Requirement for BUB1B/BUBR1 in tumor progression of lung adenocarcinoma

A whole-genome CRISPR screen identifies the spindle accessory checkpoint as a locus of nab-paclitaxel resistance in a pancreatic cancer cell line

Pancreatic adenocarcinoma is one of the most aggressive and lethal forms of cancer. Chemotherapy is the primary treatment for pancreatic cancer, but resistance to the drugs used remains a major challenge. A genome-wide CRISPR interference and knockout screen in the PANC-1 cell line with the drug nab-paclitaxel has identified a group of spindle assembly checkpoint (SAC) genes that enhance survival in nab-paclitaxel. Knockdown of these SAC genes (BUB1B, BUB3, and TTK) attenuates paclitaxel-induced cell death. Cells treated with the small molecule inhibitors BAY 1217389 or MPI 0479605, targeting the threonine tyrosine kinase (TTK), also enhance survival in paclitaxel. Overexpression of these SAC genes does not affect sensitivity to paclitaxel. These discoveries have helped to elucidate the mechanisms behind paclitaxel cytotoxicity. The outcomes of this investigation may pave the way for a deeper comprehension of the diverse responses of pancreatic cancer to therapies including paclitaxel. Additionally, they could facilitate the formulation of novel treatment approaches for pancreatic cancer.

m6A demethylase ALKBH5 maintains stemness of intrahepatic cholangiocarcinoma by sustaining BUB1B expression and cell proliferation

ALKBH5 plays critical roles in various cellular processes via post-transcriptional regulation of oncogenes or tumor suppressors in an N6-methyladenosine (m6A)-dependent manner. However, its function in intrahepatic cholangiocarcinoma (ICC) remains unclear. In the present study, bioinformatic analyses of The Cancer Genome Atlas (TCGA) data were performed, and the association of ALKBH5 in predicting overall survival in patients with ICC was investigated. Then, the clinical data of patients from The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University (Changzhou, China) was used to reveal the overall survival of patients with ICC with different ALKBH5 expression levels by Kaplan-Meier survival analysis. Subsequently, in vitro and in vivo studies were conducted to explore and verify the downstream genes regulated by ALKBH5. The results from TCGA data demonstrated that ALKBH5 expression is elevated in ICC and that patients with high ALKBH5 expression exhibited poor survival compared with patients with low expression. In addition, in vitro assays demonstrated that ALKBH5 promoted cell viability and maintained the stemness of ICC cells, leading to ICC progression. The present study also demonstrated that BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) is the downstream gene regulated by ALKBH5 and targeting BUB1B suppressed cell growth. The in vitro and vivo experiments revealed that ALKBH5 might function through BUB1B to maintain the stemness of ICC and that altering BUB1B may suppress ICC progression.

Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients

Background

Programmed cell death (PCD) plays a critical role in tumor progression and malignancy, and exploring its relationship with lung adenocarcinoma (LUAD)'s survival outcomes is important for personalized diagnosis and treatment. This study aimed to identify survival-related genes and construct an effective prognostic indicator for LUAD based on 12 forms of PCD.

Methods

A total of 1,933 candidate genes related to PCD were collected from published studies and public data center. A prognostic gene signature, called the cell death index (CDI), was established based on RNA-Seq and immunohistochemistry (IHC). IHC staining on tissue microarray was applied for the validation of protein level. Moreover, GSE42127, GSE72094 were used as validation datasets.

Results

The CDI based on expression level of nine genes (CCNB2, HMGA1, CACNA2D2, BUB1B, BTG2, KIF14, PTGDS, SERPINB5, BRCA1) was highly predictive for overall survival (OS) of LUAD in our cohort [36-month area under the curve (AUC): 0.750, 60-month AUC: 0.809]. The CDI was further validated in independent cohorts (GSE72094, 36-month AUC: 0.717, 60-month AUC: 0.737; GSE42127, 12-month AUC: 0.829, 60-month AUC: 0.663). And the CDI was found to be an independent prognostic factor after adjusting for other clinical characteristics. Furthermore, the high-CDI group was associated with upregulated tumor immune infiltration compared to the low-CDI group.

Conclusions

This study identified a 9-gene signature (CDI) based on PCD-related genes that accurately predicted the prognosis of LUAD patients. The CDI could serve as a valuable prognostic indicator and guide personalized therapeutic strategies for LUAD.

A whole-genome CRISPR screen identifies the spindle accessory checkpoint as a locus of nab-paclitaxel resistance in pancreatic cancer cells

Pancreatic adenocarcinoma is one of the most aggressive and lethal forms of cancer. Chemotherapy is the primary treatment for pancreatic cancer, but resistance to the drugs used remains a major challenge. A genome-wide CRISPR interference and knockout screen in the PANC-1 cell line with the drug nab-paclitaxel has identified a group of spindle assembly checkpoint (SAC) genes that enhance survival in nab-paclitaxel. Knockdown of these SAC genes (BUB1B, BUB3, and TTK) attenuates paclitaxel-induced cell death. Cells treated with the small molecule inhibitors BAY 1217389 or MPI 0479605, targeting the threonine tyrosine kinase (TTK), also enhance survival in paclitaxel. Overexpression of these SAC genes does not affect sensitivity to paclitaxel. These discoveries have helped to elucidate the mechanisms behind paclitaxel cytotoxicity. The outcomes of this investigation may pave the way for a deeper comprehension of the diverse responses of pancreatic cancer to therapies including paclitaxel. Additionally, they could facilitate the formulation of novel treatment approaches for pancreatic cancer.

