Overexpression of BUB1B, CCNA2, CDC20, and CDK1 in tumor tissues predicts poor survival in pancreatic ductal adenocarcinoma

High expression of H2AX/γ-H2AX is associated with distinct biological pathway alterations and shorter survival in oropharyngeal squamous cell carcinoma

BACKGROUND

The histone gene H2AX and its phosphorylated protein γ-H2AX play a crucial role in the DNA damage response. This study investigates the expression of H2AX mRNA and its phosphorylated γ-H2AX protein in oropharyngeal squamous cell carcinoma (OPSCC), its association with distinct biological pathway alterations and its potential as a biomarker.

MATERIALS AND METHODS

Expression of H2AX mRNA in 76 OPSCC from The Cancer Genome Atlas (TCGA) cohort was analyzed. Patients were stratified into H2AXhigh- and H2AXlow OPSCC based on a survival-associated cutoff. Differentially expressed genes were identified using DESeq2, followed by pathway enrichment analyses. Immunohistochemical staining of γ-H2AX protein expression was performed on an independent cohort of 209 OPSCC, followed by survival and Cox regression analyses.

RESULTS

High H2AX mRNA expression was a significant prognostic factor associated with shorter OS in the TCGA OPSCC cohort (HR 4.77, p = 0.04). In H2AXhigh tumors, differential gene expression analysis revealed upregulation of genes regulating DNA repair and cell cycle (CDK1, CCNB1, ZWINT). High γ-H2AX protein expression was significantly associated with HPV-negative OPSCC (p = 0.005), and remained an independent predictor of poor survival in the total OPSCC cohort (HR 2.24, p = 0.03) and particularly in HPV-negative patients (HR 3.67, p = 0.007).

CONCLUSION

H2AX/γ-H2AX expression is a potential prognostic biomarker in OPSCC, with elevated levels indicating poor survival, especially in HPV-negative cases. These findings suggest distinct molecular behaviors in OPSCC based on H2AX expression and highlight the need for further investigation into its therapeutic implications.

Integrated analysis of cell cycle and p53 signaling pathways related genes in breast, colorectal, lung, and pancreatic cancers: implications for prognosis and drug sensitivity for therapeutic potential

Cancer, a leading cause of death worldwide, is projected to increase by 76.6% in new cases and 89.7% in mortality by 2050 (WHO 2022). Among various types, lung cancer is the most prevalent with high morbidity, while breast, colorectal, and pancreatic cancers also show high mortality rates. Cancer progression often involves disruption in cell cycle regulation and signaling pathways, with mutations in genes like TP53, EGFR, and K-RAS playing significant roles. In this study, we analyzed gene expression datasets to identify common molecular signatures across breast, colorectal, lung and pancreatic cancers. Our focus was on genes related to cell cycle regulation and p53 signaling pathway, intending to discover potential biomarkers for improved diagnosis and treatment strategies. The study analyzed GEO datasets; GSE45827, GSE9348, GSE30219, and GSE62165 for breast, colorectal, lung, and pancreatic cancers respectively. Differentially expressed genes (DEGs) were identified using GEO2R, and functional annotation and pathway analysis were performed using WebGestalt. Common cell cycle and p53 signaling genes were acquired from MSigDB using GSEA. A protein-protein interaction network was constructed using STRING and Cytoscape, identifying top hub genes. Validation of hub genes at mRNA and protein levels was done via GEPIA2 and Human Protein Atlas. Survival analysis was conducted using TCGA data by GEPIA2 and LASSO, and drug sensitivity was analyzed with the GSCA drug bank database, highlighting potential therapeutic targets. The study identified 411 common DEGs among these four cancers. Pathway and functional enrichment revealed key biological processes and pathways like p53 signaling, and cell cycle. The intersection of these DEGs with genes involved in cell cycle and p53 signaling, identified 23 common genes that were used for constructing a PPI network. The top 10 hub genes were validated both for mRNA and protein expression, revealing they are significantly overexpressed in all studied cancers. Prognostic relevance showed that MCM4, MCM6, CCNA2, CDC20, and CHEK1 are associated with survival. Additionally, drug sensitivity analysis highlighted key gene-drug interactions, suggesting potential targets for therapeutic intervention.

Integrated analysis of hub genes and intrinsically disordered regions in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most prevalent breast cancer subtype. Its prognosis is poor because there are no effective treatment targets. Despite several attempts, the molecular pathways of TNBC remain unknown, posing a significant clinical barrier in the search for viable targets. Two microarray datasets were used to identify possible targets for TNBC, GSE38959 and GSE45827, retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in TNBC samples compared with normal samples were identified using the GEO2R program. KEGG pathway enrichment and Gene Ontology functions were assessed for DEG pathways and functional annotation using ShinyGO 0.77. The STRING database and Cytoscape program were used for protein-protein interaction (PPI) analysis. Furthermore, we evaluated the predictive significance of hub gene expression in TNBC patients using the GEPIA2 online tool. We developed a comprehensive technique to assess whether intrinsically disordered regions (IDRs) are present in the TNBC hub genes. There were 48 DEGs were identified, all of which were upregulated. A putative protein complex containing these four core genes was selected for further analysis. Breast cancer patients with TTK, TOP2A, CENPF, and CCNA2 upregulation had a poor prognosis; TTK and CCNA2 were partially disordered, whereas TOP2A and CENPF were primarily disordered, according to IDR analysis. According to our study, TOP2A and CENPF may be useful therapeutic targets for disruption of the TNBC PPI network.

[Expression and its clinical significance of cell-cycle dependent kinase 1 in malignant peripheral nerve sheath tumors]

Objective

To explore the role and clinical significance of cell-cycle dependent kinase 1 (CDK1) and its upstream and downstream molecules in the development of malignant peripheral nerve sheath tumor (MPNST) through the analysis of clinical tissue samples.

Methods

A total of 56 tumor samples from MPNST patients ("Tianjin" dataset) who underwent surgical resection, confirmed by histology and pathology between September 2011 and March 2020, along with 17 normal tissue samples, were selected as the research subjects. MPNST-related hub genes were identified through transcriptome sequencing, bioinformatics analysis, immunohistochemistry staining, and survival analysis, and their expression levels and prognostic associations were analyzed.

Results

Transcriptome sequencing and bioinformatics analysis revealed that upregulated genes in MPNST were predominantly enriched in cell cycle-related pathways, with CDK1 occupying a central position among all differentially expressed genes. Further differential analysis demonstrated that CDK1 mRNA expression in sarcoma tissues was significantly higher than in normal tissues [based on searching the cancer genome atlas (TCGA) dataset, P<0.05]. In MPNST tissues, CDK1 mRNA expression was not only significantly higher than in normal tissues (based on Tianjin, GSE141438 datasets, P<0.05), but also significantly higher than in neurofibromatosis (NF) and plexiform neurofibromas (PNF) (based on GSE66743 and GSE145064 datasets, P<0.05). Immunohistochemical staining results indicated that the expression rate of CDK1 protein in MPNST tissues was 40.31%. Survival analysis results demonstrated that CDK1 expression was associated with poor prognosis. The survival time of MPNST patients with high CDK1 mRNA expression was significantly lower than that of the low expression group ( P<0.05), and the overall survival trend of patients with positive CDK1 protein expression was worse than that of patients with negative CDK1 expression. Additionally, differential analysis of CDK family genes (CDK1-8) revealed that only CDK1 was significantly upregulated in MPNST, NF, and PNF.

