Spindle assembly checkpoint gene BUB1B is essential in breast cancer cell survival

Identification of novel candidate predisposing genes in familial nonmedullary thyroid carcinoma implicating DNA damage repair pathways

The genetic basis of nonsyndromic familial nonmedullary thyroid carcinoma (FNMTC) is still poorly understood, as the susceptibility genes identified so far only account for a small percentage of the genetic burden. Recently, germline mutations in DNA repair-related genes have been reported in cases with thyroid cancer. In order to clarify the genetic basis of FNMTC, 94 genes involved in hereditary cancer predisposition, including DNA repair genes, were analyzed in 48 probands from FNMTC families, through targeted next-generation sequencing (NGS). Genetic variants were selected upon bioinformatics analysis and in silico studies. Structural modeling and network analysis were also performed. In silico results of NGS data unveiled likely pathogenic germline variants in 15 families with FNMTC, in genes encoding proteins involved in DNA repair (ATM, CHEK2, ERCC2, BRCA2, ERCC4, FANCA, FANCD2, FANCF, and PALB2) and in the DICER1, FLCN, PTCH1, BUB1B, and RHBDF2 genes. Structural modeling predicted that most missense variants resulted in the disruption of networks of interactions between residues, with implications for local secondary and tertiary structure elements. Functional annotation and network analyses showed that the involved DNA repair proteins functionally interact with each other, within the same DNA repair pathway and across different pathways. MAPK activation was a common event in tumor progression. This study supports that rare germline variants in DNA repair genes may be accountable for FNMTC susceptibility, with potential future utility in patients' clinical management, and reinforces the relevance of DICER1 in disease etiology.

Doxorubicin downregulates cell cycle regulatory hub genes in breast cancer cells

Breast cancer (BC) is the leading commonly diagnosed cancer in the world, with complex mechanisms underlying its development. There is an urgent need to enlighten key genes as potential therapeutic targets crucial to advancing BC treatment. This study sought to investigate the influence of doxorubicin (DOX) on identified key genes consistent across numerous BC datasets obtained through bioinformatic analysis. To date, a meta-analysis of publicly available coding datasets for expression profiling by array from the Gene Expression Omnibus (GEO) has been carried out. Differentially Expressed Genes (DEGs) identified using GEO2R revealed a total of 23 common DEGs, including nine upregulated genes and 14 downregulated genes among the datasets of three platforms (GPL570, GPL6244, and GPL17586), and the commonly upregulated DEGs, showed significant enrichment in the cell cycle in KEGG analysis. The top nine genes, NUSAP1, CENPF, TPX2, PRC1, ANLN, BUB1B, AURKA, CCNB2, and CDK-1, with higher degree values and MCODE scores in the cytoscape program, were regarded as hub genes. The hub genes were activated in disease states commonly across all the subclasses of BC and correlated with the unfavorable overall survival of BC patients, as verified by the GEPIA and UALCAN databases. qRT-PCR confirmed that DOX treatment resulted in reduced expression of these genes in BC cell lines, which reinforces the evidence that DOX remains an effective drug for BC and suggests that developing modified formulations of doxorubicin to reduce toxicity and resistance, could enhance its efficacy as an effective therapeutic option for BC.

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.

BubR1 and cyclin B1 immunoexpression in pleomorphic adenoma and polymorphous adenocarcinoma of minor salivary glands

