Elevated mRNA Levels of AURKA, CDC20 and TPX2 are associated with poor prognosis of smoking related lung adenocarcinoma using bioinformatics analysis

Particulate matters 2.5 induce tumor progression in lung cancer by increasing the activity of hnRNPA2B1 resulting in retarding mRNA decay of oxidative phosphorylation

Particulate matter 2.5 (PM2.5) has been implicated in lung injury and various cancers, yet its precise mechanistic role remains elusive. To elucidate the key signaling pathways underpinning PM2.5-induced lung cancer progression, we embarked on a study examining the impact of PM2.5 both in vitro and in vivo. Lung cancer cell lines, A549 and H157, were employed for the in vitro investigations. Overexpression or knockdown techniques targeting the hnRNPA2B1 protein were implemented. Lung cancer cells were treated with a medium containing PM2.5 and subsequently prepared for in vitro evaluations. Cell growth, invasion, and migration were gauged using transwell and CCK-8 assays. Apoptosis was ascertained through flow cytometry and western blotting of pertinent proteins. Seahorse analyses probed the influence of PM2.5 on lung cancer energy metabolism. The RNA stability assay was employed to discern the impact of PM2.5 on the stability of oxidative phosphorylation-related genes in lung cancer. Our findings revealed that PM2.5 augmented cell proliferation, migration, and invasion rates. Similarly, a diminished apoptosis rate was observed in PM2.5-treated cells. Elevated expression of hnRNPA2B1 was detected in lung cancer cells exposed to PM2.5. Moreover, in cells treated with PM2.5, hnRNPA2B1 knockdown markedly curtailed cell proliferation by inducing G1-S cell cycle arrest and bolstered lung cancer cell apoptosis in vitro; it also curbed xenograft tumor growth. Mechanistically, our data suggest that PM2.5 undermines the stability of mRNA transcripts associated with oxidative phosphorylation (OXPHOS) and augments the formation of processing bodies (P-bodies), leading to an upsurge in OXPHOS levels. In conclusion, PM2.5 appears to drive lung cancer progression and migration by modulating the energy metabolism of lung cancer in a hnRNPA2B1-dependent manner.

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

Hypopharyngeal cancer is 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.

Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature

Objective

This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD).

Methods

A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.

Results

High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).

Conclusion

Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.

A novel prognostic signature based on smoking-associated genes for predicting prognosis and immune microenvironment in NSCLC smokers

BACKGROUND

As a highly heterogeneous tumor, non-small cell lung cancer (NSCLC) is famous for its high incidence and mortality worldwide. Smoking can cause genetic changes, which leading to the occurrence and progress of NSCLC. Nevertheless, the function of smoking-related genes in NSCLC needs more research.

METHODS

We downloaded transcriptome data and clinicopathological parameters from Gene Expression Omnibus (GEO) databases, and screened smoking-related genes. Lasso regression were applied to establish the 7-gene signature. The associations between the 7-gene signature and immune microenvironment analysis, survival analysis, drug sensitivity analysis and enriched molecular pathways were studied. Ultimately, cell function experiments were conducted to research the function of FCGBP in NSCLC.

RESULTS

Through 7-gene signature, NSCLC samples were classified into high-risk group (HRG) and low-risk group (LRG). Significant difference in overall survival (OS) between HRG and LRG was found. Nomograms and ROC curves indicated that the 7-gene signature has a stable ability in predicting prognosis. Through the analysis of immune microenvironment, we found that LRG patients had better tumor immune activation. FCGBP showed the highest mutation frequency among the seven prognostic smoking related genes (LRRC31, HPGD, FCGBP, SPINK5, CYP24A1, S100P and FGG), and was notable down-regulated in NSCLC smokers compared with non-smoking NSCLC patients. The cell experiments confirmed that FCGBP knockdown promoting proliferation, migration, and invasion in NSCLC cells.

CONCLUSION

This smoking-related prognostic signature represents a promising tool for assessing prognosis and tumor microenvironment in smokers with NSCLC. The role of FCGBP in NSCLC was found by cell experiments, which can be served as diagnostic biomarker and immunotherapy target for NSCLC.