Conclusion

Increased expression of CDK1 is associated with poor prognosis in MPNST patients. Compared to other CDK family members, CDK1 exhibits a unique expression pattern, suggesting its potential as a therapeutic target for MPNST.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.

Based on machine learning, CDC20 has been identified as a biomarker for postoperative recurrence and progression in stage I & II lung adenocarcinoma patients

Objective

By utilizing machine learning, we can identify genes that are associated with recurrence, invasion, and tumor stemness, thus uncovering new therapeutic targets.

Methods

To begin, we obtained a gene set related to recurrence and invasion from the GEO database, a comprehensive gene expression database. We then employed the Weighted Gene Co-expression Network Analysis (WGCNA) to identify core gene modules and perform functional enrichment analysis on them. Next, we utilized the random forest and random survival forest algorithms to calculate the genes within the key modules, resulting in the identification of three crucial genes. Subsequently, one of these key genes was selected for prognosis analysis and potential drug screening using the Kaplan-Meier tool. Finally, in order to examine the role of CDC20 in lung adenocarcinoma (LUAD), we conducted a variety of in vitro and in vivo experiments, including wound healing assay, colony formation assays, Transwell migration assays, flow cytometric cell cycle analysis, western blotting, and a mouse tumor model experiment.

Results

First, we collected a total of 279 samples from two datasets, GSE166722 and GSE31210, to identify 91 differentially expressed genes associated with recurrence, invasion, and stemness in lung adenocarcinoma. Functional enrichment analysis revealed that these key gene clusters were primarily involved in microtubule binding, spindle, chromosomal region, organelle fission, and nuclear division. Next, using machine learning, we identified and validated three hub genes (CDC45, CDC20, TPX2), with CDC20 showing the highest correlation with tumor stemness and limited previous research. Furthermore, we found a close association between CDC20 and clinical pathological features, poor overall survival (OS), progression-free interval (PFI), progression-free survival (PFS), and adverse prognosis in lung adenocarcinoma patients. Lastly, our functional research demonstrated that knocking down CDC20 could inhibit cancer cell migration, invasion, proliferation, cell cycle progression, and tumor growth possibly through the MAPK signaling pathway.

Conclusion

CDC20 has emerged as a novel biomarker for monitoring treatment response, recurrence, and disease progression in patients with lung adenocarcinoma. Due to its significance, further research studying CDC20 as a potential therapeutic target is warranted. Investigating the role of CDC20 could lead to valuable insights for developing new treatments and improving patient outcomes.

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.

Positive feedback regulation between glycolysis and histone lactylation drives oncogenesis in pancreatic ductal adenocarcinoma

BACKGROUND

Metabolic reprogramming and epigenetic alterations contribute to the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). Lactate-dependent histone modification is a new type of histone mark, which links glycolysis metabolite to the epigenetic process of lactylation. However, the role of histone lactylation in PDAC remains unclear.

METHODS

The level of histone lactylation in PDAC was identified by western blot and immunohistochemistry, and its relationship with the overall survival was evaluated using a Kaplan-Meier survival plot. The participation of histone lactylation in the growth and progression of PDAC was confirmed through inhibition of histone lactylation by glycolysis inhibitors or lactate dehydrogenase A (LDHA) knockdown both in vitro and in vivo. The potential writers and erasers of histone lactylation in PDAC were identified by western blot and functional experiments. The potential target genes of H3K18 lactylation (H3K18la) were screened by CUT&Tag and RNA-seq analyses. The candidate target genes TTK protein kinase (TTK) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were validated through ChIP-qPCR, RT-qPCR and western blot analyses. Next, the effects of these two genes in PDAC were confirmed by knockdown or overexpression. The interaction between TTK and LDHA was identified by Co-IP assay.

RESULTS

Histone lactylation, especially H3K18la level was elevated in PDAC, and the high level of H3K18la was associated with poor prognosis. The suppression of glycolytic activity by different kinds of inhibitors or LDHA knockdown contributed to the anti-tumor effects of PDAC in vitro and in vivo. E1A binding protein p300 (P300) and histone deacetylase 2 were the potential writer and eraser of histone lactylation in PDAC cells, respectively. H3K18la was enriched at the promoters and activated the transcription of mitotic checkpoint regulators TTK and BUB1B. Interestingly, TTK and BUB1B could elevate the expression of P300 which in turn increased glycolysis. Moreover, TTK phosphorylated LDHA at tyrosine 239 (Y239) and activated LDHA, and subsequently upregulated lactate and H3K18la levels.

CONCLUSIONS

The glycolysis-H3K18la-TTK/BUB1B positive feedback loop exacerbates dysfunction in PDAC. These findings delivered a new exploration and significant inter-relationship between lactate metabolic reprogramming and epigenetic regulation, which might pave the way toward novel lactylation treatment strategies in PDAC therapy.

Exploring risk factors and molecular targets in leukemia patients with COVID-19: a bioinformatics analysis of differential gene expression

The molecular mechanism of COVID-19's pathogenic effects in leukemia patients is still poorly known. Our study investigated the possible disease mechanism of COVID-19 and its associated risk factors in patients with leukemia utilizing differential gene expression analysis. We also employed network-based approaches to identify molecular targets that could potentially diagnose and treat COVID-19-infected leukemia patients. Our study demonstrated a shared set of 60 genes that are expressed differentially among patients with leukemia and COVID-19. Most of these genes are expressed in blood and bone marrow tissues and are predominantly implicated in the pathogenesis of different hematologic malignancies, increasingly imperiling COVID-19 morbidity and mortality among the affected patients. Additionally, we also found that COVID-19 may influence the expression of several cancer-associated genes in leukemia patients, such as CCR7, LEF1, and 13 candidate cancer-driver genes. Furthermore, our findings reveal that COVID-19 may predispose leukemia patients to altered blood homeostasis, increase the risk of COVID-19-related liver injury, and deteriorate leukemia-associated injury and patient prognosis. Our findings imply that molecular signatures, like transcription factors, proteins such as TOP21, and 25 different microRNAs, may be potential targets for diagnosing and treating COVID-19-infected leukemia patients. Nevertheless, additional experimental studies will contribute to further validating the study's findings.

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

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

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.

Cyclin A2 Expression as Predictive Biomarker in Muscle-Invasive Upper Tract Urothelial Carcinoma

INTRODUCTION

The aim was to evaluate the prognostic value of altered Cyclin A2 (CCNA2) gene expression in upper tract urothelial carcinoma (UTUC) and to assess its predictive potential as a prognostic factor for overall survival (OS) and disease-free survival.