The immunoexpression of BubR1 and cyclin B1 in pleomorphic adenoma (PA) and polymorphic adenocarcinoma (PAC) in minor salivary glands is poorly studied. Thus, a retrospective and observational study was performed to provide a better understanding of the role and immunopositivity patterns of these proteins in these lesions. Sixteen cases of PA and 16 cases of PAC were selected. Parenchyma cells were submitted to quantitative immunohistochemical analysis through the labeling index. Cytoplasmic immunoexpression of BubR1 was observed in neoplastic cells from all analyzed PA and PAC cases. All PA cases and 93.7% of PAC exhibited nuclear immunoexpression of BubR1. Higher cytoplasmic and nuclear immunoexpression of BubR1 was observed in PAC (p = 0.001 and p = 0.122, respectively). Cytoplasmic immunoexpression of cyclin B1 was observed in all cases of PA and PAC, with a higher labeling index in the latter (p < 0.001). There was a significant positive correlation between nuclear and cytoplasmic BubR1 immunoexpressions (p < 0.001) in PA and a significant negative correlation between BubR1 and cyclin B1 cytoplasmic immunoexpressions (p = 0.014) in PAC. The higher cytoplasmic and nuclear immunoexpression of BubR1 in PACs suggests the continuous maintenance of neoplastic cells in the cell cycle and migration. Higher immunoexpression of cyclin B1 supports this lesion's enhanced proliferative and migration ability.

Protooncogenic Role of ARHGAP11A and ARHGAP11B in Invasive Ductal Carcinoma: Two Promising Breast Cancer Biomarkers

Invasive duct carcinoma (IDC) is one of the most common types of breast cancer (BC) in women worldwide, with a high risk of malignancy, metastasis, recurrence, and death. So far, molecular patterns among IDC cases have not been fully defined. However, extensive evidence has shown that dysregulated Rho family small GTPases (Rho GTPases) including Rho GTPase activating proteins (RhoGAPs) have important roles in the invasive features of IDCs. In the current study, we analyzed the expression levels of two RhoGAP genes, ARHGAP11A and ARHGAP11B, in The Cancer Genome Atlas (TCGA) breast cancer (BRCA) and also our 51 IDC tumors compared to their matched normal tissues using quantitative polymerase chain reaction (qPCR). Our TCGA data analysis revealed higher expression of ARHGAP11A and ARHGAP11B in various cancers comprising BCs. Also, we found correlations between these genes and other genes in TCGA-BRCA. Moreover, our methylation analysis showed that their promotor methylation had a negative correlation with their overexpression. QPCR revealed their significant upregulation in our tumor samples. Furthermore, we found that the expression level of ARHGAP11A was considerably lower in women who were breastfeeding. Moreover, it had overexpression in cases who had regular menstrual cycles and early age (younger than 14) at menarche. However, ARHGAP11B had a higher expression in HER2-positive tumors versus HER2-positive and ER-positive tumors. Our study found possible protooncogenic roles for these genes and their involvement in IDC pathogenesis and malignancy. Therefore, they can be considered novel prognostic and diagnostic biomarkers for IDC.

[Prognostic Value of PCMT1 Expression in Gastric Cancer and Its Regulatory Effect on Spindle Assembly Checkpoints]

Objective

The study was conducted to investigate the expression of protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) in gastric cancer and its effect on the prognosis, and to analyze its potential mechanism.

Methods

UALCAN, a cancer data analysis platform, was used to conduct online analysis of the expression of PCMT1 in gastric cancer tissues. Through the Database for Annotation, Visualization and Integrated Discovery (DAVID), Gene Ontology (GO) annotation and signaling pathway enrichment by Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to analyze the possible functions and signaling pathways. A total of 120 patients who underwent radical gastrectomy for gastric cancer between January 2014 and December 2017 in our hospital were enrolled for the study. Immunohistochemical staining was performed to determine the expression of PCMT1 and Ki67 in gastric cancer tissues. Cox regression, Kaplan-Meier curve, and receiver operating characteristic (ROC) curves were used for prognostic analysis of 5-year survival in gastric cancer patients after surgery. Lentivirus was used to construct PCMT1-interfering or PCMT1-overexpressing vectors, which were then used to transfect human gastric cancer cell lines of MGC-803 and HGC-27 cells. The interfering empty vector (sh-NC) group, the interfering PCMT1 vector (sh-PCMT1) group, the overexpressing empty vector (LV-Vec) group, and the overexpressing PCMT1 vector (LV-PCMT1) group were set up. Western blot was performed to determine the protein expression levels of PCMT1, CyclinB1, and CDC20. CCK-8 assay was performed to measure the proliferation of gastric cancer cells. Flow cytometry was performed to determine the cell cycle. MGC-803 cells were injected in four groups of nude mice to construct a subcutaneous xenograft tumor model, with three nude mice in each group. The body mass of the nude mice was measured. The nude mice were sacrificed after 14 days and the tumor volume was monitored. The expression levels of CyclinB1 and CDC20 proteins in the tumor tissues were determined by Western blot assay.