Clinical translation for targeting DNA damage repair in non-small cell lung cancer: a review

Despite significant advancements in screening, diagnosis, and treatment of non-small cell lung cancer (NSCLC), it remains the primary cause of cancer-related deaths globally. DNA damage is caused by the exposure to exogenous and endogenous factors and the correct functioning of DNA damage repair (DDR) is essential to maintain of normal cell circulation. The presence of genomic instability, which results from defective DDR, is a critical characteristic of cancer. The changes promote the accumulation of mutations, which are implicated in cancer cells, but these may be exploited for anti-cancer therapies. NSCLC has a distinct genomic profile compared to other tumors, making precision medicine essential for targeting actionable gene mutations. Although various treatment options for NSCLC exist including chemotherapy, targeted therapy, and immunotherapy, drug resistance inevitably arises. The identification of deleterious DDR mutations in 49.6% of NSCLC patients has led to the development of novel target therapies that have the potential to improve patient outcomes. Synthetic lethal treatment using poly (ADP-ribose) polymerase (PARP) inhibitors is a breakthrough in biomarker-driven therapy. Additionally, promising new compounds targeting DDR, such as ATR, CHK1, CHK2, DNA-PK, and WEE1, had demonstrated great potential for tumor selectivity. In this review, we provide an overview of DDR pathways and discuss the clinical translation of DDR inhibitors in NSCLC, including their application as single agents or in combination with chemotherapy, radiotherapy, and immunotherapy.

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.

Exploring potential genes and pathways related to lung cancer: a graph theoretical analysis

Lung cancer is the primary and third most frequently detected form of cancer in both males and females. The present study tries to perform integrated analysis in male as well as female patients inclusively both smoker and non-smokers. This study aims to identify diagnostic biomarkers and therapeutic targets for lung cancer patients using human microarray profile datasets. Differentially expressed genes (DEGs) were identified using a PPI network from the String database, and major modules or clusters were extracted using MCODE. The Cytohubba plug-in was used to find hub genes from the PPI network using centralities approaches. Twenty significant hub genes (CCND1, CDK1, CCNB1, CDH1, TP53, CTNNB1, EGFR, ESR1, CDK2, CCNA2, RHOA, EGF, FN1, HSP90AA1, STAT3, JUN, NOTCH1, IL6, SRC, and CD44) were identified as promising diagnostic biomarkers and therapeutic targets for lung cancer treatment. Survival analysis and hub gene validation were also conducted. GO enrichment and pathway analysis were conducted to identify their important functions. These hub genes were also used to identify targeted drugs. The findings suggest that the identified genes have the potential to be used as diagnostic biomarkers and therapeutic targets for lung cancer treatment.

The potential role of CDC20 in tumorigenesis, cancer progression and therapy: A narrative review

The cell division cycle 20 homologue (CDC20) is known to regulate the cell cycle. Many studies have suggested that dysregulation of CDC20 is associated with various pathological processes in malignant solid tumors, including tumorigenesis, progression, chemoradiotherapy resistance, and poor prognosis, providing a biomarker for cancer diagnosis and prognosis. Some researchers have demonstrated that CDC20 also regulates apoptosis, immune microenvironment, and tumor angiogenesis. In this review, we have systematically summarized the biological functions of CDC20 in solid cancers. Furthermore, we briefly synthesized multiple medicines that inhibited CDC20. We anticipate that CDC20 will be a promising and effective biomarker and therapeutic target for the treatment of human cancer.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein–protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein–protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients’ prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

The role of radiotherapy-related autophagy genes in the prognosis and immune infiltration in lung adenocarcinoma

Background

There is a close relationship between radiotherapy and autophagy in tumors, but the prognostic role of radiotherapy-related autophagy genes (RRAGs) in lung adenocarcinoma (LUAD) remains unclear.

Methods

Data used in the current study were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) was executed to recognize module genes associated with radiotherapy. The differentially expressed genes (DEGs) between different radiotherapy response groups were filtered via edgeR package. The differentially expressed radiotherapy-related autophagy genes (DERRAGs) were obtained by overlapping the module genes, DEGs, and autophagy genes (ATGs). Then, prognostic autophagy genes were selected by Cox analyses, and a risk model and nomogram were subsequently built. Gene Set Enrichment Analysis (GSEA) and single-sample Gene Set Enrichment Analysis (ssGSEA) were performed to investigate potential mechanisms through which prognostic autophagy signatures regulate LUAD. Radiotherapy-resistant cell lines (A549IR and PC9IR) were established after exposure to hypo-fractionated irradiation. Ultimately, mRNA expression was validated by quantitative real-time PCR (qRT-PCR), and relative protein levels were measured in different cell lines by western blot.

Results

A total of 11 DERRAGs were identified in LUAD. After Cox analyses, SHC1, NAPSA, and AURKA were filtered as prognostic signatures in LUAD. Then, the risk score model was constructed using the prognostic signatures, which had a good performance in predicting the prognosis, as evidenced by receiver operating characteristics curves. Furthermore, Cox regression analyses demonstrated that risk score was deemed as an independent prognostic factor in LUAD. Moreover, GSEA and ssGSEA results revealed that prognostic RRAGs may regulate LUAD by modulating the immune microenvironment and affecting cell proliferation. The colony formation assay showed that the radiosensitivity of radiation-resistant cell lines was lower than that of primary cells. The western blot assay found that the levels of autophagy were elevated in the radiotherapy-resistant cell lines. Moreover, the expression of DERRAGs (SHC1, AURKA) was higher in the radiotherapy-resistant cells than in primary cells.