Club cell-specific telomere protection protein 1 (TPP1) protects against tobacco smoke-induced lung inflammation, xenobiotic metabolic dysregulation, and injurious responses

Inhaling xenobiotics, such as tobacco smoke is a major risk factor for pulmonary diseases, e.g., COPD/emphysema, interstitial lung disease, and pre-invasive diseases. Shelterin complex or telosome provides telomeric end protection during replication. Telomere protection protein 1 (TPP1) is one of the main six subunits of the shelterin complex supporting the telomere stability and genomic integrity. Dysfunctional telomeres and shelterin complex are associated as a disease mechanism of tobacco smoke-induced pulmonary damage and disease processes. The airway epithelium is critical to maintaining respiratory homeostasis and is implicated in lung diseases. Club cells (also known as clara cells) play an essential role in the immune response, surfactant production, and metabolism. Disrupted shelterin complex may lead to dysregulated cellular function, DNA damage, and disease progression. However, it is unknown if the conditional removal of TPP1 from Club cells can induce lung disease pathogenesis caused by tobacco smoke exposure. In this study, conditional knockout of Club-cell specific TPP1 demonstrated the instability of other shelterin protein subunits, such as TRF1, dysregulation of cell cycle checkpoint proteins, p53 and downstream targets, and dysregulation of telomeric genes. This was associated with age-dependent senescence-associated genes, increased DNA damage, and upregulated RANTES/IL13/IL33 mediated lung inflammation and injury network by cigarette smoke (CS). These phenomena are also associated with alterations in cytochrome P450 and glutathione transferases, upregulated molecular pathways promoting lung lesions, bronchial neoplasms, and adenocarcinomas. These findings suggest a pivotal role of TPP1 in maintaining lung homeostasis and injurious responses in response to CS. Thus, these data TPP1 may have therapeutic value in alleviating telomere-related chronic lung diseases.

Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression

Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.

Revealing active components and action mechanism of Fritillariae Bulbus against non-small cell lung cancer through spectrum-effect relationship and proteomics

BACKGROUND

Fritillariae Bulbus (FB) is widely used as a traditional medicine for the treatment of lung meridian diseases. It has been proved that FB has good anti-non-small cell lung cancer (NSCLC) activity. However, the active components and potential mechanism are still not clear.

PURPOSE

To reveal the bioactive components of FB against NSCLC and potential mechanism through spectrum-effect relationship and proteomics.

METHOD

First, the FB extract was chemically profiled by UHPLC-QTOF-MS and the inhibitory effect of FB extract on A549 cell viability was evaluated by Cell Counting Kit-8 assay. Second, orthogonal-partial least squares-regression analysis was applied to screen potential active compounds through correlating the chemical profile with corresponding inhibitory effect. Third, the anti-NSCLC activities of potential active components were further investigated in terms of cell proliferation, cell cycle and cell apoptosis in vitro and tumor growth in vivo. Finally, proteomics was utilized to reveal the underlying anti-NSCLC mechanism.

RESULTS

Six potential active components including verticine, verticinone, zhebeirine, ebeiedinone, yibeissine and peimisine were screened out by spectrum-effect relationship. Among them, zhebeirine showed higher inhibitory effect on A549 cell viability with IC₅₀ value of 36.93 μM and dosage-dependent inhibition of A549 xenograft tumor growth in nude mice. Proteomics and western blotting assays indicated that zhebeirine could arrest cell cycle by down-regulating the expressions of CDK1, CDK2, Cyclin A2, Cyclin B2 and inhibiting the phosphorylation of p53. Moreover, the proteins participating in p53 signaling pathway including PCNA, 14-3-3σ, CHEK1 were significantly decreased, which suggested that zhebeirine affected cell cycle progression through p53 signaling pathway.

CONCLUSION

This study not only provides scientific evidence to support the clinical application of FB against NSCLC, but also demonstrates that zhebeirine is a promising anti-NSCLC lead compound deserving further studies.

Clinical value and potential mechanisms of BUB1B up-regulation in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) has insidious onset, late clinical diagnosis and high recurrence rate, which leads to poor quality of patient life. Therefore, it is necessary to further explore the pathogenesis and therapy targets of NPC. BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) was found to be up-regulated in a variety of cancers, but only two previous study showed that BUB1B was overexpressed in NPC and the sample size was small. The clinical role of BUB1B expression and its underlying mechanism in NPC require more in-depth research. Immunohistochemical samples and public RNA-seq data indicated that BUB1B protein and mRNA expression levels were up-regulated in NPC, and summary receiver operating characteristic curve indicated that BUB1B expression level had a strong ability to distinguish NPC tissues from non-NPC tissues. Gene ontology and Kyoto Encyclopedia of genes and genomes were performed and revealed that BUB1B and its related genes were mainly involved in cell cycle and DNA replication. Protein- Protein Interaction were built to interpret the BUB1B molecular mechanism. Histone deacetylase 2 (HDAC2) could be the upstream regulation factor of BUB1B, which was verified by Chromatin Immunoprecipitation Sequencing samples. In summary, BUB1B was highly expressed in NPC, and HDAC2 may affect cell cycle by regulating BUB1B to promote cancer progression.

