Gene set enrichment analysis and meta-analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma

Investigating the evolution process of lung adenocarcinoma via random walk and dynamic network analysis

Lung adenocarcinoma (LUAD) is a typical disease regarded as having multi-stage progression. However, many existing methods often ignore the critical differences among these stages, thereby limiting their effectiveness for discovering key biological molecules and biological functions as signals at each stage. In this study, we propose a method to discover the evolution between biological molecules and biological functions by investigating the multi-stage biological molecules of LUAD. The method is based on the random walk algorithm and the Monte Carlo method to generate clusters as the modules, which were used as subgraphs of the differentiated biological molecules network in each stage. The connection between modules of adjacent stages is based on the measurement of the Jaccard coefficient. The online gene set enrichment analysis tool (DAVID) was used to obtain biological functions corresponding to the individual important modules. The core evolution network was constructed by combining the aforementioned two networks. Since the networks here are all dynamic, we also propose a strategy to visualize the dynamic information together in one network. Eventually, 12 core modules and 11 core biological functions were found through such evolutionary analyses. Among the core biological functions that we obtained, six functions are related to the disease, the biological function of neutrophil chemotaxis is not directly associated with LUAD but can serve as a predictor, two functions may serve as a predictive signal, and two functions need to be verified through more biological evidence. Compared with two alternative design methods, the method proposed in this study performed more efficiently.

A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T₁, reference; T₃, HR 1.925, 95% CI 1.104–3.355) and N classification (N₀, reference; N₁, HR 2.212, 95% CI 1.520–3.220; N₂, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

D-Mannose Inhibits Adipogenic Differentiation of Adipose Tissue-Derived Stem Cells via the miR669b/MAPK Pathway

The adipogenic differentiation of adipose tissue-derived stem cells (ADSCs) plays an important role in the process of obesity and host metabolism. D-Mannose shows a potential regulating function for fat tissue expansion and glucose metabolism. To explore the mechanisms through which D-mannose affects the adipogenic differentiation of adipose-derived stem cells in vitro, we cultured the ADSCs with adipogenic medium inducement containing D-mannose or glucose as the control. The adipogenic differentiation specific markers Pparg and Fabp4 were determined by real-time PCR. The Oil Red O staining was applied to measure the lipid accumulation. To further explore the mechanisms, microarray analysis was performed to detect the differences between glucose-treated ADSCs (G-ADSCs) and D-mannose-treated ADSCs (M-ADSCs) in the gene expression level. The microarray data were further analyzed by a Venn diagram and Gene Set Enrichment Analysis (GSEA). MicroRNA inhibitor transfection was used to confirm the role of key microRNA. Results. D-Mannose intervention significantly inhibited the adipogenic differentiation of ADSCs, compared with the glucose intervention. Microarray showed that D-mannose increased the expression of miR669b, which was an inhibitor of adipogenesis. In addition, GSEA and western blot suggested that D-mannose suppressed the adipogenic differentiation via inhibiting the MAPK pathway and further inhibited the expression of proteins related to glucose metabolism and tumorigenesis. Conclusion. D-Mannose inhibits adipogenic differentiation of ADSCs via the miR669b/MAPK signaling pathway and may be further involved in the regulation of glucose metabolism and the inhibition of tumorigenesis.

Regulatory role of DEPTOR‑mediated cellular autophagy and mitochondrial reactive oxygen species in angiogenesis in multiple myeloma

DEPTOR, an inhibitor of mammalian target of rapamycin (mTOR), is essential for the survival of multiple myeloma (MM) cells. The expression level of DEPTOR is closely related to the prognosis of patients with MM treated with the antiangiogenic agent thalidomide; however, its role in the regulation of angiogenesis has not yet been elucidated. In the present study, the expression levels of DEPTOR and vascular endothelial growth factor (VEGF), and the microvessel density (MVD) of bone marrow (BM) from patients with MM assessed. DEPTORoverexpression plasmid or CRISPR-associated protein 9 (Cas9) and single guided RNAs (sgRNAs) were used to modulate DEPTOR expression. The DEPTOR-mediated angiogenic effects were assessed using a tube formation assay of human umbilical vein endothelial cells (HUVECs) cultured in the collected conditioned medium from MM cell lines with different expression levels of DEPTOR. It was found that the expression level of DEPTOR negatively correlated with the VEGF level and BM MVD in MM. Autophagic activity was regulated by DEPTOR expression, but was not related to thalidomide-binding protein CRBN, which is required for thalidomide to play an anti-tumor and antiangiogenic role in MM cells. The disruption of DEPTOR protein decreased cellular autophagy, increased VEGF expression in MM cells, and inhibited the tube formation of HUVECs, while a high expression of DEPTOR exerted the opposite effect. Moreover, targeting DEPTOR also resulted in the production of mitochondrial reactive oxygen species (mtROS), the phosphorylation of nuclear factor-κB (NF-κB) and an increase in interleukin 6 (IL-6) secretion. Of note, these effects are fully abrogated by treatment with autophagy activator (SMER28) or mitochondrial-specific antioxidant (Mito-TEMPO). Taken together, the present study demonstrates the role of DEPTOR in the regulation of autophagy/mtROS and subsequent angiogenesis. The results provide a novel mechanism for the further understanding of the therapeutic effects of thalidomide on MM.