Conclusion

Our study explored the role of RRAGs in the prognosis of LUAD and identified three biomarkers. The findings enhanced the understanding of the relationship between radiotherapy, autophagy, and prognosis in LUAD and provided potential therapeutic targets for LUAD patients.

Characterization of glucose metabolism in breast cancer to guide clinical therapy

Background

Breast cancer (BRCA) ranks as a leading cause of cancer death in women worldwide. Glucose metabolism is a noticeable characteristic of the occurrence of malignant tumors. In this study, we aimed to construct a novel glycometabolism-related gene (GRG) signature to predict overall survival (OS), immune infiltration and therapeutic response in BRCA patients.

Materials and methods

The mRNA sequencing and corresponding clinical data of BRCA patients were obtained from public cohorts. Lasso regression was applied to establish a GRG signature. The immune infiltration was evaluated with the ESTIMATE and CIBERSORT algorithms. The drug sensitivity was estimated using the value of IC50, and further forecasted the therapeutic response of each patient. The candidate target was selected in Cytoscape. A nomogram was constructed via the R package of “rms”.

Results

We constructed a six-GRG signature based on CACNA1H, CHPF, IRS2, NT5E, SDC1 and ATP6AP1, and the high-risk patients were correlated with poorer OS (P = 2.515 × 10⁻⁷). M2 macrophage infiltration was considerably superior in high-risk patients, and CD8⁺ T cell infiltration was significantly higher in low-risk patients. Additionally, the high-risk group was more sensitive to Lapatinib. Fortunately, SDC1 was recognized as candidate target and patients had a better OS in the low-SDC1 group. A nomogram integrating the GRG signature was developed, and calibration curves were consistent between the actual and predicted OS.

Conclusions

We identified a novel GRG signature complementing the present understanding of the targeted therapy and immune biomarker in breast cancer. The GRGs may provide fresh insights for individualized management of BRCA patients.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

BACKGROUND

Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.

METHODS

A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.

RESULTS

Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61-3.53) in the validation set I, 1.35 (95% CI, 1.06-1.71) in the validation set II, and 2.71 (95% CI, 1.77-4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.

CONCLUSIONS

In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.

Identification of SRXN1 and KRT6A as Key Genes in Smoking-Related Non-Small-Cell Lung Cancer Through Bioinformatics and Functional Analyses

Background

Lung cancer is the leading cause of cancer-related mortality worldwide. Although cigarette smoking is an established risk factor for lung cancer, few reliable smoking-related biomarkers for non-small-cell lung cancer (NSCLC) are available. An improved understanding of these biomarkers would further the development of new biomarker-targeted therapies and lead to improvements in overall patient survival.

Methods

We performed bioinformatic analysis to screened potential target genes, then quantitative PCR, western, siRNA, CCK-8, flow cytometry, tumorigenicity assays in nude mice were performed to validated the function.

Results

In this study, we identified 83 smoking-related genes (SRGs) based on an integration analysis of two Gene Expression Omnibus (GEO) datasets, and 27 hub SRGs with potential carcinogenic effects by analyzing a dataset of smokers with NSCLC in The Cancer Genome Atlas (TCGA) database. A survival analysis revealed three genes with potential prognostic value, namely SRXN1, KRT6A and JAKMIP3. A univariate Cox analysis revealed significant associations of elevated SRXN1 and KRT6A expression with prognosis. A receiver operating characteristic (ROC) curve analysis indicated the high diagnostic value of SRXN1 and KRT6A for smoking and cancer. Quantitative PCR and western blotting validated the increased expression of SRXN1 and KRT6A mRNA and protein, respectively, in lung cancer cell lines and NSCLC tissues. In patients with NSCLC, SRXN1 and KRT6A expression was associated with the tumor–node–metastasis (TNM) stage, presence of metastasis, history of smoking and daily smoking consumption. Furthermore, inhibition of SRXN1 or KRT6A suppressed viability and enhanced apoptosis in the A549 human lung carcinoma cell line. Tumorigenicity assays in nude mice confirmed that the siRNA-mediated downregulation of SRXN1 and KRT6A expression inhibited tumor growth in vivo.

