Overexpression of BUB1B contributes to progression of prostate cancer and predicts poor outcome in patients with prostate cancer

Overexpression of BubR1 Mitotic Checkpoint Protein Predicts Short Survival and Influences the Progression of Cholangiocarcinoma

Budding Uninhibited by Benzimidazole-Related 1 (BubR1) or BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B) is an essential component of the spindle assembly checkpoint (SAC), which controls chromosome separation during mitosis. Overexpression of BubR1 has been associated with the progression of various cancers. This study demonstrated that high expression of BubR1 correlated with cholangiocarcinogenesis in a hamster cholangiocarcinoma (CCA) model and was associated with shorter survival in patients with CCA. Co-expression of BubR1 and MPS1, which is a SAC-related protein, indicated a shorter survival rate in patients with CCA. Knockdown of BubR1 expression by specific siRNA (siBubR1) significantly decreased cell proliferation and colony formation while inducing apoptosis in CCA cell lines. In addition, suppression of BubR1 inhibited migration and invasion abilities via epithelial-mesenchymal transition (EMT). A combination of siBubR1 and chemotherapeutic drugs showed synergistic effects in CCA cell lines. Taken together, this finding suggested that BubR1 had oncogenic functions, which influenced CCA progression. Suppression of BubR1 might be an alternative option for CCA treatment.

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.

Comprehensive data mining reveals RTK/RAS signaling pathway as a promoter of prostate cancer lineage plasticity through transcription factors and CNV

Prostate cancer lineage plasticity is a key driver in the transition to neuroendocrine prostate cancer (NEPC), and the RTK/RAS signaling pathway is a well-established cancer pathway. Nevertheless, the comprehensive link between the RTK/RAS signaling pathway and lineage plasticity has received limited investigation. In particular, the intricate regulatory network governing the interplay between RTK/RAS and lineage plasticity remains largely unexplored. The multi-omics data were clustered with the coefficient of argument and neighbor joining algorithm. Subsequently, the clustered results were analyzed utilizing the GSEA, gene sets related to stemness, multi-lineage state datasets, and canonical cancer pathway gene sets. Finally, a comprehensive exploration of the data based on the ssGSEA, WGCNA, GSEA, VIPER, prostate cancer scRNA-seq data, and the GPSAdb database was conducted. Among the six modules in the clustering results, there are 300 overlapping genes, including 3 previously unreported prostate cancer genes that were validated to be upregulated in prostate cancer through RT-qPCR. Function Module 6 shows a positive correlation with prostate cancer cell stemness, multi-lineage states, and the RTK/RAS signaling pathway. Additionally, the 19 leading-edge genes of the RTK/RAS signaling pathway promote prostate cancer lineage plasticity through a complex network of transcriptional regulation and copy number variations. In the transcriptional regulation network, TP63 and FOXO1 act as suppressors of prostate cancer lineage plasticity, whereas RORC exerts a promoting effect. This study provides a comprehensive perspective on the role of the RTK/RAS pathway in prostate cancer lineage plasticity and offers new clues for the treatment of NEPC.

Knockdown of BUB1B Inhibits the Proliferation, Migration, and Invasion of Colorectal Cancer by Regulating the JNK/c-Jun Signaling Pathway

Background: Colorectal cancer (CRC) ranks as the third most common cancer, accounting for a significant number of cancer-related deaths worldwide every year. Yet, the molecular mechanisms responsible for the progression of this malignancy are not fully understood. Numerous studies indicate that BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) plays a role in the progression of various malignant tumors. However, the specific biological functions and the detailed mechanisms of how BUB1B influences CRC are still not completely known. This study aimed to explore the expression and role of BUB1B in CRC. Materials and Methods: To achieve this, the expression levels of BUB1B in human CRC tissues and cell lines were examined using real-time polymerase chain reaction and Western blotting. The role and associated mechanisms of BUB1B in CRC cell progression were assessed both in vitro and in vivo using RNA interference. Results: The findings of this study revealed an elevated expression of BUB1B in both CRC tissues and cell lines. The silencing of BUB1B in CRC cell lines notably inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest and apoptosis. In addition, the knockdown of BUB1B inhibited the JNK/c-Jun signaling pathway, increased the expression of proapoptotic proteins, and decreased the expression of antiapoptotic proteins. The effects of BUB1B knockdown on CRC cell progression were reversed by the JNK activator PAF(C-16). Conclusions: In summary, the suppression of BUB1B hindered malignant tumor progression and heightened apoptosis and cell cycle arrest in CRC cells via the JNK/c-Jun pathway. Importantly, the removal of BUB1B expression curtailed tumor growth in human CRC xenografts in nude mice, suggesting its potential as a promising therapeutic target for CRC patients. ClinicalTrials.gov ID: No.2019 K-C086.

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.

Budding uninhibited by benzimidazoles 1 promotes cell proliferation, invasion, and epithelial-mesenchymal transition via the Wnt/β-catenin signaling in glioblastoma

The pathogenesis and progression of GBM (glioblastoma), as one of the most frequently occurring malignancies of the central nervous system, are regulated by several genes. BUB1 (budding uninhibited by benzimidazoles 1) is a mitotic checkpoint that plays an important role in chromosome segregation as well as in various tumors. However, its role in glioma is unknown. The current study discovered prominently elevated BUB1 in glioma and a significant relationship between BUB1 expression, a high World Health Organization grade, and a poor prognosis in glioma patients. Moreover, BUB1 triggered EMT (epithelial-mesenchymal transition) apart from promoting glioma cell proliferation, migration, and infiltration. Besides, BUB1 promoted EMT by activating the Wnt/β-catenin axis. As implied by our study, BUB1 probably has the potential as a target for GBM management.

Pan-cancer investigation of psoriasis-related BUB1B gene: genetical alteration and oncogenic immunology

Unknown factors contribute to psoriasis' hyperproliferative, chronic, inflammatory, and arthritic features. Psoriasis patients have been linked to an increased risk of cancer, though the underlying genetics remain unknown. Since our prior research indicated that BUB1B contributes to the development of psoriasis, we designed and carried out this investigation using bioinformatics analysis. Using the TCGA database, we investigated the oncogenic function of BUB1B in 33 tumor types. To sum up, our work sheds light on BUB1B's function in pan-cancer from various perspectives, including its pertinent signaling pathways, mutation locations, and connection to immune cell infiltration. BUB1B was shown to have a non-negligible role in pan-cancer, which is connected to immunology, cancer stemness, and genetic alterations in a variety of cancer types. BUB1B is highly expressed in a variety of cancers and may serve as a prognostic marker. This study is anticipated to offer molecular details on the elevated cancer risk that psoriasis sufferers experience.

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.

Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database

Objective

The data of lung adenocarcinoma- (LUAD-) related gene expression profiles were mined from the Cancer Genome Atlas (TCGA) database using bioinformatics methods and potential biomarkers related to the occurrence, development, and prognosis of LUAD were screened out to explore the key prognostic genes and clinical significance.

Methods

Following the LUAD gene expression profile data that were initially exported from the TCGA database, R software DESeq2 was employed to analyze the difference between the expression profiles of LUAD and normal tissues. The R package "clusterProfiler" was subsequently utilized to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differential genes. A protein-protein interaction (PPI) network was constructed via the String database, and cytohubba, a plugin of Cytoscape, was applied to screen hub genes using the MCC algorithm. The Gene Expression Profile Data Interactive Analysis (GEPIA) was used to analyze expressions of 10 candidate genes in LUAD samples and healthy lung samples, and the selected genes were employed for survival analysis.

Results

A total of 1,598 differential genes were identified through differential analyses and data mining, with 1,394 genes upregulated and 204 downregulated. A total of 10 hub genes CCNA2, CDC20, CCNB2, KIF11, TOP2A, BUB1, BUB1B, CENPF, TPX2, and KIF2C were obtained using the cytohubba plugin. The results of the GEPIA analysis indicated that compared with normal lung tissue, the mRNA expression level of the described hub genes in LUAD tissue was significantly increased (P < 0.05). Survival analysis revealed that these genes had a significant impact on the overall survival time of LUAD patients (P < 0.05).

Conclusion

The previously described key genes related to LUAD identified in the TCGA database may be used as potential prognostic biomarkers, which will contribute to further comprehension of the occurrence and development of LUAD and provide references for its diagnosis and treatment.

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.

ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG May Serve as Diagnostic and Prognostic Biomarkers in Endometrial Carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC), the most common gynecologic malignancy in developed countries, remains to be a major public health problem. Further studies are surely needed to elucidate the tumorigenesis of UCEC. Herein, intersecting 203 differentially expressed genes (DEGs) were identified with the GSE17025, GSE63678, and The Cancer Genome Atlas-UCEC datasets. The Gene Ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis and protein-protein interaction (PPI) network were performed on those 203 DEGs. Intriguingly, 6 of the top 10 nodes in the PPI network were related to unfavorable prognosis, that is, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG. The mRNA and protein expression levels of the 6 hub genes were elevated in UCEC tissues compared to normal tissues. Higher expression of the 6 hub genes was associated with poor prognostic clinicopathological characteristics. The receiver operating characteristic curve suggested the significant diagnostic ability of the 6 hub genes for UCEC. Then, underlying pathogeneses of UCEC including promoter methylation level, TP53 mutation status, genomic genetic variation, and immune cells infiltration were analyzed. The mRNA expression level of the 6 hub genes was also higher in cervical squamous cell carcinoma and endocervical adenocarcinoma, uterine carcinosarcoma, and ovarian serous cystadenocarcinoma tissues than in corresponding normal tissues. In conclusion, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG may be considered diagnostic and prognostic biomarkers in UCEC.

Bioinformatics Analysis Reveals FOXM1/BUB1B Signaling Pathway as a Key Target of Neosetophomone B in Human Leukemic Cells: A Gene Network-Based Microarray Analysis

Abnormal expression of Forkhead box protein M1 (FOXM1) and serine/threonine kinase Budding uninhibited by benzimidazoles 1 (BUB1B) contributes to the development and progression of several cancers, including chronic myelogenous leukemia (CML). However, the molecular mechanism of the FOXM1/BUB1B regulatory network and the role of Neosetophomone-B (NSP-B) in leukemia remains unclear. NSP-B, a meroterpenoid fungal secondary metabolite, possesses anticancer potential in human leukemic cells lines; however, the underlying mechanism has not been elucidated. The present study aimed to explore the role of NSP-B on FOXM1/BUB1B signaling and the underlying molecular mechanism of apoptosis induction in leukemic cells. We performed gene expression profiling of NSP-B-treated and untreated leukemic cells to search for differentially expressed genes (DEGs). Interestingly BUB1B was found to be significantly downregulated (logFC -2.60, adjusted p = 0.001) in the treated cell line with the highest connectivity score among cancer genes. Analysis of TCGA data revealed overexpression of BUB1B compared to normal in most cancers and overexpression was associated with poor prognosis. BUB1B also showed a highly significant positive correlation with FOXM1 in all the TCGA cancer types. We used human leukemic cell lines (K562 and U937) as an in vitro study model to validate our findings. We found that NSP-B treatment of leukemic cells suppressed the expression of FOXM1 and BUB1B in a dose-dependent manner. In addition, NSP-B also resulted in the downregulation of FOXM1-regulated genes such as Aurora kinase A, Aurora kinase B, CDK4, and CDK6. Suppression of FOXM1 either by siRNA or NSP-B reduced BUB1B expression and enhanced cell survival inhibition and induction of apoptosis. Interestingly combination treatment of thiostrepton and NSP-B suppressed of cell viability and inducted apoptosis in leukemic cells via enhancing the activation of caspase-3 and caspase-8 compared with single-agent treatment. These results demonstrate the important role of the FOXM1/BUB1B pathway in leukemia and thus a potential therapeutic target.

Aberrant Expression of BUB1B Contributes to the Progression of Thyroid Carcinoma and Predicts Poor Outcomes for Patients

Objective: This study aimed to clarify the function and potential mechanism of BUB1B in THCA.

Methods: Expression of BUB1B in THCA was firstly determined, and its important prognostic value was then demonstrated. The potential mechanism was initially predicted by KEGG analysis. To explore the specific function of BUB1B in THCA, we used lentivirus infection to knock down the BUB1B, and then performed flow cytometry, colony formation, transwell, and wound-healing assays. Related protein expression was detected through western blotting. Additionally, we predicted the BUB1B-regulated pathways involved in THCA by GSEA analysis.

Results: BUB1B expression was highly increased in THCA tissues relative to normal controls. We further found that BUB1B was essential for tumor cell proliferation, and BUB1B high expression predicted a shorter PFS time of THCA patients. More importantly, Cox regression determined the BUB1B as an independent prognostic factor for PFS in THCA. BUB1B was initially found to participate in the cell cycle pathway from KEGG analysis. Unexpectedly, we did not detect the disturbing effect on the cell cycle distribution of THCA cells with BUB1B knockdown. But, BUB1B knockdown inhibited the proliferation, invasion, and migration of THCA cells, as well as increased apoptotic cells, and the results were further confirmed by western blotting. Through GSEA analysis, we predicted a positive correlation between BUB1B and metastasis-related pathways such as mTOR and NF-kappa B signaling pathways.