Hyper-active RAS/MAPK introduces cancer-specific mitotic vulnerabilities

Aneuploidy, the incorrect number of whole chromosomes, is a common feature of tumors that contributes to their initiation and evolution. Preventing aneuploidy requires properly functioning kinetochores, which are large protein complexes assembled on centromeric DNA that link mitotic chromosomes to dynamic spindle microtubules and facilitate chromosome segregation. The kinetochore leverages at least two mechanisms to prevent aneuploidy: error correction and the spindle assembly checkpoint (SAC). BubR1, a factor involved in both processes, was identified as a cancer dependency and therapeutic target in multiple tumor types; however, it remains unclear what specific oncogenic pressures drive this enhanced dependency on BubR1 and whether it arises from BubR1's regulation of the SAC or error-correction pathways. Here, we use a genetically controlled transformation model and glioblastoma tumor isolates to show that constitutive signaling by RAS or MAPK is necessary for cancer-specific BubR1 vulnerability. The MAPK pathway enzymatically hyperstimulates a network of kinetochore kinases that compromises chromosome segregation, rendering cells more dependent on two BubR1 activities: counteracting excessive kinetochore-microtubule turnover for error correction and maintaining the SAC. This work expands our understanding of how chromosome segregation adapts to different cellular states and reveals an oncogenic trigger of a cancer-specific defect.

Identification of the Key Genes Involved in the Tumorigenesis and Prognosis of Prostate Cancer

Background. Prostate cancer (PCa) is a malignant tumor in males, with a majority of the cases advancing to metastatic castration resistance. Metastasis is the leading cause of mortality in PCa. The traditional early detection and prediction approaches cannot differentiate between the different stages of PCa. Therefore, new biomarkers are necessary for early detection and clear differentiation of PCa stages to provide precise therapeutic intervention. Methods. The objective of the study was to find significant differences in genes and combine the three GEO datasets with TCGA-PRAD datasets (DEG). Weighted gene coexpression network analysis (WGCNA) determined the gene set and PCa clinical feature correlation module utilizing the TGGA-PRAD clinical feature data. The correlation module genes were rescreened using the biological information analysis tools, with the three hub genes (TOP2A, NCAPG, and BUB1B) for proper verification. Finally, internal (TCGA) and external (GSE32571, GSE70770) validation datasets were used to validate and predict the value of last hub genes. Results. The hub gene was abnormally upregulated in PCa samples during verification. The expression of each gene was favorably connected with the Gleason score and TN tumor grade in clinical samples but negatively correlated with the overall survival rate. The expression of these genes was linked to CD8 naive cells and macrophages, among other cells. Antitumor immune cells like NK and NKT were favorably and adversely correlated with infiltrating cells, respectively. Simultaneously, the GSCV and GSEA indicated that the hub gene is connected with cell proliferation, death, and androgen receptor, among other signaling pathways. Therefore, these genes could influence the incidence and progression of PCa by participating in or modulating various signaling pathways. Furthermore, using the online tool of CMap, we examined the individual medications for Hughes and determined that tipifarnib could be useful for the clinical therapy of PCa. Conclusion. TOP2A, NCAPG, and BUB1B are important genes intimately linked to the clinical prognosis of PCa and can be employed as reliable biomarkers for early diagnosis and prognosis. Moreover, these genes can provide a theoretical basis for precision differentiation and treatment of PCa.

Construction of novel lncRNA-miRNA-mRNA ceRNA networks associated with prognosis of hepatitis C virus related hepatocellular carcinoma

Background

Hepatitis C virus (HCV) infection contribute to liver fibrosis and cirrhosis, which significantly increases the risk of hepatocellular carcinoma (HCC) development. Previous studies have demonstrated the pivotal role of competitive endogenous RNA (ceRNA) networks in tumorigenesis and cancer progression. Consequently, we herein seek to identify and evaluate the prognostic relevance of a novel ceRNA network associated with HCV-related HCC.

Methods

Differentially expressed genes (DEGs) in GSE140846 dataset from GEO were identified using Network Analyst, and GO, KEGG and Reactome analyses were performed. Furthermore, a protein-protein interaction network was generated, and hub genes were detected. Hub gene expression levels, as well as those of their upstream lncRNAs and miRNAs and associated survival analyses were conducted using appropriate bioinformatics databases. Predicted target relationships were used to establish putative ceRNA networks for HCV-related HCC.

Results

A total of 372 and 360 up- and down-regulated DE-mRNA were identified, which were associated with nuclear division, cell cycle, and ATPase activity. A PPI network containing 704 DE-mRNAs was constructed, and the 6 hub gene with the highest degree of connectivity were selected for subsequent analysis. We discovered that 22 miRNAs and 4 lncRNAs upstream of 11 hub gene were significantly associated with poor prognosis of HCV-related HCC, and used them to constructe a prognostic ceRNA network. Further experiments confirmed the ceRNA-regulatory relationship of BUB1-hsa-miR-193a-3p-MALAT1.

Conclusion

This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network, and reveals potential lncRNA biomarkers in HCV related HCC.

Anoikis-Associated Lung Cancer Metastasis: Mechanisms and Therapies

Simple Summary

Anoikis is a programmed cell death process resulting from the loss of interaction between cells and the extracellular matrix. Therefore, it is necessary to overcome anoikis when tumor cells acquire metastatic potential. In lung cancer, the composition of the extracellular matrix, cell adhesion-related membrane proteins, cytoskeletal regulators, and epithelial–mesenchymal transition are involved in the process of anoikis, and the initiation of apoptosis signals is a critical step in anoikis. Inversely, activation of growth signals counteracts anoikis. This review summarizes the regulators of lung cancer-related anoikis and explores potential drug applications targeting anoikis.