Long non-coding RNA expression profiles and related regulatory networks in areca nut chewing-induced tongue squamous cell carcinoma

Areca nut chewing is an important risk factor for developing tongue squamous cell carcinoma (TSCC), although the underlying molecular mechanism is unknown. To determine the potential molecular mechanisms of areca nut chewing-induced TSCC, the present study performed whole-genome detection with five pairs of TSCC and adjacent normal tissues, via mRNA- and long non-coding (lnc)RNA-gene chip analysis. A total of 3,860 differentially expressed genes were identified, including 2,193 lncRNAs and 1,667 mRNAs. Gene set-enrichment analysis revealed that the differentially expressed mRNAs were enriched in chromosome 22q13, 8p21 and 3p21 regions, and were regulated by nuclear factor kappa B (NF-κB) and interferon regulatory factors (IRFs). The results of ingenuity pathway analysis revealed that these mRNAs were significantly enriched for inflammatory immune-related signaling pathways. A co-expression network of mRNAs and lncRNAs was constructed by performing weighted gene co-expression network analysis. The present study focused on NF-κB-, IRF- and Th cell-signaling pathway-related lncRNAs and the corresponding mRNA-lncRNA regulatory networks. To the best of our knowledge, the present study was the first to investigate differential mRNA- and lncRNA-expression profiles in TSCCs induced by areca nut chewing. Inflammation-related mRNA-lncRNA regulatory networks driven by IRFs and NF-κB were identified, as well as the Th cell-related signaling pathways that play important carcinogenic roles in areca nut chewing-induced TSCC. These differentially expressed mRNAs and lncRNAs, and their regulatory networks provide insight for further analysis on the molecular mechanism of areca nut chewing-induced TSCC, candidate molecular markers and targets for further clinical intervention.

Identification of candidate genes and prognostic value analysis in patients with PDL1-positive and PDL1-negative lung adenocarcinoma

Background

Increasing bodies of evidence reveal that targeting a programmed cell death protein 1 (PD-1) monoclonal antibody is a promising immunotherapy for lung adenocarcinoma. Although PD receptor ligand 1 (PDL1) expression is widely recognized as the most powerful predictive biomarker for anti-PD-1 therapy, its regulatory mechanisms in lung adenocarcinoma remain unclear. Therefore, we conducted this study to explore differentially expressed genes (DEGs) and elucidate the regulatory mechanism of PDL1 in lung adenocarcinoma.

Methods

The GSE99995 data set was obtained from the Gene Expression Omnibus (GEO) database. Patients with and without PDL1 expression were divided into PDL1-positive and PDL1-negative groups, respectively. DEGs were screened using R. The Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) networks of DEGs was visualized using Cytoscape, and the MNC algorithm was applied to screen hub genes. A survival analysis involving Gene Expression Profiling Interactive Analysis was used to verify the GEO results. Mutation characteristics of the hub genes were further analyzed in a combined study of five datasets in The Cancer Genome Atlas (TCGA) database.

Results

In total, 869 DEGs were identified, 387 in the PDL1-positive group and 482 in the PDL1-negative group. GO and KEGG analysis results of the PDL1-positive group mainly exhibited enrichment of biological processes and pathways related to cell adhesion and the peroxisome proliferators-activated receptors (PPAR) signaling pathway, whereas biological process and pathways associated with cell division and repair were mainly enriched in the PDL1-negative group. The top 10 hub genes were screened during the PPI network analysis. Notably, survival analysis revealed BRCA1, mainly involved in cell cycle and DNA damage responses, to be a novel prognostic indicator in lung adenocarcinoma. Moreover, the prognosis of patients with different forms of lung adenocarcinoma was associated with differences in mutations and pathways in potential hub genes.

Conclusions

PDL1-positive lung adenocarcinoma and PDL1-negative lung adenocarcinoma might be different subtypes of lung adenocarcinoma. The hub genes might play an important role in PDL1 regulatory pathways. Further studies on hub genes are warranted to reveal new mechanisms underlying the regulation of PDL1 expression. These results are crucial for understanding and applying precision immunotherapy for lung adenocarcinoma.