Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer gene regulatory networks and therapeutic molecules are a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Identification of T-cell exhaustion-related genes and prediction of their immunotherapeutic role in lung adenocarcinoma

Background: Lung adenocarcinoma ranks as the second most widespread form of cancer globally, accompanied by a significant mortality rate. Several studies have shown that T cell exhaustion is associated with immunotherapy of tumours. Consequently, it is essential to comprehend the possible impact of T cell exhaustion on the tumor microenvironment. The purpose of this research was to create a TEX-based model that would use single-cell RNA-seq (scRNA-seq) and bulk-RNA sequencing to explore new possibilities for assessing the prognosis and immunotherapeutic response of LUAD patients. Methods: RNA-seq data from LUAD patients was downloaded from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). 10X scRNA sequencing data, as reported by Bischoff P et al., was utilized for down-sampling clustering and subgroup identification using TSNE. TEX-associated genes were identified through gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). We utilized LASSO-Cox analysis to establish predicted TEX features. External validation was conducted in GSE31210 and GSE30219 cohorts. Immunotherapeutic response was assessed in IMvigor210, GSE78220, GSE35640 and GSE100797 cohorts. Furthermore, we investigated differences in mutational profiles and immune microenvironment between various risk groups. We then screened TEXRS key regulatory genes using ROC diagnostic curves and KM survival curves. Finally, we verified the differential expression of key regulatory genes through RT-qPCR. Results: Nine TEX genes were identified as highly predictive of LUAD prognosis and strongly correlated with disease outcome. Univariate and multivariate analysis revealed that patients in the low-risk group had significantly better overall survival rates compared with those in the high-risk group, highlighting the model's ability to independently predict LUAD prognosis. Our analysis revealed significant variation in the biological function, mutational landscape, and immune cell infiltration within the tumor microenvironment of both high-risk and low-risk groups. Additionally, immunotherapy was found to have a significant impact on both groups, indicating strong predictive efficacy of the model. Conclusions: The TEX model showed good predictive performance and provided a new perspective for evaluating the efficacy of preimmunization, which provides a new strategy for the future treatment of lung adenocarcinoma.

Transcriptomic characterization revealed that METTL7A inhibits melanoma progression via the p53 signaling pathway and immunomodulatory pathway

METTL7A is a protein-coding gene expected to be associated with methylation, and its expression disorder is associated with a range of diseases. However, few research have been carried out to explore the relationship between METTL7A and tumor malignant phenotype as well as the involvement potential mechanism. We conducted our research via a combination of silico analysis and molecular biology techniques to investigate the biological function of METTL7A in the progression of cancer. Gene expression and clinical information were extracted from the TCGA database to explore expression variation and prognostic value of METTL7A. In vitro, CCK8, transwell, wound healing and colony formation assays were conducted to explore the biological functions of METT7A in cancer cell. GSEA was performed to explore the signaling pathway involved in METTL7A and validated via western blotting. In conclusion, METTL7A was downregulated in most cancer tissues and its low expression was associated with shorter overall survival. In melanoma, METTL7A downregulation was associated with poorer clinical staging, lower levels of TIL infiltration, higher IC50 levels of chemotherapeutic agents, and poorer immunotherapy outcomes. QPCR results confirm that METTL7A is down-regulated in melanoma cells. Cell function assays showed that METTL7A knockdown promoted proliferation, invasion, migration and clone formation of melanoma cells. Mechanistic studies showed that METTL7A inhibits tumorigenicity through the p53 signaling pathway. Meanwhile, METTL7A is also a potential immune regulatory factor.

Comprehensive analyses of genome-wide methylation and RNA epigenetics identify prognostic biomarkers, regulating the tumor immune microenvironment in lung adenocarcinoma

The aim of our study was to identify a signature of immune-regulated molecules and reveal its prognostic role in lung adenocarcinoma (LUAD). We downloaded RNA-Sequencing data and DNA methylation data from the Gene Expression Omnibus (GEO) database. GEO2R was used to analyze differentially expressed mRNAs (DEmRNAs). we used "factoextra" R package to do the principal component analysis (PCA) of DEmRNAs. "Limma" R package was used to identify DEmRNAs, differentially expressed miRNAs (DEmiRNAs), differentially expressed lncRNAs (DElncRNAs) from The Cancer Genome Atlas (TCGA) database. Three R packages "org.Hs.eg.db", "clusterProfiler", "ggplot2″ were used to show enrichment results. Considering about methylation and mutation data, TEK and SOX17 mediated cancer signaling pathways. Through tumor-immune system interactions database (TISIDB) and Tumor Immune Estimation Resource (TIMER), higher methylated and lower expressed TEK may act as a prognostic marker, regulating the tumor immunity in LUAD. Through four databases (MEXPRESS, DNMIVD, MethSurv, Firehose), we further verified the methylation (P = 2.33e-23) and mutation about TEK. A signature of immune-associated TEK to predict survival of LUAD patients was validated. Prognostic, methylation, immune microenvironment analysis showed new light on potential novel therapeutic targets in LUAD.

