• Aurora kinase A
• Colorectal cancer
• Invasion
• Microtubule nucleation factor
• Migration
• Proliferation
• Targeting protein for Xklp2

• ESPL1
• biomarker
• cell proliferation
• endometrial cancer
• separase

• Female
• Humans
• Middle Aged
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Cell Line, Tumor
• Cell Proliferation/genetics
• Endometrial Neoplasms/genetics
• Endometrial Neoplasms/metabolism
• Endometrial Neoplasms/pathology
• Gene Expression Regulation, Neoplastic
• Kaplan-Meier Estimate
• Prognosis
• Separase/metabolism
• Separase/genetics
• Up-Regulation

• Diagnosis
• Kinesin
• Marker
• Treatment
• cancer
• microRNA

• Humans
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Kinesins/metabolism
• Kinesins/genetics
• Neoplasms/genetics
• Neoplasms/pathology
• Neoplasms/metabolism
• Gene Expression Regulation, Neoplastic
• Animals
• Cell Proliferation/genetics
• Disease Progression
• Signal Transduction
• Apoptosis/genetics
• Cell Movement/genetics

• DEGs
• NSCLC
• SCLC
• meta-analysis
• virtual screening

• Colorectal cancer
• DDX39A
• Gene-drug-interaction
• LRP8
• Precision medicine
• TPX2
• TTK

• Humans
• Colonic Neoplasms/genetics
• Colorectal Neoplasms/drug therapy
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/metabolism
• Adenocarcinoma/pathology
• Molecular Docking Simulation
• Cell Proliferation
• Gene Expression Regulation, Neoplastic
• Microtubule-Associated Proteins/genetics
• Microtubule-Associated Proteins/metabolism
• Cell Cycle Proteins/genetics
• Cell Cycle Proteins/metabolism
• Protein-Tyrosine Kinases/metabolism
• Protein Serine-Threonine Kinases/metabolism

• QB13 Qatar Biomedical Research Institute

• endocrine disruptive chemicals
• environmental health risk assessment
• gene ontology
• molecular biomarkers
• prostate cancer
• protein–protein interaction

• Male
• Humans
• Gene Expression Profiling
• Endocrine Disruptors
• Gene Regulatory Networks
• Microarray Analysis
• Prostatic Neoplasms/metabolism
• Computational Biology
• Gene Expression Regulation, Neoplastic

• Natural Science Foundation of Shanghai

• BIRC5/hsa-miR-218-5p/NEAT1
• UBE2C/hsa-miR-483-3p/NEAT1
• biomarkers
• ceRNA
• prostate cancer

This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients.

Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO‐Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence‐free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs).

Notably, the risk score could significantly identify BCRFS by time‐dependent receiver operating characteristic (t‐ROC) curves in the training set (3‐year area under the curve (AUC) = 0.820, 5‐year AUC = 0.809) and the validation set (3‐year AUC = 0.723, 5‐year AUC = 0.733).

Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa.

• LASSO-Cox regression
• biochemical recurrence-free survival
• gene signature
• primary prostate cancer
• radical therapy

• 3D-cell culture
• DNA damage (comet assay)
• ROS - reactive oxygen species
• apoptosis
• lapachol
• naphtoquinones

• E26 transformation specific sequence 1
• endocrine-independent
• metastasis
• prostate carcinoma
• the targeting protein for Xklp2

• Biochemical recurrence
• Immunotherapy
• Prognosis
• Prostate cancer

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

• DNA methylation
• RND3
• hypermethylation
• predictive signature
• prognosis
• prostate cancer
• systems biology

• CDK1
• EMT
• ERK/GSK3β/SNAIL
• High gleason grade prostate cancer
• TPX2

• LASSO
• PRAD
• expression pattern
• m6A
• m6Avar
• prognosis model
• regulator
• variant

The morbidity and mortality of prostate carcinoma has increased in recent years and has become the second most common ale malignant carcinoma worldwide. The interaction mechanisms between different genes and signaling pathways, however, are still unclear.

Variation analysis of GSE38241, GSE69223, GSE46602 and GSE104749 were realized by GEO2R in Gene Expression Omnibus database. Function enrichment was analyzed by DAVID.6.8. Furthermore, the PPI network and the significant module were analyzed by Cytoscape, STRING and MCODE.GO. Pathway analysis showed that the 20 candidate genes were closely related to mitosis, cell division, cell cycle phases and the p53 signaling pathway. A total of six independent prognostic factors were identified in GSE21032 and TCGA PRAD. Oncomine database and The Human Protein Atlas were applied to explicit that six core genes were over expression in prostate cancer compared to normal prostate tissue in the process of transcriptional and translational. Finally, gene set enrichment were performed to identified the related pathway of core genes involved in prostate cancer.