Conclusions: Present study identified BUB1B as a promising clinical prognostic factor in THCA, as well as a potential novel therapeutic target for cancer treatment.

BUBs Are New Biomarkers of Promoting Tumorigenesis and Affecting Prognosis in Breast Cancer

Background

A tumor occurs because of abnormal cell multiplication caused by many variables like a significant disturbance in the regulation of cell growth and the instability of chromosome mitosis. Budding uninhibited by benzimidazoles 1 (BUB1), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and budding uninhibited by benzimidazoles 3 (BUB3) are key regulators of mitosis, and their abnormal expression is highly correlated with breast cancer (BrCa), sarcoma, hepatic carcinoma, and other malignant tumors. However, the occurrence of BUBs (BUB1, BUB1B, and BUB3) and the development of BrCa have not been systematically explained.

Methods

Find out the target gene by looking up literature on PubMed and CNKI. Using the R software, TCGA, GEO, Kaplan-Meier Plotter, TIMER, and other databases, we studied the level of transcription, genetic changes, and physiological functions of BUBs in BrCa patients and their relationship with the origin, development, prognosis, immunity, and drug resistance of BrCa patients. Findings. We found that the high expression level of BUBs in BrCa tissues proposed a poor prognosis. The multivariate Cox regression analysis suggested that BUB1B and BUB3 might be independent prognostic factors of BrCa. In addition, the Metascape functional enrichment analysis showed that BUBs may be involved in the composition of the spindle, chromosome, and other structures and play a role in mitosis, sister chromatid separation, and other processes. Pathway enrichment suggests that BUBs may affect the cell cycle and lead to abnormal proliferation. Meanwhile, we also found that BUB3 can negatively regulate B lymphocytes, and BUB1 and BUB1B inhibit immune responses by promoting the secretion level of checkpoint molecules of the immune system, leading to immune escape of tumor cells.

Conclusion

We speculate that BUB1, BUB1B, and BUB3 may be therapeutic targets for BrCa patients and also provide new therapeutic strategies for BrCa treatment.

Identification and Validation of Hub Genes with Poor Prognosis in Hepatocellular Carcinoma by Integrated Bioinformatical Analysis

Background

Hepatocellular carcinoma (HCC) is the reason for the world’s second largest cancer-related death. It is clinically valuable to study the molecular mechanisms of HCC occurrence and development for formulating more effective diagnosis and treatment strategies.

Methods

The five microarray data sets GSE45267, GSE101685, GSE84402, GSE62232 and GSE45267 were downloaded from Gene Expression Omnibus (GEO) database, including 165 HCC tissues and 73 normal tissues. Differential expressed genes (DEGs) between HCC tissues and normal tissues were determined by GEO2R. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and the protein–protein interaction network (PPI) network analysis were employed to identify DEGs and to evaluate the clinical significance in prognosis of HCC.

Results

A total of 152 genes differentially expressed in HCC tissues and normal tissues were identified. GO and KEGG functional enrichment analysis revealed that 39 up-regulated genes were mainly enriched in mitosis, cell cycle and oocyte meiosis, while those down-regulated genes (113) were concentrated in exogenous drug catabolism and the metabolism of cytochrome P450 on exogenous drugs. Totally, 19 hub genes were chosen by PPI network and module analysis and verified by The Cancer Genome Atlas (TCGA) database. Finally, 8 hub genes were selected, including CDK1, CYP2C8, CCNB1, AURKA, CYP2C9, BUB1B, MAD2L1 and TTK, which were associated with the overall survival rate of HCC patients.

Conclusion

This study presented eight target genes connected to the prognosis of HCC patients. Those mainly exists in cell cycle and drug catabolism, which may be latent targets for clinical treatment.

The Interplay of Cohesin and RNA Processing Factors: The Impact of Their Alterations on Genome Stability

Cohesin, a multi-subunit protein complex, plays important roles in sister chromatid cohesion, DNA replication, chromatin organization, gene expression, transcription regulation, and the recombination or repair of DNA damage. Recently, several studies suggested that the functions of cohesin rely not only on cohesin-related protein-protein interactions, their post-translational modifications or specific DNA modifications, but that some RNA processing factors also play an important role in the regulation of cohesin functions. Therefore, the mutations and changes in the expression of cohesin subunits or alterations in the interactions between cohesin and RNA processing factors have been shown to have an impact on cohesion, the fidelity of chromosome segregation and, ultimately, on genome stability. In this review, we provide an overview of the cohesin complex and its role in chromosome segregation, highlight the causes and consequences of mutations and changes in the expression of cohesin subunits, and discuss the RNA processing factors that participate in the regulation of the processes involved in chromosome segregation. Overall, an understanding of the molecular determinants of the interplay between cohesin and RNA processing factors might help us to better understand the molecular mechanisms ensuring the integrity of the genome.

Androgen receptor negatively regulates mitotic checkpoint signaling to induce docetaxel resistance in castration-resistant prostate cancer

BACKGROUND

Despite multiple treatment advances for castration-resistant prostate cancer (CRPC), there are currently no curative therapies and patients ultimately to succumb to the disease. Docetaxel (DTX) is the standard first-line chemotherapy for patients with metastatic CRPC; however, drug resistance is inevitable and often develops rapidly, leading to disease progression in nearly all patients. In contrast, when DTX is deployed with androgen deprivation therapy in castration-sensitive disease, more durable responses and improved outcomes are observed, suggesting that aberrant androgen receptor (AR) signaling accelerates DTX resistance in CRPC. In this study, we demonstrate that AR dysregulates the mitotic checkpoint, a critical pathway involved in the anticancer action of DTX.

METHODS

Androgen-dependent and independent cell lines were used to evaluate the role of AR in DTX resistance. Impact of drug treatment on cell viability, survival, and cell-cycle distribution were determined by plate-based viability assay, clonogenic assay, and cell-cycle analysis by flow cytometry, respectively. Mitotic checkpoint kinase signal transduction and apoptosis activation was evaluated by Western blotting. Pathway gene expression analysis was evaluated by RT-PCR. A Bliss independence model was used to calculate synergy scores for drug combination studies.

RESULTS

Activation of AR in hormone-sensitive cells induces a rescue phenotype by increasing cell viability and survival and attenuating G2/M arrest in response to DTX. Analysis of mitotic checkpoint signaling shows that AR negatively regulates spindle checkpoint signaling, resulting in premature mitotic progression and evasion of apoptosis. This phenotype is characteristic of mitotic slippage and is also observed in CRPC cell lines where we demonstrate involvement of AR splice variant AR-v7 in dysregulation of checkpoint signaling. Our findings suggest that DTX resistance is mediated through mechanisms that drive premature mitotic exit. Using pharmacologic inhibitors of anaphase-promoting complex/cyclosome and polo-like kinase 1, we show that blocking mitotic exit induces mitotic arrest, apoptosis, and synergistically inhibits cell survival in combination with DTX.