Identification of a novel survival predictor, CSF2RB, for female lung cancer in never smokers (LCNS) by a bioinformatics analysis

Lung cancer in never smokers (LCNS) has been considered as a separate disease and the 7th cause of cancer-related death worldwide. However, limited research has focused on "female" cohorts, which have presented a higher incidence rate. In this study, the microarray data of lung cancer tissues derived from 54 female lung cancer patients, consisting of 43 nonsmokers and 11 smokers, were selected from GSE2109 dataset. A total of 249 differentially expressed genes (DEGs) including 102 up- and 147 down-regulated genes were identified and further analyzed for gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. By constructing protein-protein interaction (PPI) network and calculating key modules, 10 hub genes were screened out. The module analysis of the PPI network presented that the progression of female LCNS was significantly associated with immune response as chemokine activity and lipopolysaccharide response, and these biological processes (BP) might be mediated by chemokine signaling pathway and cytokine-cytokine receptor interaction. Then, survival analysis by Kaplan-Meier (K-M) Plotter online platform presented down-regulated gene colony stimulating factor 2 receptor beta common subunit (CSF2RB) of female LCNS might be involved in poor clinical outcome. Female LCNS with high expression of CSF2RB might be relevant with relative risk reduction of mortality, longer median survival time and higher 5-year survival rate, while female LCNS with low expression of CSF2RB might be implicated in a poor clinical outcome. In short, our results support CSF2RB to be a candidate survival predictor for female LCNS.

Evidence Based on an Integrative Analysis of Multi-Omics Data on METTL7A as a Molecular Marker in Pan-Cancer

Methyltransferase-like protein 7A (METTL7A), an RNA N6-methyladenosine (m6A) methyltransferase, has attracted much attention as it has been found to be closely associated with various types of tumorigenesis and progression. This study provides a comprehensive assessment of METTL7A from a pan-cancer perspective using multi-omics data. The gene ontology enrichment analysis of METTL7A-binding proteins revealed a close association with methylation and lipid metabolism. We then explored the expression of METTL7A in normal tissues, cell lines, different subtypes and cancers, and found that METTL7A was differentially expressed in various cancer species, tumor molecular subtypes and immune subtypes. Evaluation of the diagnostic and prognostic value of METTL7A in pan-cancer revealed that METTL7A had high accuracy in tumor prediction. Moreover, the low expression of METTL7A significantly correlated with the poor prognosis, including kidney renal clear cell carcinoma (KIRC), mesothelioma and sarcoma, indicating that METTL7A could be a potential biomarker for tumor diagnosis and prognosis. We focused on KIRC after pre-screening and analyzed its expression and prognostic value in various clinical subgroups. We found that METTL7A was significantly related to tumor stage, metastasis stage, pathologic stage, primary therapy outcome, histologic grade and gender, and that low METTL7A expression was associated with poorer outcomes. Finally, we analyzed the immune infiltration and co-expressed genes of METTL7A as well as the differentially expressed genes in the high and low expression groups. In conclusion, METTL7A is a better molecular marker for pan-cancer diagnosis and prognosis and has high potential as a diagnostic and prognostic biomarker for KIRC.

