Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

Identification of hub genes and pathways in hepatitis B virus-associated hepatocellular carcinoma: A comprehensive in silico study

Background and Aim

The hepatitis B virus (HBV) is one of the most common causes of liver cancer in the world. This study aims to provide a better understanding of the mechanisms involved in the development and progression of HBV-associated hepatocellular carcinoma (HCC) by identifying hub genes and the pathways related to their functions.

Methods

GSE83148 and GSE94660 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a |logFC| ≥1 were identified. Common DEGs of two data sets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene Expression Profiling Interactive Analysis (GEPIA) analysis was carried out to confirm the target genes.

Results

One hundred and ninety-eight common DEGs and 49 hub genes have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in the G1/S transition of cell cycle mitotic, cell cycle, spindle, and extracellular matrix structural constituent. The expression of four genes (TOP2A, CDK1, CCNA2, and CCNB2) with high scores in module 1 were more in tumor samples and have been identified by GEPIA analysis.

Conclusion

In this study, the hub genes and their related pathways involved in the development of HBV-associated HCC were identified. These genes, as potential diagnostic biomarkers, may provide a potent opportunity to detect HBV-associated HCC at the earliest stages, resulting in a more effective treatment.

Anti-Tumor Activity and Mechanism of Silibinin Based on Network Pharmacology and Experimental Verification

Silibinin is a flavonoid compound extracted from the seeds of Silybum marianum (L.) Gaertn. It has the functions of liver protection, blood-lipid reduction and anti-tumor effects. However, the potential molecular mechanism of silibinin against tumors is still unknown. This study aimed to assess the anti-tumor effects of silibinin in adenoid cystic carcinoma (ACC2) cells and Balb/c nude mice, and explore its potential mechanism based on network pharmacology prediction and experimental verification. A total of 347 targets interacting with silibinin were collected, and 75 targets related to the tumor growth process for silibinin were filtrated. Based on the PPI analysis, CASP3, SRC, ESR1, JAK2, PRKACA, HSPA8 and CAT showed stronger interactions with other factors and may be the key targets of silibinin for treating tumors. The predicted target proteins according to network pharmacology were verified using Western blot analysis in ACC2 cells and Balb/c nude mice. In the pharmacological experiment, silibinin was revealed to significantly inhibit viability, proliferation, migration and induce the apoptosis of ACC2 cells in vitro, as well as inhibit the growth and development of tumor tissue in vivo. Western blot analysis showed that silibinin affected the expression of proteins associated with cell proliferation, migration and apoptosis, such as MMP3, JNK, PPARα and JAK. The possible molecular mechanism involved in cancer pathways, PI3K-Akt signaling pathway and viral carcinogenesis pathway via the inhibition of CASP3, MMP3, SRC, MAPK10 and CDK6 and the activation of PPARα and JAK. Overall, our results provided insight into the pharmacological mechanisms of silibinin in the treatment of tumors. These results offer a support for the anti-tumor uses of silibinin.

Silencing of KIF2C enhances the sensitivity of hepatocellular carcinoma cells to cisplatin through regulating the PI3K/AKT/MAPK signaling pathway

In the treatment of unresectable advanced hepatocellular carcinoma (HCC), cisplatin is administered transhepatic arterially for local treatment, but the clinical application of cisplatin drugs is frequently hindered by the emergence of drug resistance. Kinesin family member 2C( KIF2C ) has been shown as oncogene in a variety of tumors. Nevertheless, its effect on cisplatin sensitivity has yet to be ascertained. Herein, we aim to investigate the impact of the KIF2C gene on cisplatin sensitivity within HCC and the plausible underlying molecular mechanism. We examined the expression level of the KIF2C gene in HCC cells by real-time quantitative reverse transcription PCR and Western blot analysis, and analyzed bioinformatically by The Gene Expression Omnibus database and The Cancer Genome Atlas database. The KIF2C gene was silenced using the small interfering RNA technology, and its effect on cisplatin drug sensitivity in HCC cells was evaluated by flow cytometry, cell proliferation, cell migration, and invasion assays. Our results indicated that KIF2C was highly expressed in HCC cells. KIF2C silencing inhibits HCC cell proliferation, migration and invasion, promotes apoptosis, and keeps the cell cycle in G2 phase. In addition, KIF2C silencing enhanced the sensitivity of HCC cells to cisplatin. KIF2C silencing down-regulates the expression levels of phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT) and mitogen-activated protein kinase 3 (MAPK3) proteins. In conclusion, KIF2C silencing amplifies the sensitivity of HCC cells to cisplatin by regulating the PI3K/AKT/MAPK signaling pathway. Consequently, targeting KIF2C shows great application potential as a strategy for enhancing the effectiveness of HCC treatment.

KIF2C: An important factor involved in signaling pathways, immune infiltration, and DNA damage repair in tumorigenesis

BACKGROUNDS

Poorly regulated mitosis and chromosomal instability are common characteristics in malignant tumor cells. Kinesin family member 2 C (KIF2C), also known as mitotic centromere-associated kinesin (MCAK) is an essential component during mitotic regulation. In recent years, KIF2C was shown to be dysregulated in several tumors and was involved in many aspects of tumor self-regulation. Research on KIF2C may be a new direction and target for anti-tumor therapy.

OBJECT

The article aims at reviewing current literatures and summarizing the research status of KIF2C in malignant tumors as well as the oncogenic signaling pathways associated with KIF2C and its role in immune infiltration.

RESULT

In this review, we summarize the KIF2C mechanisms and signaling pathways in different malignant tumors, and briefly describe its involvement in pathways related to classical chemotherapeutic drug resistance, such as MEK/ERK, mTOR, Wnt/β-catenin, P53 and TGF-β1/Smad pathways. KIF2C upregulation was shown to promote tumor cell migration, invasion, chemotherapy resistance and inhibit DNA damage repair. It was also highly correlated with microRNAs, and CD4 +T cell and CD8 +T cell tumor immune infiltration.

CONCLUSION

This review shows that KIF2C may function as a new anticancer drug target with great potential for malignant tumor treatment and the mitigation of chemotherapy resistance.