Results

Analysis with UALCAN showed that PCMT1 was highly expressed in gastric cancer tissues. Moreover, elevated expression was found in gastric tumor tissues of different pathological stages and grades and those with lymph node metastasis (P<0.05). GO and KEGG enrichment analyses showed that PCMT1 was mainly involved in the signal regulation of mitosis, spindle assembly checkpoints, and cell cycle. The immunohistochemical results showed that PCMT1 and Ki67 were highly expressed in gastric cancer tissues and that they were positively correlated with each other (P<0.05). Cox multivariate analysis showed that high PCMT1 expression (hazard ratio [HR]=2.921, 95% confidence interval [CI]:1.628-5.239) was one of the independent risk factors affecting the 5-year survival rate of gastric cancer patients after surgery. Kaplan-Meier curve showed that patients with high PCMT1 expression had a lower 5-year survival after surgery (16.7%, HR=4.651, 95% CI: 2.846-7.601) than patients with low PCMT1 expression (70.0%, HR=0.215, 95% CI: 0.132-0.351) did. The ROC curve showed that PCMT1 had an area under the curve (AUC) of 0.764 (95% CI: 0.674-0.854) for predicting 5-year patient survival after surgery. Western blot results showed that lentiviral interference or overexpression of PCMT1 cell lines was successfully constructed. The results of CCK-8 showed that the proliferative ability of MGC-803 and HGC-27 cells was weakened with the downregulation of PCMT1, and the overexpression of PCMT1 promoted cell proliferation (P<0.05). With the interference of PCMT1, the expression of CDC20 protein was decreased, the expression of CyclinB1 protein was increased, and the cell cycle was arrested in the G2/M phase. In contrast, the overexpression of PCMT1 led to the opposite trends (P<0.05). In the sh-PCMT1 group, the tumor volume and mass were decreased and the expression of CDC20 protein was decreased and the expression of CyclinB1 protein was increased in the tumor tissues of the nude mice (P<0.05, compared with those of the sh-NC group. In contrast, the LV-PCMT1 group showed the opposite trends (P<0.05, compared with those of the LV-Vec group).

Conclusion

The high expression of PCMT1 in gastric cancer tissues is associated with poor prognosis in patients and may affect tumor cell malignant proliferation via regulating spindle checkpoints in the process of mitosis.

Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. Researchers have discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs was performed using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database. The protein–protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was completed using cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using a Kaplan–Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12,145 edges. Elevated expression of the five hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation causing aneuploidy. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

Identification of Differentially Methylated Genes Associated with Clear Cell Renal Cell Carcinoma and Their Prognostic Values

Objective

Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes among which clear cell RCC (ccRCC) accounts for 70% of all RCC cases. DNA methylation constitutes a main part of the molecular mechanism of cancer evolution and prognosis. In this study, we aim to identify differentially methylated genes related to ccRCC and their prognostic values.

Methods

The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between ccRCC tissues and paired tumor-free kidney tissues. DEGs were submitted to public databases for functional and pathway enrichment analysis, protein-protein interaction (PPI) analysis, promoter methylation analysis, and survival correlation analysis.

Results

In the setting of |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues were sorted out. The most enriched pathways were "T cell activation" and "cytokine-cytokine receptor interaction." After PPI analysis, 22 hub genes related to ccRCC stood out, among which CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM exhibited higher methylation levels, and BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues. Among these differentially methylated genes, TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (p < 0.001).

Conclusion

Our study indicates the DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK may be promising results for the prognosis of ccRCC.

Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV

BACKGROUND

The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.