Circ-GSK3B up-regulates GSK3B to suppress the progression of lung adenocarcinoma

GSK3B is the mRNA form of glycogen synthase kinase 3 beta (GSK-3β), which is a critical repressor of Wnt/β-catenin signaling pathway and generally inhibited in cancer cells. Plenty of researches have disclosed that circular RNAs, namely circRNAs exert important functions in the progression of various human malignancies including lung adenocarcinoma (LUAD). Therefore, we attempted to explore whether there existed certain circRNAs that could mediate LUAD development by regulating GSK3B expression and Wnt/β-catenin pathway. In the present research, circ-GSK3B (hsa_circ_0066903) was found to be significantly down-regulated in LUAD tissues and cells and it suppressed the proliferation, migration and stemness of LUAD cells. Furthermore, it was discovered that circ-GSK3B competitively sponged miR-3681-3p and miR-3909 to elevate GSK3B expression. Circ-GSK3B could impair the binding ability of FKBP51 to GSK-3β to inhibit the phosphorylation of GSK-3β^S9, resulting in the inactivation of Wnt/β-catenin signaling. In addition, the regulatory effect of circ-GSK3B on LUAD tumorigenesis and cell progression was testified through in vitro and in vivo rescue experiments. In conclusion, circ-GSK3B suppressed LUAD development through up-regulating and activating GSK3B.

Up-Regulated Proteins Have More Protein-Protein Interactions than Down-Regulated Proteins

Microarray technology has been successfully used in many biology studies to solve the protein–protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process begins with transcription and ends with the translation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can precisely give high accuracy expression levels at normal and cancer-affected cells, which can be useful for the identification of disease-related genes. First, the differentially expressed genes (DEGs) are extracted from the cancer microarray dataset in order to identify the genes that are up-regulated and down-regulated during cancer progression in the human body. Then, proteins corresponding to these genes are collected from NCBI, and then the STRING web server is used to build the PPI network of these proteins. Interestingly, up-regulated proteins have always a higher number of PPIs compared to down-regulated proteins, although, in most of the datasets, the majority of these DEGs are down-regulated. We hope this study will help to build a relevant model to analyze the process of cancer progression in the human body.

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan–Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

A bioinformatics-based study on the Cisplatin-resistant lung cancer cells; what are the orchestrators of this phenom?

Lung cancer represents a significant global health issue and is among the central causes of mortality and morbidity around the world. Unfortunately, the majority of lung cancer patients acquire drug resistant to chemotherapy either intrinsically or acquired after Cisplatin treatment. It is indicated that increasing or decreasing the expression of particular genes can affect chemotherapeutic sensitivity or resistance. As a result, gaining a deeper knowledge of the changed expression of genes implicated in lung cancer drug resistance, as well as developing novel therapeutic techniques, are critical targets for continued advancement in lung cancer treatment. In the present study, we aimed to find key regulatory genes in the progression of Cisplatin resistance in A-549 lung cancer cells. In this regard, microarray dataset of Cisplatin-resistant and Cisplatin-sensitive was retrieved from the Gene Expression Omnibus (GEO) with accession number of GSE108214. Then, differentially expressed genes (DEGs) between sensitive and resistant lung cancer cells were obtained by using R software v4.0.2 and related packages. We recognized CEACAM1, DGKA, ARHGEF4, and THSD4 are involved in the drug resistance. Experimentally, Cisplatin-resistant A-549 cells were developed and analyzed by MTT assay. Besides, the expression of candidate genes were analyzed in these cells compared to Cisplatin-sensitive A-549 cells by qRT-PCR. The findings presented that the expression of CEACAM1, DGKA, ARHGEF4, and THSD4 was altered following the induction of Cisplatin resistance in A549 cells.

PLK1 Is a Potential Prognostic Factor Associated with the Tumor Microenvironment in Lung Adenocarcinoma

More than 40% of lung cancers are lung adenocarcinoma (LUAD) worldwide. However, the prognosis of LUAD is poor for the lack of effective treatment methods. Our study identified PLK1 as a novel prognosis biomarker and treatment target for LUAD. Based on the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) from 551 LUAD cases were analyzed for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. To explore the biological pathways and the tumor-infiltrating immune cells (TICs) using gene set variation analysis (GSVA) and the CIBERSORT, as well as to analyze DEGs, a protein-protein interaction (PPI) network and Cox regression analysis were performed. Validation of DEGs was achieved through quantitative real-time PCR (qPCR) and immunoblotting. DEGs associated with the cell cycle were sorted out. Cell cycle scores were positively correlated with age, clinical stages, and metastasis and negatively correlated with overall survival of LUAD patients. PPI and Cox analyses showed that PLK1 could be a prognostic factor for LUAD patients. CIBERSORT analysis revealed a positive correlation between the transcription level of PLK1 and the function of CD8+ and activated memory CD4+ T cells, as well as a negative correlation with activated natural killer cells. Furthermore, PLK1 overexpression increased immune cytotoxicity, as measured by the cytolytic activity score, IFN- score, and IFN- level. There is a strong correlation between PLK1 and key features of TICs, indicating its potential as a promising prognostic biomarker for LUAD.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( $p<0.05$ ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