Unravelling hub genes as potential therapeutic targets in lung cancer using integrated transcriptomic meta-analysis and in silico approach

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Smoking has been identified as the main contributing cause of the disease's development. The study aimed to identify the key genes in small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), the two major types of LC. Meta-analysis was performed with two datasets GSE74706 and GSE149507 obtained from Gene Expression Omnibus (GEO). Both the datasets comprised samples from cancerous and adjacent non-cancerous tissues. Initially, 633 differentially expressed genes (DEGs) were identified. To understand the underlying molecular mechanism of the identified genes, pathway enrichment, gene ontology (GO) and protein-protein interaction (PPI) analyses were done. A total of 9 hub genes were identified which were subjected to mutation study analysis in LC patients using cBioPortal. These   9 genes (i.e. AURKA, AURKB, KIF23, RACGAP1, KIF2C, KIF20A, CENPE, TPX2 and PRC1) have shown overexpression in LC patients and can be explored as potential candidates for prognostic biomarkers. TPX2 reported a maximum mutation of 4 % . This was followed with high throughput screening and docking analysis to identify the potential drug candidates following competitive inhibition of the AURKA-TPX2 complex. Four compounds, CHEMBL431482, CHEMBL2263042, CHEMBL2385714, and CHEMBL1206617 were identified. The results signify that the selected 9 genes can be explored as biomarkers in disease prognosis and targeted therapy. Also, the identified 4 compounds can be further analyzed as promising therapeutic candidates.Communicated by Ramaswamy H. Sarma.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

A genomic and transcriptomic study toward breast cancer

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

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

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

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

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

BACKGROUND

Molecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype.

METHODS

Meta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA).

RESULTS

We identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%.

CONCLUSION

Integrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

Upregulation of Centromere Proteins as Potential Biomarkers for Esophageal Squamous Cell Carcinoma Diagnosis and Prognosis

Esophageal squamous cell carcinoma (ESCC) has a high incidence and low survival rate, necessitating the identification of novel specific biomarkers. Centromere-associated proteins (CENPs) have been reported to be biomarkers for many cancers, but their roles in ESCC have seldom been investigated. Here, the potential clinical roles of CENPs in ESCC patients were demonstrated by a systematic bioinformatics analysis. Most CENP-encoding genes were differentially expressed between tumor and normal tissues. CENPA, CENPE, CENPF, CENPI, CENPM, CENPN, CENPQ, and CENPR were upregulated universally in the three datasets. Survival analysis demonstrated that high expression of CENPE and CENPQ was positively correlated with the outcomes of ESCC patients. The CENPE-based forecast model was more accurate than the tumor-node-metastasis (TNM) staging-based model, which was classified as stage I/II vs. III/IV. More importantly, the forecast model based on the commonly upregulated CENPs exhibited a much higher area under the curve (AUC) value (0.855) than the currently known TTL, ZNF750, AC016205.1, and BOLA3 biomarkers. The nomogram model integrating the CENPs, TNM stage, and sex was highly accurate in the prognosis of ESCC patients ( $\text{AUC}=0.906$ ). Besides, gene set enrichment analysis (GSEA) demonstrated that CENPE expression is significantly correlated with cell cycle, G2/M checkpoint, mitotic spindle, p53, etc. Finally, in validation experiments, we also found that CENPE and CENPQ were significantly overexpressed in esophageal cancer cells. Taken together, these results clearly suggest that CENPs are clinically promising diagnostic and prognostic biomarkers for ESCC patients.

Analysis of Breast Cancer Based on the Dysregulated Network

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

Anticonvulsant activity of melatonin and its success in ameliorating epileptic comorbidity-like symptoms in zebrafish

Epilepsy is one of common neurological disorders, greatly distresses the well-being of the sufferers. Melatonin has been used in clinical anti-epileptic studies, but its effect on epileptic comorbidities is unknown, and the underlying mechanism needs further investigation. Herein, by generating PTZ-induced zebrafish seizure model, we carried out interdisciplinary research using neurobehavioral assays, bioelectrical detection, molecular biology, and network pharmacology to investigate the activity of melatonin as well as its pharmacological mechanisms. We found melatonin suppressed seizure-like behavior by using zebrafish regular locomotor assays. Zebrafish freezing and bursting activity assays revealed the ameliorative effect of melatonin on comorbidity-like symptoms. The preliminary screening results of neurobehavioral assays were further verified by the expression of key genes involved in neuronal activity, neurodevelopment, depression and anxiety, as well as electrical signal recording from the midbrain of zebrafish. Subsequently, network pharmacology was introduced to identify potential targets of melatonin and its pathways. Real-time qPCR and protein-protein interaction (PPI) were conducted to confirm the underlying mechanisms associated with glutathione metabolism. We also found that melatonin receptors were involved in this process, which were regulated in response to melatonin exposure before PTZ treatment. The antagonists of melatonin receptors affected anticonvulsant activity of melatonin. Overall, current study revealed the considerable ameliorative effects of melatonin on seizure and epileptic comorbidity-like symptoms and unveiled the underlying mechanism. This study provides an animal model for the clinical application of melatonin in the treatment of epilepsy and its comorbidities.

Study on the effect of active components of Schisandra chinensis on liver injury and its mechanisms in mice based on network pharmacology

The aim of this study was to analyze the active components of Schisandra chinensis on liver injury and its mechanism in mice by network pharmacology. The active components of S. chinensis were found through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and their corresponding targets were predicted. The targets of liver injury were searched through Therapeutic Targets Database (TTD), DisGeNET and drugbank databases, and the Venn diagram was constructed to obtain the action targets. The "drug-active component-target" network and protein-protein interaction network (PPI) were constructed by using STRING database and Cytoscape software, and the key targets were further screened by the enrichment analysis of relevant KEGG pathways. Finally, a CCl4-induced mouse liver injury model was established to verify the efficacy and related targets of S. chinensis and clarify its mechanism. Eight active components and 56 related targets of S. chinensis were screened out based on their oral bioavailability (OB) and drug likeness (DL). Five targets of S. chinensis related to liver injury were found by using the Venn diagram. The key targets, namely Ptgs2 and Nos2 genes, were further screened out by constructing a PPI network, and Schisandrol B (SCB) was considered the key component most closely related to the liver injury in S. chinensis. The results indicate that SCB may play a role in the treatment of the CCl4-induced liver injury by down-regulating the expression of iNOS and COX-2, and regulating the expression of NF-κB and IL-17 signaling pathway to inhibit the expression of proinflammatory factors.