Conclusions

In summary, SRXN1 and KRT6A act as oncogenes in NSCLC and might be potential biomarkers of smoking exposure and the early diagnosis and prognosis of NSCLC in smokers, which is vital for lung cancer therapy.

Prediction of Genes Involved in Lung Cancer with a Systems Biology Approach Based on Comprehensive Gene Information

Over the past few years, hundreds of genes have been reported in relation to lung cancer. Systems biology studies can help validate this association and find the most valid genes to use in the diagnosis and treatment. We reviewed the candidate genes for lung cancer in 120 published articles from September 1, 1993, to September 1, 2020. We obtained 134 up- and 36 downregulated genes for lung cancer in this article. The genes extracted from the articles were imported to Search Tool for the Retrieval of Interacting genes/proteins (STRING) to construct the protein-protein interaction (PPI) Network and pathway enrichment. GO ontology and Reactome databases were used for describing the genes, average length of survival, and constructing networks. Then, the ClusterONE plugin of Cytoscape software was used to analyze and cluster networks. Hubs and bottleneck nodes were defined based on their degree and betweenness. Common genes between the ClusterONE plugin and network analysis consisted of seven genes (BRCA1-TP53-CASP3-PLK1-VEGFA-MDM2-CCNB1 and PLK1), and two genes (PLK1 and TYMS) were selected as survival factors. Our drug-gene network showed that CASP3, BRCA1, TP53, VEGFA, and MDM2 are common genes that are involved in this network. Also, among the drugs recognized in the drug-gene network, five drugs such as paclitaxel, oxaliplatin, carboplatin, irinotecan, and cisplatin were examined in different studies. It seems that these seven genes, with further studies and confirmatory tests, could be potential markers for lung cancer, especially PLK1 that has a significant effect on the survival of patients. We provide the novel genes into the pathogenesis of lung cancer, and we introduced new potential biomarkers for this malignancy.

Nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed.

Aims

To identify biomarkers associated with the sub‐groups and prognosis of LUAD.

Materials and Methods

Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients.

Results

Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions.

Discussion

Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses.

Conclusions

By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD.

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.

Identification of prognostic markers by weighted gene co-expression network analysis in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the fatal tumors and is associated with a poor prognosis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify the proportions of 22 types of immune cells. Weighted gene co-expression network analysis (WGCNA) was established from the GSE37745 data, and key modules correlating most with CD8⁺ T cell infiltration were determined. Genes that manifested a high module connectivity in the key module were identified as hub genes. Three bioinformatics online databases were used to evaluate hub gene expression levels in tumor and normal tissues. Finally, survival analysis was conducted for these hub genes. In this study, we chose four hub genes (AURKB, CDC20, TPX2 and KIF2C) based on the comprehensive bioinformatics analyses. All hub genes were overexpressed in tumor tissue, and high expression of AURKB, CDC20, TPX2, and KIF2C correlated with the poor prognosis of these patients. In vitro experiments confirmed that CDC20 knockdown inhibited cell proliferation and growth. The above results indicated that AURKB, CDC20, TPX2, and KIF2C are potential CD8⁺ T cell infiltration-related biomarkers and therapeutic targets.

Repression of the AURKA-CXCL5 axis induces autophagic cell death and promotes radiosensitivity in non-small-cell lung cancer

Aurora kinase A (AURKA) regulates apoptosis and autophagy in various diseases and has shown promising clinical effects. Nevertheless, the complex regulatory mechanism of AURKA and autophagy in non-small-cell lung cancer (NSCLC) radiosensitivity remains to be elucidated. Here, we showed that AURKA was upregulated in NSCLC cell lines and tissues and that AURKA overexpression was significantly related to a poor prognosis, tumor stage and lymph node metastasis in NSCLC. Interestingly, AURKA expression was significantly increased after 8Gy radiotherapy. Silencing of AURKA enhanced radiosensitivity and impaired migration and invasion in vivo and in vitro. Mechanistically, we determined that CXCL5, a member of the chemokine family, was a key downstream effector of AURKA, and the phenotype induced by AURKA silencing was partly due to CXCL5 inhibition. We further demonstrated that the AURKA-CXCL5 axis played an essential role in NSCLC autophagy and that the activation of cytotoxic autophagy attenuated the malignant biological behavior of NSCLC cells mediated by AURKA-CXCL5. In general, we revealed the role of the AURKA-CXCL5 axis and autophagy in regulating the sensitivity of NSCLC cells to radiotherapy, which may provide potential therapeutic targets and new strategies for combatting NSCLC resistance to radiotherapy.

Application of weighted gene co-expression network analysis to identify the hub genes in H1N1

OBJECTIVE

Identifying the disease-associated interactions between different genes helps us to find novel therapeutic targets and predictive biomarkers.