The centromere-associated protein CENPU promotes cell proliferation, migration, and invasiveness in lung adenocarcinoma

CENPU, encoding an important factor involved in kinetochore assembly during mitosis, is associated with shorter survival rates in lung adenocarcinoma (LUAD) patients. CENPU promotes growth rates and invasive behavior of LUAD cells; however, its mechanism of action in LUAD progression remains to be elucidated. CENPU mRNA and protein expression were elevated in LUAD tumors, and high CENPU gene expression was associated with inferior survival prognosis in LUAD patients. CENPU knockdown inhibited LUAD cell proliferation, clone formation, migration, invasion, and epithelial-mesenchymal transition (EMT) in addition to inducing cell cycle arrest and apoptosis in vitro and reduced LUAD xenograft tumor growth in vivo. Furthermore, we identified CENPU-regulated genes significantly enriched for proliferation and apoptosis pathways, and identified HSP Family Member C10 (DNAJC10) as putative effector of CENPU. CENPU knockdown produced DNAJC10 protein downregulation, and DNAJC10 overexpression partially rescued the phenotypic effects of CENPU knockdown in LUAD cells. Moreover, CENPU's coiled-coil domain was essential for CENPU's phenotypic effects in LUAD cells. In conclusion, the kinetochore component CENPU plays a critical role in LUAD cell proliferation and invasiveness. Targeting CENPU-DNAJC10 axis may inhibit LUAD tumor cell proliferation and metastasis.

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

A Systematic Analysis Identifies Key Regulators Involved in Cell Proliferation and Potential Drugs for the Treatment of Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common and malignant cancer types. Abnormal cell proliferation, exemplified by cell cycle and cell division dysregulation, is one of the most prominent hallmarks of cancer and is responsible for recurrence, metastasis, and resistance to cancer therapy. However, LUAD-specific gene regulation and clinical significance remain obscure. Here, by using both tissues and cells from LUAD and normal lung samples, 434 increased and 828 decreased genes of biological significance were detected, including 127 cell cycle-associated genes (95 increased and 32 decreased), 66 cell division-associated genes (56 increased and 10 decreased), and 81 cell proliferation-associated genes (34 increased and 47 decreased). Among them, 12 increased genes (TPX2, CENPF, BUB1, PLK1, KIF2C, AURKB, CDKN3, BUB1B, HMGA2, CDK1, ASPM, and CKS1B) and 2 decreased genes (TACC1 and MYH10) were associated with all the three above processes. Importantly, 2 (CDKN3 and CKS1B) out of the 11 increased genes (except HMGA2) are previously uncharacterized ones in LUAD and can potentially be prognostic markers. Moreover, PLK1 could be a promising therapeutic target for LUAD. Besides, protein–protein interaction network analysis showed that CDK1 and CDC20 were the hub genes, which might play crucial roles in cell proliferation of LUAD. Furthermore, transcriptional regulatory network analysis suggested that the transcription factor E2F1 could be a key regulator in controlling cell proliferation of LUAD via expression modulation of most cell cycle-, cell division-, and cell proliferation-related DEGs. Finally, trichostatin A, hycanthone, vorinostat, and mebeverine were identified as four potential therapeutic agents for LUAD. This work revealed key regulators contributing to cell proliferation in human LUAD and identified four potential therapeutic agents for treatment strategy.

Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to construct a competing endogenous RNA (ceRNA) network to predict the progression in LUAD.

METHODS

Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC|> 1.0 and a false discovery rate (FDR) < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs.

RESULTS

A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were associated with the KEGG pathway terms "microRNAs in cancer", "pathways in cancer", "cell cycle", "HTLV-1 infection", and the "PI3K-Akt signalling pathway". K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P< 0.05). Furthermore, 15 LUAD drugs interacting with 29 significant DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, PRKCE, DLC1, LATS2, and DPY19L1 were incorporated into the risk score system, and the results suggested that LUAD patients who had the high-risk score always suffered from a poorer overall survival. Additionally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 significant DEL-DEG pairs, NAV2-AS2 - PRKCE (r= 0.430, P< 0.001) and NAV2-AS2 - LATS2 (r= 0.338, P< 0.001). And NAV2-AS2 - mir-31 - PRKCE and NAV2-SA2 - mir-31 - LATS2 were finally identified as ceRNA network involved in the progression of LUAD.

