CDCA8 expression and its clinical relevance in patients with bladder cancer

Identification of early diagnostic biomarkers for breast cancer through bioinformatics analysis

In the realm of clinical practice, there is currently an insufficiency of distinct biomarkers available for the detection of breast cancer. It is of utmost importance to promptly employ bioinformatics methodologies to investigate prospective biomarkers for breast cancer, with the ultimate goal of achieving early diagnosis of the disease. The initial phase of this investigation involved the identification of 2 breast cancer gene chips meeting the specified criteria within the gene expression omnibus database. Subsequently, paired data analysis was conducted on these datasets, leading to the identification of differentially expressed genes (DEGs). In addition, this study executed Gene Ontology enrichment analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. The subsequent stage involved the construction of a protein-protein interaction network graph using the STRING website and Cytoscape software, facilitating the calculation of Hub genes. Lastly, the UALCAN database and Kaplan-Meier survival plots were utilized to perform differential expression and survival analysis on the selected Hub genes. A total of 733 DEGs were identified from the combined analysis of 2 datasets. Among these DEGs, 441 genes were found to be downregulated, while 292 genes were upregulated. The selected DEGs underwent comprehensive analysis, including gene ontology enrichment analysis, Kyoto encyclopedia of genes and genomes pathway enrichment analysis, and establishing a protein-protein interaction network. As a result, 10 Hub genes closely associated with early diagnosis of breast cancer were identified: PDZ-binding kinase, cell cycle protein A2, cell division cycle-associated protein 8, maternal embryonic leucine zipper kinase, nucleolar and spindle-associated protein 1, BIRC5, cell cycle protein B2, hyaluronan-mediated motility receptor, mitotic arrest deficient 2-like 1, and protein regulator of cytokinesis 1. The findings of this study unveiled the significant involvement of the identified 10 Hub genes in facilitating the growth and proliferation of cancer cells, particularly cell cycle protein A2, cell division cycle-associated protein 8, maternal embryonic leucine zipper kinase, nucleolar and spindle-associated protein 1, hyaluronan-mediated motility receptor, and protein regulator of cytokinesis 1, which demonstrated a more pronounced connection with the onset and progression of breast cancer. Further analysis through differential expression and survival analysis reaffirmed their strong correlation with the incidence of breast cancer. Consequently, the investigation of these 10 pertinent Hub genes presents novel prospects for potential biomarkers and valuable insights into the early diagnosis of breast cancer.

TRAIP serves as a potential prognostic biomarker and correlates with immune infiltrates in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the major types of lung cancer with high morbidity and mortality. The TRAF-interacting protein (TRAIP) is a ring-type E3 ubiquitin ligase which has been recently identified to play pivotal roles in various cancers. However, the expression and function of TRAIP in LUAD remain elusive.

METHODS

In this study, we used bioinformatic tools as well as molecular experiments to explore the exact role of TRAIP and the underlying mechanism.

RESULTS

Data mining across the UALCAN, GEPIA and GTEx, GEO and HPA databases revealed that TRAIP was significantly overexpressed in LUAD tissues than that in adjacent normal tissues. Kaplan-Meier curve showed that high TRAIP expression was associated with poor overall survival (OS) and relapse-free survival (RFS). Univariate and multivariate cox regression analysis revealed that TRAIP was an independent risk factor in LUAD. And the TRAIP-based nomogram further supported the prognostic role of TRAIP in LUAD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that TRAIP-associated genes were mainly involved in DNA replication, cell cycle and other processes. The immune infiltration analysis indicated that TRAIP expression was tightly correlated with the infiltration of diverse immune cell types, including B cell, CD8 + T cell, neutrophil and dendritic cell. Moreover, TRAIP expression was observed to be significantly associated with tumor infiltrating lymphocytes (TILs) and immune checkpoint molecules. In vitro experiments further confirmed knockdown of TRAIP inhibited cell migration and invasion, as well as decreasing chemokine production and inhibiting M2-like macrophage recruitment. Lastly, CMap analysis identified 10 small molecule compounds that may target TRAIP, providing potential therapies for LUAD.

CONCLUSIONS

Collectively, our study found that TRAIP is an oncogenic gene in LUAD, which may be a potential prognostic biomarker and promising therapeutic target for LUAD.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

TMED3 promotes the development of malignant melanoma by targeting CDCA8 and regulating PI3K/Akt pathway

BACKGROUND

Transmembrane emp24 domain containing (TMED) proteins are known to play pivotal roles in normal development, but have been reported to be implicated in pancreatic disease, immune system disorders, and cancers. As far as TMED3 is concerned, its roles in cancers are controversial. However, evidence describing TMED3 in the context of malignant melanoma (MM) is scarce.