METHODS

COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.

RESULTS

In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.

CONCLUSION

In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

A genomic and transcriptomic study toward breast cancer

Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes.

Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server.

Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy.

Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.

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.

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.

Bioinformatics Analysis Highlight Differentially Expressed CCNB1 and PLK1 Genes as Potential Anti-Breast Cancer Drug Targets and Prognostic Markers

Breast cancer is one of the most common malignant tumors in women worldwide. Early diagnosis, treatment, and prognosis of breast cancer are global challenges. Identification of valid predictive diagnosis and prognosis biomarkers and drug targets are crucial for breast cancer prevention. This study characterizes differentially expressed genes (DEGs) based on the TCGA database by using DESeq2, edgeR, and limma. A total of 2032 DEGs, including 1026 up-regulated genes and 1006 down-regulated genes were screened. Followed with WGCNA, PPI analysis, GEPIA 2, and HPA database verification, thirteen hub genes including CDK1, BUB1, BUB1B, CDC20, CCNB2, CCNB1, KIF2C, NDC80, CDCA8, CENPF, BIRC5, AURKB, PLK1, MAD2L1, and CENPE were obtained, and they may serve as potential therapeutic targets of breast cancer. Especially, overexpression of CCNB1 and PLK1 are strongly associated with the low survival rate of breast cancer patients, demonstrating their potentiality as prognostic markers. Moreover, CCNB1 and PLK1 are highly expressed in all breast cancer stages, suggesting that they could be further studied as potential drug targets. Taken together, our study highlights CCNB1 and PLK1 as potential anti-breast cancer drug targets and prognostic markers.

Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways

BACKGROUND

The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders.

METHODS

The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples.

RESULTS

Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples.

CONCLUSION

This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches.

NF2 Gene Participates in Regulation of the Cell Cycle of Meningiomas by Restoring Spindle Assembly Checkpoint Function and Inhibiting the Binding of Cdc20 Protein to Anaphase Promoting Complex/Cyclosome

BACKGROUND

The neurofibromatosis type 2 (NF2) gene mutation is the leading genetic event in meningiomas, usually accompanied by malignant features. Dysfunction of the spindle assembly checkpoint (SAC) induces tumorigenesis. However, the crosstalk between NF2 and SAC in meningiomas remains unclear.

METHODS

Cell proliferation, invasion, apoptosis, and cell cycle of meningiomas were determined by cell counting kit-8 assay, transwell assay, and flow cytometry, respectively. The expression of SAC in meningioma cells was detected by quantitative real-time polymerase chain reaction and Western blot. The interaction between anaphase promoting complex/cyclosome (APC/C) and cell division cycle 20 (Cdc20) protein in meningioma cells was further explored by co-immunoprecipitation.

RESULTS

We found that the expression of NF2/merlin was low or absent in malignant meningiomas. Overexpression of NF2 suppressed the proliferation and invasion of meningioma cells, prolonged the G₂/M phase, and elevated the expression of SAC proteins at posttranscription. Furthermore, the interaction between APC/C and Cdc20 was inhibited by NF2.

CONCLUSIONS

Our findings suggested that NF2 might restore SAC function by impairing the binding of APC/C and Cdc20, thereby limiting the mitotic rate and inhibiting proliferation of meningiomas.

Identification of a Prognostic Model Based on 2-Gene Signature and Analysis of Corresponding Tumor Microenvironment in Alcohol-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with the poor prognosis. Nowadays, alcohol is becoming a leading risk factor of HCC in many countries. In our study, we obtained the DEGs in alcohol-related HCC through two databases (TCGA and GEO). Subsequently, we performed enrichment analyses (GO and KEGG), constructed the PPI network and screened the 53 hub genes by Cytoscape. Two genes (BUB1B and CENPF) from hub genes was screened by LASSO and Cox regression analyses to construct the prognostic model. Then, we found that the high risk group had the worse prognosis and verified the clinical value of the risk score in alcohol-related HCC. Finally, we analyzed the tumor microenvironment between high and low risk groups through CIBERSORT and ESTIMATE. In summary, we constructed the two-gene prognostic model that could predict the poor prognosis in patients with alcohol-related HCC.