Abstract

Tumor metastasis occurs in lung cancer, resulting in tumor progression and therapy failure. Anoikis is a mechanism of apoptosis that combats tumor metastasis; it inhibits the escape of tumor cells from the native extracellular matrix to other organs. Deciphering the regulators and mechanisms of anoikis in cancer metastasis is urgently needed to treat lung cancer. Several natural and synthetic products exhibit the pro-anoikis potential in lung cancer cells and in vivo models. These products include artonin E, imperatorin, oroxylin A, lupalbigenin, sulforaphane, renieramycin M, avicequinone B, and carbenoxolone. This review summarizes the current understanding of the molecular mechanisms of anoikis regulation and relevant regulators involved in lung cancer metastasis and discusses the therapeutic potential of targeting anoikis in the treatment of lung cancer metastasis.

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.

Chromatin Separation Regulators Predict the Prognosis and Immune Microenvironment Estimation in Lung Adenocarcinoma

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear.

Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database.

Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk.

Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma

Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been trying to identify robust prognostic gene expression signatures. Even for high performance signatures, they usually have few shared genes. This is not totally unexpected, since a prognostic signature is associated with patient survival and may contain no upstream regulators. Identification of master regulators driving disease progression is a vital step to understand underlying molecular mechanisms and develop new treatments.

Methods: In this study, we have utilized a robust workflow to identify potential master regulators that drive poor prognosis in patients with lung adenocarcinoma. This workflow takes gene expression signatures that are associated with poor survival of early-stage lung adenocarcinoma, EGFR-TKI resistance, and responses to immune checkpoint inhibitors, respectively, and identifies recurrent master regulators from seven public gene expression datasets by a regulatory network-based approach.

Results: We have found that majority of the master regulators driving poor prognosis in early stage LUAD are cell-cycle related according to Gene Ontology annotation. However, they were demonstrated experimentally to promote a spectrum of processes such as tumor cell proliferation, invasion, metastasis, and drug resistance. Master regulators predicted from EGFR-TKI resistance signature and the EMT pathway signature are largely shared, which suggests that EMT pathway functions as a hub and interact with other pathways such as hypoxia, angiogenesis, TNF-α signaling, inflammation, TNF-β signaling, Wnt, and Notch signaling pathways. Master regulators that repress immunotherapy are enriched with MYC targets, E2F targets, oxidative phosphorylation, and mTOR signaling.

Conclusion: Our study uncovered possible mechanisms underlying recurrence, resistance to targeted therapy, and immunotherapy. The predicted master regulators may serve as potential therapeutic targets in patients with lung adenocarcinoma.

RBL2/DREAM-mediated repression of the Aurora kinase A/B pathway determines therapy responsiveness and outcome in p53 WT NSCLC

Wild-type p53 is a stress-responsive transcription factor and potent tumor suppressor. P53 activates or represses genes involved in cell cycle progression or apoptosis in order to arrest the cell cycle or induce cell death. Transcription repression by p53 is indirect and requires repressive members of the RB-family (RB1, RBL1, RBL2) and formation of repressor complexes of RB1-E2F and RBL1/RBL2-DREAM. Many aurora kinase A/B (AURKA/B) pathway genes are repressed in a p53-DREAM-dependent manner. We found heightened expression of RBL2 and reduced expression of AURKA/B pathway genes is associated with improved outcomes in p53 wild-type but not p53 mutant non-small cell lung cancer (NSCLC) patients. Knockdown of p53, RBL2, or the DREAM component LIN37 increased AURKA/B pathway gene expression and reduced paclitaxel and radiation toxicity in NSCLC cells. In contrast, pharmacologic inhibition of AURKA/B or knockdown of AURKA/B pathway components increased paclitaxel and IR sensitivity. The results support a model in which p53-RBL2-DREAM-mediated repression of the AURKA/B pathway contributes to tumor suppression, improved tumor therapy responses, and better outcomes in p53 wild-type NSCLCs.

Identification of a Gene Signature Closely Related to Immunosuppressive Tumour Microenvironment Predicting Prognosis of Patients in EGFR Mutant Lung Adenocarcinoma

Lung adenocarcinomas (LUADs) harbouring epidermal growth factor receptor (EGFR) mutations are generally unable to benefit from immune checkpoint inhibitors (ICIs) due to an immunosuppressive tumour microenvironment (TME) and a lower tumour mutation burden. Currently, no gene signature can comprehensively evaluate the TME and predict the prognosis of patients with EGFR-mutant LUAD. Using the Cancer Genome Atlas database of EGFR-mutant LUAD based on the immune score derived from the ESTIMATE algorithm, we divided 80 patients with EGFR-mutant LUAD samples into high and low immune score groups with different immune microenvironments. Subsequently, we screened 396 differentially expressed immune-related genes with prognostic value. The top Gene Ontology terms were significantly enriched in biological functions related to T cell differentiation, immune response, cell cycle, and cell proliferation, which are closely related to the immune microenvironment of tumours. In addition, the KEGG pathway enrichment analysis mainly focused on cell cycle, cell adhesion molecules, and cytokine-cytokine receptor interaction, which also had a relationship with the immune response. Subsequently, we identified a three-gene signature including BTLA, BUB1B, and CENPE using the LASSO Cox regression model. The three-gene signature could accurately identify patients at risk of EGFR-mutant LUAD in the training and validation sets and high-risk patients from both the sets exhibited significantly shorter overall survival (p=0.0053 and p=0.035, respectively). CIBERSORT was used to evaluate the abundance of immune cell infiltration in the EGFR-mutant LUAD microenvironment. The immune activity of B cells and macrophages was higher in the low-risk group, while the immune activity of natural killer cells and T cells was higher in the high-risk group. Thus, the three-gene signature closely related to immunosuppressive TME could predict the risk and prognosis in patients with EGFR-mutant LUAD.

Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

Background

Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.

Methods

The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.

Results

In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.

Conclusion

Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00837-6.

Long non-coding RNA NORAD upregulation induced by airborne particulate matter (PM10) exposure leads to aneuploidy in A549 lung cells

Air pollution is a worldwide problem that affects human health predominantly in the largest cities. Particulate matter of 10 μm or less in diameter (PM₁₀) is considered a risk factor for multiple diseases, including lung cancer. The long non-coding RNA NORAD and the components of the spindle assembly checkpoint (SAC) ensure proper chromosomal segregation. Alterations in the SAC cause aneuploidy, a feature associated with carcinogenesis. In this study, we demonstrated that PM₁₀ treatment increased the expression levels of NORAD as well as those of SAC components mitotic arrest deficient 1 (MAD1L1), mitotic arrest deficient 2 (MAD2L1), BubR1 (BUB1B), aurora B (AURKB), and survivin (BIRC5) in the lung A549 cell line. We also demonstrated that MAD1L1, MAD2L1, and BUB1B expression levels were reduced when cells were transfected with small interfering RNAs (siRNAs) against NORAD. Interestingly, the expression levels of AURKB and BIRC5 (survivin) were not affected by transfection with NORAD siRNAs. Cells treated with PM₁₀ exhibited a decrease in mitotic arrest and an increase in micronuclei frequency in synchronized A549 cells. PM₁₀ exposure induced aneuploidy events as a result of SAC deregulation. We also observed a reduction in the protein levels of Pumilio 1 after PM₁₀ treatment. Our results provide novel clues regarding the effect of PM₁₀ in the generation of chromosomal instability, a phenotype observed in lung cancer cells.

Identification of the Hub Genes Associated with the Prognosis of Ovarian Cancer Patients via Integrated Bioinformatics Analysis and Experimental Validation

Background

This study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.

Methods

Four microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein–protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.

Results

A total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.

Conclusion

Our results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.

BUB1B promotes extrahepatic cholangiocarcinoma progression via JNK/c-Jun pathways

Currently, the controversy regarding the expression profile and function of BUB1B in different malignancies still exist. In this project, we aimed to explore the role and molecular mechanism of BUB1B in the progression of extrahepatic cholangiocarcinoma (ECC). The expression levels of BUB1B in human ECC were evaluated by immunohistochemistry, western blot, and real-time PCR. The role and mechanism of BUB1B in CCA cell proliferation and invasion were investigated in both in vitro and in vivo functional studies. To indicate the clinical significance, a tissue microarray was performed on 113 ECC patients, followed by univariate and multivariate analyses. The expression of BUB1B was increased in both human CCA tissues and CCA cells. Results from loss-of-function and gain-of-function experiments suggested that the inhibition of BUB1B decreased the proliferation and invasiveness of CCA cells in vitro and in vivo, while overexpression of BUB1B achieved the opposite effect. Furthermore, the activation of c-Jun N-terminal kinase-c-Jun (JNK)-c-Jun pathway was regulated by BUB1B. BUB1B regulated the proliferation and invasiveness of CAA cells in a JNK-c-Jun-dependent manner. Clinically, ECC patients with BUB1B high expression had worse overall survival and recurrence-free survival than those with BUB1B low expression. Multivariate analysis identified that BUB1B was an independent predictor for postoperative recurrence and overall survival of ECC patients. In conclusion, BUB1B promoted ECC progression via JNK/c-Jun pathways. These findings suggested that BUB1B could be a potential therapeutic target and a biomarker for predicting prognosis for ECC patients.

BUB1B promotes hepatocellular carcinoma progression via activation of the mTORC1 signaling pathway

Background and Aims

Accumulating studies identified that BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) is integrally involved in the initiation and development of tumors. Nevertheless, the precise biological role and underlying mechanisms of BUB1B in hepatocellular carcinoma (HCC) remain indistinct.

Method

To figure out the role of BUB1B in HCC, we first assessed its expression using The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then verified BUB1B expression in HCC tissues, nontumor tissues, and HCC cell lines through western blotting, quantitative reverse transcription‐polymerase chain reaction, and immunohistochemistry. To explore the specific function of BUB1B in HCC in vivo and in vitro, we performed the flow cytometry, Cell Counting Kit‐8, 5‐ethynyl‐2′‐deoxyuridine incorporation, colony formation, Transwell, wound‐healing, subcutaneous tumor growth, and metastasis assays. Additionally, we identified the BUB1B‐regulated pathways involved in HCC by using gene set enrichment analysis.