CONCLUSIONS

The lncRNA-miRNA-mRNA ceRNA network plays an essential role in predicting the progression of LUAD. These results may improve our understanding and provide novel mechanistic insights to explore prognosis and therapeutic drugs for LUAD patients.

IP3 R attenuates oxidative stress and inflammation damage in smoking-induced COPD by promoting autophagy

Tobacco smoking is one of the most important risk factors for chronic obstructive pulmonary disease (COPD). However, the most critical genes and proteins remain poorly understood. Therefore, we aimed to investigate these hub genes and proteins in tobacco smoke-induced COPD, together with the potential mechanism(s). Differentially expressed genes (DEGs) were analysed between smokers and patients with COPD. mRNA expression and protein expression of IP3 R were confirmed in patients with COPD and extracted smoke solution (ESS)-treated human bronchial epithelial (HBE) cells. Moreover, expression of oxidative stress, inflammatory cytokines and/or autophagy-related protein was tested when IP3 R was silenced or overexpressed in ESS-treated and/or 3-MA-treated cells. A total of 30 DEGs were obtained between patients with COPD and smoker samples. IP3 R was identified as one of the key targets in tobacco smoke-induced COPD. In addition, IP3 R was significantly decreased in patients with COPD and ESS-treated cells. Loss of IP3 R statistically increased expression of oxidative stress and inflammatory cytokines in ESS-treated HBE cells, and overexpression of IP3 R reversed the above functions. Furthermore, the autophagy-related proteins (Atg5, LC3 and Beclin1) were statistically decreased, and p62 was increased by silencing of IP3 R cells, while overexpression of IP3 R showed contrary results. Additionally, we detected that administration of 3-MA significantly reversed the protective effects of IP3 R overexpression on ESS-induced oxidative stress and inflammatory injury. Our results suggest that IP3 R might exert a protective role against ESS-induced oxidative stress and inflammation damage in HBE cells. These protective effects might be associated with promoting autophagy.

Identification and characterization of prognosis-related genes in the tumor microenvironment of esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the main pathological subtype of esophageal cancer with high incidence and mortality. Immune and stromal cells in the tumor microenvironment (TME) profoundly affect the development of ESCC.

METHODS

In this study, we used the ESTIMATE algorithm to calculate the immune and stromal scores of ESCC samples in The Cancer Genome Atlas (TCGA) database. Next, we used the R package limma to identify differentially expressed genes (DEGs) from high- versus low-immune/stromal score groups and these DEGs were further utilized to analyze the functional annotations, protein-protein interaction (PPI) networks and overall survival of patients with ESCC. Finally, we identified the biological roles of core gene C3AR1 in the TME of ESCC using the TCGA database and in vitro experiments.

RESULTS

We obtained the immune and stromal scores of ESCC samples and further evaluated the impact of these scores on the prognosis and clinical parameters of patients with ESCC. Next, we identified 410 DEGs from high- versus low-immune/stromal score groups and to gain better understanding of the biological functions and characteristics of DEGs. Among these DEGs, 69 were correlated with the overall survival of patients with ESCC and C3AR1 was identified as a core gene for the regulation of most genes in the network. We found that C3AR1 was positively correlated with M2 macrophages and immune inhibitory molecules (T-cell immunoglobulin and mucin domain 3 (TIM-3), programmed cell death-1 (PD-1)), but not with M1 macrophages. We also observed a higher expression of CD163 and CD206, which were the markers for M2 macrophages in the TLQP-21 TFA (the agonist of C3AR1)groups than in the control groups.

CONCLUSION

Based on the ESTIMATE algorithm, we obtained and characterized prognosis-related genes in the TME of ESCC samples from the TCGA database. We have further revealed that C3AR1 may cause an immunosuppressive microenvironment by affecting the polarization of macrophages to M2 phenotype and lead to the progression of ESCC, which indicates that C3AR1 may be a potential target for immunotherapy.

Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis

Background

Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC.

Methods

Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature.

Results

Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively.

Conclusion

These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00181-1.

Identification of Prognostic Related Genes of Tumor Microenvironment Derived From Esophageal Cancer Patients

Background and Objective: Esophageal cancer (ESCA) is a commonly occurring cancer worldwide with poor survival and limited therapeutic options. Due to the lack of biomarkers that facilitate early detection, its treatment remains a great challenge. This study aims at identifying the tumor microenvironment (TME)-related genes, which might affect prognosis and accelerate clinical treatment for ESCA patients.