RESULTS

In this study, we characterized the functional significance of TMED3 in MM and identified TMED3 as a tumor-promoting factor in MM development. Depletion of TMED3 arrested the development of MM in vitro and in vivo. Mechanistically, we found that TMED3 could interact with Cell division cycle associated 8 (CDCA8). Knocking down CDCA8 suppressed cell events associated with MM development. On the contrary, elevating CDCA8 augmented cell viability and motility and even reversed the inhibitory effects of TMED3 knockdown on MM development. On the other hand, we found that the levels of P-Akt and P-PI3K were decreased in response to TMED3 downregulation, which was partially abolished following SC79 treatment. Thus, our suspicion was that TMED3 exacerbates MM progression via PI3K/Akt pathway. More notably, previously decreased P-Akt and P-PI3K in TMED3-depleted cells were rescued after overexpressing CDCA8. Also, previously impaired cell events due to CDCA8 depletion were ameliorated after SC79 addition, implying that TMED3 regulates PI3K-AKT pathway via CDCA8, thereby promoting MM development.

CONCLUSIONS

Collectively, this study established the link between TMED3 and MM, and provides a potential therapeutic intervention for patients with MM harboring abundant TMED3.

Cell division cycle-associated 8 is a prognostic biomarker related to immune invasion in hepatocellular carcinoma

BACKGROUND

Cell division cycle-associated 8 (CDCA8) is involved in numerous signaling networks, and it serves a crucial modulatory function in multiple malignant tumors. However, its significance in prognosis and immune infiltration in hepatocellular carcinoma (HCC) remains unclear.

MATERIALS AND METHODS

Herein, we examined the CDCA8 levels in tumor tissues, as well as its associated signaling pathways and correlation with immune infiltration. Additionally, we further clarified the prognostic significance of CDCA8 among HCC patients. HCC patient information was recruited from The Cancer Genome Atlas (TCGA). Using bioinformatics, the following parameters were analyzed among HCC patients: CDCA8 expression, enrichment analysis, immune infiltration, and prognosis analysis. Moreover, we employed in vitro investigations, such as, qRT-PCR, immunohistochemistry (IHC), and cell functional experiments to validate our results.

RESULTS

Elevated CDCA8 expression in HCC patients was markedly associated with T stage, pathological status (PS), tumor status (TS), histologic grade (HG), and AFP. Elevated CDCA8 expression HCC patients exhibited reduced overall survival (OS) (p < 0.001), disease-specific survival (DSS) (p < 0.001), and progress free interval (PFI) H(p < 0.001). According to the ROC analysis, the area under the curve (AUC) was 0.997. Multivariate analysis revealed that CDCA8 was a stand-alone prognostic indicator of patient OS (p = 0.009) and DSS (p = 0.006). A nomogram was then generated based on the multivariate analysis, and the C-indexes and calibration chart revealed excellent predictive performance in determining HCC patient outcome. Based on the GSEA analysis, CDCA8 modulated the P53, Notch, PPAR, E2F networks. We observed a direct link between CDCA8 levels and Th2 and T helper cells, and a negative link between CDCA8 levels and dendritic cells (DC), neutrophils, cytotoxic cells, and CD8 T cells. Furthermore, CDCA8 deficiency inhibited liver cancer cell proliferation and invasion.

CONCLUSION

In conclusion, these findings indicate that CDCA8 is a new molecular bioindicator of HCC patient prognosis, and it is an excellent candidate for therapeutic target against HCC.

CDCA8/SNAI2 Complex Activates CD44 to Promote Proliferation and Invasion of Pancreatic Ductal Adenocarcinoma

Simple Summary

There is an urgent need to find an effective therapeutic target for pancreatic cancer owing to late diagnosis, tumor metastasis, and current ineffective targeted drugs. We aimed to identified potential targets for the treatment of pancreatic cancer. In this study, the specific mechanism by which the CDCA8 contributes to pancreatic cancer progression via the activation of CD44 was clarified, and CDCA8 knockdown inhibited the proliferation and metastasis of pancreatic cancer. This finding may provide a promising target for future targeted therapies of pancreatic cancer.

Abstract

(1) Background: Recently, cell division cycle associated 8 (CDCA8) was found to be overexpressed in pancreatic ductal adenocarcinoma (PDAC). Here, we aimed to explore the specific mechanism of action of CDCA8 in PDAC progression. (2) Methods: All human PDAC samples and clinical data were collected from Huashan Hospital, Fudan University. All experimental studies were carried out using many in vitro and in vivo assays, including lentiviral transfection, real-time quantitative polymerase chain reaction (qPCR), western blotting, co-immunoprecipitation (Co-IP), chromatin IP (ChIP)-qPCR, dual-luciferase reporter, and in vivo imaging assays. (3) Results: Clinical data analysis of human PDAC samples revealed that CDCA8 overexpression were positively and negatively associated with tumor grade (p = 0.007) and overall survival (p = 0.045), respectively. CDCA8 knockdown inhibited PDAC proliferation and invasion in in vitro and in vivo assays. CD44 was also up-regulated by CDCA8 during PDAC progression. CDCA8 could be combined with SNAI2 to form a CDCA8/SNAI2 complex to integrate with the CD44 promoter as indicated through ChIP-qPCR and dual-luciferase reporter assays. (4) Conclusion: We showed that CDCA8-CD44 axis plays a key role in the proliferation and invasion of PDAC, which provides a potential target for treatment.