METHODS

62 patients who underwent surgical treatment for UTUC were included. Gene expression of CCNA2, MKI67, and p53 was analyzed by quantitative reverse transcriptase polymerase chain reaction. Survival analyses were performed using the Kaplan-Meier method and the log-rank test. For Cox regression analyses, uni- and multivariable hazard ratios were calculated. Spearman correlation was used to analyze correlation of CCNA2 expression with MKI67 and p53.

RESULTS

The median age of the cohort was 73 years, and it consisted of 48 males (77.4%) and 14 females (22.6%). Patients with high CCNA2 expression levels showed longer OS (HR 0.33; 95% CI: 0.15-0.74; p = 0.0073). Multivariable Cox regression analyses identified CCNA2 overexpression (HR 0.37; 95% CI: 0.16-0.85; p = 0.0189) and grading G2 (vs. G3) (HR 0.39; 95% CI: 0.17-0.87; p = 0.0168) to be independent predictors for longer OS. CCNA2 expression correlated positively with MKI67 expression (Rho = 0.4376, p = 0.0005).

CONCLUSION

Low CCNA2 expression is significantly associated with worse OS. Thus, CCNA2 might serve as a potential biomarker in muscle-invasive UTUC and may be used to characterize a subset of patients having an unfavorable outcome and for future risk assessment scores.

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.

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

Pancreatic cancer (PC) is a digestive malignancy with worse overall survival. Tumor immune environment (TIME) alters the progression and proliferation of various solid tumors. Hence, we aimed to detect the TIME-related classifier to facilitate the personalized treatment of PC. Based on the 1612 immune-related genes (IRGs), we classified patients into Immune_rich and Immune_desert subgroups via consensus clustering. Patients in distinct subtypes exhibited a difference in sensitivity to immune checkpoint blockers (ICB). Next, the immune-related signature (IRS) model was established based on 8 IRGs (SYT12, TNNT1, TRIM46, SMPD3, ANLN, AFF3, CXCL9 and RP1L1) and validated its predictive efficiency in multiple cohorts. RT-qPCR experiments demonstrated the differential expression of 8 IRGs between tumor and normal cell lines. Patients who gained lower IRS score tended to be more sensitive to chemotherapy and immunotherapy, and obtained better overall survival compared to those with higher IRS scores. Moreover, scRNA-seq analysis revealed that fibroblast and ductal cells might affect malignant tumor cells via MIF-(CD74+CD44) and SPP1-CD44 axis. Eventually, we identified eight therapeutic targets and one agent for IRS high patients. Our study screened out the specific regulation pattern of TIME in PC, and shed light on the precise treatment of PC.

The Cyclin-dependent kinase 1: more than a cell cycle regulator

The Cyclin-dependent kinase 1, as a serine/threonine protein kinase, is more than a cell cycle regulator as it was originally identified. During the last decade, it has been shown to carry out versatile functions during the last decade. From cell cycle control to gene expression regulation and apoptosis, CDK1 is intimately involved in many cellular events that are vital for cell survival. Here, we provide a comprehensive catalogue of the CDK1 upstream regulators and substrates, describing how this kinase is implicated in the control of key 'cell cycle-unrelated' biological processes. Finally, we describe how deregulation of CDK1 expression and activation has been closely associated with cancer progression and drug resistance.

Emerging roles of PHLPP phosphatases in lung cancer

Pleckstrin homologous domain leucine-rich repeating protein phosphatases (PHLPPs) were originally identified as protein kinase B (Akt) kinase hydrophobic motif specific phosphatases to maintain the cellular homeostasis. With the continuous expansion of PHLPPs research, imbalanced-PHLPPs were mainly found as a tumor suppressor gene of a variety of solid tumors. In this review, we simply described the history and structures of PHLPPs and summarized the recent achievements in emerging roles of PHLPPs in lung cancer by 1) the signaling pathways affected by PHLPPs including Phosphoinositide 3-kinase (PI3K)/AKT, RAS/RAF/mitogen-activated protein kinase (MEK)/extracellular signal-regulated kinase (ERK) and Protein kinase C (PKC) signaling cascades. 2) function of PHLPPs regulatory factor USP46 and miR-190/miR-215, 3) the potential roles of PHLPPs in disease prognosis, Epidermal growth factor receptors (EGFR)- tyrosine kinase inhibitor (TKI) resistance and DNA damage, 4) and the possible function of PHLPPs in radiotherapy, ferroptosis and inflammation response. Therefore, PHLPPs can be considered as either biomarker or prognostic marker for lung cancer treatment.

MicroRNA analysis of Natural Killer cell-derived exosomes: the microRNA let-7b-5p is enriched in exosomes and participates in their anti-tumor effects against pancreatic cancer cells

Natural Killer (NK) cells are important components of the immune system in the defense against tumor growth and metastasis. They release exosomes containing proteins and nucleic acids, including microRNAs (miRNAs). NK-derived exosomes play a role in the anti-tumor NK cell function since they are able to recognize and kill cancer cells. However, the involvement of exosomal miRNAs in the function of NK exosomes is poorly understood. In this study, we explored the miRNA content of NK exosomes by microarray as compared to their cellular counterparts. The expression of selected miRNAs and lytic potential of NK exosomes against childhood B acute lymphoblastic leukemia cells after co-cultures with pancreatic cancer cells were also evaluated. We identified a small subset of miRNAs, including miR-16-5p, miR-342-3p, miR-24-3p, miR-92a-3p and let-7b-5p that is highly expressed in NK exosomes. Moreover, we provide evidence that NK exosomes efficiently increase let-7b-5p expression in pancreatic cancer cells and induce inhibition of cell proliferation by targeting the cell cycle regulator CDK6. Let-7b-5p transfer by NK exosomes could represent a novel mechanism by which NK cells counteract tumor growth. However, both cytolytic activity and miRNA content of NK exosomes were reduced upon co-culture with pancreatic cancer cells. Alteration in the miRNA cargo of NK exosomes, together with their reduced cytotoxic activity, could represent another strategy exerted by cancer to evade the immune response. Our study provides new information on the molecular mechanisms used by NK exosomes to exert anti-tumor-activity and offers new clues to integrate cancer treatments with NK exosomes.

Network and functional analyses of differentially expressed genes in gastric cancer provide new biomarkers associated with disease pathogenesis

BACKGROUND

Gastric cancer is a dominant source of cancer-related death around the globe and a serious threat to human health. However, there are very few practical diagnostic approaches and biomarkers for the treatment of this complex disease.

METHODS

This study aimed to evaluate the association between differentially expressed genes (DEGs), which may function as potential biomarkers, and the diagnosis and treatment of gastric cancer (GC). We constructed a protein-protein interaction network from DEGs followed by network clustering. Members of the two most extensive modules went under the enrichment analysis. We introduced a number of hub genes and gene families playing essential roles in oncogenic pathways and the pathogenesis of gastric cancer. Enriched terms for Biological Process were obtained from the "GO" repository.

