages of buffalo. Further analysis revealed a total of 191 overlapping circRNAs among differentially expressed (DE) circRNAs and the co-expression module. A candidate circRNA was found, 21:6969877|69753491 (circRNA_ID), with a reported involvement in lipid metabolism. This circRNA is stably expressed and originates from the MARK3 gene, hence the name circMARK3. circMARK3 is highly expressed in adipose tissue and mature adipocytes and is located in the cytoplasm. Gain-of-function experiments demonstrated that circMARK3 promoted adipogenic differentiation of buffalo adipocytes and 3T3-L1 cells by up-regulating the expression levels of adipogenic marker genes PPARG, C/EBPα and FABP4.

Conclusion

These results indicate that circMARK3 is a potential factor that promotes fat deposition by regulating adipocyte differentiation and adipogenesis in buffalo.

Weighted Gene Co-Expression Network Analysis (WGCNA) Reveals the Functions of Syndecan-1 to Regulate Immune Infiltration by Influenced T Cells in Glioma

Our previous studies shown that syndecan-1 (SDC1) may be a novel class of biomarkers for the diagnosis and treatment of glioma, but its specific roles and the in-depth molecular mechanism remain elusive. Here, we used Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithms and single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms to evaluate the immune score of tumor samples and quantify the relative infiltration of immune cells in the tumor microenvironment (TME), respectively, in different data sets obtained from the Chinese Glioma Genome Atlas and The Cancer Gene Atlas. Next, we calculate the correlation of the immune score and immune cells with SDC1, respectively. To identify the specific process regulated by SDC1, the differentially expressed genes (DEGs) analysis between the high and low expression of SDC1 of glioma samples were used to discover the hub genes through Weighted Gene Coexpression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed cardinal biological processes and pathways involved in genes and tumor grade correlation and survival analysis verified its significance in glioma. The results show that SDC1 is associated with the immune infiltration of glioma in the TME, especially activated CD4+T cells and CD8+T cells. The three data sets filter 8,887 DEGs, the genes in the blue modules were selected as hub genes in WGCNA. GO and KEGG analysis found eight genes in the blue modules involved in antigen processing and presentation in T cells in glioma. Kaplan–Meier estimator and log-rank test statistic determined that the introduced genes are associated with poor prognosis in glioma. Protein–protein network interaction analysis showed that SDC1 may regulate antigen processing and presentation through CTSL or CD4 in glioma. Finally, this study provided insights and clues for the next research direction of SDC1 and identified the key pathways and genes that might participate in the immune escape of glioma. These results might provide a new insight on the study of immune infiltration of glioma in the future.

Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma

Objective: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis. Methods: The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Genotype Tissue Expression databases. The weighted gene co-expression network and differentially expressed genes were used to identify the central regulatory genes for the development of LUAD. Univariate Cox, LASSO, and multivariate Cox regression analyses were utilized to identify prognosis-related genes. Results: The top 10 central regulatory genes of LUAD included IL6, PECAM1, CDH5, VWF, THBS1, CAV1, TEK, HGF, SPP1, and ENG. Genes that have an impact on survival included PECAM1, HGF, SPP1, and ENG. The favorable prognosis genes included KDF1, ZNF691, DNASE2B, and ELAPOR1, while unfavorable prognosis genes included RPL22, ENO1, PCSK9, SNX7, and LCE5A. The areas under the receiver operating characteristic curves of the risk score model in the training and testing datasets were .78 and .758, respectively. Conclusion: Bioinformatics methods were used to identify genes involved in the development and prognosis of LUAD, which will provide a basis for further research on the treatment and prognosis of LUAD.

Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis

BACKGROUND

Lung squamous cell carcinoma (LUSC) is one of the most common types of lung carcinoma and has specific clinicopathologic characteristics. In this study, we screened novel molecular biomarkers relevant to the prognosis of LUSC to explore new diagnostic and treatment approaches for this disease.

METHODS

We downloaded GSE73402 from the Gene Expression Omnibus (GEO) database. GSE73402 contains 62 samples, which could be classified as four subtypes according to their pathology and stages. Via weighted gene coexpression network analysis (WGCNA), the main module was identified and was further analyzed using differentially expressed genes (DEGs) analysis. Then, by protein-protein interaction (PPI) network and Gene Expression Profiling Interactive Analysis (GEPIA), hub genes were screened for potential biomarkers of LUSC.