Hierarchical clustering analysis revealed that these 20 core genes were mostly related to carcinogenesis and development. CKS2, TK1, MKI67, TOP2A, CCNB1 and RRM2 directly related to the recurrence and prognosis of prostate cancer. This result was verified by TCGA database and GSE21032.

These core genes play a crucial role in tumor carcinogenesis, development, recurrence, metastasis and progression. Identifying these genes could help us to understand the molecular mechanisms and provide potential biomarkers for the diagnosis and treatment of prostate cancer.

• Biomarkers
• GEO
• Prognosis factor
• Prostate cancer
• TCGA

• AKT signaling pathway
• Chronic rhinosinusitis
• Inflammatory cytokines
• MicroRNA-335-5p
• TPX2

• Animals
• Cell Cycle Proteins/metabolism
• Chronic Disease
• Glycogen Synthase Kinase 3 beta/metabolism
• Inflammation/metabolism
• Mice
• Mice, Inbred C57BL
• MicroRNAs/physiology
• Microtubule-Associated Proteins/metabolism
• Nuclear Proteins/metabolism
• Proto-Oncogene Proteins c-akt/metabolism
• Rhinitis/metabolism
• Signal Transduction
• Sinusitis/metabolism

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

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

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

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

• Abnormally expressed genes
• Gene co-expression network
• Mutual information
• Pearson correlation coefficient
• TCGA

• Heterogeneous nuclear ribonucleoprotein F
• bladder cancer
• cell proliferation
• targeting protein for Xenopus kinesin-like protein 2

• TPX2
• cancer
• meta-analysis
• prognostic value

• Bioinformatic analysis
• Breast cancer
• Metastasis
• Prognosis
• WGCNA

• Biomarkers, Tumor
• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Breast Neoplasms/mortality
• Breast Neoplasms/pathology
• Computational Biology/methods
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Neoplasm Metastasis
• Neoplasm Staging
• Prognosis
• Protein Interaction Mapping
• Protein Interaction Maps
• ROC Curve
• Reproducibility of Results
• Transcriptome

• apoptosis
• lentivirus
• liver cancer
• proliferation
• targeting protein for Xklp2

• BUB1
• Biochemical recurrence
• Biomarker
• Castration-resistant prostate cancer
• Cell cycle
• E2F2
• Gleason 3 + 4 = 7 prostate cancer
• HES6
• Prostate cancer prognosticators
• SRD5A2

• disease-free survival
• meta-analysis
• overall survival
• prognosis
• solid tumors
• targeting protein for xklp2

• Biomarkers, Tumor/metabolism
• Cell Cycle Proteins/metabolism
• Humans
• Microtubule-Associated Proteins/metabolism
• Neoplasms/metabolism
• Neoplasms/therapy
• Nuclear Proteins/metabolism
• Prognosis

Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R² = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.

• GEO
• TCGA
• breast cancer
• prognosis
• weighted gene co-expression network analysis (WGCNA)

Targeting protein for Xenopus kinesin-like protein 2 (TPX2) activates Aurora kinase A during mitosis and targets its activity to the mitotic spindle, serving an important role in mitosis. It has been associated with different types of cancer and is considered to promote tumor growth. The aim of the present study was to explore the role of TPX2 in diagnosing prostate cancer (PCa). It was identified that TPX2 expression in PCa tissues was increased compared with benign prostate tissues. Microarray analysis demonstrated that TPX2 was positively associated with the Gleason score, tumor-node-metastasis (TNM) stage, clinicopathological stage, metastasis, overall survival and biochemical relapse-free survival. In vitro studies revealed that the high expression of TPX2 in PCa cells improved proliferative, invasive and migratory abilities, and repressed apoptosis of the PCa cells, without affecting tolerance to docetaxel. The results suggested that TPX2 serves as a tumorigenesis-promoting gene in PCa, and a potential therapeutic target for patients with PCa.