Methods: We integrated the expression profiles from ESCA patients in The Cancer Genome Atlas. Then, we determined the stromal and immune scores of each sample using the R package. The Gene Expression Omnibus database was used to validate the expression profile of the key genes.

Results: Tumor mutational burden showed a significant difference between the groups of ESCA patients with high and low ESTIMATE scores. We identified 859 intersection genes among patients with different immune and stromal scores. Moreover, gene ontology analysis demonstrated that these 859 intersection genes were closely related to adaptive immune response and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes showed the enrichment of cytokine-cytokine receptor interaction and chemokine signaling pathway in the TME. Furthermore, the protein–protein interaction network consisted of 175 nodes. We selected 35 hub genes, including ITGAM, CXCL10, CCR2, CCR5, and CCR1. Of these, 23 intersection genes predicted the overall survival rate. C1QA and FCER1G correlated with overall survival of the ESCA patients in the two databases.

Conclusion: We identified a set of stromal and immune score-related prognostic differentially expressed genes that could influence the complexity of the TME. C1QA and FCER1G were identified and validated with respect to their role in the progression of ESCA.

Bioinformatics analysis identified CDC20 as a potential drug target for cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma (CCA) is a malignancy that originates from bile duct cells. The incidence and mortality of CCA are very high especially in Southeast Asian countries. Moreover, most CCA patients have a very poor outcome. Presently, there are still no effective treatment regimens for CCA. The resistance to several standard chemotherapy drugs occurs frequently; thus, searching for a novel effective treatment for CCA is urgently needed.

METHODS

In this study, comprehensive bioinformatics analyses for identification of novel target genes for CCA therapy based on three microarray gene expression profiles (GSE26566, GSE32225 and GSE76297) from the Gene Expression Omnibus (GEO) database were performed. Based on differentially expressed genes (DEGs), gene ontology and pathway enrichment analyses were performed. Protein-protein interactions (PPI) and hub gene identifications were analyzed using STRING and Cytoscape software. Then, the expression of candidate genes from bioinformatics analysis was measured in CCA cell lines using real time PCR. Finally, the anti-tumor activity of specific inhibitor against candidate genes were investigated in CCA cell lines cultured under 2-dimensional and 3-dimensional cell culture models.

RESULTS

The three microarray datasets exhibited an intersection consisting of 226 DEGs (124 up-regulated and 102 down-regulated genes) in CCA. DEGs were significantly enriched in cell cycle, hemostasis and metabolism pathways according to Reactome pathway analysis. In addition, 20 potential hub genes in CCA were identified using the protein-protein interaction (PPI) network and sub-PPI network analysis. Subsequently, CDC20 was identified as a potential novel targeted drug for CCA based on a drug prioritizing program. In addition, the anti-tumor activity of a potential CDC20 inhibitor, namely dinaciclib, was investigated in CCA cell lines. Dinaciclib demonstrated huge anti-tumor activity better than gemcitabine, the standard chemotherapeutic drug for CCA.

CONCLUSION

Using integrated bioinformatics analysis, CDC20 was identified as a novel candidate therapeutic target for CCA.

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Carcinoma diagnosis and prognosis are still hindered by the lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method's robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic least absolute shrinkage and selection operator (LASSO) regression method [the averaged 1- to 4-years predictive area under the receiver operating characteristic curve (AUC) measure, 0.6998], the proposed one outperforms more accurately by 0.7208 with fewer predictors, particularly its averaged 1- to 3-years AUC reaches 0.723, vs. classic 0.6917 on The Cancer Genome Atlas (TCGA). In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results, are available at https://github.com/gladex/PM-FSOR.

Microenvironment-related prognostic genes in esophageal cancer

Background

Esophageal cancer is one of the most common malignant tumors. The role of tumor microenvironment in esophageal cancer is unclear.

Methods

The gene expression profiles and clinical data of 158 patients with esophageal cancer were extracted from The Cancer Genome Atlas database. Immune scores and stromal scores were calculated based on ESTIMATE algorithm. According to different immune/stromal scores, differentially expressed genes (DEGs) were identified. The function enrichment, protein interactions of shared DEGs and their associations with overall survival were analyzed.