Results

Our data displayed that higher BUB1B expression was detected in HCC tissues and HCC cell lines. The overexpression of BUB1B was positively correlated with adverse clinicopathological characteristics. Survival analyses showed that lower recurrence‐free and overall survival rates were correlated with the overexpression of BUB1B in patients with HCC. Moreover, the malignancy of HCC was facilitated by BUB1B both in vivo and in vitro. Lastly, the results were confirmed by western blots, which showed that BUB1B upregulated mTORC1 signaling pathway in HCC. Meanwhile, the oncogenic effect of BUB1B will be impaired when the mTORC1 signaling pathway was inhibited by rapamycin.

Conclusion

We highlighted that BUB1B played an oncogenic role in HCC and was identified as a possible clinical prognostic factor and a potential novel therapeutic target for HCC.

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis.A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway.Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.

Identification of new candidate genes and signalling pathways associated with the development of neuroendocrine pancreatic tumours based on next generation sequencing data

Despite advances in classification, treatment, and imaging, neuroendocrine tumours remain a clinically complex subject. In this work, we studied the genetic profile of well-differentiated pancreatic neuroendocrine tumours (PanNETs) in a cohort of Caucasian patients and analysed the signalling pathways and candidate genes potentially associated with the development of this oncological disease. Twenty-four formalin-fixed paraffin-embedded (FFPE) samples of well-differentiated PanNETs were subjected to massive parallel sequencing using the targeted gene panel (409 genes) of the Illumina NextSeq 550 platform (San Diego, USA). In 24 patients, 119 variants were identified in 54 genes. The median mutation rate per patient was 5 (2.8-7). The detected genetic changes were dominated by missense mutations (67%) and nonsense mutations (29%). 18% of the mutations were activating, 35% of the variants led to a loss of function of the encoded protein, and 52% were not classified. Twenty-six variants were described as new. Functionally significant changes in the tertiary structure and activity of the protein molecules in an in silico assay were predicted for 5 new genetic variants. The 5 highest priority candidate genes were selected: CREB1, TCF12, PRKAR1A, BCL11A, and BUB1B. Genes carrying the identified mutations participate in signalling pathways known to be involved in PanNETs; in addition, 38% of the cases showed genetic changes in the regulation of the SMAD2/3 signalling pathway. Well-differentiated PanNETs in a Russian cohort demonstrate various molecular genetic features, including new genetic variations and potential driver genes. The highlighted molecular genetic changes in the SMAD2/3 signalling pathway suggest new prospects for targeted therapy.

Deregulation of Chromosome Segregation and Cancer

The mitotic spindle assembly checkpoint (SAC) is an intricate cell signaling system that ensures the high fidelity and timely segregation of chromosomes during cell division. Mistakes in this process can lead to the loss, gain, or rearrangement of the genetic material. Gross chromosomal aberrations are usually lethal but can cause birth and development defects as well as cancer. Despite advances in the identification of SAC protein components, important details of the interactions underpinning chromosome segregation regulation remain to be established. This review discusses the current understanding of the function, structure, mode of regulation, and dynamics of the assembly and disassembly of SAC subcomplexes, which ultimately safeguard the accurate transmission of a stable genome to descendants. We also discuss how diverse oncoviruses take control of human cell division by exploiting the SAC and the potential of this signaling circuitry as a pool of drug targets to develop effective cancer therapies.

The genetic association between type 2 diabetic and hepatocellular carcinomas

Background

Type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC) are both major health problems throughout the world. It has been reported that T2DM is an independent risk factor for HCC, although the pathophysiology is still unclear.

Methods

In order to identify differentially expressed genes (DEGs) in T2DM and HCC, gene expression datasets for T2DM (GSE15653), HCC (GSE60502) and metformin-treated cells (GSE69850) were obtained from the Gene Expression Omnibus database repository. Protein-protein interaction (PPI) networks for the DEGs were constructed and gene clusters selected for functional enrichment analysis. Ten genes with the highest degree of connectivity were selected as hub genes and prognostic analysis together with analysis of gene expression and protein distribution were performed for these genes. Lastly, we investigated associations between the hub genes and genes associated with metformin treatment in hepatocarcinoma cells.

Results

In total, 256 common DEGs, including 155 up-regulated genes and 101 down-regulated genes, were identified. Enrichment analyses showed that the genes of the major module were largely associated with the cell cycle. All of the 10 hub genes (CCNA2, CCNB1, MAD2L1, BU1B, RACGAP1, CHEK1, BUB1, ASPM, NCAPG and TTK) have a strong association with lower overall survival in liver cancer patients and four genes (CCNA2, CCNB1, CHEK1 and BUB1) have reduced expression in metformin-treated samples.

Conclusions

This study identified a number of genes that may play important roles in the association of T2DM and HCC, including four genes which may be the target of metformin treatment for diabetes and HCC. The specific mechanisms involved remain to be identified.

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.