• prostate cancer
• prostate cancer diagnosis
• prostate cancer prognosis
• targeting protein for Xenopus kinesin-like protein 2

Hepatocellular carcinoma (HCC) has an increasing incidence and high mortality. Surgical operation is not a comprehensive strategy for liver cancer. Moreover, tolerating systemic chemotherapy is difficult for patients with HCC because hepatic function is often impaired due to underlying cirrhosis. Therefore, a comprehensive strategy for cancer treatment should be developed. DTL (Cdc10-dependent transcript 2) is a critical regulator of cell cycle progression and genomic stability. In our previous study, the upregulation of DTL expression in aggressive HCC correlated positively with tumor grade and poor patient survival. We hypothesize that targeting DTL may provide a novel therapeutic strategy for liver cancer. DTL small interference RNAs were used to knock down DTL protein expression.

A clonogenic assay, immunostaining, double thymidine block, imaging flow cytometry analysis, and a tumor spheroid formation assay were used to analyze the role of DTL in tumor cell growth, cell cycle progression, micronucleation, ploidy, and tumorigenicity.

Our results demonstrated that targeting DTL reduced cell cycle regulators and chromosome segregation genes, resulting in increased cell micronucleation. DTL depletion inhibited liver cancer cell growth, increased senescence, and reduced tumorigenesis. DTL depletion resulted in the disruption of the mitotic proteins cyclin B, CDK1, securin, seprase, Aurora A, and Aurora B as well as the upregulation of the cell cycle arrest gene p21. A rescue assay indicated that DTL should be targeted through TPX2 downregulation for cancer cell growth inhibition. Moreover, DTL silencing inhibited the growth of patient-derived primary cultured HCC cells.

Our study results indicate that DTL is a potential novel target gene for treating liver cancer through liver cancer cell senescence induction. Furthermore, our results provide insights into molecular mechanisms for targeting DTL in liver cancer cells. The results also indicate several other starting points for future preclinical and clinical studies on liver cancer treatment.

• DTL
• TPX2
• cell cycle
• hepatocellular carcinoma
• senescence

• TPX2
• chemosensitivity
• miR-1294
• proliferation

• OC cell line
• Ovarian cancer
• TPX2
• migration
• proliferation

• Adult
• Aged
• Apoptosis/genetics
• CDC2 Protein Kinase/metabolism
• Cell Cycle Proteins/genetics
• Cell Cycle Proteins/metabolism
• Cell Line, Tumor
• Cell Movement/genetics
• Cell Proliferation
• Cell Survival/genetics
• Cyclin D1/metabolism
• Female
• Gene Expression Regulation, Neoplastic
• Gene Knockdown Techniques
• Humans
• Immunohistochemistry
• Microtubule-Associated Proteins/genetics
• Microtubule-Associated Proteins/metabolism
• Middle Aged
• Nuclear Proteins/genetics
• Nuclear Proteins/metabolism
• Ovarian Neoplasms/genetics
• Ovarian Neoplasms/metabolism
• Ovarian Neoplasms/pathology
• Protein Serine-Threonine Kinases/genetics
• Protein Serine-Threonine Kinases/metabolism
• Proto-Oncogene Proteins/genetics
• Proto-Oncogene Proteins/metabolism
• RNA, Small Interfering
• Young Adult
• Polo-Like Kinase 1

• Kaplan-Meier analysis
• biomarkers
• differentially expressed genes
• gene ontology
• lung adenocarcinoma

MicroRNA-7 (miR-7) is a non-coding RNA of 23-nucleotides that has been shown to act as a tumor suppressor in various cancers including breast cancer. Although there have been copious studies on the action mechanisms of miR-7, little is known about how the miR is controlled in the mammary cell. In this study, we performed a genome-wide expression analysis in miR-7-transfected MCF-10A breast cell line to explore the upstream regulators of miR-7. Analysis of the dysregulated target gene pool predicted hepatocyte growth factor (HGF) as the most plausible upstream regulator of miR-7. MiR-7 was upregulated in MCF-10A cells by HGF, and subsequently downregulated upon treatment with siRNA against HGF. However, the expression of HGF did not significantly change through either an upregulation or downregulation of miR-7 expression, suggesting that HGF acts upstream of miR-7. In addition, the target genes of miR-7, such as EGFR, KLF4, FAK, PAK1 and SET8, which are all known oncogenes, were downregulated in HGF-treated MCF-10A; in contrast, knocking down HGF recovered their expression. These results indicate that miR-7 mediates the activity of HGF to suppress oncogenic proteins, which inhibits the development of normal cells, at least MCF-10A, into cancerous cells.