METHODS

Gene expression data GSE82050 from H1N1 and control human samples were acquired from the NCBI GEO database. Highly co-expressed genes were grouped into modules. Through Person's correlation coefficient calculation between the module and clinical phenotype, notable modules were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted, and the hub genes within the module of interest were identified. Also, gene expression data GSE27131 were acquired from the GEO database to verify differential key gene expression analysis. The CIBERSORT was used to evaluate the immune cells infiltration and the GSVA was performed to identify the differentially regulated pathways in H1N1. The receiver operating characteristic (ROC) curves were used to assess the diagnostic values of the hub genes.

RESULT

The black module was shown to have the highest correlation with the clinical phenotype, mainly functioning in the signaling pathways such as the mitochondrial inner membrane, DNA conformation change, DNA repair, and cell cycle phase transition. Through analysis of the black module, we found 5 genes that were highly correlated with the H1N1 phenotype. The H1N1 project from GSE27131 confirmed an increased expression of these genes.

CONCLUSION

By using the WGCNA we analyzed and predicted the key genes in H1N1. BRCA1, CDC20, MAD2L1, MCM2, and UBE2C were found to be the most relevant genes, which may be therapeutic targets and predictive biomarkers for H1N1 therapy.

Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

Objective

The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).

Methods

In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.

Results

Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87.

Conclusions

Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05371-7.

Cell division cycle proteinising prognostic biomarker of breast cancer

Cell division cycle protein (CDC20) has been observed to be expressed higher in various kinds of human cancers and was associated with poor prognosis. However, studies on role of CDC20 in breast cancer are seldom reported till now, most of which are not systematic and conclusive. The present study was performed to analyze the expression pattern, potential function, and distinct prognostic effect of CDC20 in breast cancer using several online databases including Oncomine, bc-GenExMiner, PrognoScan, and UCSC Xena. To verify the results from databases, we compared the mRNA CDC20 expression in breast cancer tissues and adjacent normal tissues of patients by real-time PCR. We found that CDC20 was expressed higher in different types of breast cancer, comparing with normal tissues. Moreover, the patients with a more advanced stage of breast cancer tended to express higher level CDC20. CDC20 was expressed higher in breast cancer tissues than normal tissues from patients in our hospital, consistent with the results from databases. Estrogen receptor (ER) and progesterone receptor (PR) status were negatively correlated with CDC20 level. Conversely, Scarff–Bloom–Richardson (SBR) grade, Nottingham prognostic index (NPI), epidermal growth factor receptor-2 (HER-2) status, basal-like status, and triple-negative status were positively related to CDC20 expression in breast cancer patients with respect to normal individuals. Higher CDC20 expression correlated with worse survival. Finally, a positive correlation between CDC20 and Targeting protein for Xenopus kinesin-like protein 2 (TPX2) expression was revealed. CDC20 could be considered as a potential predictive indicator for prognosis of breast cancer with co-expressed TPX2 gene.

High expression of SPP1 in patients with chronic obstructive pulmonary disease (COPD) is correlated with increased risk of lung cancer

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airway inflammation and fixed airflow obstruction. Patients with COPD have increased risk of lung cancer (LC), and the coexistence of both diseases is associated with poorer survival. However, the mechanisms predisposing patients with COPD to LC development and poor prognosis remain unclear. Gene expression profiles were downloaded from the Gene Expression Omnibus. Twenty‐two data sets were included (n = 876). We identified 133 DEGs and 145 DEGs in patients with COPD and LC compared with healthy controls, respectively. There were 1544 DEGs in patients with LC and coexisting COPD compared with COPD, and these DEGs are mainly involved in the cell cycle, DNA replication, p53 signalling and insulin signalling. The biological processes primarily associated with these DEGs are oxidation reduction and apoptosis. SPP1 was the only overlapping DEG that was up‐regulated in patients with COPD and/or LC, and this was validated by qPCR in an independent cohort. The area under the curve value for SPP1 was 0.893 (0.822–0.963) for the prediction of LC in patients with COPD. High expression of SPP1 in patients with LC was associated with shorter survival time. Up‐regulation of SPP1 may be associated with increased risk of LC in patients with COPD and therefore may have potential as a therapeutic target for LC in patients with COPD.