Integrative genomics analysis of hub genes and their relationship with prognosis and signaling pathways in esophageal squamous cell carcinoma

The main purpose of the present study was to recognize the integrative genomics analysis of hub genes and their relationship with prognosis and signaling pathways in esophageal squamous cell carcinoma (ESCC). The mRNA gene expression profile data of GSE38129 were downloaded from the Gene Expression Omnibus database, which included 30 ESCC and 30 normal tissue samples. The differentially expressed genes (DEGs) between ESCC and normal samples were identified using the GEO2R tool. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the functions and related pathways of the genes. The protein‑protein interaction (PPI) network of these DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes and visualized with a molecular complex detection plug‑in via Cytoscape. The top five important modules were selected from the PPI network. A total of 928 DEGs, including ephrin‑A1 (EFNA1), collagen type IV α1 (COL4A1),  C‑X‑C chemokine receptor 2 (CXCR2), adrenoreceptor β2 (ADRB2), P2RY14, BUB1B, cyclin A2 (CCNA2), checkpoint kinase 1 (CHEK1), TTK, pituitary tumor transforming gene 1 (PTTG1) and COL5A1, including 498 upregulated genes, were mainly enriched in the 'cell cycle', 'DNA replication' and 'mitotic nuclear division', whereas 430 downregulated genes were enriched in 'oxidation‑reduction process', 'xenobiotic metabolic process' and 'cell‑cell adhesion'. The KEGG analysis revealed that 'ECM‑receptor interaction', 'cell cycle' and 'p53 signaling pathway' were the most relevant pathways. According to the degree of connectivity and adjusted P‑value, eight core genes were selected, among which those with the highest correlation were CHEK1, BUB1B, PTTG1, COL4A1 and CXCR2. Gene Expression Profiling Interactive Analysis in The Cancer Genome Atlas database for overall survival (OS) was applied among these genes and revealed that EFNA1 and COL4A1 were significantly associated with a short OS in 182 patients. Immunohistochemical results revealed that the expression of PTTG1 in esophageal carcinoma tissues was higher than that in normal tissues. Therefore, these genes may serve as crucial predictors for the prognosis of ESCC.

Survival-based bioinformatics analysis to identify hub genes and key pathways in non-small cell lung cancer

BACKGROUND

Lung cancer is one of the leading causes of cancer mortality worldwide. Here, we performed an integrative bioinformatics analysis to screen hub genes and critical pathways in non-small cell lung cancer (NSCLC) based on the overall survival rate of differentially expressed genes (DEGs).

METHODS

Four datasets from the gene expression omnibus (GEO) were used to identify the DEGs. To obtain robust DEGs in NSCLC, only the DEGs that co-existed in the four datasets were selected for subsequent analysis. To identify the genes correlated with overall survival, the overall survival of these genes was then analyzed using the Kaplan-Meier plotter database. The genes significantly correlated with survival were used to perform gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis; next, these genes were used to construct a protein-protein interaction network. MCODE and CytoHubba were used to identify the clusters and hub genes. Finally, the hub genes were validated in the Cancer Genome Atlas (TCGA) and the Human Protein Atlas (HPA).

RESULTS

We found 522 up-regulated DEGs, and 989 down-regulated DEGs between the NSCLC and normal lung tissue, and 895 of them were correlated with a higher overall survival. GO analysis showed that the DEGs that were associated with a higher overall survival were enriched in cell division, cell cycle, DNA replication, angiogenesis, and cell migration. KEGG analysis was consistent with GO analysis and showed that p53 signaling pathway, pyrimidine metabolism, cGMP-PKG signaling pathway and renin secretion pathway were associated with overall survival in NSCLC. In the protein-protein analysis, we identified seven clusters and six hub genes which were BUB1B, CCNB1, CENPE, KIF18A, NDC10, and MAD2L1. Of these genes, CENPE and KIF18A had not been reported until now. Finally, the dysregulated expression of the six hub genes was validated by the data from the TCGA and HPA.

CONCLUSIONS

We identified the hub genes and potential mechanisms of NSCLC based on multiple-microarray analysis and overall survival; then, validated the hub genes in the TCGA and HPA database. These hub genes may serve as potential therapeutic targets.

Bioinformatics analysis of a long non‑coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing

Long non‑coding RNAs (lncRNAs) have been proven to be critical gene regulators of development and disease. The main aim of the present study was to elucidate the lncRNA‑mRNA regulation network in ischemic stroke induced by middle cerebral artery occlusion (MCAO) using RNA sequencing (RNA‑seq) in rats. lncRNA expression profiles were screened in brain tissues to identify a number of differentially expressed lncRNAs (DELs) and genes (DEGs) by RNA‑seq. Reverse transcription‑quantitative polymerase chain reaction was performed to further confirm the lncRNA expression data. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to mine mRNA functions, and a lncRNA‑mRNA network was constructed. Additionally, cis‑ and trans‑regulatory gene analyses of DELs were predicted. A total of 134 DELs (fold change >2, false discovery rate <0.05) and 1,006 DEGs (fold change >2 and P<0.05) were identified. Eighteen lncRNAs were predicted to regulate heme oxygenase 1, mitotic checkpoint serine/threonine kinase B, chemokine ligand 2 and DNA Topoisomerase IIα, amongst other genes. These genes are all associated with a cellular response to inorganic substances, alkaloids, estradiol, reactive oxygen species, metal ions, oxidative stress, and are associated with metabolic pathways, chemokine signaling pathways, malaria, Parkinson's disease, the cell cycle and other GO and KEGG pathway enrichments. The present study identifies novel DELs and an lncRNA‑mRNA regulatory network that may allow for an improved understanding of the molecular mechanism of ischemic stroke induced by MCAO.

Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

Background

Lung cancer is one of the most common malignant tumors. Despite advances in lung cancer therapies, prognosis of non-small-cell lung cancer is still unfavorable. The aim of this study was to identify the prognostic value of key genes in lung tumorigenesis.