CONCLUSION

Our results suggest that targeting the mechanisms of dysregulated mitotic checkpoint signaling in AR-reactivated tumors has significant clinical potential to extend treatment benefit with DTX and improve outcomes in patients with lethal prostate cancer.

Integrative Analysis of Epigenome and Transcriptome Data Reveals Aberrantly Methylated Promoters and Enhancers in Hepatocellular Carcinoma

DNA methylation is a key transcription regulator, whose aberration was ubiquitous and important in most cancers including hepatocellular carcinoma (HCC). Whole-genome bisulfite sequencing (WGBS) was conducted for comparison of DNA methylation in tumor and adjacent tissues from 33 HCC patients, accompanying RNA-seq to determine differentially methylated region-associated, differentially expressed genes (DMR-DEGs), which were independently replicated in the TCGA-LIHC cohort and experimentally validated via 5-aza-2-deoxycytidine (5-azadC) demethylation. A total of 9,867,700 CpG sites showed significantly differential methylation in HCC. Integrations of mRNA-seq, histone ChIP-seq, and WGBS data identified 611 high-confidence DMR-DEGs. Enrichment analysis demonstrated activation of multiple molecular pathways related to cell cycle and DNA repair, accompanying repression of several critical metabolism pathways such as tyrosine and monocarboxylic acid metabolism. In TCGA-LIHC, we replicated about 53% of identified DMR-DEGs and highlighted the prognostic significance of combinations of methylation and expression of nine DMR-DEGs, which were more efficient prognostic biomarkers than considering either type of data alone. Finally, we validated 22/23 (95.7%) DMR-DEGs in 5-azadC-treated LO2 and/or HepG2 cells. In conclusion, integration of epigenome and transcriptome data depicted activation of multiple pivotal cell cycle-related pathways and repression of several metabolic pathways triggered by aberrant DNA methylation of promoters and enhancers in HCC.

BUB1 drives the occurrence and development of bladder cancer by mediating the STAT3 signaling pathway

Background

The incidence of bladder urothelial carcinoma (UC), a common malignancy of the urinary tract, is approximately three times higher in men than in women. High expression of the mitotic kinase BUB1 is associated with the occurrence and development of several cancers, although the relationship between BUB1 and bladder tumorigenesis remains unclear.

Methods

Using a microarray approach, we found increased BUB1 expression in human BCa. The association between BUB1 and STAT3 phosphorylation was determined through molecular and cell biological methods. We evaluated the impact of pharmacologic inhibition of BUB1 kinase activity on proliferation and BCa progression in vitro and in vivo.

Results

In this study, we found that BUB1 expression was increased in human bladder cancer (BCa). We further identified through a series of molecular and cell biological approaches that BUB1 interacted directly with STAT3 and mediated the phosphorylation of STAT3 at Ser727. In addition, the findings that pharmacologic inhibition of BUB1 kinase activity significantly suppressed BCa cell proliferation and the progression of bladder cancer in vitro and in vivo were further verified. Finally, we found that the BUB1/STAT3 complex promoted the transcription of STAT3 target genes and that depletion of BUB1 and mutation of the BUB1 kinase domain abrogated this transcriptional activity, further highlighting the critical role of kinase activity in the activation of STAT3 target genes. A pharmacological inhibitor of BUB1 (2OH-BNPP1) was able to significantly inhibit the growth of BCa cell xenografts.

Conclusion

This study showed that the BUB1 kinase drives the progression and proliferation of BCa by regulating the transcriptional activation of STAT3 signaling and may be an attractive candidate for therapeutic targeting in BCa.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02179-z.

Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis

Independent Component Analysis is a matrix factorization method for data dimension reduction. ICA has been widely applied for the analysis of transcriptomic data for blind separation of biological, environmental, and technical factors affecting gene expression. The study aimed to analyze the publicly available esophageal cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways and molecular signatures involved in this cancer type. In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used. A bioinformatics tool « BiODICA-Independent Component Analysis of Big Omics Data» was applied to compute independent components (ICs). Gene Set Enrichment Analysis (GSEA) and ToppGene uncovered the most significantly enriched pathways. Construction and visualization of gene networks and graphs were performed using the Cytoscape, and HPRD database. The correlation graph between decompositions into 30 ICs was built with absolute correlation values exceeding 0.3. Clusters of components-pseudocliques were observed in the structure of the correlation graph. The top 1,000 most contributing genes of each ICs in the pseudocliques were mapped to the PPI network to construct associated signaling pathways. Some cliques were composed of densely interconnected nodes and included components common to most cancer types (such as cell cycle and extracellular matrix signals), while others were specific to EC. The results of this investigation may reveal potential biomarkers of esophageal carcinogenesis, functional subsystems dysregulated in the tumor cells, and be helpful in predicting the early development of a tumor.

Identification of potential biomarkers associated with immune infiltration in papillary renal cell carcinoma

Background

Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune‐mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune‐related biomarkers for pRCC.

Methods

The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC samples. Univariate Cox analysis was used to select the prognostic‐related tumor‐infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic‐related TIICs. Then, these pRCC samples were divided into low‐ and high‐risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high‐ and low‐risk groups. The hub genes were identified using a weighted gene co‐expression network analysis (WGCNA) and a protein‐protein interaction (PPI) analysis. The hub genes were subsequently validated by multiple clinical traits and databases.

Results

According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune‐related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF.

Conclusion

In this study, our data revealed the crucial TIICs and potential immune‐related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC.

Gene Expression Analysis Reveals Key Genes and Signalings Associated with the Prognosis of Prostate Cancer

It is urgent to identify novel biomarkers for prostate cancer (PCa) prognosis and to understand the mechanisms regulating the tumorigenesis for PCa treatment. In this study, GSE17951 and TCGA were used to identify the differentially expressed genes (DEGs). Our study demonstrated that 1533 genes with increased expression and 2301 genes with decreased expression in PCa. Bioinformatics analysis data indicated that these up-regulated genes had an association with the modulation of mitotic nuclear division, sister chromatid cohesion, cell division, and cell cycle. Additionally, our results revealed downregulated genes took part in modulating extracellular matrix organization, angiogenesis, signal transduction, and Ras signaling pathway. Hub upregulated and downregulated PPI networks were identified by protein-protein interaction (PPI) network analysis and MCODE analysis. Of note, 12 cell cycle regulators, comprising CCNB1, CCNB2, PLK1, TTK, AURKA, CDC20, BUB1, PTTG1, CDC45, CDC25C, CCNA2, and BUB1B, were demonstrated to function crucially in PCa development. By detecting their expression in PCa cell lines, we confirmed that these cell cycle regulator expressions were heightened in PCa cells. GEPIA databases analysis showed that higher expression of these cell cycle regulators was correlated to shorter disease-free survival (DFS) time in PCa samples. Our findings collectively suggested targeting cell cycle pathways may offer novel prognosis and treatment biomarkers for PCa.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

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.

