Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Up-Regulation of MELK Promotes Cell Growth and Invasion by Accelerating G1/S Transition and Indicates Poor Prognosis in Lung Adenocarcinoma

Maternal embryonic leucine zipper kinase (MELK) is an oncogene in many tumors, although its contribution to lung adenocarcinoma (LUAD) is unclear. We examined MELK expression in patient LUAD tissue and matched healthy lung tissues. We investigated the connection between MELK expression and tumor differentiation, lymph node metastasis, and patient survival. We downregulated MELK expression using small-hairpin RNA to assess its impact on LUAD cell proliferation, clonogenicity, and invasion. We also investigated the molecular mechanism underlying these effects. MELK expression was significantly heightened in LUAD tissue as opposed to the matching healthy lung tissues. LUAD patients who had MELK overexpression had a worse prognosis. Suppression of MELK hinders proliferation, clonogenicity, and invasion of LUAD cells. The MELK suppression led to the arrest of the cell cycle's G1/S phase by reducing the cyclin E1 and cyclin D expression. Our outcomes manifest that MELK can function as a beneficial prognostic indication and a new therapy target for LUAD. MELK has an essential function in progressing LUAD, manifesting potential as a viable target for therapeutic intervention in this disease management.

Transcriptomic analysis reveals the potential biological mechanism of AIS and lung adenocarcinoma

Introduction

Acute ischemic stroke (AIS) and lung adenocarcinoma (LUAD) are associated with some of the highest morbidity and mortality rates worldwide. Despite reports on their strong correlation, the causal relationship is not fully understood. The study aimed to identify and annotate the biological functions of hub genes with clinical diagnostic efficacy in AIS and LUAD.

Methods

Transcriptome and single-cell datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified the differentially expressed genes (DEGs) upregulated in AIS and LUAD and found 372 genes intersecting both datasets. Hub genes were identified using protein-protein interaction (PPI) networks, and the diagnostic and prognostic utility of these hub genes was then investigated using receiver operating characteristic (ROC) curves, survival analysis, and univariable Cox proportional hazard regression. Single-cell analysis was used to detect whether the hub genes were expressed in tumor epithelial cells. The immune microenvironment of AIS and LUAD was assessed using the CIBERSORT algorithm. The protein expression of these hub genes was tracked using the Human Protein Atlas (HPA). We calculated the number of positive cells using the digital pathology software QuPath. Finally, we performed molecular docking after using the Enrichr database to predict possible medicines.

Results

We identified the molecular mechanisms underlying hub genes in AIS and LUAD and found that CCNA2, CCNB1, CDKN2A, and CDK1 were highly expressed in AIS and LUAD tissue samples compared to controls. The hub genes were mainly involved in the following pathways: the cell cycle, cellular senescence, and the HIF-1 signaling pathway. Using immunohistochemical slices from the HPA database, we confirmed that these hub genes have a high diagnostic capability for AIS and LUAD. Further, their high expression is associated with poor prognosis. Finally, curcumin was tested as a potential medication using molecular docking modeling.

Discussion

Our findings suggest that the hub genes we found in this study contribute to the development and progression of AIS and LUAD by altering the cellular senescence pathway. Thus, they may be promising markers for diagnosis and prognosis.

Maternal embryonic leucine zipper kinase in tumor cell and tumor microenvironment: Emerging player and promising therapeutic opportunities

Maternal embryonic leucine zipper kinase (MELK) is a member of the AMPK (AMP-activated protein kinase) protein family, which is widely and highly expressed in multiple cancer types. Through direct and indirect interactions with other proteins, it mediates various cascades of signal transduction processes and plays an important role in regulating tumor cell survival, growth, invasion and migration and other biological functions. Interestingly, MELK also plays an important role in the regulation of the tumor microenvironment, which can not only predict the responsiveness of immunotherapy, but also affect the function of immune cells to regulate tumor progression. In addition, more and more small molecule inhibitors have been developed for the target of MELK, which exert important anti-tumor effects and have achieved excellent results in a number of clinical trials. In this review, we outline the structural features, molecular biological functions, potential regulatory mechanisms and important roles of MELK in tumors and tumor microenvironment, as well as substances targeting MELK. Although many molecular mechanisms of MELK in the process of tumor regulation are still unknown, it is worth affirming that MELK is a potential tumor molecular therapeutic target, and its unique superiority and important role provide clues and confidence for subsequent basic research and scientific transformation.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Objective

To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.

Methods

Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool.

Results

CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate.