Regulators CDCA8 as potential targets and biomarkers for the prognosis of human skin cutaneous melanoma

Cutaneous melanoma (CM) is considered as the most malignant skin tumor with high distant metastasis and poor prognosis. Cell division cycle-associated protein (CDCA) family has a role in regulating cell proliferation and modulating immune cell and tumor cell proliferation in the tumor microenvironment to regulate tumor oncogenesis, development and affect patient outcomes. However, the differential expression pattern and prognostic value of CDCA factors (CDCAs) have not been clarified. In this study, the role of CDCAs in CM was analyzed by using bioinformatics and found that the transcriptional expressions of CDCA1/2/3/5/6/8 were upregulating in CM samples than in normal compares. CM patients with downregulated of CDCA1/3/4/5/6/8 and high transcriptional levels of CDCA7 suggest a significantly better prognosis. Furthermore, the significant correlations among the expression of CDCAs and the infiltration of immune cells. In terms of the protein level, we found CDCA8 was upregulated in CM patients. In conclusion, CDCA8 is a powerful prognostic biomarker for CM and can be a novel target for immunotherapy.

Upregulation of KIF18B facilitates malignant phenotype of esophageal squamous cell carcinoma by activating CDCA8/mTORC1 pathway

Background

Kinesin family member 18B (KIF18B) has been regarded as an oncogene that is abnormally overexpressed in some cancers, but its mechanism in esophageal squamous cell carcinoma (ESCC) remains unclear, which is thereby investigated in this study.

Methods

Bioinformatics analysis was performed to analyze the expression of KIF18B in esophageal carcinoma (ESCA). Quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to detect KIF18B expression in ESCC cells. After KIF18B overexpression or cell division cycle associated 8 (CDCA8) deficiency, ESCC cells were subjected to determination of qRT‐PCR, Western blot, cell counting kit‐8 assay, flow cytometry, wound healing, and Transwell assay. The mechanism of KIF18B in the mechanistic target of rapamycin complex 1 (mTORC1) pathway was detected by Western blot.

Results

KIF18B was overexpressed in ESCA samples and ESCC cells. Upregulation of KIF18B enhanced the viability, accelerated cell cycle by elevating CDK4 and Cyclin D3 levels as well as promoted the migration and invasion by decreasing E‐cadherin level and increasing Vimentin and N‐cadherin levels in ESCC cells, which was counteracted by CDCA8 silencing. The expression of CDCA8 in ESCC cells was upregulated by KIF18B overexpression. KIF18B overexpression activated the mTORC1 pathway by upregulating phosphorylated (p)‐/p70S6K and p‐/mTOR levels in the ESCC cells, which was reversed by CDCA8 silencing.

Conclusion

KIF18B overexpression promotes the proliferation, migration, and invasion of ESCC cells via CDCA8‐mediated mTORC1 signaling pathway in vitro.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

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.

Borealin Promotes Tumor Growth and Metastasis by Activating the Wnt/β-Catenin Signaling Pathway in Hepatocellular Carcinoma

Background and Aims

Hepatocellular carcinoma (HCC) is a common malignant disease with high morbidity and mortality throughout the world. While Borealin is a putative oncogene that is dysregulated in multiple tumors, its exact role in HCC remains less investigated.

Methods

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) assays were employed to examine the relative amount of Borealin. Gene set enrichment analysis (GSEA) and other bioinformatic analyses were implemented to probe into the potential functions of Borealin. The biological roles and mechanisms of Borealin in the tumorigenesis and development of HCC were further evaluated using a battery of functional assays in vivo and in vitro.

Results

Borealin was enhanced in the HCC tissue samples and hepatoma cells when compared with the nontumor tissues and normal liver cells. Higher Borealin expression was positively linked with advanced pathological phenotypes and inferior overall survival. The overexpression of Borealin promoted the cells’ abilities on proliferation, invasion and epithelial–mesenchymal transition (EMT) in vitro, facilitated tumor growth and lung metastasis in vivo, whereas the silencing of Borealin inhibited these capabilities in vitro. Furthermore, Borealin interacted with β-catenin and further activated the Wnt/β-catenin signaling pathway, which endowed HCC cells with highly aggressive and metastatic capabilities.

Conclusion

Borealin was identified as an oncogene that could promote HCC growth and metastasis by activating the WNT/β-catenin signaling pathway. These findings extended the understanding of Borealin in HCC tumorigenesis and development and highlighted the significance of Borealin in HCC diagnosis and treatment.