AURKA and FAM83A are prognostic biomarkers and correlated with Tumor-infiltrating Lymphocytes in smoking related Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) has become the main histologic type, which account for nearly 40% of lung cancer. The present study aimed to investigate the gene expression signature in smoking related LUAD. A total of 45 smoking related DEGs in LUAD were identified and functional enrichment analysis was also performed. Then Cox's regression model and Kaplan-Meier analysis were used to screen potential prognostic genes. Finally, AURKA and FAM83A were left for further immune-related mechanism exploration. Kaplan-Meier analysis indicated survival rates are related to different immune cell (B cell and Dendritic cell) infiltration levels. Mechanistically, we further explore the correlation between AURKA and FAM83A gene expression levels and tumor-infiltrating lymphocytes (TILs) level as well as their response to immunomodulators. The results suggested that AURKA and FAM83A are highly expressed in smoking related LUAD, and negatively correlated to B cell and Dendritic cell infiltration levels. At the same time, B cell and Dendritic cell infiltration levels also related to the prognosis of LUAD. We further revealed AURKA and FAM83A could be novel targets to improve the prognosis of LUAD through regulated the response to immunomodulators.

Clinical Implications of HSC70 Expression in Clear Cell Renal Cell Carcinoma

Purpose: The role of heat shock protein 70 (HSC70) in the progression of clear cell renal cell carcinoma (ccRCC) is unclear. This study explored the effect of the HSC70 on the survival of ccRCC patients. Methods: Immunohistochemical analysis was performed to determine HSC70 expression in samples obtained from 121 ccRCC patients with at least 5 years of follow-up. We also analyzed the association between HSC70 expression and clinicopathological characteristics. Furthermore, the association of overall survival (OS) with HSC70 expression was analyzed using Kaplan-Meier curves. Finally, we used the Oncomine and CCLE databases to determine the effects of HSC70 mRNA expression on ccRCC. Results: HSC70 expression was associated with distant metastasis and death of ccRCC patients. HSC70 was expressed in the nucleus and/or cytoplasm of ccRCC cells. The incidence of distant organ metastasis and death was higher in patients with HSC70 expression than in those without it. Survival analysis revealed that patients with HSC70 expression had significantly shorter OS. Oncomine analyses also showed that the HSC70 mRNA was significantly upregulated in ccRCC tissues. Conclusions: HSC70 expression was related to adverse prognosis, and patients with HSC70 expression had a worse prognosis than those without HSC70 expression. HSC70 may thus serve as a potential therapeutic target for ccRCC.

Promising Therapy in Lung Cancer: Spotlight on Aurora Kinases

Simple Summary

Lung cancer has remained one of the major causes of death worldwide. Thus, a more effective treatment approach is essential, such as the inhibition of specific cancer-promoting molecules. Aurora kinases regulate the process of mitosis—a process of cell division that is necessary for normal cell proliferation. Dysfunction of these kinases can contribute to cancer formation. In this review, we present studies indicating the implication of Aurora kinases in tumor formation, drug resistance, and disease prognosis. The effectivity of using Aurora kinase inhibitors in the pre-clinical and clinical investigations has proven their therapeutic potential in the setting of lung cancer. This work may provide further information to broaden the development of anticancer drugs and, thus, improve the conventional lung cancer management.

Abstract

Despite tremendous efforts to improve the treatment of lung cancer, prognosis still remains poor; hence, the search for efficacious therapeutic option remains a prime concern in lung cancer research. Cell cycle regulation including mitosis has emerged as an important target for cancer management. Novel pharmacological agents blocking the activities of regulatory molecules that control the functional aspects of mitosis such as Aurora kinases are now being investigated. The Aurora kinases, Aurora-A (AURKA), and Aurora B (AURKB) are overexpressed in many tumor entities such as lung cancer that correlate with poor survival, whereby their inhibition, in most cases, enhances the efficacy of chemo-and radiotherapies, indicating their implication in cancer therapy. The current knowledge on Aurora kinase inhibitors has increasingly shown high potential in ensuing targeted therapies in lung malignancies. In this review, we will briefly describe the biology of Aurora kinases, highlight their oncogenic roles in the pre-clinical and clinical studies in lung cancer and, finally, address the challenges and potentials of Aurora kinases to improve the therapy of this malignancy.

Identification of crucial genes associated with lung adenocarcinoma by bioinformatic analysis