Conclusion

CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Interfering with CALCRL expression inhibits glioma proliferation, promotes apoptosis, and predicts prognosis in low-grade gliomas

Background

CALCRL is involved in a variety of key biological processes, including cell proliferation, apoptosis, angiogenesis, and inflammation. However, the role of CALCRL in glioma remains unknown. The purpose of this study was to investigate the effect of differential CALCRL expression on the malignant progression of glioma and its value in glioma prognosis.

Methods

Sequencing data from glioma and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, and the downloaded data were statistically analyzed using bioinformatics tools and the corresponding R package. The expression of CALCRL in normal brain tissue and different grades of glioma tissue was detected by pathological and immunohistochemical staining of clinical glioma specimens. The expression of CALCRL in different glioma cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR), and the U87 cell line with high expression was selected to construct the CALCRL knockdown model by transfection with short hairpin (shRNA). The cell proliferation ability was detected by Celigo assay and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, the ability of cell clone formation was detected by clone formation assay, and the level of apoptosis was detected by flow cytometry.

Results

The expression of CALCRL in glioma was significantly upregulated compared with that of normal tissue, especially in low-grade glioma (LGG) compared to glioblastoma, and the differential expression of CALCRL correlated significantly with the prognosis of LGG. Clinical pathology and immunohistochemistry showed that the expression of CALCRL was related to the pathological grade of glioma, and the highest expression was found in World Health Organization (WHO) grade Ⅲ glioma. The results of qRT-PCR showed that CALCRL expression was highest in the U87 cell line. After knockdown of CALCRL expression, the proliferation and clonogenic ability of U87 cells were significantly decreased, and the apoptosis rate was significantly increased.

Conclusions

CALCRL is highly expressed in LGG. Interfering with CALCRL expression inhibits glioma cell proliferation and promotes apoptosis, and thus has potential as a biomarker and therapeutic target for the prognosis of those with LGGs.

CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

Background

The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach.

Methods

We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log₂ fold change (FC) | >2.

Results

A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape.

Conclusions

We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

Proliferating cell nuclear antigen clamp associated factor, a potential proto-oncogene with increased expression in malignant gastrointestinal tumors

Gastrointestinal (GI) cancers, including malignancies in the gastrointestinal tract and accessory organs of digestion, represent the leading cause of death worldwide due to the poor prognosis of most GI cancers. An investigation into the potential molecular targets of prediction, diagnosis, prognosis, and therapy in GI cancers is urgently required. Proliferating cell nuclear antigen (PCNA) clamp associated factor (PCLAF), which plays an essential role in cell proliferation, apoptosis, and cell cycle regulation by binding to PCNA, is a potential molecular target of GI cancers as it contributes to a series of malignant properties, including tumorigenesis, epithelial-mesenchymal transition, migration, and invasion. Furthermore, PCLAF is an underlying plasma prediction target in colorectal cancer and liver cancer. In addition to GI cancers, PCLAF is also involved in other types of cancers and autoimmune diseases. Several pivotal pathways, including the Rb/E2F pathway, NF-κB pathway, and p53-p21 cascade, are implicated in PCLAF-mediated diseases. PCLAF also contributes to some diseases through dysregulation of the p53 pathway, WNT signal pathway, MEK/ERK pathway, and PI3K/AKT/mTOR signal cascade. This review mainly describes in detail the role of PCLAF in physiological status and GI cancers. The signaling pathways involved in PCLAF are also summarized. Suppression of the interaction of PCLAF/PCNA or the expression of PCLAF might be potential biological therapeutic strategies for GI cancers.

Peripheral blood transcriptome heterogeneity and prognostic potential in lung cancer revealed by RNA-Seq

Understanding of the complex interaction between the peripheral immune system and lung cancer (LC) remains incomplete, limiting patient benefit. Here, we aimed to characterize the host peripheral immune response to LC and investigate its potential prognostic value. Bulk RNA-sequencing data of peripheral blood leucocytes (PBLs) from healthy volunteers and LC patients (n = 142) were analysed for characterization of host systemic immunity in LC. We observed broad blood transcriptome perturbations in LC patients that were heterogeneous, as two new subtypes were established independent of histology. Functionally, the heterogeneity between the two subtypes included dysregulation of diverse biological processes, such as the cell cycle, blood coagulation and inflammatory signalling pathways, together with the abundance and activity of blood cells, particularly lymphocytes and neutrophils, ultimately manifesting as differences in antitumour immune status. Based on these findings, a prognostic model composed of ten genes dysregulated in one LC subtype with relatively poor immune status was developed and validated in a Gene Expression Omnibus (GEO) data set (n = 108), helping to generate a prognostic nomogram. Collectively, our study provides novel and comprehensive insight into the heterogeneity of the host peripheral immune response to LC. The expression heterogeneity-based predictive model may help guide prognostic management for LC patients.