RESULTS

A total of 307 DEGs were identified between GC and their corresponding normal adjacent tissue samples in GSE63089 datasets, including 261 upregulated and 261 downregulated genes. The top five hub genes in the PPI network were CDK1, CCNB1, CCNA2, CDC20, and PBK. They are involved in focal adhesion formation, extracellular matrix remodeling, cell migration, survival signals, and cell proliferation. No significant survival result was found for these hub genes.

CONCLUSIONS

Using comprehensive analysis and bioinformatics methods, important key pathways and pivotal genes related to GC progression were identified, potentially informing further studies and new therapeutic targets for GC treatment.

Identification of potential ATP-competitive cyclin-dependent kinase 1 inhibitors: De novo drug generation, molecular docking, and molecular dynamics simulation

Cyclin-dependent kinases 1 (CDK1) has been identified as a potential target for the search for new antitumor drugs. However, no clinically effective CDK1 inhibitors are now available for cancer treatment. Therefore, this study aimed to offer potential CDK1 inhibitors using de novo drug generation, molecular docking, and molecular dynamics (MD) simulation studies. We first utilized the BREED algorithm (a de novo drug generation approach) to produce a novel library of small molecules targeting CDK1. To initially obtain novel potential CDK1 inhibitors with favorable physicochemical properties and excellent druggability, we performed a virtual rule-based rational drug screening on our generated library and found ten initial hits. Then, the molecular interactions and dynamic stability of these ten initial hits and CDK1 complexes during their all-atom MD simulations (total 18 μs) and binding pose metadynamics simulations were investigated, resulting in five final hits. Furthermore, another MD simulation (total 2.1 μs) with different force fields demonstrated the binding ability of the five hits to CDK1. It was found that these five hits, CBMA001 (ΔG = -29.88 kcal/mol), CBMA002 (ΔG = -34.89 kcal/mol), CBMA004 (ΔG = -32.47 kcal/mol), CBMA007 (ΔG = -31.16 kcal/mol), and CBMA008 (ΔG = -34.78 kcal/mol) possessed much greater binding affinity to CDK1 than positive compound Flavopiridol (FLP, ΔG = -25.38 kcal/mol). Finally, CBMA002 and CBMA004 were identified as excellent selective CDK1 inhibitors in silico. Together, this study provides a workflow for rational drug design and two promising selective CDK1 inhibitors that deserve further investigation.

Construction and validation of an aging-related gene signature predicting the prognosis of pancreatic cancer

Background: Pancreatic cancer is a malignancy with a high mortality rate and worse prognosis. Recently, public databases and bioinformatics tools make it easy to develop the prognostic risk model of pancreatic cancer, but the aging-related risk signature has not been reported. The present study aimed to identify an aging-related risk signature with potential prognostic value for pancreatic cancer patients. Method: Gene expression profiling and human clinical information of pancreatic cancer were derived from The Cancer Genome Atlas database (TCGA). Aging-related gene sets were downloaded from The Molecular Signatures Database and aging-related genes were obtained from the Human Ageing Genomic Resources database. Firstly, Gene set enrichment analysis was carried out to investigate the role of aging process in pancreatic cancer. Secondly, differentially expressed genes and aging-related prognostic genes were screened on the basis of the overall survival information. Then, univariate COX and LASSO analysis were performed to establish an aging-related risk signature of pancreatic cancer patients. To facilitate clinical application, a nomogram was established to predict the survival rates of PCa patients. The correlations of risk score with clinical features and immune status were evaluated. Finally, potential therapeutic drugs were screened based on the connectivity map (Cmap) database and verified by molecular docking. For further validation, the protein levels of aging-related genes in normal and tumor tissues were detected in the Human Protein Atlas (HPA) database. Result: The genes of pancreatic cancer were markedly enriched in several aging-associated signaling pathways. We identified 14 key aging-related genes related to prognosis from 9,020 differentially expressed genes and establish an aging-related risk signature. This risk model indicated a strong prognostic capability both in the training set of TCGA cohort and the validation set of PACA-CA cohort and GSE62452 cohort. A nomogram combining risk score and clinical variables was built, and calibration curve and Decision curve analysis (DCA) have proved that it has a good predictive value. Additionally, the risk score was tightly linked with tumor immune microenvironment, immune checkpoints and proinflammatory factors. Moreover, a candidate drug, BRD-A47144777, was screened and verified by molecular docking, indicating this drug has the potential to treat PCa. The protein expression levels of GSK3B, SERPINE1, TOP2A, FEN1 and HIC1 were consistent with our predicted results. Conclusion: In conclusion, we identified an aging-related signature and nomogram with high prediction performance of survival and immune cell infiltration for pancreatic cancer. This signature might potentially help in providing personalized immunotherapy for patients with pancreatic cancer.

Six Cell Cycle-related Genes Serve as Potential Prognostic Biomarkers and Correlated with Immune Infiltrates in Hepatocellular Carcinoma

Background: Cell cycle-related genes (CDK1, CDK5, CDC20, CCNA2, CCNB1, and CCNB2) play important roles in the regulation of mitotic cell cycle in eukaryotes. However, the correlation between cell cycle-related genes and tumor-infiltrating and prognosis of hepatocellular carcinoma (HCC) needs further investigation. Methods: Two public websites, Tumor Immune Estimate Resource (TIMER) and Oncomine, were used to assess the expression levels of cycle-related genes. We also analyzed the protein expression levels of six cell cycle-related genes using the HPA database. In addition, Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA) database were used to investigate the impact of cell cycle-related gene expression levels on the clinical prognosis of HCC. The correlation between cell cycle-related genes and cancer immune infiltrates was analyzed through TIMER site. Subsequently, GEPIA and TIMER database were used to assess the correlation between the expression of six cell cycle-related genes and polygenic markers in monocytes and macrophages, respectively. The cell cycle-related genes were also analyzed to find the associated genes with the highest alteration frequency, by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) approaches of Metascape and String database, respectively. Results: The expression levels of cell cycle-related genes were up-regulated in tumor tissues compared with normal tissues. Subsequently, the expression of high cell cycle-related genes was positively correlated with poor overall survival (OS) and progression-free survival (PFS) in HCC, for CDK1 (OS: HR = 2.15, P = 1.1E-05 PFS: HR = 2.03, P = 2.3E-06), CDK5 (OS: HR = 1.85, P = 0.0035 PFS: HR = 1.26, P = 0.17), CDC20 (OS: HR = 2.49, P = 5.1E-07 PFS: HR = 1.77, P = 0.00012), CCNA2 (OS: HR = 1.92, P = 0.00018 PFS: HR = 1.96, P = 5.2E-06), CCNB1 (OS: HR = 2.34, P = 3.4E-05 PFS: HR = 1.97, P = 5.3E-06), and CCNB2 (OS: HR = 1.91, P = 0.0013 PFS: HR = 1.63, P = 0.0011), respectively. Furthermore, the transcription level of cell cycle-related genes was significantly correlated with immune infiltrating levels of CD4+ T and CD8+ T cells, neutrophils, macrophages, and dendritic cells (DCs) in HCC, respectively. Amongst them, the expression levels of CDK1, CDC20, CCNA2, CCNB1 and CCNB2 manifest strongly correlated with diverse immune marker sets in HCC. Conclusions: Our results demonstrated that cell cycle-related genes played key roles in the prognosis of HCC. Meanwhile, they were significantly correlated with immune infiltrating levels of CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages and DCs in HCC, respectively. In addition, CDK1, CDC20, CCNA2, CCNB1 and CCNB2 expressions may be involved in the regulation of monocytes and tumor-associated macrophages (TAMs) in HCC, respectively. These findings strongly suggested that cell cycle-related genes could be used as novel biomarkers for exploring the prognosis and immune cells infiltration of HCC.