A 7-gene signature predicts the prognosis of patients with bladder cancer

The biomarkers have an important guiding role in prognosis and treatment of patients with bladder cancer (BC). The aim of the present study was to identify and evaluate a prognostic gene signature in BC patients. The gene expression profiles of BC samples and the corresponding clinicopathological data were downloaded from GEO and TCGA. The differentially expressed genes (DEGs) were identified by R software. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct the prognostic score model. A nomogram was established with the identified prognostic factors to predict the overall survival rates of BC patients. The discriminatory and predictive capacity of the nomogram was evaluated based on the concordance index (C‐index), calibration curves and decision curve analysis (DCA). A 7-gene signature (KLRB1, PLAC9, SETBP1, NR2F1, GRHL2, ANXA1 and APOL1) was identified from 285 DEGs by univariate and LASSO Cox regression analyses. Univariate and multivariate Cox regression analyses showed that age, lymphovascular invasion, lymphatic metastasis, metastasis and the 7-gene signature risk score was an independent predictor of BC patient prognosis. A nomogram that integrated these independent prognostic factors was constructed. The C-index (0.73, CI 95%, 0.693–0.767) and calibration curve demonstrated the good performance of the nomogram. DCA of the nomogram further showed that this model exhibited good net benefit. The combined 7-gene signature could serve as a biomarker for predicting BC prognosis. The nomogram built by risk score and other clinical factors could be an effective tool for predicting the prognosis of patients with BC.

Identification of biomarkers related to glycolysis with weighted gene co-expression network analysis in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is the most common tumor in the oral cavity and maxillofacial region. Increasing evidence suggests that aerobic glycolysis plays an important role in the occurrence, development, and prognosis of OSCC. Therefore, the identification of biomarkers related to glycolysis in OSCC represents considerable potential for improving its treatment.

METHODS

In the present study, a single-sample gene-set enrichment analysis (ssGSEA) algorithm with weighted gene co-expression network analysis (WGCNA) were used to quantify the degree of glycolysis and identify key modules with the greatest correlation with glycolysis.

RESULTS

Glycolytic scores significantly correlated with prognosis. In the key module 5 HUB genes were finally selected, which displayed a robust predictive effect. The expressions of key genes were associated with glycolysis.

CONCLUSIONS

The research comprehensively analyzed the glycolysis of OSCC and identified several biomarkers related to glycolysis. These biomarkers may represent potential therapeutic targets for future OSCC therapy.

CDCA1/2/3/5/7/8 as novel prognostic biomarkers and CDCA4/6 as potential targets for gastric cancer

Background

Increasing evidence had suggested that cell division cycle-associated (CDCA) family proteins play prominent roles in multiple types of cancer. However, the expression pattern and prognostic value of CDCAs in gastric cancer were still poorly understood.

Methods

In this study, bioinformatics was used for the first time to comprehensively discuss the expression changes of the CDCA protein family in gastric cancer. We studied the transcription and survival data of CDCAs in patients with gastric cancer in Oncomine, GEPIA, DAVID, cBioportal, and other databases.

Results

We identified that the CDCA 1/2/3/4/5/6/7/8 were overexpressed gastric cancer than in normal tissues. There was no significant difference in CDCAs expression among different gastric cancer stages. High expression of CDCA4/6 in patients with gastric cancer was closely related to low overall survival (OS), first progression survival (FPS), and post-progression survival (PPS). In contrast, high CDCA1/2/3/5/7/8 expression predicted a better prognosis. The genetic mutation rate of CDCA2 and CDCA4 was 4%, ranking first. The main biological process of CDCAs protein family enrichment was cell division, the main cell component involved was centromeres of chromosomes, and the main molecular function involved was protein binding.

Conclusions

The study suggested that CDCA1/2/3/5/7/8 were expected to be new prognostic markers for gastric cancer, and CDCA4/6 might be potential targets for the treatment of gastric cancer.

miR-133b inhibits cell proliferation, migration, and invasion of lung adenocarcinoma by targeting CDCA8

OBJECTIVE

Lung adenocarcinoma (LUAD) is the most common type of lung cancer. This study aims to explore the mechanism by which CDCA8 regulates cell proliferation, invasion, and migration of LUAD, and to generate novel insights into targeted therapy of LUAD.

METHODS

Expression profiles of mature microRNAs (miRNAs) and mRNAs, along with clinical data of LUAD were downloaded from TCGA database for differential analysis and survival analysis to mine differentially expressed mRNAs. qRT-PCR was used to detect the expression of CDCA8 and miR-133b in LUAD cell lines, and western blot was used to detect protein expression. The effects of CDCA8 on the proliferation, migration, and invasion of LUAD cells were detected by CCK-8 assay, scratch healing assay, and Transwell assay. Bioinformatics predicted the target miRNA of CDCA8, and dual-luciferase reporter gene assay was used to verify the binding relationship between miR-133b and CDCA8.

RESULTS

Data from TCGA-LUAD showed that CDCA8 was significantly overexpressed in LUAD tissue, while its upstream miRNA (miR-133b) was significantly lowly expressed. The result of dual-luciferase test showed that miR-133b targeted CDCA8. The results of in vitro functional experiments showed that overexpression of CDCA8 could promote the proliferation, invasion, and migration of LUAD cells, and miR-133b could reverse this promotion by targeting CDCA8.