RESULTS

Via WGCNA, the yellow module containing 349 genes was identified, and it is strongly related to the subtype of CIS (carcinoma in situ). DEGs analysis detected 180 genes that expressed differentially between the subtype of CIS and subtype of early-stage carcinoma (Stage I and Stage II). A PPI network of DEGs was constructed, and the top 20 genes with the highest correlations were selected for GEPIA database to explore their effect on LUSC survival prognosis. Finally, ITGA5, TUBB3, SCNN1B, and SERPINE1 were screened as hub genes in LUSC.

CONCLUSIONS

ITGA5, TUBB3, SCNN1B, and SERPINE1 may have great diagnostic and prognostic significance for LUSC and have great potential to be new treatment targets for LUSC.

Weighted gene co-expression network analysis of hub genes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a tumor with high incidence. This study aimed to identify the central genes of LUAD. LUAD were analyzed by weighted gene co-expression network (WGCNA), and differentially expressed genes (DEGs) were identified. Samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases and included 515 LUAD samples and 347 normal samples. The WGCNA algorithm generated a total of 10 modules. The top 2 modules (MEturquoise and MEblue) with the highest correlation to LUAD were selected. Ten Hub genes (IL6, CDH1, PECAM1, SPP1, THBS1, HGF, SNCA, CDH5, CAV1, and DLC1) were screened in the intersecting genes of DEGs and WGCNA (MEturquoise and MEblue). Only SPP1 was correlated with LUAD poor survival, indicating that SPP1 may be a key Hub gene for LUAD. The competing endogenous RNA (ceRNA) network was constructed to analyze the regulatory relationship of Hub genes, and SPP1 may be directly regulated by 4 microRNAs (miRNAs) and indirectly regulated by 49 long noncoding RNAs (lncRNAs).

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.

Key microRNAs and hub genes associated with poor prognosis in lung adenocarcinoma

In the study, we obtained 36 pairs of lung adenocarcinoma (LUAD) tissues and adjacent non-tumorous tissues. Then, we chose a specific hub-target gene of miRNA and used qRT-PCR to evaluate the expression of PECAM1. We found that the expression level of PECAM1 mRNA in LUAD was significantly lower than that in adjacent nontumor tissues (P<0.0001). Univariate and multivariate analyses were conducted on 481 LUAD patients from The Cancer Genome Atlas (TCGA) according to the Cox proportional hazard regression model to evaluate the impact of PECAM1 expression and other clinicopathological factors on survival. The results showed that the low expression of PECAM1 was an important independent predictor of poor overall survival (HR, 0.704; 95% CI, 0.518-0.957; P = 0.025). Based on the Tumor Immune Estimation Resource (TIMER) database, the relationship between PECAM1 expression and B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration was weak in LUAD (P<0.01). In particular, a more significant positive correlation between PECAM1 expression and HLA-complex members, CD1C, NRP1, and ITGAX expression in dendritic cell was detected in LUAD. The mechanism which PECAM1 involved in the development of LUAD may be closely related to changes in the immune microenvironment.

Comprehensive analysis of circular RNA expression profiles in cisplatin-resistant non-small cell lung cancer cell lines

Platinum-based drugs such as cisplatin are widely used in combination chemotherapy for non-small cell lung cancer (NSCLC) owing to their high clinical response rate; however, acquired resistance to cisplatin is eventually inevitable. Circular RNAs (circRNAs) are involved in the development of diverse types of cancers, but their connection to cisplatin-resistance in NSCLC has not been studied. In the present study, two cisplatin-resistant NSCLC cell lines (A549/DDP and PC9/DDP) were established by gradually increasing concentrations of cisplatin in the media. The resulting cell lines possessed high resistance to cisplatin and strong proliferation, migration, and colony formation abilities compared to the parental cells. Microarray analysis identified 19,161 circRNAs that were dysregulated in cisplatin-resistant cell lines (fold change abs>2), including 11,915 up-regulated and 7246 down-regulated circRNAs. The expression of the top five up-regulated and down-regulated circRNAs was validated using real-time quantitative polymerase chain reaction. A circRNA-micro RNA (miRNA) network of the top 20 dysregulated circRNAs and their predicted miRNAs was constructed using Cytoscape. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the host genes of the identified circRNAs were involved in the regulation of MAP kinase kinase kinase kinase activity, 6-phosphofructo-2-kinase activity, focal adhesion, ErbB signaling, and ECM-receptor interactions, which may contribute to cisplatin resistance in NSCLC. In summary, this is the first report on circRNA profiling in cisplatin-resistant NSCLC cells and it provides new potential targets for the reversal of cisplatin resistance in NSCLC.

Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p = 0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.