MELK expression in breast cancer is associated with infiltration of immune cell and pathological compete response (pCR) after neoadjuvant chemotherapy

In experimental settings, maternal embryonic leucine zipper kinase (MELK), an apical member of the snf1/AMPK serine-threonine kinases family, plays a role in tumor growth. We investigated the clinical relevance of MELK expression by performing silico analyses of 7,135 breast cancer patients using multiple independent large cohorts. In triple negative breast cancer (TNBC) found that elevated MELK expression significantly correlates with Nottingham histologic grade and tumor growth according to American Joint Committee Cancer (AJCC) stage. High MELK tumor enriched cell proliferation-related gene sets as well as DNA repair, unfolded protein response, and MTORC signaling gene sets. In two independent cohorts a high mutation rate and worse survival was significantly associated with high MELK tumor. In immune-related gene sets including, allograft rejection, interferon (IFN)-α response, and IFN-γ response, high MELK tumor significantly enriched. Pro-cancer regulatory T cells, T helper type 2 cells and anti-cancer immune cells including CD4⁺ memory T cells, T helper type1 cells, CD8⁺ T cells, M1 macrophages, gamma-delta T cells, and dendritic cells with high levels of cytolytic activity (CYT) were highly infiltrated. MELK expression did not correlate with the responses to any of the drugs tested in cell lines. However, pathologic complete response was significantly associated with high MELK following NAC in both TNBC and ER-positive plus HER2-negative breast cancer. In conclusion, cell proliferation, immune response, and NAC breast cancer response was associated with MELK expression.

CKS2 (CDC28 protein kinase regulatory subunit 2) is a prognostic biomarker in lower grade glioma: a study based on bioinformatic analysis and immunohistochemistry

Gliomas account for the highest cases of primary brain malignancies. Whereas previous studies have demonstrated the roles of CDC28 Protein Kinase Regulatory Subunit 2 (CKS2) in various cancer types, its functions in lower grade gliomas (LGGs) remain elusive. This study aimed to profile the expression and functions of CKS2 in LGG. Multiple online databases such as The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), Gene Expression Profiling Interactive Analysis 2nd edition (GEPIA2), Tumor Immune Estimation Resource 2nd edition (TIMER2.0) as well as Gene Expression Omnibus (GEO) were used in this study. Immunohistochemistry (IHC) was performed to evaluate CKS2 protein expression. Our data demonstrated upregulation of CKS2 in LGG tissues at both mRNA and protein level, especially in grade III gliomas. Similarly, there was increased expression of CKS2 in isocitrate dehydrogenase 1 (IDH1) wildtype gliomas. In addition, increased DNA copy number and DNA hypomethylation might be associated with the upregulation of the CKS2 in LGG. Using the Kaplan–Meier survival analysis and the Cox regression analysis, CKS2 was shown to be independently associated with poor prognosis of LGG patients. Receiver operating characteristic (ROC) analysis revealed that CKS2 could effectively predict the 1-, 3- and 5-year survival rates of LGG patients. Enrichment analyses revealed that CKS2 was mainly involved in the regulation of the cell cycle in LGG. Taken together, our study demonstrated that CKS2 might be a candidate prognostic biomarker for LGG and could predict the survival rates of LGG patients.

Abbreviations: LGG: lower grade glioma; CKS2: CDC28 protein kinase regulatory subunit 2; TCGA: The Cancer Genome Atlas; CGGA: the Chinese Glioma Genome Atlas; GEO: Gene Expression Omnibus; GEPIA: Gene Expression Profiling Interactive Analysis; TIMER: Tumor Immune Estimation Resource; IHC: immunohistochemistry; qRT-PCR: quantitative real-time polymerase chain reaction; PBS: phosphate buffered saline; DAB: diaminobenzidine tetrachloride; OS: overall survival; CAN: copy number alteration; IDH: Isocitrate dehydrogenase; GSEA: Gene Set Enrichment Analysis; DEG: differentially expressed gene; KEGG: Kyoto encyclopedia of genes and genomes; GO: Gene ontology; BP: biological process; CC: cellular component; MF: molecular function; NES: normalized enrichment score; NOM: nominal; FDR: false discovery rate

WBP2 promotes BTRC mRNA stability to drive migration and invasion in triple-negative breast cancer via NF-κB activation

WW‐domain‐binding protein 2 (WBP2) is an oncogene that drives breast carcinogenesis through regulating Wnt, estrogen receptor (ER), and Hippo signaling. Recent studies have identified neoteric modes of action of WBP2 other than its widely recognized function as a transcriptional coactivator. Here, we identified a previously unexplored role of WBP2 in inflammatory signaling in breast cancer via an integrated proteogenomic analysis of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) dataset. WBP2 was shown to enhance the migration and invasion in triple‐negative breast cancer (TNBC) cells especially under tumor necrosis factor alpha (TNF‐α) stimulation. Molecularly, WBP2 potentiates TNF‐α‐induced nuclear factor kappa B (NF‐κB) transcriptional activity and nuclear localization through aggrandizing ubiquitin‐mediated proteasomal degradation of its upstream inhibitor, NF‐κB inhibitor alpha (NFKBIA; also known as IκBα). We further demonstrate that WBP2 induces mRNA stability of beta‐transducin repeat‐containing E3 ubiquitin protein ligase (BTRC), which targets IκBα for ubiquitination and degradation. Disruption of IκBα rescued the impaired migratory and invasive phenotypes in WBP2‐silenced cells, while loss of BTRC ameliorated WBP2‐driven migration and invasion. Clinically, the WBP2‐BTRC‐IκBα signaling axis correlates with poorer prognosis in breast cancer patients. Our findings reveal a pivotal mechanism of WBP2 in modulating BTRC‐IκBα‐NF‐κB pathway to promote TNBC aggressiveness.

Study on the Mechanism of the Danggui-Chuanxiong Herb Pair on Treating Thrombus through Network Pharmacology and Zebrafish Models

Danggui-Chuanxiong (DC) is a commonly used nourishing and activating blood medicine pair in many gynecological prescriptions and modern Chinese medicine. However, its activating blood mechanism has not been clearly elucidated. Our research aimed at investigating the activating blood mechanisms of DC using network pharmacology and zebrafish experiments. Network pharmacology was used to excavate the potential targets and mechanisms of DC in treating thrombus. The antithrombotic, anti-inflammatory, antioxidant, and vasculogenesis activities of DC and the main components of DC, ferulic acid (DC2), ligustilide (DC7), and levistilide A (DC17), were evaluated by zebrafish models in vivo. A total of 24 compounds were selected as the active ingredients with favorable pharmacological parameters for this herb pair. A total of 89 targets and 18 pathways related to the thrombus process were gathered for active compounds. The genes, TNF, CXCR4, IL2, ESR1, FGF2, HIF1A, CXCL8, AR, FOS, MMP2, MMP9, STAT3, and RHOA, might be the main targets for this herb pair to exert cardiovascular activity from the analysis of protein-protein interaction and KEGG pathway results, which were mainly related to inflammation, vasculogenesis, immunity, hormones, and so forth. The zebrafish experiment results showed that DC had antithrombotic, anti-inflammatory, antioxidant, and vasculogenesis activities. The main compounds had different effects of zebrafish activities. Especially, the antithrombotic activity of the DC17H group, anti-inflammatory activities of DCH and DC2H groups, antioxidant activities of DCM, DCH, DC2, DC7, and DC17 groups, and vasculogenesis activities of DCM, DCH, and DC2 groups were stronger than those of the positive group. The integrated method coupled zebrafish models with network pharmacology provided the insights into the mechanisms of DC in treating thrombus.