CONCLUSION

This study found that CDCA8 was a carcinogenic factor in LUAD cells and it was regulated by upstream miR-133b. miR-133b could inhibit proliferation, invasion, and migration of LUAD cells by targeting CDCA8.

CDCA8 as an independent predictor for a poor prognosis in liver cancer

Background

Human cell division cycle associated 8 (CDCA8) a key regulator of mitosis, has been described as a potential prognostic biomarker for a variety of cancers, such as breast, colon and lung cancers. We aimed to evaluate the potential role of CDCA8 expression in the prognosis of liver cancer by analysing data from The Cancer Genome Atlas (TCGA).

Methods

The Wilcoxon rank-sum test was used to compare the difference in CDCA8 expression between liver cancer tissues and matched normal tissues. Then, we applied logistic regression and the Wilcoxon rank-sum test to identify the association between CDCA8 expression and clinicopathologic characteristics. Cox regression and the Kaplan–Meier method were used to examine the clinicopathologic features correlated with overall survival (OS) in patients from the TCGA. Gene set enrichment analysis (GSEA) was performed to explore possible mechanisms of CDCA8 according to the TCGA dataset.

Results

CDCA8 expression was higher in liver cancer tissues than in matched normal tissues. Logistic regression and the Wilcoxon rank-sum test revealed that the increased level of CDCA8 expression in liver cancer tissues was notably related to T stage (OR = 1.64 for T1/2 vs. T3/4), clinical stage (OR = 1.66 for I/II vs. III/IV), histologic grade (OR = 6.71 for G1 vs. G4) and histological type (OR = 0.24 for cholangiocarcinoma [CHOL] vs. hepatocellular carcinoma [LIHC]) (all P-values < 0.05). Kaplan–Meier survival analysis indicated that high CDCA8 expression was related to a poor prognosis in liver cancer (P = 2.456 × 10⁻⁶). Univariate analysis showed that high CDCA8 expression was associated with poor OS in liver cancer patients, with a hazard ratio (HR) of 1.85 (95% confidence interval [CI]: 1.47–2.32; P = 1.16 × 10^–7). Multivariate analysis showed that CDCA8 expression was independently correlated with OS (HR = 1.74; CI: 1.25–12.64; P = 1.27 × 10^–5). GSEA revealed that the apoptosis, cell cycle, ErbB, MAPK, mTOR, Notch, p53 and TGF-β signaling pathways were differentially enriched in the CDCA8 high expression phenotype.

Conclusions

High CDCA8 expression is a potential molecular predictor of a poor prognosis in liver cancer.

CDCA8, targeted by MYBL2, promotes malignant progression and olaparib insensitivity in ovarian cancer

Ovarian cancer is the most lethal gynecologic malignancy. Poly (ADP-ribose) polymerase inhibitors (PARPi) are effective in treating ovarian cancer. However, cancer cell insensitivity and resistance remain challenges. Determination of the exact chemoresistance mechanisms and potential targeted therapies is urgent. CDCA8 (cell division cycle associated 8) participates in the tumorigenesis of various cancers; however, the exact biological function of CDCA8 in ovarian cancer remains obscure. Here, we found that CDCA8 was overexpressed in ovarian cancer and that high expression of CDCA8 promoted the proliferation of ovarian cancer cells in vitro and in vivo. Moreover, silencing of CDCA8 sensitized ovarian cancer cells to olaparib and cisplatin by inducing G2/M arrest, accelerating apoptosis, increasing DNA damage and interfering with RAD51 accumulation in vitro. In addition, MYBL2 (MYB proto-oncogene-like 2), identified as an upstream transcription factor of CDCA8, was positively correlated with the expression level of CDCA8 in ovarian cancer. Finally, MYBL2 enhanced the aggressive characteristics of ovarian cancer cells by regulating CDCA8. In conclusion, high CDCA8 expression was involved in the tumorigenesis, aggressiveness and chemoresistance of ovarian cancer. CDCA8 silencing combined with olaparib treatment might lead to substantial progress in ovarian cancer targeted therapy.

Microarray Data Mining and Preliminary Bioinformatics Analysis of Hepatitis D Virus-Associated Hepatocellular Carcinoma

Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.

Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8⁺ T cells while CDCA8 with CD4⁺ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

Identification of candidate biomarkers correlated with the pathogenesis and prognosis of breast cancer via integrated bioinformatics analysis

BACKGROUND

This study was carried out to identify potential key genes associated with the pathogenesis and prognosis of breast cancer (BC).

METHODS

Seven GEO datasets (GSE24124, GSE32641, GSE36295, GSE42568, GSE53752, GSE70947, GSE109169) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between BC and normal breast tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Hub genes related to the pathogenesis and prognosis of BC were verified by employing protein-protein interaction (PPI) network.