Methods

Differentially expressed genes (DEGs) were screened out by GEO2R from three Gene Expression Omnibus cohorts. Common DEGs were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein– protein interaction networks were constructed by the STRING database and visualized by Cytoscape software. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database, and their genomic alterations were identified by performing the cBioportal. Finally, overall survival analysis of hub genes was performed using Kaplan–Meier Plotter.

Results

From three datasets, 169 DEGs (70 upregulated and 99 downregulated) were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that upregulated DEGs were significantly enriched in cell cycle, p53 pathway, and extracellular matrix–receptor interactions; the downregulated DEGs were significantly enriched in PPAR pathway and tyrosine metabolism. The protein–protein interaction network consisted of 71 nodes and 305 edges, including 49 upregulated and 22 downregulated genes. The hub genes, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, were overexpressed compared with the normal group by Gene Expression Profiling Interactive Analysis analysis, and associated with reduced overall survival in lung cancer patients. In the genomic alterations analysis, two hotspot mutations (S2021C/F and E314K/V) were identified in Pfam protein domains.

Conclusion

DEGs, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, might be potential biomarkers for the prognosis and treatment of lung adenocarcinoma.

The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers

The Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) is an expert-curated description of the genes driving human cancer that is used as a standard in cancer genetics across basic research, medical reporting and pharmaceutical development. After a major expansion and complete re-evaluation, the 2018 CGC describes in detail the effect of 719 cancer-driving genes. The recent expansion includes functional and mechanistic descriptions of how each gene contributes to disease generation in terms of the key cancer hallmarks and the impact of mutations on gene and protein function. These functional characteristics depict the extraordinary complexity of cancer biology and suggest multiple cancer-related functions for many genes, which are often highly tissue-dependent or tumour stage-dependent. The 2018 CGC encompasses a second tier, describing an expanding list of genes (currently 145) from more recent cancer studies that show supportive but less detailed indications of a role in cancer.

Hub genes and key pathways of non-small lung cancer identified using bioinformatics

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in 'biological processes' (BP), including 'cell cycle and nuclear division'; the downregulated DEGs were also significantly enriched in BP, including 'response to wounding', 'anatomical structure morphogenesis' and 'response to stimulus'. Upregulated DEGs were also enriched in 'cell cycle', 'DNA replication' and the 'tumor protein 53 signaling pathway', while the downregulated DEGs were also enriched in 'complement and coagulation cascades', 'malaria' and 'cell adhesion molecules'. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included 'metabolism of amino acid' and the other primarily contained 'tumor necrosis signaling' pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

A broadly protective therapeutic antibody against influenza B virus with two mechanisms of action

Influenza B virus (IBV) causes annual influenza epidemics around the world. Here we use an in vivo plasmablast enrichment technique to isolate a human monoclonal antibody, 46B8 that neutralizes all IBVs tested in vitro and protects mice against lethal challenge of all IBVs tested when administered 72 h post infection. 46B8 demonstrates a superior therapeutic benefit over Tamiflu and has an additive antiviral effect in combination with Tamiflu. 46B8 binds to a conserved epitope in the vestigial esterase domain of hemagglutinin (HA) and blocks HA-mediated membrane fusion. After passage of the B/Brisbane/60/2008 virus in the presence of 46B8, we isolated three resistant clones, all harbouring the same mutation (Ser301Phe) in HA that abolishes 46B8 binding to HA at low pH. Interestingly, 46B8 is still able to protect mice against lethal challenge of the mutant viruses, possibly owing to its ability to mediate antibody-dependent cellular cytotoxicity (ADCC).

Influenza B virus (IBV) co-circulates with influenza A virus to cause annual epidemics. Here, Chai et al. isolate a human monoclonal antibody that binds to a conserved epitope in the viral HA protein, neutralizes IBV strains in vitro, and protects mice against IBV infection.

Prognostic Significance of Major Histocompatibility Complex Class II Expression in Pediatric Adrenocortical Tumors: A St. Jude and Children's Oncology Group Study

PURPOSE

Histologic markers that differentiate benign and malignant pediatric adrenocortical tumors are lacking. Previous studies have implicated an association of MHC class II expression with adrenocortical tumor prognosis. Here, we determined the expression of MHC class II as well as the cell of origin of these immunologic markers in pediatric adrenocortical tumor. The impact of MHC class II gene expression on outcome was determined in a cohort of uniformly treated children with adrenocortical carcinomas.

EXPERIMENTAL DESIGN

We analyzed the expression of MHC class II and a selected cluster of differentiation genes in 63 pediatric adrenocortical tumors by Affymetrix Human U133 Plus 2.0 or HT HG-U133+PM gene chip analyses. Cells expressing MHC class II were identified by morphologic and immunohistochemical assays.

RESULTS

MHC class II expression was significantly greater in adrenocortical adenomas than in carcinomas (P = 4.8 ×10^-6) and was associated with a higher progression-free survival (PFS) estimate (P = 0.003). Specifically, HLA-DPA1 expression was most significantly associated with PFS after adjustment for tumor weight and stage. HLA-DPA1 was predominantly expressed by hematopoietic infiltrating cells and undetectable in tumor cells in 23 of 26 cases (88%).

CONCLUSIONS

MHC class II expression, which is produced by tumor-infiltrating immune cells, is an indicator of disease aggressiveness in pediatric adrenocortical tumor. Our results suggest that immune responses modulate adrenocortical tumorigenesis and may allow the refinement of risk stratification and treatment for this disease. Clin Cancer Res; 22(24); 6247-55. ©2016 AACR.