Exploring the mechanism of curcumin in the treatment of colon cancer based on network pharmacology and molecular docking

Objective: Curcumin is a plant polyphenol extracted from the Chinese herb turmeric. It was found that curcumin has good anti-cancer properties in a variety of cancers, but the exact mechanism is not clear. Based on the network pharmacology and molecular docking to deeply investigate the molecular mechanism of curcumin for the treatment of colon cancer, it provides a new research direction for the treatment of colon cancer. Methods: Curcumin-related targets were collected using PharmMapper, SwissTargetPrediction, Targetnet and SuperPred. Colon cancer related targets were obtained using OMIM, DisGeNET, GeneCards and GEO databases. Drug-disease intersection targets were obtained via Venny 2.1.0. GO and KEGG enrichment analysis of drug-disease common targets were performed using DAVID. Construct PPI network graphs of intersecting targets using STRING database as well as Cytoscape 3.9.0 and filter core targets. Molecular docking via AutoDockTools 1.5.7. The core targets were further analyzed by GEPIA, HPA, cBioPortal and TIMER databases. Results: A total of 73 potential targets of curcumin for the treatment of colon cancer were obtained. GO function enrichment analysis yielded 256 entries, including BP(Biological Progress):166, CC(celluar component):36 and MF(Molecular Function):54. The KEGG pathway enrichment analysis yielded 34 signaling pathways, mainly involved in Metabolic pathways, Nucleotide metabolism, Nitrogen metabolism, Drug metabolism - other enzymes, Pathways in cancer,PI3K-Akt signaling pathway, etc. CDK2, HSP90AA1, AURKB, CCNA2, TYMS, CHEK1, AURKA, DNMT1, TOP2A, and TK1 were identified as core targets by Cytoscape 3.9.0. Molecular docking results showed that the binding energies of curcumin to the core targets were all less than 0 kJ-mol-1, suggesting that curcumin binds spontaneously to the core targets. These results were further validated in terms of mRNA expression levels, protein expression levels and immune infiltration. Conclusion: Based on network pharmacology and molecular docking initially revealed that curcumin exerts its therapeutic effects on colon cancer with multi-target, multi-pathway. Curcumin may exert anticancer effects by binding to core targets. Curcumin may interfere with colon cancer cell proliferation and apoptosis by regulating signal transduction pathways such as PI3K-Akt signaling pathway,IL-17 signaling pathway, Cell cycle. This will deepen and enrich our understanding of the potential mechanism of curcumin against colon cancer and provide a theoretical basis for subsequent studies.

Integrated analysis of Dendrobium nobile extract Dendrobin A against pancreatic ductal adenocarcinoma based on network pharmacology, bioinformatics, and validation experiments

Background: Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC). Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells. Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase. Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.

C118P exerted potent anti-tumor effects against melanoma with induction of G2/M arrest via inhibiting the expression of BUB1B

BACKGROUND

The incidence of melanoma rapidly increased in the past decades, and the clinical treatment of melanoma met huge challenges because of tumor heterogeneity and drug resistance. C118P, a novel tubulin polymerization inhibitor, exhibited strong anticancer effects in many tumors. However, there was no data regarding the potential effects of C118P in melanoma cells.

OBJECTIVE

To investigate of the efficacy and potential target of C118P in melanoma cells.

METHODS

Human melanoma cells were treated with C118P, followed by assessments of proliferation, apoptosis and cell cycle distribution. Subsequently, RNA sequencing was performed to further identify the drug targets of C118P in melanoma cells. GO analysis and protein-protein interaction networks analysis were used to screen the potential targets, and verified by a series of assays. Finally, the anti-growth activity of C118P was evaluated in A375-xenografted nude mice, and the expression of BUB1B (BUB1 mitotic checkpoint serine/threonine kinase B), Ki67 and Tunel were determined.

RESULTS

We found that C118P concentration-dependently inhibited proliferation of melanoma cells. Moreover, C118P simultaneously triggered dramatic G2/M arrest and apoptosis via independent mechanisms in melanoma cells in vitro. C118P exerted anti-melanoma effects by inducing potent G2/M arrest, which was mechanistically related to downregulation of the expression of BUB1B. Importantly, C118P inhibited the tumor growth in A375-xenografted nude, and increased the staining of Ki-67 and Tunel and suppressed the expression of BUB1B in melanoma tissues, which was consistent with in vitro study.

CONCLUSION

C118P might provide a novel strategy for the clinical treatment of melanoma by inhibition of BUB1B.

Identification of hub genes and construction of prognostic nomogram for patients with Wilms tumors

Background

In children, Wilms' tumors are the most common urological cancer with unsatisfactory prognosis, but few molecular prognostic markers have been discovered for it. With the rapid development of high-throughput quantitative proteomic and transcriptomic approaches, the molecular mechanisms of various cancers have been comprehensively explored. This study aimed to uncover the molecular mechanisms underlying Wilms tumor and build predictive models by use of microarray and RNA-seq data.

Methods

Gene expression datasets were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. Bioinformatics methods wereutilized to identified hub genes, and these hub genes were validated by experiment. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables.

Results

CDC20, BUB1 and CCNB2 were highly expressed in tumor tissues and able to affect cell proliferation and the cell cycle of SK-NEP-1 cells. This may reveal molecular biology features and a new therapeutic target of Wilms tumour.7 genes were selected as prognostic genes after univariate, Lasso, and multivariate Cox regression analyses and had good accuracy, a prognostic nomogram combined gene model with clinical factors was completed with high accuracy.

Conclusions

The current study discovered CDC20,BUB1 and CCNB2 as hub-genes associated with Wilms tumor, providing references to understand the pathogenesis and be considered a novel candidate to target therapy and construct novel nomogram, incorporating both clinical risk factors and gene model, could be appropriately applied in preoperative individualized prediction of malignancy in patients with Wilms tumor.