RESULTS

Ten hub genes with high degree were identified, including CDK1, CDC20, CCNA2, CCNB1, CCNB2, BUB1, BUB1B, CDCA8, KIF11, and TOP2A. Lastly, the Kaplan-Meier plotter (KM plotter) online database demonstrated that higher expression levels of these genes were related to lower overall survival. Experimental validation showed that all 10 hub genes had the same expression trend as predicted.

CONCLUSION

The findings of this research would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of BC, which could be used as a new biomarker for diagnosis and to guide the combination medicine of BC.

Identification of cell division cycle 20 as a candidate biomarker and potential therapeutic target in bladder cancer using bioinformatics analysis

PURPOSE

As bladder cancer (BC) is very heterogeneous and complicated in the genetic level, exploring genes to serve as biomarkers and therapeutic targets is practical.

MATERIALS AND METHODS

We searched Gene Expression Omnibus (GEO) and downloaded the eligible microarray datasets. After intersection analysis for identified differentially expressed genes (DEGs) of included datasets, overlapped DEGs were identified and subsequently analyzed with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-Protein Interaction (PPI) and hub genes identification. Hub genes were further analyzed with mRNA expression comparation in Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) database, proteomics-based validation in The Human Protein Atlas (THPA) and survival analysis in GEO and Oncolnc database.

RESULTS

We analyzed five eligible GEO datasets and identified 76 overlapped DEGs mapped into PPI network with 459 edges which were mainly enriched in cell cycle pathway and related terms in GO and KEGG analysis. Among five identified hub genes, which are Cyclin-Dependent Kinase 1 (CDK1), Ubiquitin-Conjugating Enzyme E2 C (UBE2C), Cell Division Cycle 20 (CDC20), Microtubule Nucleation Factor (TPX2) and Cell Division Cycle Associated 8 (CDCA8); CDC20 and CDCA8 were confirmed as significant in mRNA expression comparation and proteomics-based validation. However, only CDC20 was considered prognostically significant in both GEO and Oncolnc database.

CONCLUSIONS

CDC20 and CDCA8 were identified as candidate diagnostic biomarkers for BC in the present study; however, only CDC20 was validated as prognostically valuable and may possibly serve as a candidate prognostic biomarker and potential therapeutic target. Still, further validation studies are essential and indispensable.

Identification of key genes associated with the progression of intrahepatic cholangiocarcinoma using weighted gene co-expression network analysis

The present study aimed to identify the key genes that are associated with the progression of intrahepatic cholangiocarcinoma through weighted gene co-expression network analysis (WGCNA). A total of three gene datasets were downloaded from the Gene Expression Omnibus database, including GSE107943, GSE119336 and GSE26566. Differentially expressed genes (DEGs) between intrahepatic cholangiocarcinoma tissues and adjacent liver tissues were identified using GSE107943, while tissue specific genes between bile duct and liver tissues were identified using GSE26566. Following the removal of tissue-specific genes, real DEGs were used to construct the WGCNA to investigate the association between gene modules and clinical traits. Following functional analysis, pathway enrichment analysis and the construction of a protein-protein interaction (PPI) network were performed, hub genes were selected and their diagnostic value was verified in GSE119336 using a receiver operating characteristic curve. Finally, the protein levels of the hub genes were also verified in intrahepatic cholangiocarcinoma tissues. A total of 1,643 real DEGs were identified and used to construct the WGCNA. Additionally, a total of seven co-expressed gene modules were identified following WGCNA, while genes in brown and yellow modules were identified to be associated with multiple clinical traits (the number of clinical traits >3) and used as key modules. A total of 63 core key module genes were subsequently identified, and it was indicated that these genes were most enriched in the nucleus (Gene Ontology term) and the cell cycle pathway (Kyoto Encyclopedia of Genes and Genomes term). Finally, a total of eight genes, including cyclin B1, cell division cycle 20, cell division cycle associated 8, cyclin dependent kinase 1, centrosomal protein 55, kinesin family member 2C, DNA topoisomerase IIα and TPX2 microtubule nucleation factor, exhibited the highest score in PPI analysis and had a high diagnostic value for intrahepatic cholangiocarcinoma. In addition, the protein levels of these genes were also revealed to be increased in most intrahepatic cholangiocarcinoma tissues. These eight genes may be used as novel biomarkers for the diagnosis of intrahepatic cholangiocarcinoma.