Results

In regard to the association of the immune/stromal scores and disease stage, pathological type and overall survival, only the stromal scores among the different stages were significantly different (P=0.015). In the high immune and stromal score groups, 603 shared up-regulated genes were found. The related function and pathways included regulation of lymphocyte activation, cytokine binding and chemokine signaling pathway. Protein-protein interaction analysis showed that ITGAM had the most connections, followed by CXCL10 and CCR2. High expression of 11 genes, including MS4A7, TMIGD3, MS4A4A, EVI2A, MS4A6A, FCER1G, AIF1, GNGT2, LCP2, DNAJC5B and RNASE6, were found to be associated with shorter overall survival.

Conclusions

Microenvironment-associated functions and pathways were analyzed in esophageal cancer, and 11 microenvironment-associated genes were correlated to poor prognoses. Further studies on these genes may be helpful to understand the tumor microenvironment and provide new therapies for esophageal cancer.

Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM).

Methods

Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes.

Results

We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD.

Conclusions

The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.

Ceramide Pathway Regulators Predict Clinical Prognostic Risk and Affect the Tumor Immune Microenvironment in Lung Adenocarcinoma

Purpose

The ceramide pathway is strongly associated with the regulation of tumor proliferation, differentiation, senescence, and apoptosis. This study aimed to explore the gene signatures, prognostic value, and immune-related effects of ceramide-regulated genes in lung adenocarcinoma (LUAD).

Methods

Public datasets of LUAD from The Cancer Genome Atlas and Gene Expression Omnibus were selected. Consensus clustering was adopted to classify LUAD patients, and a least absolute shrinkage and selection operator (LASSO) regression model was employed to develop a prognostic risk signature. CIBERSORT algorithm was used to estimate the association between the risk signature and the tumor immune microenvironment.

Results

Most of the 22 ceramide-regulated genes were differentially expressed between LUAD and normal samples. LUAD patients were classified into two subgroups (cluster 1 and 2) and cluster 2 was associated with a poor prognosis. Furthermore, a prognostic risk signature was developed based on the three ceramide-regulated genes, Cytochrome C (CYCS), V-rel reticuloendotheliosis viral oncogene homolog A (RELA) and Fas-associated via death domain (FADD). LUAD patients with low- and high-risk scores differed concerning the subtypes of tumor-infiltrating immune cells. A moderate to weak correlation was observed between the risk score and tumor-infiltrating immune cells.

Conclusions

Ceramide-regulated genes could predict clinical prognostic risk and affect the tumor immune microenvironment in LUAD.

METTL7B (methyltransferase-like 7B) identification as a novel biomarker for lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is still one of the major causes of cancer-related mortality across the globe. Therefore, there is a dire need to identify early specific and sensitive biomarkers or drug targets of LUAD for developing improved diagnosis and clinical management. We aimed to investigate the role of methyltransferase-like 7B (METTL7B) on LUAD tumor development and progression in this study.

Methods

METTL7B’s expression was confirmed in two independent clinical cohort samples, including LUAD tissues microarray (TMA) via immunohistochemistry (IHC) and serum samples via enzyme-linked immunosorbent assay (ELISA). The correlation between METTL7B expression with clinicopathological features and overall survival rate in LUAD patients was then further analyzed. Meanwhile, the messenger ribonucleic acid (mRNA) and protein levels of METTL7B were verified in cell lines and in vitro experiments, including cell proliferation assay, and migration. Invasion assays were conducted to explore the effects of METTL7B on LUAD by silencing the protein expression.

Results

METTL7B was remarkably overexpressed in clinical LUAD tumor tissues and serum compared to the normal control group and in LUAD cell lines. The expression level of METTL7B was significantly correlated with tumor size, advanced tumor node and metastases (TNM) stages, and lymph node metastasis. The Kaplan-Meier survival curves proved that high METTL7B expression was significantly associated with a reduced survival rate in LUAD patients (P<0.05), and univariate analysis showed that high METTL7B expression was significantly associated with poor overall survival [hazard ratio (HR) =2.220, 95% confidence interval (CI): 1.211–4.086; P=0.010]. In vitro assays showed that METTL7B overexpression augmented cell proliferation, migration, and the invasion in LUAD.

Conclusions

METTL7B was overexpressed in LUAD and significantly associated with the poor progression, showing that METTL7B may serve as a potential novel biomarker for the diagnosis and prognosis of LUAD. Moreover, METTL7B plays a role in promoting tumor proliferation, migration, and invasion in LUAD.