Gene Expression Profiling of Olfactory Neuroblastoma Helps Identify Prognostic Pathways and Define Potentially Therapeutic Targets

Simple Summary

The gene expression profile of ONB defines a group of patients with a dismal prognosis and identifies potentially targetable pathways. Better prognostic stratification may offer new tailored approaches for the treatment and follow-up of ONB. The integration of new therapeutic agents with standard surgical and RT strategies may improve the outcomes in cases with worse prognoses. Furthermore, the ontogenesis of ONB in basal and neural subtypes is mirrored by different transcriptional pathways, paving the way towards different therapeutic approaches.

Abstract

Olfactory neuroblastoma (ONB) is a rare sinonasal neoplasm with a peculiar behavior, for which limited prognostic factors are available. Herein, we investigate the transcriptional pathways altered in ONB and correlate them with pathological features and clinical outcomes. We analyze 32 ONB patients treated with curative intent at two independent institutions from 2001 to 2019 for whom there is available pathologic and clinical data. We perform gene expression profiling on primary ONB samples and carry out functional enrichment analysis to investigate the key pathways associated with disease-free survival (DFS). The median age is 53.5 years; all patients undergo surgery and a pure endoscopic approach is adopted in the majority of cases (81.2%). Most patients have advanced disease (stages III–IV, 81.2%) and 84.4% undergo adjuvant (chemo)radiotherapy. The median follow-up is 35 months; 11 (26.8%) patients relapse. Clinical characteristics (gender, stage and Hyams’ grade) are not associated with the outcomes. In contrast, TGF-beta binding, EMT, IFN-alpha response, angiogenesis, IL2-STAT5 and IL6-JAK-STAT3 signaling pathways are enriched in patients experiencing recurrence, and significantly associated with shorter DFS. Clustering of transcriptional profiles according to pathological features indicates two distinct molecular groups, defined by either cytokeratin-positive or -negative immunostaining. Definition of the characterizing ONB transcriptomic pathways may pave the way towards tailored treatment approaches.

Investigation of the active components and mechanisms of Schisandra chinensis in the treatment of asthma based on a network pharmacology approach and experimental validation

The aim of this paper was to investigate the active components of Schisandra chinensis in the treatment of asthma and the related mechanisms by a network pharmacology approach. The active components of Schisandra chinensis and the corresponding targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Eight active components in Schisandra chinensis and 56 related targets were screened out according to two indicators, oral bioavailability (OB) and drug-likeness (DL). A total of 132 targets related to asthma were screened out through Therapeutic Target Database (TTD) data. The String database and Cytoscape software were used to build the "drug-active compound-target" network and protein-protein interaction (PPI) network. The key targets were further predicted by the analysis of related biological processes and the pathway-enrichment. A total of 10 intersection targets between Schisandra chinensis and asthma were obtained by building Venn diagrams, and lignans in Schisandra chinensis were found to be associated with asthma. The key targets Ptgs2 and Nos2 were further screened out, and schisandrol B (SCB) was predicted as the most related key component to asthma. A mouse asthma model was established with ovalbumin and aluminum hydroxide for verifying the effect of SCB and related mechanisms. The results showed that SCB could inhibit the gene expression of proinflammatory factors to play a therapeutic role in asthma by reducing the expression of Nos2 and Ptgs2 and regulating the NF-κB signaling pathway to intervene in the process of cell metabolism in mice. These results suggest that SCB can alleviate the severity of asthma through the mechanisms predicted by network pharmacology, and provide a basis for further understanding of the application of Schisandra chinensis in the treatment of asthma.

MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

High nuclear TPX2 expression correlates with TP53 mutation and poor clinical behavior in a large breast cancer cohort, but is not an independent predictor of chromosomal instability

BACKGROUND

Targeting Protein for Xenopus Kinesin Like Protein 2 (TPX2) is a microtubule associated protein that functions in mitotic spindle assembly. TPX2 also localizes to the nucleus where it functions in DNA damage repair during S-phase. We and others have previously shown that TPX2 RNA levels are strongly associated with chromosomal instability (CIN) in breast and other cancers, and TPX2 RNA levels have been demonstrated to correlate with aggressive behavior and poor clinical outcome across a range of solid malignancies, including breast cancer.

METHODS

We perform TPX2 IHC on a cohort of 253 primary breast cancers and adopt a clinically amenable scoring system to separate tumors into low, intermediate, or high TPX2 expression. We then correlate TPX2 expression against diverse pathologic parameters and important measures of clinical outcome, including disease-specific and overall survival. We link TPX2 expression to TP53 mutation and evaluate whether TPX2 is an independent predictor of chromosomal instability (CIN).

RESULTS

We find that TPX2 nuclear expression strongly correlates with high grade morphology, elevated clinical stage, negative ER and PR status, and both disease-specific and overall survival. We also show that increased TPX2 nuclear expression correlates with elevated ploidy, supernumerary centrosomes, and TP53 mutation. TPX2 nuclear expression correlates with CIN via univariate analyses but is not independently predictive when compared to ploidy, Ki67, TP53 mutational status, centrosome number, and patient age.

CONCLUSIONS

Our findings demonstrate a strong correlation between TPX2 nuclear expression and aggressive tumor behavior, and show that TPX2 overexpression frequently occurs in the setting of TP53 mutation and elevated ploidy. However, TPX2 expression is not an independent predictor of CIN where it fails to outperform existing clinical and pathologic metrics.

Identification of Four Genes as Prognosis Signatures in Lung Adenocarcinoma Microenvironment

Background

Tumor microenvironment (TME) cells constitute a vital element of tumor tissues. Increasing evidence has shown that immune response in the microenvironment plays an active role in tumor invasion, metastasis, and recurrence, and is an important factor affecting tumor prognosis. Our study aimed to identify the gene signatures in lung adenocarcinoma (LUAD) microenvironment for prognosis and immunotherapy.