Knockdown of CDCA8 inhibits the proliferation and enhances the apoptosis of bladder cancer cells

Bladder cancer is a tumour of the urinary system with high mortality, and there is also a great lack of therapeutic targets in the clinic. Cell division cycle associated 8 (CDCA8), an important component of the vertebrate chromosomal passenger complex, is highly expressed in various tumours and promotes tumour development. However, the role of CDCA8 in bladder cancer is not fully understood. This study aimed to reveal the function of CDCA8 in bladder cancer by determining the relationship between CDCA8 expression and proliferation, metastasis and apoptosis of bladder cancer cells. Firstly, we studied the mRNA expression of CDCA8 through the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases and analysed the correlation between CDCA8 expression and prognosis of patients with bladder cancer. We also verified CDCA8 expression in bladder cancer tissues by immunohistochemistry. In addition, CDCA8 expression was inhibited in bladder cancer T24 and 5637 cells, and the effects of CDCA8 on the proliferation, migration and invasion of bladder cancer cell lines were investigated using cell counting kit-8, colony formation, cell cycle, apoptosis, wound healing and Transwell invasion assays. Results showed that CDCA8 was highly expressed in bladder cancer compared with normal tissues, and the high CDCA8 expression was significantly correlated with the poor prognosis of patients. Inhibiting CDCA8 expression inhibited the proliferation, migration and invasion of T24 and 5637 cells and induced the apoptosis of bladder cancer cells. CDCA8 was involved in the regulation of the growth cycle of bladder cancer cells. Bioinformatics-based mechanism analysis revealed that high CDCA8 expression may affect the cell cycle and P53 signalling pathways. In conclusion, our results suggest that CDCA8 is highly expressed in bladder cancer and can promote tumour development. Hence, CDCA8 may serve as an effective therapeutic target for treatment of bladder cancer.

Identification of core genes associated with prostate cancer progression and outcome via bioinformatics analysis in multiple databases

Abstract

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.

Methods

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.

Result

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.

Conclusion

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.

Analysis of the Expression of Cell Division Cycle-Associated Genes and Its Prognostic Significance in Human Lung Carcinoma: A Review of the Literature Databases

Background

Lung cancer (LC) has become the top cause responsible for cancer-related deaths. Cell division cycle-associated (CDCA) genes exert an important role in the life process. Dysregulation in the process of cell division may lead to malignancy.

Methods

Transcriptional data on CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

Results

CDCA1/2/3/4/5/7/8 expression levels were higher in lung adenocarcinoma tissues, and the CDCA1/2/3/4/5/6/7/8 expression levels were increased in squamous cell LC tissues compared with those in noncarcinoma lung tissues. The expression levels of CDCA1/2/3/4/5/8 showed correlation with tumor classification. The Kaplan-Meier Plotter database was employed to carry out survival analysis, indicating that increased CDCA1/2/3/4/5/6/7/8 expression levels were increased in squamous cell LC tissues compared with those in noncarcinoma lung tissues. The expression levels of P < 0.05). Only LC patients with increased CDCA3/4/5/8 expression were significantly related to lower post-progression survival (PPS) (P < 0.05). Only LC patients with increased CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA8, INCENP, AURKB, and BIRC5); CORUM: 127: NDC80 kinetochore complex; M129: the PID PLK1 pathway; and GO: 0007080: mitotic metaphase plate congression, all of which were remarkably modulated since the alterations affected CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

Conclusions

Upregulated CDCA genes' expression levels in LC tissues probably play a crucial part in LC oncogenesis. The upregulated CDCA genes' expression levels are used as the potential prognostic markers to improve patient survival and the LC prognostic accuracy. CDCA genes probably exert their functions in tumorigenesis through the PLK1 pathway.CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

Systematic investigation of the prognostic value of cell division cycle-associated proteins for clear cell renal cell carcinoma patients

Aim: To explore the prognostic value of the cell division cycle-associated proteins (CDCA) family in clear cell renal cell carcinoma. Methods: Gene profiles were collected from the The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), the GSE29609 and GSE22541 datasets. Genetic alteration and DNA methylation data were downloaded from the cBioPortal and MethSurv. The functional enrichment data were analyzed by Metascape. Results: The mRNA expression of the CDCAs, except CBX2, was significantly increased in clear cell renal cell carcinoma patients. Genetic alterations might affect the expression of CDCAs, but promotor methylation does not affect CDCA gene expression. The overall expression of the CDCAs, according to the The Cancer Genome Atlas-KIRC database (hazard ratio [HR]: 2.18), the GSE29609 (HR: 6.08) and GSE22541 (HR: 6.73), was significantly associated with unfavorable overall survival. In addition, genes co-expressed with CDCAs (R² ≥0.3) were highly associated with cell division and the FOXM1 pathway. Conclusion: Our study demonstrated that the aberrant expression of CDCA gene family members plays an indispensable role in tumorigenesis.

KIF18B promotes the proliferation of pancreatic ductal adenocarcinoma via activating the expression of CDCA8

Pancreatic cancer is one of the malignant tumors with the worst prognosis, and the 5-year survival rate of this disease is less than 1%. About 90% of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), and targeting therapy has become a promising treatment for PDAC in recent years. To improve the survival rate, novel therapeutic targets for PDAC are still urgently needed. KIF18B was initially identified as a member of the kinesin family and involved in multiple cellular processes, such as separation of chromosomes in mitosis. Recently, it was found that KIF18B was involved in the growth and development of multiple cancers. However, the potential link between KIF18B and PDAC is still unclear. In this study, we demonstrated that KIF18B was highly expressed in human PDAC tissues, and related with the poor prognosis and clinical features, such as tumor size (*p = .013) and pTNM stage (*p = .025), of patients with PDAC. We further found that KIF18B knockdown blocked the cell proliferation of PDAC in vitro and in vivo, and the cell cycle was arrested caused by KIF18B depletion. Additionally, we also found that KIF18B bound to the promoter region of the cell division cycle associated 8 and thus activated its transcription. Taken together, this study explored the molecular mechanism underlying KIF18B promoting PDAC and provided a novel therapeutic target of this disease.