A review on the role of cyclin dependent kinases in cancers

The Cyclin-dependent kinase (CDK) class of serine/threonine kinases has crucial roles in the regulation of cell cycle transition and is mainly involved in the pathogenesis of cancers. The expression of CDKs is controlled by a complex regulatory network comprised of genetic and epigenetic mechanisms, which are dysregulated during the progression of cancer. The abnormal activation of CDKs results in uncontrolled cancer cell proliferation and the induction of cancer stem cell characteristics. The levels of CDKs can be utilized to predict the prognosis and treatment response of cancer patients, and further understanding of the function and underlying mechanisms of CDKs in human tumors would pave the way for future cancer therapies that effectively target CDKs. Defects in the regulation of cell cycle and mutations in the genes coding cell-cycle regulatory proteins lead to unrestrained proliferation of cells leading to formation of tumors. A number of treatment modalities have been designed to combat dysregulation of cell cycle through affecting expression or activity of CDKs. However, effective application of these methods in the clinical settings requires recognition of the role of CDKs in the progression of each type of cancer, their partners, their interactions with signaling pathways and the effects of suppression of these kinases on malignant features. Thus, we designed this literature search to summarize these findings at cellular level, as well as in vivo and clinical levels.

Alzheimer's disease-associated inflammatory pathways might contribute to osteoporosis through the interaction between PROK2 and CSF3

This study aimed to explore the potential molecular pathways and targets of Alzheimer's disease leading to osteoporosis using bioinformatics tools. The Alzheimer's and osteoporosis microarray gene expression data were retrieved from the Gene Expression Omnibus, and differentially expressed genes in the blood microenvironment related to Alzheimer's disease and osteoporosis were identified. The intersection of the three datasets (GSE97760, GSE168813, and GSE62402) was used to obtain 21 co-expressed targets in the peripheral blood samples in patients with Alzheimer's disease and osteoporosis. Based on the degree algorithm, the top 10 potential core target genes related to these diseases were identified, which included CLEC4D, PROK2, SIGLEC7, PDGFB, PTCRA, ECH1, etc. Two differentially expressed mRNAs, Prokineticin 2 (PROK2) and three colony-stimulating factor 3 (CSF3), were screened in the GSE62402 dataset associated with osteoporosis. Protein–protein rigid docking with ZDOCK revealed that PROK2 and CSF3 could form a stable protein docking model. The interaction of PROK2 and CSF3, core genes related to osteoporosis inflammation, plays an important role in the mechanism of osteoporosis in patients with Alzheimer's. Therefore, abnormalities or alterations in the inflammatory pathways in the peripheral blood samples of Alzheimer's patients may affect the course of osteoporosis.

Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma

Background

In children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis to build potential miRNA-gene-TF networks.

Methods

We collected three gene expression profiles and one miRNA expression profile from the Gene Expression Omnibus (GEO) database. We used the limma R package to identify overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs in RB tissues compared to noncancer tissues. The robust rank aggregation (RRA) method was implemented to identify key genes among the DEGs. Then, miRNA-key gene-TF networks were built using the online tools TransmiR and miRTarBase. Next, we used RT-qPCR to confirm the results.

Results

We identified 180 DEGs in RB tissues compared to nontumor tissues using integrative analysis, among which 109 genes were upregulated and 71 were downregulated. Gene ontology (GO) analysis revealed that these DEGs were primarily involved with chromosome segregation, condensed chromosome and DNA replication origin binding. The most highly enriched pathways obtained in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were cell cycle, DNA replication, homologous recombination, P53 signaling pathway and pyrimidine metabolism. Furthermore, two key differentially expressed miRNAs (DEMs) were also established: let-7a and let-7b. Finally, the potential regulatory networks of miRNA-target gene-TFs were examined.

Conclusions

This study identified key genes and built miRNA-target gene-TF regulatory networks in RB, which will deepen our understanding of the molecular mechanisms involved in the development of RB. These key genes and miRNAs may be potential targets and biomarkers for RB diagnosis and therapy.

Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide, and its pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis and expression analysis of the hub genes were performed. ROC curve analysis was performed for further test of the diagnostic efficacy. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Altogether, 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, and cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (P < 0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis and ROC curve analysis showed good diagnostic accuracy. In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

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

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

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Cancer is one of the leading causes of death worldwide, bringing a significant burden to human health and society. Accurate cancer diagnosis and biomarkers that can be used as robust therapeutic targets are of great importance as they facilitate early and effective therapies. Shared etiology among cancers suggests the existence of pan-cancer biomarkers, performance of which could benefit from the large sample size and the heterogeneity of the studied patients. In this study, we conducted a systematic RNA-seq study of 9,213 tumors and 723 para-cancerous tissue samples of 28 solid tumors from the Cancer Genome Atlas (TCGA) database, and 7,008 normal tissue samples from the Genotype-Tissue Expression (GTEx) database. By differential gene expression analysis, we identified 214 up-regulated and 186 downregulated differentially expressed genes (DEGs) in more than 80% of the studied tumors, respectively, and obtained 20 highly linked up- and downregulated hub genes from them. These markers have rarely been reported in multiple tumors simultaneously. We further constructed pan-cancer diagnostic models to classify tumors and para-cancerous tissues using 10 up-regulated hub genes with an AUC of 0.894. Survival analysis revealed that these hub genes were significantly associated with the overall survival of cancer patients. In addition, drug sensitivity predictions for these hub genes in a variety of tumors obtained several broad-spectrum anti-cancer drugs targeting pan-cancer. Furthermore, we predicted immunotherapy sensitivity for cancers based on tumor mutational burden (TMB) and the expression of immune checkpoint genes (ICGs), providing a theoretical basis for the treatment of tumors. In summary, we identified a set of biomarkers that were differentially expressed in multiple types of cancers, and these biomarkers can be potentially used for diagnosis and used as therapeutic targets.

Identification of Key Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

Objective

This study aimed to identify key genes associated with the pathogenesis of nasopharyngeal carcinoma (NPC) by bioinformatics analysis.

Methods

Datasets (GSE13597 and GSE34573) were screened and downloaded from the comprehensive gene expression database (GEO). GEO2R online tool was adopted to analyze microarray data GSE13597 and GSE34573 related to NPC. Volcano plot was generated using Bioconductor in R software. "Pheatmap" was used to draw heatmaps based on the top 10 regulated genes of GSE13597 and GSE34573. GO and KEGG analyses were conducted via online tool DAVID. We uploaded the DEGs of NPC to STRING software and then used Cytoscape software to draw PPI network of DEGs.

Results

216 DEGs were obtained in GSE13597 between patient and control group (111 up-regulated DEGs and 105 down-regulated DEGs). 1101 DEGs were obtained in GSE34573 (470 up-regulated DEGs and 641 down-regulated DEGs). 63 common differential genes were screened named co-DEGs in the two datasets. These DEGs were mainly associated with defense response to bacterium, cell-matrix adhesion, chemokine-mediated signaling pathway, tissue homeostasis, humoral immune response, cilium movement, cilium organization, cilium assembly, and epithelial cilium movement. KEGG pathway enrichment analysis showed that DEGs were mainly involved in viral protein interaction with cytokine and cytokine receptor, salivary secretion, p53 signaling pathway, IL-17 signaling pathway, cell cycle, PI3K-Akt signaling pathway, and ECM-receptor interaction. We identified seven hub genes, including FN1, MMP-10, MUC1, KIF23, CDK1, MUC5B, and MUC5AC.