Genetics of congenital solid tumors

When we discuss the genetics of tumors, we cannot fail to remember that in the second decade of the twentieth century, more precisely in 1914, Theodore Boveri defined for the first time the chromosomal bases of cancer. In the last 30 years, progresses in genetics have only confirmed Boveri's remarkable predictions made more than 80 years ago. Before the cloning of the retinoblastoma 1 (RB1) gene, the existence of a genetic component in most, if not all, solid childhood tumors were well known. The existence of familial tumor aggregations has been found much more frequently than researchers expected to find at random. Sometimes, the demonstration of this family predisposition was very difficult, because the survival of children diagnosed as having a certain tumor, up to an age at which reproduction and procreation is possible, was very rare. In recent years, advances in the diagnosis and treatment of these diseases have made it possible for these children to survive until the age when they were able to start their own families, including the ability to procreate. Four distinct groups of so-called cancer genes have been identified: oncogenes, which promote tumor cell proliferation; tumor suppressor genes, which inhibit this growth/proliferation; anti-mutational genes, with a role in deoxyribonucleic acid (DNA) stability; and micro-ribonucleic acid (miRNA) genes, with a role in the posttranscriptional process.

KIF4A knockdown suppresses ovarian cancer cell proliferation and induces apoptosis by downregulating BUB1 expression

Ovarian cancer is one of the most common lethal gynecological malignancies worldwide. Abnormal kinesin family member 4A (KIF4A) expression has been implicated in ovarian cancer progression; however, the potential mechanism underlying KIF4A in ovarian cancer is not completely understood. The present study aimed to clarify the molecular basis of KIF4A in ovarian cancer. KIF4A and budding uninhibited by benzimidazoles 1 (BUB1) expression levels were detected via reverse transcription-quantitative PCR and western blotting. Cell Counting Kit-8, colony formation, wound healing, TUNEL and flow cytometry assays were performed to assess cell proliferation, migration, apoptosis and cell cycle distribution, respectively. Ki67 expression levels were detected by conducting immunofluorescence assays. The expression levels of migration- and apoptosis-related proteins were measured via western blotting. A co-immunoprecipitation assay was conducted to determine the association between KIF4A and BUB1. The results demonstrated that KIF4A was expressed at significantly higher levels in ovarian cancer cell lines compared with IOSE-80 cells. Compared with the short hairpin RNA-negative control group, KIF4A knockdown significantly inhibited cell viability, colony formation and migration, and markedly induced cell apoptosis. The results indicated that KIF4A could bind to BUB1 and regulate BUB1 expression. BUB1 overexpression weakened KIF4A knockdown-mediated effects on cell viability, colony formation, migration and apoptosis. Overall, the present study demonstrated that KIF4A knockdown suppressed ovarian cancer progression by regulating BUB1, and suggested the potential value of KIF4A and BUB1 as therapeutic targets for ovarian cancer.

Expression and prognosis analyses of BUB1, BUB1B and BUB3 in human sarcoma

Budding Uninhibited By Benzimidazoles are a group of genes encoding proteins that play central roles in spindle checkpoint during mitosis. Improper mitosis may lead to aneuploidy which is found in many types of tumors. As a key mediator in mitosis, the dysregulated expression of BUBs has been proven to be highly associated with various malignancies, such as leukemia, gastric cancer, breast cancer, and liver cancer. However, bioinformatic analysis has not been applied to explore the role of the BUBs in sarcomas. Herein, we investigate the transcriptional and survival data of BUBs in patients with sarcomas using Oncomine, Gene Expression Profiling Interactive Analysis, Cancer Cell Line Encyclopedia, Kaplan-Meier Plotter, LinkedOmics, and the Database for Annotation, Visualization and Integrated Discovery. We found that the expression levels of BUB1, BUB1B and BUB3 were higher in sarcoma samples and cell lines than in normal controls. Survival analysis revealed that the higher expression levels of BUB1, BUB1B and BUB3 were associated with lower overall and disease-free survival in patients with sarcomas. This study implies that BUB1, BUB1B and BUB3 are potential treatment targets for patients with sarcomas and are new biomarkers for the prognosis of sarcomas.

Bioinformatics analysis identified hub genes in prostate cancer tumorigenesis and metastasis

OBJECTIVE

Prostate cancer (PCa) is the most frequent cancer found in males worldwide, and its mortality rate is increasing every year. To discover key molecular changes in PCa development and metastasis, we analyzed microarray data of localized PCa, metastatic PCa and normal prostate tissue samples from clinical specimens.

METHODS

Gene expression profiling datasets of PCa were analyzed online. The DAVID was used to perform GO functional and KEGG pathway enrichment analyses. CytoHubba in Cytoscape software was applied to identify hub genes. Survival data were downloaded from GEPIA. Gene expression data were obtained from ONCOMINE and UALCAN.

RESULTS

We obtained 4 sets of differentially expressed genes (DEGs), DEGs 1: a comparison of the gene expression between 4 normal prostate and 5 localized PCa samples in GSE27616; DEGs 2: a comparison of the gene expression between 6 normal prostate and 7 localized PCa samples in GSE3325; DEGs 3: a comparison of the gene expression between 5 localized PCa and 4 metastatic PCa samples in GSE27616; DEGs 4: a comparison of the gene expression between 7 localized PCa and 6 metastatic PCa samples in GSE3325. A comparison of these 4 sets of genes revealed 51 overlapped genes. GO function analysis revealed enrichment of the 51 DEGs in functions related to the proteinaceous extracellular matrix and centrosome, protein homodimerization activity and chromatin binding were the main functions of these genes, which participated in regulating cell division, mitotic nuclear division, proteinaceous extracellular matrix, cell adhesion and apoptotic process. KEGG pathway analysis indicated that these identified DEGs were mainly enriched in progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle. We defined the 16 genes with the highest degree of connectivity as the hub genes in the 51 overlapped DEGs. Cox regression revealed TOP2A, CCNB2, BUB1, CDK1 and EZH2 were related to Disease-free survival (DFS). The expression levels of the 5 genes were 2.232-, 1.786-, 2.303-, 1.699-, and 1.986-fold higher in PCa than the levels in normal tissues, respectively (P < 0.05). We obtained 20 hub genes from DEGs by the comparison of normal prostate tissue vs. localized cancer tissue. Among them, KIF20A, CDKN3, PBK and CDCA2, were expressed higher in PCa than in normal tissues, and were associated with the DFS of PCa patients. Meanwhile, we obtained 20 hub genes from DEGs by the comparison of localized cancer tissue vs. metastatic cancer tissue. Cox regression revealed PLK1, CCNA2 and CDC20, were associated with both the DFS and overall survival of PCa patients.