Weighted gene coexpression network analysis identifies a new biomarker of CENPF for prediction disease prognosis and progression in nonmuscle invasive bladder cancer

BACKGROUND

The dreadful prognosis of nonmuscle invasive bladder cancer mainly results from the delay in recognition of individuals with a high risk of progression. Thus, the emphasis of this work lies in developing valuable biomarkers that is conducive to accurately predicting the progression of NMIBC.

METHODS

Microarray data from GSE32894 including 209 NMIBC samples were performed by weighted gene coexpression network analysis (WGCNA), which could find modules of highly correlated genes and relate modules to external sample traits. Besides, we constructed a protein-protein interaction to facilitate screening the hub gene. At last, we used RNA-seq and microarray data and clinical information from ArrayExpress (E-MTAB-4321) and GSE13507 to select and validate the candidate gene.

RESULTS

In current paper, blue module of 13 gene coexpression clusters we identified was selected as the key modules. Seven genes namely: CDCA8, CENPF, MCM6, MELK, PRC1, STIL, and TPX2 have been identified as candidate genes. Notably, among them, only elevated CENPF in NIMBC tissue was closely associated with low progression-free survival (PFS) and overall survival (OS) rate in three datasets and had a large area under receiver operating characteristic (ROC) curve. Finally, CENPF was identified as an effective biomarker in NMIBC.

CONCLUSION

Therefore, our findings submit a new progressive and prognostic molecular marker and therapeutic target for NMIBC. Moreover, these genes that deserve to be further researched may improve the comprehension about the occurrence and development of superficial bladder cancer.

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

BACKGROUND

Breast cancer is one of the most common endocrine cancers among females worldwide. Distant metastasis of breast cancer is causing an increasing number of breast cancer-related deaths. However, the potential mechanisms of metastasis and candidate biomarkers remain to be further explored.

RESULTS

The gene expression profiles of GSE102484 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screen for the most potent gene modules associated with the metastatic risk of breast cancer, and a total of 12 modules were identified based on the analysis. In the most significant module (R2 = 0.68), 21 network hub genes (MM > 0.90) were retained for further analyses. Next, protein-protein interaction (PPI) networks were used to further explore the biomarkers with the most interactions in gene modules. According to the PPI networks, five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) were identified as key genes associated with breast cancer progression. Furthermore, the prognostic value and differential expression of these genes were validated based on data from The Cancer Genome Atlas (TCGA) and Kaplan-Meier (KM) Plotter. Receiver operating characteristic (ROC) curve analysis revealed that the mRNA expression levels of these five hub genes showed excellent diagnostic value for breast cancer and adjacent tissues. Moreover, these five hub genes were significantly associated with worse distant metastasis-free survival (DMFS) in the patient cohort based on KM Plotter.

CONCLUSION

Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) associated with the risk of distant metastasis were extracted for further research, which might be used as biomarkers to predict distant metastasis of breast cancer.

Cell Division Cycle Associated 8 Is a Key Regulator of Tamoxifen Resistance in Breast Cancer

Purpose

Breast cancer (BC) is one of the most common malignancies globally, and millions of women worldwide are diagnosed with BC every year. Up to 70% of BC patients are estrogen receptor (ER)-positive. Numerous studies have shown that tamoxifen has a significant therapeutic effect on both primary and metastatic ER-positive BC patients. Although tamoxifen is currently one of the most successful therapeutic agents for BC, a significant proportion of patients will eventually become resistant to tamoxifen, leading to tumor recurrence and metastasis. Knowledge about the development of tamoxifen resistance in BC patients is still limited.

Methods

We applied a loss-and-gain method to study the biological functional role of cell division cycle associated 8 (CDCA8) in tamoxifen resistance in BC cells.

Results

We found that CDCA8 was significantly elevated in tamoxifen-resistant BC cells. Knockdown of CDCA8 expression significantly inhibited the proliferation of tamoxifen-resistant BC cells and reduced their resistance to tamoxifen. In contrast, overexpression of CDCA8 promoted the growth of tamoxifen-sensitive BC cells and induced their resistance to tamoxifen.

Conclusion

In this study, we reported that CDCA8 is a key regulator of tamoxifen resistance in BC, suggesting that CDCA8 may serve as a potential therapeutic target for BC treatment.

Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis

Background

Endometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.

Methods

Download the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the “limma” R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein–protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the “WGCNA” package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.

Results

Functional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.

Conclusions

In summary, these 11 genes may become new therapy targets for EC treatment.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0859-1) contains supplementary material, which is available to authorized users.