Methods

In this study, we evaluated, for the first time, the stromal and immune scores of 594 patients from The Cancer Genome Atlas (TCGA) database with LUAD using the ESTIMATE algorithm. Three hundred and sixty-seven dysregulated immune-related genes were identified. Then, we performed functional enrichment analysis of these genes, and found the best gene model and construct the signature through univariate, Lasso and multivariate COX regression analysis. To assess the independently prognostic ability of the signature, the Kaplan–Meier survival analysis and Cox’s proportional hazards model were performed.

Results

Functional enrichment analysis and protein–protein interaction networks showed that the immune-related genes mainly played a role in immune response, activation/proliferation of immune-related cells, and chemokine activity. A prognostic model involving 6 genes was constructed and the signature was identified as an independent prognostic factor and significantly associated with the overall survival (OS) of LUAD. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the 6 genes signature in predicting the 3-year survival rate was 0.708. Finally, four genes (FOXN4, KLHL4, FAM83F and CCR2) can be used as candidate prognostic biomarkers for LUAD.

Conclusion

Our findings will help evaluate the prognosis of LUAD and provide new ideas for exploring the potential relationship between TME and LUAD treatment and prognosis.

Kinesin Family Member C1 (KIFC1) Regulated by Centrosome Protein E (CENPE) Promotes Proliferation, Migration, and Epithelial-Mesenchymal Transition of Ovarian Cancer

Background

Centrosome amplification is recognized as a hallmark of cancer. Kinesin family member C1 (KIFC1), a centrosome-clustering molecule, is essential for the viability of extra centrosome-bearing cancer cells and may be the basis for the progression of ovarian cancer. However, its biological function and mechanism in ovarian cancer have not yet been studied.

Material/Methods

Quantitative reverse-transcription polymerase chain reaction was performed to detect the levels of KIFC1 and centrosome protein E (CENPE). Further, cell viability was analyzed with CCK-8 assay, and immunofluorescence was used to measure the expression of Ki67 and PCNA. Cell migration was analyzed with wound healing and transwell assays. Western blot analysis was performed to measure the expression of proteins in ovarian cancer cells. The relationship between KIFC1 and CENPE was investigated by performing co-immunoprecipitation.

Results

KIFC1 was upregulated in ovarian cancer cells, especially in SKOV3 cells. Additionally, we found that KIFC1 silencing in SKOV3 cells inhibited cell proliferation and downregulated the expression of Ki67 and PCNA. Further, the knockdown of KIFC1 suppressed cell migration and epithelial-mesenchymal transition (EMT) and regulated the expression of matrix metalloproteinase (MMP)2, MMP9, E-cadherin, N-cadherin, Snail, and ZEB1. Next, we found that KIFC1 bound to and positively regulated CENPE, a tumor promoter in certain human cancers. All the suppressive effects triggered by KIFC1 inhibition were reversed by CENPE overexpression.

Conclusions

KIFC1 contributed to cell proliferation, migration, and EMT via interacting with CENPE in ovarian cancer. KIFC1 might be a potential biomarker and therapeutic target in ovarian cancer patients.

Study on Network Pharmacological Analysis and Preliminary Validation to Understand the Mechanisms of Plantaginis Semen in Treatment of Gouty Nephropathy

Plantaginis Semen (PS) has been used to promote diuresis and clear away dampness. Recent reports have shown that PS can be used to treat gouty nephropathy (GN). However, the action and mechanism of PS have not been well defined in treating GN. The present study aimed to define the molecular mechanisms of PS as a potential therapeutic approach to treat GN. A combination of network pharmacology and validation experiments in GN is used to understand the potential mechanism. Information on pharmaceutically active compounds in PS and gene information related to GN was obtained from public databases. The compound target network and protein-protein interaction network were constructed to study the mechanism of action of PS in the treatment of GN. The mechanism of action of PS in the treatment of GN was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment. Validation experiments were performed to verify the core targets. The GN rat model was prepared by the method of combining yeast and adenine. Hematoxylin-eosin (HE) staining was used to observe the morphology of renal tissue in rats. ELISA was applied to detect TGF-β1, TNF-α, and IL-1β levels in renal tissue. The expressions of TGF-β1, TNF-α, and IL-1β were determined using immunohistochemistry. Through the results of network pharmacology, we obtained 9 active components, 118 predicted targets, and 149 GN targets from the public database. Based on the protein-protein interaction (PPI), 26 hub genes for interaction with PS treating for GN were screened, including MMP9, TNF, IL1β, and IL6. The enrichment analysis results showed that the treatment of GN with PS was mainly involved in the TGF-β1 signaling pathway, MAPK signaling pathway, TNF signaling pathway, NF-κB signaling pathway, and PI3K Akt signaling pathway. Validation experiment results showed that PS could reduce the content of urinary protein and UA and deregulate the expression of TGF-β1, TNF-α, and IL-1β in the treatment of GN. The molecular mechanism of PS in the treatment of GN indicated the synergistic features of multicomponent, multitarget, and multipathway of traditional Chinese medicine, which provided an essential scientific basis for further elucidating the mechanism of PS in the treatment of GN.

Functional genomics of AP-2α and AP-2γ in cancers: in silico study

BACKGROUND

Among all causes of death, cancer is the most prevalent and is only outpaced by cardiovascular diseases. Molecular theory of carcinogenesis states that apoptosis and proliferation are regulated by groups of tumor suppressors or oncogenes. Transcription factors are example of proteins comprising representatives of both cancer-related groups. Exemplary family of transcription factors which exhibits dualism of function is Activating enhancer-binding Protein 2 (AP-2). Scientific reports concerning their function in carcinogenesis depend on particular family member and/or tumor type which proves the issue to be unsolved. Therefore, the present study examines role of the best-described AP-2 representatives, AP-2α and AP-2γ, through ontological analysis of their target genes and investigation what processes are differentially regulated in 21 cancers using samples deposited in Genomic Data Analysis Center (GDAC) Firehose.

METHODS

Expression data with clinical annotation was collected from TCGA-dedicated repository GDAC Firehose. Transcription factor targets were obtained from Gene Transcription Regulation Database (GTRD), TRANScription FACtor database (TRANSFAC) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Monocle3 R package was used for global samples profiling while Protein ANalysis THrough Evolutionary Relationships (PANTHER) tool was used to perform gene ontology analysis.