CONCLUSIONS

The results suggested that the functions of KIF20A, CDKN3, PBK and CDCA2 may contribute to PCa development and the functions of PLK1, CCNA2 and CDC20 may contribute to PCa metastasis. Meanwhile, the functions of TOP2A, CCNB2, BUB1, CDK1 and EZH2 may contribute to both PCa development and metastasis.

Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated With Distant Metastasis and Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy with high distant metastasis rate. Long non-coding RNAs (LncRNAs) are reported to be upregulated or downregulated in multiple cancers and play a crucial role in the metastasis of tumors or prognosis. Therefore, the purpose of our study is to construct a prognostic signature for ccRCC based on distant metastasis-related lncRNAs and explore the involved potential competitive endogenous RNA (ceRNA) network. The differentially expressed genes (DEGs) screened from the database of the cancer genome atlas (TCGA) were used to construct a co-expression network and identify the distant metastasis-related module by weighted gene co-expression network analysis (WGCNA). Key genes with metastatic and prognostic significance were identified through rigorous screening, including survival analysis, correlation analysis, and expression analyses in stage, grade, and distant metastasis, and were verified in the data set of gene expression omnibus (GEO) and the database from gene expression profiling interactive analysis (GEPIA). The potential upstream miRNAs and lncRNAs were predicted via five online databases and LncBase. Here, we constructed a ceRNA network of key genes that are significantly associated with the distant metastasis and prognosis of patients with ccRCC. The distant metastasis-related lncRNAs were used to construct a risk score model through the univariate, least absolute shrinkage selection operator (LASSO), and multivariate Cox regression analyses, and the patients were divided into high- and low-risk groups according to the median of the risk score. The Kaplan–Meier survival analysis demonstrated that mortality was significantly higher in the high-risk group than in the low-risk group. Considering the other clinical phenotype, the Cox regression analyses indicated that the lncRNAs model could function as an independent prognostic factor. Quantitative real-time (qRT)-PCR in the tissues and cells of ccRCC verified the high-expression level of three lncRNAs. Gene set enrichment analysis (GSEA) revealed that the lncRNA prognostic signature was mainly enriched in autophagy- and immune-related pathways, indicating that the autophagy and immune functions may play an important role in the distant metastasis of ccRCC. In summary, the constructed distant metastasis-related lncRNA signature could independently predict prognosis in patients with ccRCC, and the related ceRNA network provided a new sight on the potential mechanism of distant metastasis and a promising therapeutic target for ccRCC.

Identification of Core Genes Related to Progression and Prognosis of Hepatocellular Carcinoma and Small-Molecule Drug Predication

Background

Hepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential.

Methods and Results

In this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database.

Conclusion

In conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

INTRODUCTION

BUB1 mitotic checkpoint serine/threonine kinase B encoded by BUB1B gene is a member of the spindle assembly checkpoint family. Several reports have demonstrated that overexpression of BUB1B is associated with cancer progression and prognosis.

OBJECTIVE

This study aims to clarify the expression and function of BUB1B in renal cell carcinoma (RCC).

METHODS

The expression of BUB1B was determined using immunohistochemistry and bioinformatics analysis in RCC. The effects of BUB1B knockdown on cell growth and invasion were evaluated. We analyzed the interaction between BUB1B, cancer stem cell markers, p53, and PD-L1 in RCC.

RESULTS

In 121 cases of RCC, immunohistochemistry showed that 30 (25%) of the RCC cases were positive for BUB1B. High BUB1B expression was significantly correlated with high nuclear grade, T stage, and M stage. A Kaplan-Meier analysis showed that the high expression of BUB1B was associated with poor overall survival after nephrectomy. High BUB1B expression was associated with CD44, p53, and PD-L1 in RCC. Knockdown of BUB1B suppressed cell growth and invasion in RCC cell lines. Knockdown of BUB1B also suppressed the expression of CD44 and increased the expression of phospho-p53 (Ser15). In silico analysis showed that BUB1B was associated with inflamed CD8+, exhausted T-cell signature, IFN-γ signature, and the response to nivolumab.

CONCLUSION

These results suggest that BUB1B plays an oncogenic role and may be a promising predictive biomarker for survival in RCC.

Identification of hub genes in triple-negative breast cancer by integrated bioinformatics analysis

Background

Triple negative breast cancer (TNBC) is usually aggressive and accompanied by a poor prognosis. The molecular biological mechanism of TNBC pathogenesis is still unclear, and requires more detailed research. The aim of this study was to screen and verify potential biomarkers of TNBC, and provide new clues for the treatment and diagnosis of TNBC.

Methods

In this work, GSE76250 was downloaded from the Gene Expression Omnibus (GEO) database and included 165 TNBC samples and 33 paired normal breast tissues. The R software and its related software package were used for data processing and analysis. Compared with normal tissues, genes with a false discovery rate (FDR) <0.01 and log fold change (logFC) ≥1 or ≤-1 were identified as differentially expressed genes (DEGs) by limma package. Survival prognoses were analyzed by Kaplan-Meier plotter database.

Results

In total, 160 up-regulated and 180 down-regulated genes were identified. The biological mechanism of enrichment analysis presented that DEGs were significantly enriched in chromosome segregation, extracellular matrix, and extracellular matrix structural constituent, among others. A total of 8 hub genes (CCNB1, CDK1, TOP2A, MKI67, TTK, CCNA2, BUB1, and PLK1) were identified by the protein-protein interaction network (PPIN) and Cytoscape software. Survival prognosis of these hub genes showed that they were negatively correlated with overall survival.

Conclusions

The 8 hub genes and pathways that were identified might be involved in tumorigenesis and become new candidate biomarkers for TNBC treatment.

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.

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.

METHODS

The bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.

RESULTS

We ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.

CONCLUSION

The present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients

We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years’ following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05–8.70), N3 vs. N0 hazard ratio: 4.32 (1.41–13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95–6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.

Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis

Lung adenocarcinoma (LUAD) accounts for the majority of cancer-related deaths worldwide. Our study identified key LUAD genes and their potential mechanism via bioinformatics analysis of public datasets. GSE10799, GSE40791, and GSE27262 microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database. The RobustRankAggreg package was used to perform a meta-analysis, and 50 upregulated genes and 87 downregulated genes overlapped in three datasets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Furthermore, protein-protein interaction (PPI) networks of the differentially expressed genes (DEGs) were built by the Search Tool for the Retrieval of Interacting Genes (STRING) and 22 core genes were identified by Molecular Complex Detection (MCODE) and visualized with Cytoscape. Subsequently, these core genes were analyzed by the Kaplan-Meier Plotter and Gene Expression Profiling Interactive Analysis (GEPIA). The results showed that all 22 genes were significantly associated with reduced survival rates. For GEPIA, the expression of only one gene was not significantly different between LUAD tissues and normal tissues. A KEGG pathway enrichment reanalysis of the 21 genes identified five key genes (CCNB1, BUB1B, CDC20, TTK, and MAD2L1) in the cell cycle pathway. Finally, the Comparative Toxicogenomics Database (CTD) website was used to explore the relationship between these key genes and certain drugs. Based on the bioinformatics analysis, five key genes were identified in LUAD, and drugs closely associated these genes can provide clues for the treatment and prognosis of LUAD.

Comprehensive analysis of transcriptome data stemness indices identifies key genes for controlling cancer stem cell characteristics in gastric cancer

BACKGROUND

Cancer stem cells (CSCs) are the tumor cell of origin with self-renewing ability and multi-differentiation potency. CSCs can play vital roles in gastric cancer (GC) metastasis and relapse. However, the genes that regulate the stemness maintenance of CSCs in GC patients remain largely unknown. In the present study, we sought to determine the key genes associated with stemness in GC patients.

METHODS

mRNA expression-based stemness index (mRNA SI) was analyzed with regard to the differential expression levels between normal and GC tissues, as well as clinical features and survival outcomes. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules of interest and key genes. The differences in mRNA expression of key genes between normal and GC tissues were calculated by "ggpubr" package in R. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were carried out to annotate the function of key genes. Protein-protein interaction (PPI) and gene co-expression analyses were conducted using STRING and "corrplot" package in R, respectively.

RESULTS

mRNA SI score was markedly increased in GC tumor compared to normal tissues. High mRNA SI score was remarkably associated with more advanced tumor stage and higher pathologic grade, but longer survival times. Based on the results of WGCNA, 19 key genes (i.e., BUB1, BUB1B, KIF14, NCAPH, RACGAP1, KIF15, CENPF, TPX2, RAD54L, KIF18B, TTX, KIF4A, SGO2, PLK4, ARHGAP11A, XRCC2, C1orf112, NCAPG, ORC6) were identified. GO and KEGG functional analyses revealed that these 19 key genes were mainly related to cell proliferation. From PPI and gene co-expression analyses, these 19 key genes were discovered to be intensively associated with each other at both protein and transcription levels.

CONCLUSIONS

our study identified 19 key genes that play vital roles in the stemness maintenance of CSCs in GC patients. Targeting these key genes may help to control CSC characteristics in GC.

Identification of G2 and S Phase-Expressed-1 as a Potential Biomarker in Patients with Prostate Cancer

Background

This study aimed to predict and explore the possible clinical value and mechanism of genetic markers in prostate cancer (PCa) using a bioinformatics analysis method.

Materials and Methods

The RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database to identify the differentially expressed genes (DEGs). The hub genes were screened by building protein–protein interaction (PPI) subnetworks with four topological analysis methods. The overall survival analysis of hub genes was conducted using Kaplan–Meier curves. Furthermore, the bioinformatics results were confirmed in 102 PCa samples collected in our hospital. Gene Set Enrichment Analysis (GSEA) was performed to provide information about the molecular mechanisms underlying PCa.

Results

Among 13 hub genes, the high expression of GTSE1 or KIF18B was associated with worse overall survival according to the TCGA samples. Immunoreactive scores for GTSE1 staining were significantly higher in PCa tissues than in paracancerous tissues (P<0.01). The overall survival time of patients with high GTSE1 expression was shorter than that of patients with low GTSE1 expression (P=0.015). GSEA demonstrated that high GTSE1 expression was mainly enriched in the cell cycle (P<0.001), DNA replication (P<0.001), mismatch repair (P<0.001), and p53 signaling pathway (P<0.001).

Conclusion

GTSE1 expression was significantly high in PCa and associated with poor prognosis. GTSE1 may serve as a potential biomarker and therapeutic target in PCa 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.

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Background

With characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified.

Methods

The mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis.

Results

GC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037-19.051; P < 0.001). We constructed a prognostic nomogram with well-fitted calibration curve based on risk score and clinical data.

Conclusion

The 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.

Deep Proteomic Deconvolution of Interferons and HBV Transfection Effects on a Hepatoblastoma Cell Line

Interferons are commonly utilized in the treatment of chronic hepatitis B virus (HBV) infection but are not effective for all patients. A deep understanding of the limitations of interferon treatment requires delineation of its activity at multiple "omic" levels. While myriad studies have characterized the transcriptomic effects of interferon treatment, surprisingly, few have examined interferon-induced effects at the proteomic level. To remedy this paucity, we stimulated HepG2 cells with both IFN-α and IFN-λ and performed proteomic analysis versus unstimulated cells. Alongside, we examined the effects of HBV transfection in the same cell line, reasoning that parallel IFN and HBV analysis might allow determination of cases where HBV transfection counters the effects of interferons. More than 6000 proteins were identified, with multiple replicates allowing for differential expression analysis at high confidence. Drawing on a compendium of transcriptomic data, as well as proteomic half-life data, we suggest means by which transcriptomic results diverge from our proteomic results. We also invoke a recent multiomic study of HBV-related hepatocarcinoma (HCC), showing that despite HBV's role in initiating HCC, the regulated proteomic landscapes of HBV transfection and HCC do not strongly align. Special focus is applied to the proteasome, with numerous components divergently altered under IFN and HBV-transfection conditions. We also examine alterations of other protein groups relevant to HLA complex peptide display, unveiling intriguing alterations in a number of ubiquitin ligases. Finally, we invoke genome-scale metabolic modeling to predict relevant alterations to the metabolic landscape under experimental conditions. Our data should be useful as a resource for interferon and HBV researchers.