Lung cancer is the world's most common malignancies and ranks first among all cancer-related deaths. Lung adenocarcinoma (LUAD) is the most frequent histological type in lung cancer. Its pathogenesis has not yet been fully elucidated, so it is of great significance to explore related genes for elucidating the molecular mechanism involved in occurrence and development of LUAD.To explore the crucial genes associated with LUAD development and progression, microarray datasets GSE7670, GSE10072, and GSE31547 were acquired from the Gene Expression Omnibus (GEO) database. R language Limma package was adopted to screen the differentially expressed genes (DEGs). The clusterProfiler package was used for enrichment analysis and annotation of the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for DEGs. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to construct the protein interaction network for DEGs, while Cytoscape was adopted to visualize it. The functional module was screened with Cytoscape's MCODE (The Molecular Complex Detection) plugin. The crucial genes associated with LUAD were identified by cytoHubba plugin. Kaplan-Meier plotter online tool was used to perform survival analysis of the hub gene.Three hundred twenty-one DEGs in total were screened, of which 105 were upregulated and 216 were downregulated. It was found that some GO terms and pathways (e.g., collagen trimer, extracellular structure organization, heparin binding, complement and coagulation cascades, malaria, protein digestion and absorption, and PPAR signaling pathway) were considerably enriched in DEGs. UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, PRC1, and CDK1 were identified as crucial genes. Survival analysis showed that the overexpression of UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, and PRC1 significantly reduced the overall survival of LUAD patients. One of the crucial genes: UBE2C was validated by immunohistochemistry to be upregulated in LUAD tissues.This study screened out potential biomarkers of LUAD, providing a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of LUAD.

Screening and identification of biomarkers associated with the diagnosis and prognosis of lung adenocarcinoma

BACKGROUND

In this study, we aimed to identify the pathogenesis and prognostic biomarkers of lung adenocarcinoma (LUAD).

METHODS

Differentially expressed mRNAs (DEmRNAs) and single nucleotide polymorphism (SNP) mutant genes were screened. In addition, enrichment and protein-protein interaction (PPI) network analyses of the SNP-mutated genes were performed. Thereafter, the correlation between gene mutation and expression was analyzed. Finally, the mutated genes associated with LUAD prognosis were validated on the basis of The Cancer Genome Atlas (TCGA) database.

RESULTS

A total of 2502 DEmRNAs were initially screened in this study. We identified 756 SNP-mutated genes from more than 30 cases. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the mutated genes involved in LUAD were mainly associated with the ECM-receptor interaction, focal adhesion, and calcium signaling pathways. Tumor protein p53 (TP53) and neurexin 1 (NRXN1) with the higher degree were chosen as the hub genes in the PPI network. In addition, the correlation analysis revealed six genes, including assembly factor for spindle microtubules (ASPM), centromere protein F (CENPF), contactin 3 (CNTN3), catenin delta 2 (CTNND2), PKHD1 like 1 (PKHD1L1), and semaphorin 6D (SEMA6D), and three SNP mutations at ASPM rs368020495, CENPF rs762653487, and PKHD1L1 rs768349010 sites that were found to be associated with LUAD prognosis. Further validation showed that among the aforementioned six mutated genes, CENPF was upregulated and SEMA6D was downregulated.

CONCLUSION

CENPF, SEMA6D, TP53, and NRXN1 were found to be closely associated with the development of LUAD.

The Differential Effect of Carbon Dots on Gene Expression and DNA Methylation of Human Embryonic Lung Fibroblasts as a Function of Surface Charge and Dose

This study presents a toxicological evaluation of two types of carbon dots (CD), similar in size (<10 nm) but differing in surface charge. Whole-genome mRNA and miRNA expression (RNAseq), as well as gene-specific DNA methylation changes, were analyzed in human embryonic lung fibroblasts (HEL 12469) after 4 h and 24 h exposure to concentrations of 10 and 50 µg/mL (for positive charged CD; pCD) or 10 and 100 µg/mL (for negative charged CD, nCD). The results showed a distinct response for the tested nanomaterials (NMs). The exposure to pCD induced the expression of a substantially lower number of mRNAs than those to nCD, with few commonly differentially expressed genes between the two CDs. For both CDs, the number of deregulated mRNAs increased with the dose and exposure time. The pathway analysis revealed a deregulation of processes associated with immune response, tumorigenesis and cell cycle regulation, after exposure to pCD. For nCD treatment, pathways relating to cell proliferation, apoptosis, oxidative stress, gene expression, and cycle regulation were detected. The expression of miRNAs followed a similar pattern: more pronounced changes after nCD exposure and few commonly differentially expressed miRNAs between the two CDs. For both CDs the pathway analysis based on miRNA-mRNA interactions, showed a deregulation of cancer-related pathways, immune processes and processes involved in extracellular matrix interactions. DNA methylation was not affected by exposure to any of the two CDs. In summary, although the tested CDs induced distinct responses on the level of mRNA and miRNA expression, pathway analyses revealed a potential common biological impact of both NMs independent of their surface charge.

Identification of key candidate genes and pathways in anaplastic thyroid cancer by bioinformatics analysis

BACKGROUND

Anaplastic thyroid carcinoma (ATC) is a refractory and poor prognosis tumor Present study aimed to investigate the underlying biological functions and pathways involved in the development of ATC and to identify potential hub genes and candidate biomarkers of ATC.