KLF5-induced BBOX1-AS1 contributes to cell malignant phenotypes in non-small cell lung cancer via sponging miR-27a-5p to up-regulate MELK and activate FAK signaling pathway

Background

Non-small cell lung cancer (NSCLC) is a major histological subtype of lung cancer with high mortality and morbidity. A substantial amount of evidence demonstrates long non-coding RNAs (lncRNA) as critical regulators in tumorigeneis and malignant progression of human cancers. The oncogenic role of BBOX1 anti-sense RNA 1 (BBOX1-AS1) has been reported in several tumors. As yet, the potential functions and mechanisms of BBOX1-AS1 in NSCLC are obscure.

Methods

The gene and protein expression was detected by qRT-PCR and western blot. Cell function was determined by CCK-8, colony forming, would healing and transwell assays. Bioinformatics tools, ChIP assays, dual luciferase reporters system and RNA pull-down experiments were used to examine the interaction between molecules. Subcutaneous tumor models in nude mice were established to investigate in vivo NSCLC cell behavior.

Results

BBOX1-AS1 was highly expressed in NSCLC tissues and cells. High BBOX1-AS1 expression was associated with worse clinical parameters and poor prognosis. BBOX1-AS1 up-regulation was induced by transcription factor KLF5. BBOX1-AS1 deficiency resulted in an inhibition of cell proliferation, migration, invasion and EMT in vitro. Also, knockdown of BBOX1-AS1 suppressed NSCLC xenograft tumor growth in mice in vivo. Mechanistically, BBOX1-AS1 acted act as a competetive “sponge” of miR-27a-5p to promote maternal embryonic leucine zipper kinase (MELK) expression and activate FAK signaling. miR-27a-5p was confirmed as a tumor suppressor in NSCLC. Moreover, BBOX1-AS1-induced increase of cell proliferation, migration, invasion and EMT was greatly reversed due to the overexpression of miR-27a-5p. In addition, the suppressive effect of NSCLC progression owing to BBOX1-AS1 depletion was abated by the up-regulation of MELK. Consistently, BBOX1-AS1-mediated carcinogenicity was attenuated in NSCLC after treatment with a specific MELK inhibitor OTSSP167.

Conclusions

KLF5-induced BBOX1-AS1 exerts tumor-promotive roles in NSCLC via sponging miR-27a-5p to activate MELK/FAK signaling, providing the possibility of employing BBOX1-AS1 as a therapeutic target for NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-01943-5.

Bioinformatics analysis and experimental validation of TTK as a biomarker for prognosis in non-small cell lung cancer

Background: Despite the prominent development of medical technology in recent years, the prognosis of non-small cell lung cancer (NSCLC) is still not optimistic. It is crucial to identify more reliable diagnostic biomarkers for the early diagnosis and personalized therapy of NSCLC and clarify the molecular mechanisms underlying NSCLC progression.

Methods: In the present study, bioinformatics analysis was performed on three datasets obtained from the Gene Expression Omnibus to identify the NSCLC-associated differentially expressed genes (DEGs). Immunohistochemistry-based tissue microarray of human NSCLC was used to experimental validating the potential targets obtained from bioinformatics analysis.

Results: By using protein–protein interaction (PPI) network analysis, Kaplan–Meier plotter, and Gene Expression Profiling Interactive Analysis, we selected 40 core DEGs for further study. Then, a re-analysis of 40 selected genes via Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that nine key genes involved in the cell cycle and p53 signaling pathway participated in the development of NSCLC. Then, we checked the protein level of nine key genes by semi-quantitative of IHC and checked the distribution at a single-cell level. Finally, we validated dual-specificity protein kinase TTK as a biomarker for prognosis in a tissue microarray. High TTK expression associated with a higher histological stage, advanced TNM stage, high frequency of positive lymph nodes, and worse 5-year overall survival.

Conclusions: We found nine key genes were enriched in the cell cycle and p53 signaling pathway. TTK could be considered as a potential therapeutic target and for the prognosis biomarker of NSCLC. These findings will provide new insights for the development of individualized therapeutic targets for NSCLC.

Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Venous Thromboembolism (VTE)

OBJECTIVES

To explore the key genes, and correlated pathways in venous thromboembolism (VTE) via bioinformatic analysis, and expected our findings could contribute to the development of new biomarkers and therapeutic target for VTE.

METHODS

Two VTE-related microarray expression profiles (GSE48000 and GSE19151) were downloaded from the Gene Expression Ominibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using limma package, and overlapping DEGs were identified form the above two expression profiles. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway enrichment analyses were performed by DAVID. Protein-protein interaction (PPI) network was constructed by using STRING and visualized with Cytoscape. Furthermore, module analysis plus centrality analysis of the PPI network were executed to identify the potential key genes. Finally, the pathway analysis was performed using GenCLiP 3.0.

RESULTS

A total of 173 DEGs (125 upregulated and 48 downregulated) were identified. GO analysis demonstrated that DEGs were mainly enriched in viral life cycle, ribosome and structural constituent of ribosome. Meanwhile, KEGG pathway analysis showed that these genes were enriched in ribosome, Parkinson's disease and cell cycle. Additionally, one most significant module and 12 hub genes were found. Finally, 6 key genes, namely ISG15, RPS15A, MRPL13, ICT1, MRPL15 and RPLP0, with high centrality features were identified. These key genes were mainly involved in translation, metabolism of proteins and ribosome pathway.

CONCLUSIONS

In summary, these 6 identified genes and correlated pathways should play an important role in VTE, which can provide new insight into the molecular mechanism, potential biomarkers and therapeutic targets associated with VTE.

NUCKS1 Promotes Proliferation, Invasion and Migration of Non-Small Cell Lung Cancer by Upregulating CDK1 Expression

Background

Non-small cell lung cancer (NSCLC) is a predominant type of lung cancer with a high mortality rate.

Objective

The aim of this study is to investigate the roles of nuclear casein kinase and cyclin-dependent kinase substrate 1 (NUCKS1) in NSCLC and to identify the potential mechanisms.

Materials and Methods

The expression of NUCKS1 in several NSCLC cells was detected firstly. Then, NUCKS1 was overexpressed or silenced in both A549 and NCI-H460 cells, where cell proliferation, invasion and migration were, respectively, determined, using CCK-8, colony formation assay, transwell and wound healing assays. Cell cycle analysis was performed, and the expression-associated proteins were detected by Western blotting. Subsequently, NCI-H460 cells with NUCKS1 overexpression for the subsequent tumor-bearing experiment. And the NUCKS1 expression in tumor tissues was measured by means of immunohistochemistry and Western blotting. Additionally, the STRING database predicted that Cyclin-Dependent Kinase 1 (CDK1) would bind to NUSK1, which was verified by the co-immunoprecipitation assay. Then, CDK1 was silenced by transfection with short hairpin RNA (shRNA)-CDK-1 or by exposure to CDK1 inhibitor p2767-00. And the biological characteristics of proliferation, invasion and migration were examined.

Results

Results indicated that NUCKS1 was overly expressed in NSCLC cells, and its overexpression promoted proliferation, invasion and migration of both A549 and NCI-H460 cells while NUCKS1 knockdown displayed the opposite effects. Moreover, the results of the xenograft experiments revealed that NUCKS1-upregulation promoted the tumor growth. Furthermore, the immunoprecipitation assay verified CDK1’s interaction with NUCKS1, and CDK1 knockdown alleviates the impact of NUCKS1 overexpression on NSCLC cell proliferation, invasion and migration.

Conclusion

Taken together, these findings demonstrated that NUCKS1 promotes proliferation, invasion and migration of NSCLC by upregulating CDK1, providing a novel putative target for the clinical treatment of NSCLC.

MELK/MPK38 in cancer: from mechanistic aspects to therapeutic strategies

Maternal embryonic leucine zipper kinase (MELK)/Murine protein serine-threonine kinase 38 (MPK38) is a member of the AMP-related serine-threonine kinase family, which has been reported to be involved in the regulation of many cellular events, including cell proliferation, apoptosis, and metabolism, partly by phosphorylation and regulation of several signaling molecules. The abnormal expression of MELK has been associated with tumorigenesis and malignant progression in various types of cancer. Currently, several small-molecule inhibitors of MELK are under investigation although only OTS167 has entered clinical trials. In this review, we elaborate on the relative contributions of MELK pathways in the physiological process, their oncogenic role in carcinogenesis, and targeted agents under development for the treatment of cancer.

Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

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.

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.