RESULTS

With RNA-seq data and Monocle3 or PANTHER tools we outlined differences in many processes and signaling pathways, separating tumor from normal tissues or tumors from each other. Unexpectedly, a number of alterations in basal-like breast cancer were identified that distinguished it from other subtypes, which could bring future clinical benefits.

CONCLUSIONS

Our findings indicate that while the AP-2α/γ role remains ambiguous, their activity is based on processes that underlie the cancer hallmarks and their expression could have potential in diagnosis of selected tumors.

Screening and Identification of Potential Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is one of the most lethal cancers globally. Hepatitis B virus (HBV) infection might cause chronic hepatitis and cirrhosis, leading to HCC. To screen prognostic genes and therapeutic targets for HCC by bioinformatics analysis and determine the mechanisms underlying HBV-related HCC, three high-throughput RNA-seq based raw datasets, namely GSE25599, GSE77509, and GSE94660, were obtained from the Gene Expression Omnibus database, and one RNA-seq raw dataset was acquired from The Cancer Genome Atlas (TCGA). Overall, 103 genes were up-regulated and 127 were down-regulated. A protein–protein interaction (PPI) network was established using Cytoscape software, and 12 pivotal genes were selected as hub genes. The 230 differentially expressed genes and 12 hub genes were subjected to functional and pathway enrichment analyses, and the results suggested that cell cycle, nuclear division, mitotic nuclear division, oocyte meiosis, retinol metabolism, and p53 signaling-related pathways play important roles in HBV-related HCC progression. Further, among the 12 hub genes, kinesin family member 11 (KIF11), TPX2 microtubule nucleation factor (TPX2), kinesin family member 20A (KIF20A), and cyclin B2 (CCNB2) were identified as independent prognostic genes by survival analysis and univariate and multivariate Cox regression analysis. These four genes showed higher expression levels in HCC than in normal tissue samples, as identified upon analyses with Oncomine. In addition, in comparison with normal tissues, the expression levels of KIF11, TPX2, KIF20A, and CCNB2 were higher in HBV-related HCC than in HCV-related HCC tissues. In conclusion, our results suggest that KIF11, TPX2, KIF20A, and CCNB2 might be involved in the carcinogenesis and development of HBV-related HCC. They can thus be used as independent prognostic genes and novel biomarkers for the diagnosis of HBV-related HCC and development of pertinent therapeutic strategies.

An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan-Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA-lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

Identification of mRNA and non-coding RNA hubs using network analysis in organ tropism regulated triple negative breast cancer metastasis

Triple negative breast cancer (TNBC) is aggressive in nature, resistant to conventional therapy and often ends in organ specific metastasis. In this study, publicly available datasets were used to identify miRNA, mRNA and lncRNA hubs. Using validated mRNA-miRNA, mRNA-mRNA and lncRNA-miRNA interaction information obtained from various databases, RNA interaction networks for TNBC and its subtype specific as well as organ tropism regulated metastasis were generated. Further, miRNA-mRNA-lncRNA triad classification was performed using social network analysis from subnetworks and visualized using Cytoscape. Survival analysis of the RNA hubs, oncoprint analysis for mRNAs and pathway analysis of the lncRNAs were also performed. Results indicated that two lncRNAs (NEAT1 and CASC7) and four miRNAs (hsa-miR-106b-5p, hsa-miR-148a-3p, hsa-miR-25-3p and hsa-let-7i-5p) were common between hubs identified in TNBC and TNBC associated metastasis. The exclusive hubs for TNBC associated metastasis were hsa-miR-200b-3p, SP1, HSPA4 and RAB1B. HMGA1 was the top ranked hub in mesenchymal subtype associated lung metastasis, while hsa-miR-27a-3p was identified as the top ranked hub mRNA in luminal androgen receptor subtype associated bone metastasis. When lncRNA associated pathway analysis was performed, Hs Cytoplasmic Ribosomal Protein pathway was found to be the most significant and among the selected hubs, CTNND1, SON and hsa-miR-29c emerged as TNBC survival markers. TP53, FOXA1, MTDH and HDGF were found as the top ranked mRNAs in oncoprint analysis. The pipeline proposed for the first time in this study with validated RNA interaction data integration and graph-based learning for miRNA-mRNA-lncRNA triad classification from RNA hubs may aid experimental cost reduction and its successful execution will allow it to be extended to other diseases too.

Identification of key genes for esophageal squamous cell carcinoma via integrated bioinformatics analysis and experimental confirmation

Background

Esophageal squamous cell carcinoma (ESCC) as the main subtype of esophageal cancer (EC) is a leading cause of cancer-related death worldwide. Despite advances in early diagnosis and clinical management, the long-term survival of ESCC patients remains disappointing, due to a lack of full understanding of the molecular mechanisms.

Methods

In order to identify the differentially expressed genes (DEGs) in ESCC, the microarray datasets GSE20347 and GSE26886 from Gene Expression Omnibus (GEO) database were analyzed. The enrichment analyses of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Set Enrichment Analysis (GSEA) were performed for the DEGs. The protein-protein interaction (PPI) network of these DEGs was constructed using the Cytoscape software based on the STRING database to select as hub genes for weighted co-expression network analysis (WGCNA) with ESCC samples from TCGA database.

Results

A total of 746 DEGs were commonly shared in the two datasets including 286 upregulated genes and 460 downregulated genes in ESCC. The DEGs were enriched in biological processes such as extracellular matrix organization, proliferation and keratinocyte differentiation, and were enriched in biological pathways such as ECM-receptor interaction and cell cycle. GSEA analysis also indicated the enrichment of upregulated DEGs in cell cycle. The 40 DEGs were selected as hub genes. The MEblack module was found to be enriched in the cell cycle, Spliceosome, DNA replication and Oocyte meiosis. Among the hub genes correlated with MEblack module, GSEA analysis indicated that DEGs of TCGA samples with DLGAP5 upregulation was enriched in cell cycle. Moreover, the highly endogenous expression of DLGAP5 was confirmed in ESCC cells. DLGAP5 knockdown significantly inhibited the proliferation of ESCC cells.

Conclusions

DEGs and hub genes such as DLGAP5 from independent datasets in the current study will provide clues to elucidate the molecular mechanisms involved in development and progression of ESCC.