MATERIALS AND METHODS

Bioinformatics analyses were performed to identify the differentially expressed genes (DEGs) between ATC tissue samples and adjacent normal tissue samples. Protein-protein interaction (PPI) networks of the DEGs were constructed using Search Tool for the Retrieval of Interacting Genes online tool and Cytoscape software and divided into sub-networks using the Molecular Complex Detection (MCODE) plug-in. DEGs in each module was analyzed by enrichment analysis of the KEGG Orthology Based Annotation System (KOBAS) web software version 3.0. Eventually, the hub genes from bioinformatics analysis were verified by qRT-PCR assay in different ATC cell lines.

RESULTS

Thirty hub genes were selected and three modules were built by the Cytoscape software from the PPI network. Seven genes (CDK1, CCNB2, BUB1B, CDC20, RRM2, CHEK1 and CDC45) were screened from thirty hub genes. Enrichment analysis showed that these hub genes were primarily accumulated in 'cell cycle', 'p53 signaling pathway', 'viral carcinogenesis', 'pyrimidine metabolism' and 'ubiquitin mediated proteolysis'. The results of qRT-PCR indicated that seven hub genes were unregulated in three ATC cell lines compared with normal thyroid gland cell.

CONCLUSIONS

These findings suggest that CDK1, CCNB2, BUB1B, CDC20, RRM2, CHEK1 and CDC45 may serve as novel diagnosis biomarkers and potential therapeutic target for ATC.

MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

Aim:

To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD).

Materials & methods:

We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID.

Results:

Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD.

Conclusion:

23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Background

Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD.

Methods

Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model.

Results

We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors.

Conclusion

Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases

Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario.

Norepinephrine transporter promotes the invasion of human colon cancer cells

Epidemiological studies suggested the use of antidepressants to be associated with decreased risk of colorectal cancer (CRC). However, the underlying mechanism through which this decreased risk occurs remains elusive. The norepinephrine transporter (NET) is a target of antidepressants that maintains noradrenergic transmission homeostasis; however, little is known about its function in human CRC cells. The present study, using public datasets and immunohistochemistry approaches, revealed that NET was highly expressed in human CRC tissues with metastasis and in human colon cancer cells. Furthermore, knockdown of NET inhibited the invasive capability of human colon cancer cells. Additionally, epithelial (E)-cadherin expression was increased and Notch1 signaling was inhibited in NET-depleted colon cancer cells. These findings suggest that NET is highly expressed in human colon cancer, which is associated with the invasion of human colon cancer cells by influencing cell-cell adhesion through the Notch1-E-cadherin pathway. Thus, the present study revealed a novel function for NET and its downstream effectors in colon cancer cells, which will be valuable for future studies in a clinical setting.

Clinical and Expression Significance of AKT1 by Co-expression Network Analysis in Endometrial Cancer

Introduction: Endometrial cancer is one of the most common uterine cancers worldwide. AKT is reported to regulate progesterone receptor B dependent transcription and angiogenesis in endometrial cancer. However, the potential mechanisms of AKT in the tumor progression of endometrial cancer remain unclear.

Methods: We used GSE72708 with gene expression profiles of AKT regulation from the GEO database. We performed GSEA analysis to explore pathway enrichments. We found that most upregulated enriched pathways in siAKT group were associated with acid metabolism and immune network. Endometrial cancer and various signaling pathways were downregulated enriched. Moreover, different molecular mechanism of regulation between progestin (R5020) and AKT was identified, which were related to VEGF signaling pathway. The hub genes were evaluated by immunohistochemical staining of endometrial cancer tissues.

Results: We screened out a total of 623 differentially expressed genes among different groups. According to weighted gene co-expression network analysis (WGCNA) method, four distinct modules were identified. We found brown module showed a very high positive correlation with siAKT group and a very high negative correlation with R5020 group. A total of six hub genes including PBK, BIRC5, AURKA, GTSE1, KNSTRN, and PSMB10 were finally identified associated with AKT1. In addition, the data also shows that the higher expression of AKT1, GTSE1, BIRC5, AURKA, and KNSTRN is significantly associate with poor prognosis of endometrial cancer.

Conclusion: Our study identified six hub genes related to the prognosis of endometrial cancer, which may provide new insights into the underlying biological mechanisms driving the tumorigenesis of endometrial cancer, especially in AKT1 regulation.

A Robust 8-Gene Prognostic Signature for Early-Stage Non-small Cell Lung Cancer

Background: The current staging system is imprecise for prognostic prediction of early-stage non-small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision. Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210, GSE37745, and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference. Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision. Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.

The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients' prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = -0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.