Conclusions

Seven hub genes, including FN1, MMP-10, MUC1, KIF23, CDK1, MUC5B, and MUC5AC, might be therapeutic potential biomarkers of NPC.

An Integrative Human Pan-Cancer Analysis of Cyclin-Dependent Kinase 1 (CDK1)

Simple Summary

Cyclin-dependent kinase 1 (CDK1), one of the key regulators of the G2/M checkpoint, is expressed in many cells and plays an important role in cell cycle control. However, CDK1 expression is substantially increased in many tumors of diverse origins and is associated with tumorigenesis. Targeting CDK1 shows promising results for several tumors. However, a systematic and integrative analysis of CDK1 in cancer has not been conducted. The present study aims to use pan-cancer analysis to investigate the relationship, similarities, and differences in genetic and cellular changes associated with CDK1 in various tumors and tumor microenvironments. Our findings elucidate that CDK1 expression increases in more than 20 human tumors and is highly correlated with oncogenic signature gene sets, biological pathways, immune cell infiltration, tumor mutational burden, microsatellite instability, and lower survival rate across multiple tumors. Targeting CDK1 may provide a novel and effective strategy for cancer immunotherapy.

Abstract

Cyclin-dependent kinase 1 (CDK1) is essential for cell division by regulating the G2/M phase and mitosis. CDK1 overexpression can also promote the development and progression of a variety of cancers. However, the significance of CDK1 in the formation, progression, and prognosis of human pan-cancer remains unclear. In the present study, we used The Cancer Genome Atlas database, Clinical Proteomic Tumor Analysis Consortium, Human Protein Atlas, Genotype-Tissue Expression, and other well-established databases to comprehensively examine CDK1 genetic alterations and gene/protein expression in various cancers and their relationships with the prognosis, immune reactivities, and clinical outcomes for 33 tumor types. Gene set enrichment analysis was also conducted to examine the potential mechanisms of CDK1 in tumorigenesis. The data showed that CDK1 mutation was frequently present in multiple tumors. CDK1 expression was significantly increased in various types of tumors as compared with normal tissues and was associated with poor overall and disease-free survival. In addition, CDK1 expression was significantly correlated with oncogenic genes, proteins, cellular components, myeloid-derived suppressor cell infiltration, ESTMATEScore, and signaling pathways associated with tumor development and progression and tumor microenvironments. These data indicate that CDK1 could serve as a promising biomarker for predicting tumor prognosis and a potential target for cancer treatment.

Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.

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.

The Genetic Association Identified Between Intervertebral Disc Degeneration and Associated Risk Factors Based on a Systems Biology Approach

STUDY DESIGN

Genetic cross-over study of intervertebral disc degeneration (IDD) and its associated risk factors.

OBJECTIVE

The purpose is to identify molecular biomarkers that may be involved in the IDD process and to provide effective recommendations in combination with drug analysis via systems biology methods.

SUMMARY OF BACKGROUND DATA

The pathogenesis and genetic links of IDD are still unclear. Related research is mainly based on a single data set or gene, and the impact of related risk factors on IDD is often ignored.

METHODS

Identifying disease-associated biomarkers and therapeutic targets through a systems biology approach that Integrative network-based gene and multi-omics analysis. In this study, the differential expression of genes was analyzed through NCBI datasets, followed by enrichment analysis. The central protein was identified through the establishment of protein-protein interaction (PPI) network. Key genes were screened out by VENN diagram and reasonable suggestions were put forward based on gene-chemical drug analysis.

RESULTS

The IDD database analysis revealed 669 differentially expressed genes (DEGs) which were 22, 26, 168, 5, 38, 36, and 16 common DEGs with AG, SM, DEP, NAD, CED, OB, and HFD, respectively. GO and KEGG enrichment analysis may reveal the pathway by which these DEGs were involved. PPI network identified 10 central proteins including CCNB1, RETN, HMMR, BUB1, MPO, OIP5, HP, KIF11, BUB1B, and CDC25A. Three key genes BUB1, BUB1B, and CCNB1 were screened out and their expression might be related to the pathogenesis of IDD. According to the three chemical Dexamethasone, Nicotine, and Resvera-trol obtained from the analysis of genes-chemical drugs, reasonable treatment suggestions were put forward.

CONCLUSION

Genetic association between IDD and risk factors in the general population was revealed by association network. Important gene-related molecular pathways and chemical drugs closely related to IDD have been found. Further study can provide guidance for the treatment and prognosis of IDD.Level of Evidence: N/A.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.

BUB1B (BUB1 Mitotic Checkpoint Serine/Threonine Kinase B) Promotes Lung Adenocarcinoma by Interacting with Zinc Finger Protein ZNF143 and Regulating Glycolysis

Lung adenocarcinoma (LUAD) is one of the most common causes of cancer death in men. BUB1B (BUB1 mitotic checkpoint serine/threonine kinase B) has been reported to contribute to the initiation and development of several cancers. Here, we aimed to explore the potential role of BUB1B in LUAD. We found BUB1B was upregulated in LUAD, suggesting its potential role as a biomarker for LUAD diagnosis. Significantly, LUAD patients with high BUB1B expression had a shorter survival time than those with low BUB1B expression. Knocking-out BUB1B resulted in suppression of cell proliferation, migration, and invasion in vitro, and inhibition of tumor growth in the xenograft experiment. Further analysis revealed that BUB1B regulates glycolysis in LUAD and interacting with ZNF143 in LUAD cells. The interaction was demonstrated by silencing ZNF143, which led to a decrease in proliferation, migration, and invasion in LUAD cells, whereas overexpressing BUB1B had the opposite effects. Our study suggested that the ZNF143/BUB1B axis plays a pivotal role in LUAD progression, which might be a potential target for LUAD management.

A New Oxadiazole-Based Topsentin Derivative Modulates Cyclin-Dependent Kinase 1 Expression and Exerts Cytotoxic Effects on Pancreatic Cancer Cells

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal form of cancer characterized by drug resistance, urging new therapeutic strategies. In recent years, protein kinases have emerged as promising pharmacological targets for the treatment of several solid and hematological tumors. Interestingly, cyclin-dependent kinase 1 (CDK1) is overexpressed in PDAC tissues and has been correlated to the aggressive nature of these tumors because of its key role in cell cycle progression and resistance to the induction of apoptosis. For these reasons, CDK1 is one of the main causes of chemoresistance, representing a promising pharmacological target. In this study, we report the synthesis of new 1,2,4-oxadiazole compounds and evaluate their ability to inhibit the cell growth of PATU-T, Hs766T, and HPAF-II cell lines and a primary PDAC cell culture (PDAC3). Compound 6b was the most active compound, with IC₅₀ values ranging from 5.7 to 10.7 µM. Molecular docking of 6b into the active site of CDK1 showed the ability of the compound to interact effectively with the adenosine triphosphate binding pocket. Therefore, we assessed its ability to induce apoptosis (which increased 1.5- and 2-fold in PATU-T and PDAC3 cells, respectively) and to inhibit CDK1 expression, which was reduced to 45% in Hs766T. Lastly, compound 6b passed the ADME prediction, showing good pharmacokinetic parameters. These data demonstrate that 6b displays cytotoxic activity, induces apoptosis, and targets CDK1, supporting further studies for the development of similar compounds against PDAC.