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein–protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan–Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan–Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

Mitotic checkpoint regulator RAE1 promotes tumor growth in colorectal cancer

Microtubules are among the most successful targets for anticancer therapy because they play important roles in cell proliferation as they constitute the mitotic spindle, which is critical for chromosome segregation during mitosis. Hence, identifying new therapeutic targets encoding proteins that regulate microtubule assembly and function specifically in cancer cells is critical. In the present study, we identified a candidate gene that promotes tumor progression, ribonucleic acid export 1 (RAE1), a mitotic checkpoint regulator, on chromosome 20q through a bioinformatics approach using datasets of colorectal cancer (CRC), including The Cancer Genome Atlas (TCGA). RAE1 was ubiquitously amplified and overexpressed in tumor cells. High expression of RAE1 in tumor tissues was positively associated with distant metastasis and was an independent poor prognostic factor in CRC. In vitro and in vivo analysis showed that RAE1 promoted tumor growth, inhibited apoptosis, and promoted cell cycle progression, possibly with a decreased proportion of multipolar spindle cells in CRC. Furthermore, RAE1 induced chemoresistance through its anti-apoptotic effect. In addition, overexpression of RAE1 and significant effects on survival were observed in various types of cancer, including CRC. In conclusion, we identified RAE1 as a novel gene that facilitates tumor growth in part by inhibiting apoptosis and promoting cell cycle progression through stabilizing spindle bipolarity and facilitating tumor growth. We suggest that it is a potential therapeutic target to overcome therapeutic resistance of CRC.

CRISPR-cas9 Screening Identified Lethal Genes Enriched in Cell Cycle Pathway and of Prognosis Significance in Breast Cancer

Background

Lethal genes have not been systematically analyzed in breast cancer which may have significant prognostic value. The current study aims to investigate vital genes related to cell viability by analyzing the CRISPR-cas9 screening data, which may provide novel therapeutic target for patients.

Methods

Genes differentially expressed between tumor and normal tissue from the Cancer Genome Atlas (TCGA) and genes related to cell viability by CRISPR-cas9 screening from Depmap (Cancer Dependency Map) were overlapped. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was conducted to identify which pathways of overlapped genes were enriched. GSE21653 set was randomized into training and internal validation dataset at a ratio of 3:1, and external validation was performed in GSE20685 set. The least absolute shrinkage and selection operator (LASSO) regression was used to construct a signature to predict recurrence-free survival (RFS) of breast cancer patients. Univariate and multivariate Cox regression were used to evaluate the prognostic value of this signature. Differentially expressed genes (DEGs) between high-risk and low-risk patients were then analyzed to identify the main pathways regulated by this signature. Weighted correlation network analysis (WGCNA) was conducted to recognize modules correlated with high risk. Enrichment analysis was then used to identify pathways regulated by genes shared in the overlapped genes, DEGs, and WGCNA.

Results

A total of 86 oncogenes were upregulated in TCGA database and overlapped with lethal genes in Depmap database, which were enriched in cell cycle pathway. A total of 51 genes were included in the gene signature based on LASSO regression, and the median risk score of 2.36 was used as cut-off to separate low-risk patients from high-risk patients. High-risk patients showed worse RFS compared with low-risk patients in internal training, internal validation, and external validation dataset. Time-dependent receiver operating characteristic curves of 3 and 5 years indicated that risk score was superior to tumor stage, age, and PAM50 in both entire and external validation datasets. Cell cycle was the main different pathway between the high-risk and low-risk groups. Meanwhile, cell cycle was also the main pathway enriched in the 25 genes which were shared among 86 genes, DEGs, and WGCNA.

Conclusion

Cell cycle pathway, identified by CRISPR-cas9 screening, was a key pathway regulating cell viability, which has significant prognostic values and can serve as a new target for breast cancer patient treatment.