Clinical Implications of Chromosomal Instability (CIN) and Kinetochore Abnormalities in Breast Cancers

Genetic instability is a defining property of cancer cells and is the basis of various lesions including point mutations, copy number alterations and translocations. Chromosomal instability (CIN) is part of the genetic instability of cancer and consists of copy number alterations in whole or parts of cancer cell chromosomes. CIN is observed in differing degrees in most cancers. In breast cancer, CIN is commonly part of the genomic landscape of the disease and has a higher incidence in aggressive sub-types. Tumor suppressors that are commonly mutated or disabled in cancer, such as p53 and pRB, play roles in protection against CIN, and as a result, their dysfunction contributes to the establishment or tolerance of CIN. Several structural and regulatory proteins of the centromeres and kinetochore, the complex structure that is responsible for the correct distribution of genetic material in the daughter cells during mitosis, are direct or, mostly, indirect transcription targets of p53 and pRB. Thus, despite the absence of structural defects in genes encoding for centromere and kinetochore components, dysfunction of these tumor suppressors may have profound implications for the correct function of the mitotic apparatus contributing to CIN. CIN and its prognostic and therapeutic implications in breast cancer are discussed in this article.

A topological approach for cancer subtyping from gene expression data

BACKGROUND

Gene expression data contains key information which can be used for subtyping cancer patients. However, computational methods suffer from 'curse of dimensionality' due to very high dimensionality of omics data and therefore are not able to clearly distinguish between the discovered subtypes in terms of separation of survival plots.

METHODS

To address this we propose a framework based on Topological Mapper algorithm. The novelty of this work is that we suggest a method for defining the filter function on which the mapper algorithm heavily depends. Survival analysis of the discovered cancer subtypes is carried out and evaluated in terms of minimum pairwise separation between the Kaplan-Meier plots. Furthermore, we present a method to measure the separation between the discovered subtypes based on hazard ratios.

RESULTS

Five cancer genomics datasets obtained from The Cancer Genome Atlas portal have been used for comparisons with Robust Sparse Correlation-Otrimle (RSC-Otrimle) algorithm and Similarity Network Fusion(SNF). Comparisons show that the minimum pairwise life expectancy difference (in days) between the discovered subtypes for lung, colon, breast, glioblastoma and kidney cancers is 107, 204, 20, 88 and 425 days, respectively, for the proposed methodology whereas it is only 69, 43, 6, 61 and 282 days for RSC-Otrimle and 9, 95, 18, 60 and 148 days for SNF. Hazard ratio analysis also shows that the proposed methodology performs better in four of the five datasets. A visual inspection of Kaplan-Meier plots reveals that the proposed methodology achieves lesser overlap in Kaplan-Meier plots especially for lung, breast and kidney cases. Furthermore, relevant genetic pathways for each subtype have been obtained and pathways which can be possible targets for treatment have been discussed.

CONCLUSION

The significance of this work lies in individualized understanding of cancer from patient to patient which is the backbone of Precision Medicine.

Inhibition of MELK produces potential anti-tumour effects in bladder cancer by inducing G1/S cell cycle arrest via the ATM/CHK2/p53 pathway

We aimed to investigate the biological function of MELK and the therapeutic potential of OTSSP167 in human bladder cancer (BCa). First, we observed overexpression of MELK in BCa cell lines and tissues and found that it was associated with higher tumour stage and tumour grade, which was consistent with transcriptome analysis. High expression of MELK was significantly correlated with poor prognosis in BCa patients, and MELK was found to have a role in the cell cycle, the G1/S transition in mitosis, and DNA repair and replication. Furthermore, BCa cells presented significantly decreased proliferation capacity following silencing of MELK or treatment with OTSSP167 in vitro and in vivo. Functionally, reduction in MELK or treatment of cells with OTSSP167 could induce cell cycle arrest and could suppress migration. In addition, these treatments could activate phosphorylation of ATM and CHK2, which would be accompanied by down‐regulated MDMX, cyclin D1, CDK2 and E2F1; however, p53 and p21 would be activated. Opposite results were observed when MELK expression was induced. Overall, MELK was found to be a novel oncogene in BCa that induces cell cycle arrest via the ATM/CHK2/p53 pathway. OTSSP167 displays potent anti‐tumour activities, which may provide a new molecule‐based strategy for BCa treatment.

Identification of Hub Genes Using Co-Expression Network Analysis in Breast Cancer as a Tool to Predict Different Stages

Background

Breast cancer has a high mortality rate and is the most common cancer of women worldwide. Our gene co-expression network analysis identified the genes closely related to the pathological stage of breast cancer.

Material/Methods

We performed weighted gene co-expression network analysis (WGCNA) from the Gene Expression Omnibus (GEO) database, and performed pathway enrichment analysis on genes from significant modules.

Results

A non-metastatic sample (374) of breast cancer from GSE102484 was used to construct the gene co-expression network. All 49 hub genes have been shown to be upregulated, and 19 of the 49 hub genes are significantly upregulated in breast cancer tissue. The roles of the genes CASC5, CKAP2L, FAM83D, KIF18B, KIF23, SKA1, GINS1, CDCA5, and MCM6 in breast cancer are unclear, so in order to better reveal the staging of breast cancer markers, it is necessary to study those hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that 49 hub genes were enriched to sister chromatid cohesion, spindle midzone, microtubule motor activity, cell cycle, and something else. Additionally, there is an independent data set – GSE20685 – for module preservation analysis, survival analysis, and gene validation.

Conclusions

This study identified 49 hub genes that were associated with pathologic stage of breast cancer, 19 of which were significantly upregulated in breast cancer. Risk stratification, therapeutic decision making, and prognosis predication might be improved by our study results. This study provides new insights into biomarkers of breast cancer, which might influence the future direction of breast cancer research.

Bioinformatics analysis of the expression of HOXC13 and its role in the prognosis of breast cancer

The homeobox (HOX) genes, a class of transcription factors, are known to promote embryonic development and induce tumor formation. To date, the HOXA and HOXB gene families have been reported to be associated with breast cancer. However, the expression and exact role of homeobox C13 (HOXC13) in breast cancer has not yet been investigated. In the present study, the HOXC13 expression in human breast cancer was evaluated using the Oncomine database and Cancer Cell Line Encyclopedia (CCLE). Next, the Gene expression-based Outcome for Breast cancer online database, cBioportal, University of California Santa Cruz Xena browser and bc-GenExMinerv were used to explore the specific expression of HOXC13 in breast cancer. The methylation and mutation status of HOXC13 in breast cancer was then validated using the CCLE and cBioportal databases. Finally, the co-expression of HOX transcript antisense RNA (HOTAIR) and HOXC13 in breast cancer were analyzed and their impact on clinical prognosis determined. It was found that the expression of HOXC13 was high in breast cancer compared with other types of cancer, such as gastric cancer and colon cancer. Following co-expression analysis, a significant positive association was identified between HOTAIR and HOXC13. An association between HOTAIR and HOXC13, and lymph node and distant metastasis recurrence was also revealed during the development of breast cancer. Of note, survival analysis showed that high expression of HOTAIR and HOXC13 predicted poor prognosis. These findings revealed that HOXC13 plays an important role in the progression of breast cancer. However, the specific mechanism needs to be confirmed by subsequent experiments.