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy, which is a major cause of cancer morbidity and mortality worldwide. Thus, the aim of the present study was to identify the hub genes and underlying pathways of HCC via bioinformatics analyses. The present study screened three datasets, including GSE112790, GSE84402 and GSE74656 from the Gene Expression Omnibus (GEO) database, and downloaded the RNA-sequencing of HCC from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) in both the GEO and TCGA datasets were filtered, and the screened DEGs were subsequently analyzed for functional enrichment pathways. A protein-protein interaction (PPI) network was constructed, and hub genes were further screened to create the Kaplan-Meier curve using cBioPortal. The expression levels of hub genes were then validated in different datasets using the Oncomine database. In addition, associations between expression and tumor grade, hepatitis virus infection status, satellites and vascular invasion were assessed. A total of 126 DEGs were identified, containing 70 upregulated genes and 56 downregulated genes from the GEO and TCGA databases. By constructing the PPI network, the present study identified hub genes, including cyclin B1 (CCNB1), cell-division cycle protein 20 (CDC20), cyclin-dependent kinase 1, BUB1 mitotic checkpoint serine/threonine kinase β (BUB1B), cyclin A2, nucleolar and spindle associated protein 1, ubiquitin-conjugating enzyme E2 C (UBE2C) and ZW10 interactor. Furthermore, upregulated CCNB1, CDC20, BUB1B and UBE2C expression levels indicated worse disease-free and overall survival. Moreover, a meta-analysis of tumor and healthy tissues in the Oncomine database demonstrated that BUB1B and UBE2C were highly expressed in HCC. The present study also analyzed the data of HCC in TCGA database using univariate and multivariate Cox analyses, and demonstrated that BUB1B and UBE2C may be used as independent prognostic factors. In conclusion, the present study identified several genes and the signaling pathways that were associated with tumorigenesis using bioinformatics analyses, which could be potential targets for the diagnosis and treatment of HCC.

Identification of Core Genes and Pathways in Medulloblastoma by Integrated Bioinformatics Analysis

Medulloblastoma (MB) is one of the most common intracranial malignancies in children. The present study applied integrated bioinformatics to identify potential core genes associated with the pathogenesis of MB and reveal potential molecular mechanisms. Through the integrated analysis of multiple data sets from the Gene Expression Omnibus (GEO), 414 differentially expressed genes (DEGs) were identified. Combining the protein-protein interaction (PPI) network analysis with gene set enrichment analysis (GSEA), eight core genes, including CCNA2, CCNB1, CCNB2, AURKA, CDK1, MAD2L1, BUB1B, and RRM2, as well as four core pathways, including "cell cycle", "oocyte meiosis", "p53 pathway" and "DNA replication" were selected. In independent data sets, the core genes showed superior diagnostic values and significant prognostic correlations. Moreover, in the pan-caner data of the cancer genome atlas (TCGA), the core genes were also widely abnormally expressed. In conclusion, this study identified core genes and pathways of MB through integrated analysis to deepen the understanding of the molecular mechanisms underlying the MB and provide potential targets and pathways for diagnosis and treatment of MB.

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.

Defining an embryonal rhabdomyosarcoma endotype

Rhabdomyosarcoma (RMS) is the most common childhood soft-tissue sarcoma. The largest subtype of RMS is embryonal rhabdomyosarcoma (ERMS) and accounts for 53% of all RMS. ERMS typically occurs in the head and neck region, bladder, or reproductive organs and portends a promising prognosis when localized; however, when metastatic the 5-yr overall survival rate is ∼43%. The genomic landscape of ERMS demonstrates a range of putative driver mutations, and thus the recognition of the pathological mechanisms driving tumor maintenance should be critical for identifying effective targeted treatments at the level of the individual patients. Here, we report genomic, phenotypic, and bioinformatic analyses for a case of a 3-yr-old male who presented with bladder ERMS. Additionally, we use an unsupervised agglomerative clustering analysis of RNA and whole-exome sequencing data across ERMS and undifferentiated pleomorphic sarcoma (UPS) tumor samples to determine several major endotypes inferring potential targeted treatments for a spectrum of pediatric ERMS patient cases.

BUB1 promotes proliferation of liver cancer cells by activating SMAD2 phosphorylation

Budding uninhibited by benzimidazoles 1 (BUB1) is a mitotic checkpoint serine/threonine kinase that has been reported as an oncogene or tumor suppressor gene in various types of cancer, including breast cancer, pancreatic ductal adenocarcinoma, prostate and gastric cancers. However, its role in liver cancer remains unclear. The present study aimed to explore the biological function of BUB1 in liver cancer. The present study demonstrated that BUB1 mRNA expression levels and the intensity of immunohistochemical staining were significantly increased in liver cancer tissues compared with normal tissues. The role of BUB1 in cell proliferation was also determined. Overexpression of BUB1 significantly promoted cell proliferation, whereas knockdown of BUB1 expression inhibited the proliferation of liver cancer cell lines. In experiments investigating the underlying mechanism, overexpression of BUB1 increased the levels of SMAD2 phosphorylation, whereas knockdown of BUB1 reduced the levels of SMAD2 phosphorylation. Therefore, BUB1 may promote proliferation of liver cancer cells by activating phosphorylation of SMAD2, and BUB1 may serve as a potential target in the diagnosis and/or treatment of liver cancer.

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.

TIP5 primes prostate luminal cells for the oncogenic transformation mediated by PTEN-loss

The cell of origin and the temporal order of oncogenic events in tumors play important roles for disease state. This is of particular interest for PCa due to its highly variable clinical outcome. However, these features are difficult to analyze in tumors. We established an in vitro murine PCa organoid model taking into account the cell of origin and the temporal order of events. We found that TIP5 primes luminal prostate cells for Pten-loss mediated oncogenic transformation whereas it is dispensable once the transformation is established. Cross-species transcriptomic analyses revealed a PTEN-loss gene signature that identified a set of aggressive tumors with PTEN-del, or low PTEN expression, and high-TIP5 expression. This paper provides a powerful tool to elucidate PCa mechanisms.

Prostate cancer (PCa) is the second leading cause of cancer death in men. Its clinical and molecular heterogeneities and the lack of in vitro models outline the complexity of PCa in the clinical and research settings. We established an in vitro mouse PCa model based on organoid technology that takes into account the cell of origin and the order of events. Primary PCa with deletion of the tumor suppressor gene PTEN (PTEN-del) can be modeled through Pten-down-regulation in mouse organoids. We used this system to elucidate the contribution of TIP5 in PCa initiation, a chromatin regulator that is implicated in aggressive PCa. High TIP5 expression correlates with primary PTEN-del PCa and this combination strongly associates with reduced prostate-specific antigen (PSA) recurrence-free survival. TIP5 is critical for the initiation of PCa of luminal origin mediated by Pten-loss whereas it is dispensable once Pten-loss mediated transformation is established. Cross-species analyses revealed a PTEN gene signature that identified a group of aggressive primary PCas characterized by PTEN-del, high-TIP5 expression, and a TIP5-regulated gene expression profile. The results highlight the modeling of PCa with organoids as a powerful tool to elucidate the role of genetic alterations found in recent studies in their time orders and cells of origin, thereby providing further optimization for tumor stratification to improve the clinical management of PCa.

A new method for mining information of co-expression network based on multi-cancers integrated data

Background

Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction.

Methods

In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes.

Results

We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation.

Conclusions

The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.