A New Risk Score Based on Eight Hepatocellular Carcinoma- Immune Gene Expression Can Predict the Prognosis of the Patients

Background

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism.

Methods

We analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database.

Results

The IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells.

Conclusion

Our results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients.

Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis

Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan–Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which (CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.

A network pharmacology approach to reveal the pharmacological targets and biological mechanism of compound kushen injection for treating pancreatic cancer based on WGCNA and in vitro experiment validation

BACKGROUND

Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated.

METHODS

Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis.

RESULTS

The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis.

CONCLUSIONS

Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

Cyclin Dependent Kinase-1 (CDK-1) Inhibition as a Novel Therapeutic Strategy against Pancreatic Ductal Adenocarcinoma (PDAC)

The role of CDK1 in PDAC onset and development is two-fold. Firstly, since CDK1 activity regulates the G2/M cell cycle checkpoint, overexpression of CDK1 can lead to progression into mitosis even in cells with DNA damage, a potentially tumorigenic process. Secondly, CDK1 overexpression leads to the stimulation of a range of proteins that induce stem cell properties, which can contribute to the development of cancer stem cells (CSCs). CSCs promote tumor-initiation and metastasis and play a crucial role in the development of PDAC. Targeting CDK1 showed promising results for PDAC treatment in different preclinical models, where CDK1 inhibition induced cell cycle arrest in the G2/M phase and led to induction of apoptosis. Next to this, PDAC CSCs are uniquely sensitive to CDK1 inhibition. In addition, targeting of CDK1 has shown potential for combination therapy with both ionizing radiation treatment and conventional chemotherapy, through sensitizing tumor cells and reducing resistance to these treatments. To conclude, CDK1 inhibition induces G2/M cell cycle arrest, stimulates apoptosis, and specifically targets CSCs, which makes it a promising treatment for PDAC. Screening of patients for CDK1 overexpression and further research into combination treatments is essential for optimizing this novel targeted therapy.

Dauricine inhibits human pancreatic carcinoma cell proliferation through regulating miRNAs

Pancreatic cancer is one of the most malignant digestive tract tumors with the worst prognosis. Dauricine (Dau) can inhibit the proliferation of the pancreatic cancer cell line, and has the potential to be used as an adjuvant drug against pancreatic cancer; however, the working mechanism of Dau has not been elucidated. To unravel the effects and mechanisms of Dau on proteins and metabolic pathways, we evaluated the mRNA and microRNA expression in BxPC3 cells treated with Dau. The differences in the gene expression were compared using principal component analysis using mRNA and miRNA data to detect and analyze the sample discrimination. 187 miRNA and 907 mRNA that were significantly differentially expressed were identified using Python programming. On comparing genes and miRNAs in the DISEASES database, 79 known miRNA and 47 mRNA were found to be affected by Dau. The up-regulated and down-regulated genes were annotated with GO biological processes to determine the functional effect. Interactions between mRNA and mRNA were analyzed using the STRING database and the miRBase database was queried to obtain experimentally verified interactions between miRNA and mRNA as edges of miRNA and mRNA in the network. Finally, 413 sites and 2125 sides of the network were obtained, including 1 up-regulated and 18 down-regulated miRNAs. The expression of 19 miRNAs was identified by qPCR. The analysis of the protein-protein interaction network, using the Molecular Complex Detection (MCODE) plug-in of cytoscape, helped in identifying 12 important sub-networks. Most subnets are indirectly or directly related to specific miRNAs. This study provides evidence for the anticancer effect of Dau as a potential anticancer compound.

E2F1 transcriptionally regulates CCNA2 expression to promote triple negative breast cancer tumorigenicity

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly malignant breast cancer subtype with a poor prognosis. The cell cycle regulator cyclin A2 (CCNA2) plays a role in tumor development. Herein, we explored the role of CCNA2 in TNBC.

METHODS

We analyzed CCNA2 expression in 15 pairs of TNBC and adjacent tissues and assessed the relationship between CCNA2 expression using the tissue microarray cohort. Furthermore, we used two TNBC cohort datasets to analyze the correlation between CCNA2 and E2F transcription factor 1 (E2F1) and a luciferase reporter to explore their association. Through rescue experiments, we analyzed the effects of E2F1 knockdown on CCNA2 expression and cellular behavior.

RESULTS

We found that CCNA2 expression in TNBC was significantly higher than that in adjacent tissues with similar observations in MDA-MB-231 and MDA-MB-468 cells. E2F1 was highly correlated with CCNA2 as observed through bioinformatics analysis (R= 0.80, P< 0.001) and through TNBC tissue verification analysis (R= 0.53, P< 0.001). We determined that E2F1 binds the +677 position within the CCNA2 promoter. Moreover, CCNA2 overexpression increased cell proliferation, invasion, and migration owing to E2F1 upregulation in TNBC.

CONCLUSION

Our data indicate that E2F1 promotes TNBC proliferation and invasion by upregulating CCNA2 expression. E2F1 and CCNA2 are potential candidates that may be targeted for effective TNBC treatment.

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

OBJECTIVE

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

METHODS

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

RESULT

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

CONCLUSION

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

CDC20 promotes the progression of hepatocellular carcinoma by regulating epithelial‑mesenchymal transition

Hepatocellular carcinoma (HCC) is a type of primary liver cancer, which is associated with high mortality. HCC is one of the most common malignant tumors worldwide. Cell division cycle 20 (CDC20) has been reported to be associated with the development of various malignant tumors and epithelial-mesenchymal transition (EMT) has been reported to be involved in the malignant metastasis of HCC. Therefore, the present study hypothesized that CDC20 may participate in the malignant biological behavior of HCC via EMT. The present study analyzed the expression levels of CDC20 in HCC and the association between CDC20 and poor prognosis. Furthermore, the effects of CDC20 on the proliferation, invasion and migration of HCC cells were examined using proliferation, migration and invasion assays. Finally, alterations in EMT were analyzed. The results revealed that CDC20 was highly expressed in HCC and HCC cell lines (P<0.05), and its high expression level was significantly associated with poor prognosis in patients with HCC (P<0.05). CDC20 silencing inhibited the proliferation, migration and invasion of HCC cells. Furthermore, CDC20 silencing increased the expression levels of E-cadherin, and decreased the expression levels of N-cadherin, vimentin and Ki-67. In conclusion, the present study reported that CDC20 may be a novel therapeutic target in HCC and CDC20 could promote the progression of HCC by regulating EMT.