Prognostic significance of KIF2A and KIF20A expression in human cancer: A systematic review and meta-analysis

BACKGROUND

The kinesin family (KIF) is reported to be aberrantly expressed and significantly correlated with survival outcomes in patients with various cancers. This meta-analysis was carried out to quantitatively evaluate the prognostic values of partial KIF members in cancer patients.

METHODS

Two well-known KIF members, KIF2A and KIF20A, were investigated to evaluate their potential values as novel prognostic biomarkers in human cancer. A comprehensive literature search was carried out of the PubMed, EMBASE, Cochrane Library, and Web of Science databases up to April 2019. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the association of KIF2A and KIF20A expression with overall survival (OS) and clinicopathological parameters.

RESULTS

Twenty-five studies involving 7262 patients were finally incorporated, including nine about KIF2A and sixteen about KIF20A. Our results indicated that patients with high expression of KIF2 and KIF20A tended to have shorter OS than those with low expression (HR = 2.23, 95% CI = 1.87-2.65, P < .001; HR = 1.77, 95% CI = 1.57-1.99, P < .001, respectively). Moreover, high expression of these 2 KIF members was significantly associated with advanced clinical stage (OR = 1.98, 95% CI: 1.57-2.50, P < .001; OR = 2.63, 95% CI: 2.03-3.41, P < .001, respectively), positive lymph node metastasis (OR = 2.32, 95% CI: 1.65-3.27, P < .001; OR = 2.13, 95% CI: 1.59-2.83, P < .001, respectively), and distant metastasis (OR = 2.20, 95% CI: 1.21-3.99, P = .010; OR = 5.25, 95% CI: 2.82-9.77, P < .001, respectively); only high KIF20A expression was related to poor differentiation grade (OR = 1.82, 95% CI: 1.09-3.07, P = .023).

CONCLUSIONS

High expression of KIF2 and KIF20A in human cancer was significantly correlated with worse prognosis and unfavorable clinicopathological features, suggesting that these 2 KIF members can be used as prognostic biomarkers for different types of tumors.

PROSPERO REGISTRATION NUMBER

CRD42019134928.

Exploring Pharmacological Mechanisms of Xiang Ju Tablets in the Treatment of Allergic Rhinitis via a Network Pharmacology Approach

In this study, allergic rhinitis (AR) disease targets and Xiang Ju tablet-associated targets were determined through the use of databases for the identification of putative therapeutic targets and then combined. After the production of a putative therapeutic target interaction network for Xiang Ju tablets against AR, topological analysis was used to determine the core targets of Xiang Ju tablets in AR treatment. For all putative therapeutic targets, analyses of biological function and pathway enrichment were performed to optimize the biological processes and key signaling pathways of Xiang Ju tablets in AR treatment. The top 5 therapeutic targets of Xiang Ju tablets in AR treatment were identified and included CXCL8, IL1B, IL6, IL10, and TNF. The biological processes, molecular functions, and cell composition related to the use of Xiang Ju tablets in AR treatment were predominantly associated with cytokine production, regulation of protein secretion, and regulation of peptide secretion; cytokine activity, cytokine receptor binding, and receptor ligand activity; and platelet alpha granule lumen, collagen-containing extracellular matrix, and platelet alpha granule. In addition, the top 64 key signaling pathways were identified.

Serine/threonine kinase BUB1 promotes proliferation and radio-resistance in glioblastoma

BUB1 (Budding uninhibited by benzimidazoles 1), a mitotic checkpoint serine/threonine kinase, has been linked in numerous cancers to pro-tumorigenic phenomena including elevation of cellular proliferation, tumor growth, metastatic potential, and poorer patient prognosis. However, the role of BUB1 in glioblastoma remains poorly investigated. In this study, clinical analyses determined significant enrichment of BUB1 in glioblastoma with direct correlation of elevated expression to poorer prognosis in glioma patients. Genetic inhibition of BUB1 in glioblastoma tumor cells via shRNA silencing diminished both proliferative ability and tumorigenicity in vitro and in vivo. Silencing of BUB1 was additionally determined to promote the cytotoxic effect of irradiation on glioblastoma tumor cells, and investigation of the underlying pathways revealed the roles of DNA mismatch repair, spliceosome and c-Myc pathways. Mechanistically, FOXM1 was determined to positively regulate transcription of BUB1 via direct promoter region binding. For validation, pharmacologic inhibition through administration of a BUB1 inhibitor demonstrated attenuated glioblastoma cellular proliferation in vitro as well as delayed tumor growth with prolonged survival in vivo. Collectively, this study demonstrates a novel therapeutic target for glioblastoma in the form of BUB1, which plays a pivotal role in GBM proliferative and radio-resistance capacities in a FOXM1-dependant manner.

Expression of CENPE and its Prognostic Role in Non-small Cell Lung Cancer

Background

Non-small cell lung cancer (NSCLC) is one of the most important causes of death worldwide. Most patients are diagnosed in the advanced stage and have a poor prognosis. This study was to investigate the expression and significance of CENPE in NSCLC.

Method

Collecting information about CENPE in the Oncoming database, and perform a further analysis of the data in the current database to conduct a meta-analysis for its functional role in NSCLC. Patient life cycle analysis using Kaplan-Meier Plotter and GEPIA databases are used to perform patient survival analysis.

Result

A total of 12 studies involved the expression of CENPE in NSCLC cancer tissues and normal tissues, including 1195 samples. CENPE was highly expressed in NSCLC cell carcinoma compared with the control group (P < 0.05). Moreover, the expression of CENPE was correlated with the overall survival rate of CENPE. The overall survival rate of patients with high expression of CENPE was poor, and the prognosis of patients with low expression of CENPE was better (P<0.05).

Conclusion

We propose high expression of CENPE in NSLCL tissue is related to the prognosis of NSCLC, which may provide important basis for the development of tumor drugs.