Functional genomic analysis identifies drug targetable pathways in invasive and metastatic cutaneous squamous cell carcinoma

Although cutaneous squamous cell carcinoma (cSCC) is treatable in the majority of cases, deadly invasive and metastatic cases do occur. To date there are neither reliable predictive biomarkers of disease progression nor FDA-approved targeted therapies as standard of care. To address these issues, we screened patient-derived primary cultured cells from invasive/metastatic cSCC with 107 small-molecule inhibitors. In-house bioinformatics tools were used to cross-analyze drug responses and DNA mutations in tumors detected by whole-exome sequencing (WES). Aberrations in molecular pathways with evidence of potential drug targets were identified, including the Eph-ephrin and neutrophil degranulation signaling pathways. Using a screening panel of siRNAs, we identified EPHA6 and EPHA7 as targets within the Eph-ephrin pathway responsible for mitigating decreased cell viability. These studies form a plausible foundation for detecting biomarkers of high-risk progressive disease applicable in dermatopathology and for patient-specific therapeutic options for invasive/metastatic cSCC.

Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis

Background

Sarcoidosis (SARC) is a multisystem inflammatory disease of unknown etiology and pulmonary tuberculosis (PTB) is caused by Mycobacterium tuberculosis. Both of these diseases affect lungs and lymph nodes and share similar clinical manifestations. However, the underlying mechanisms for the similarities and differences in genetic characteristics of SARC and PTB remain unclear.

Material/Methods

Three datasets (GSE16538, GSE20050, and GSE19314) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in SARC and PTB were identified using GEO2R online analyzer and Venn diagram software. Functional enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) and R packages. Two protein–protein interaction (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes database, and module analysis was performed using Cytoscape. Hub genes were identified using area under the receiver operating characteristic curve analysis.

Results

We identified 228 DEGs, including 56 common SARC-PTB DEGs (enriched in interferon-gamma-mediated signaling, response to gamma radiation, and immune response) and 172 SARC-only DEGs (enriched in immune response, cellular calcium ion homeostasis, and dendritic cell chemotaxis). Potential biomarkers for SARC included CBX5, BCL11B, and GPR18.

Conclusions

We identified potential biomarkers that can be used as candidates for diagnosis and/or treatment of patients with SARC.

Computational Approach to Identifying Universal Macrophage Biomarkers

Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, identifying a universal and reliable marker of macrophage cell becomes paramount. Traditional approaches have relied upon tissue specific expression patterns. To identify universal biomarkers of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify conserved cell cycle genes. We performed BECC analysis using the known macrophage marker CD14 as a seed gene. The main idea behind BECC is that it uses massive database of public gene expression dataset to establish robust co-expression patterns identified using a combination of correlation, linear regression and Boolean equivalences. Our analysis identified and validated FCER1G and TYROBP as novel universal biomarkers for macrophages in human and mouse tissues.

A network pharmacology study on the Tripteryguim wilfordii Hook for treatment of Crohn's disease

Background

To explore the mechanism of action of Tripterygium wilfordii Hook (TWH) in the treatment of Crohn’s disease (CD) by network pharmacology.

Methods

Traditional Chinese Medicine Systems Pharmacology database and analysis platform (TCMSP) was used to obtain the active constituents and targets of TWH. “Crohn’s disease” was used as a search term to search for related targets of CD from GeneCards database and OMIM database, thereby obtaining the targets of TWH against CD. The Cytoscape 3.7.1 software was used to construct a Chinese medicine compound-target network and STRING database to construct a protein-protein interaction network (PPI). The DAVID 6.8 online tool was used to perform gene ontology (GO) and kyoto encyclopedia of genes and genome (KEGG) pathway enrichment analysis of overlapping targets.

Results

The database results showed that there were 30 active ingredients (14 key active ingredients) in TWH and 36 targets were screened out for CD treatment. Network analysis indicated that main targets of main active components of TWH were target genes such as VEGFA, MAPK8 and CASP3, which are involved in the regulation of cancer pathway, TNF signal pathway, hepatitis B pathway, apoptosis pathway, NF-kappa B signal pathway and so forth.

Conclusions

TWH can play a multi-target and multi-channel synergistic treatment of CD by anti-angiogenesis, anti-apoptosis, anti-inflammation and immune regulation.

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.

Identification of key protein-coding genes in lung adenocarcinomas based on bioinformatic analysis

Background

Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches.

Methods

We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis.

Results

We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues.

Conclusions

ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.