AURKB: a promising biomarker in clear cell renal cell carcinoma

Overexpression of cyclin F/CCNF as an independent prognostic factor for poor survival in clear cell renal cell carcinoma

Cyclin F (encoded by CCNF gene) has been reported to be implicated in the pathobiology of several human cancers. However, its potential clinical significance in clear cell renal cell carcinoma (ccRCC) remains unknown. The present study aimed to evaluate the potential significance of cyclin F, assessed by immunohistochemical (IHC) staining and molecular (bioinformatics) techniques, as a prognostic marker in ccRCC in relation to clinicopathological features and outcomes. IHC staining was performed using two independent ccRCC tissue array cohorts, herein called tissue macroarray (TMA)_1 and tissue microarray (TMA)_2, composed of 108 ccRCCs and 37 histologically normal tissues adjacent to the tumor (NAT) and 192 ccRCCs and 16 normal kidney samples, respectively. The mRNA expression data were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) public datasets, followed by bioinformatics analysis of biological mechanisms underlying prognosis. The relationship between immune cell infiltration level and CCNF expression in ccRCC was investigated using the Tumor Immune Estimation Resource 2.0 (TIMER2) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Cyclin F expression was significantly elevated in ccRCC lesions compared to both NAT and normal renal tissues. Likewise, CCNF mRNA was markedly increased in ccRCCs relative to non-cancerous tissues. In all analyzed cohorts, tumors with features of more aggressive behavior were more likely to display cyclin F/CCNF-high expression than low. Furthermore, patients with high cyclin F/CCNF expression had shorter overall survival (OS) times than those with low expression. In addition, multivariable analysis revealed that cyclin F/CCNF-high expression was an independent prognostic factor for poor OS in ccRCC. Enrichment analysis for mechanistically relevant processes showed that CCNF and its highly correlated genes initiate the signaling pathways that eventually result in uncontrolled cell proliferation. CCNF expression was also correlated with immune cell infiltration and caused poor outcomes depending on the abundance of tumor-infiltrating immune cells in ccRCC. Our findings suggest that cyclin F/CCNF expression is likely to have an essential role in ccRCC pathobiology through regulating multiple oncogenic signaling pathways and affecting the tumor immune microenvironment and may serve as prognostic biomarker and promising therapeutic target in ccRCC.

TET1 is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Papillary Thyroid Cancer

To investigate the function of ten-eleven translocation 1 (TET1) and its underlying mechanism in papillary thyroid cancer (PTC). Using the RNA-Seq data based on GDC TCGA, we analyzed the gene expression pattern of TET1 in PTC. Immunohistochemistry was carried out to assess the TET1 protein level. Then, its diagnostic and prognostic functions were determined by various bioinformatics approaches. Enrichment analysis was performed to explore the potential pathways in which TET1 is mainly involved. Finally, the immune cell infiltration analysis was conducted and the association of TET1 mRNA expression with the expression levels of immune checkpoints, tumor mutation burden (TMB) score, microsatellite instability (MSI) score, and cancer stem cells (CSC) score was examined. TET1 expression was lower in PTC tissues compared with that in normal tissues (P < 0.01). Besides, TET1 had a certain value in diagnosing PTC, and low-TET1 mRNA expression led to favorable disease-specific survival (DSS) (P < 0.01). The enrichment analysis revealed autoimmune thyroid disease and cytokine-cytokine receptor interaction were the consistent pathways in which TET1 participated. TET1 was negatively correlated with the Stromal score and Immune score. The different proportions of immune cell subtypes were observed between high- and low-TET1 expression groups. Interestingly, TET1 mRNA expression was inversely related to the expression levels of immune checkpoints, and TMB, MSI, and CSC scores. TET1 might be a robust diagnostic and prognostic biomarker for PTC. TET1 affected the DSS of PTC patients possibly through the regulation of immune-related pathways and tumor immunity.

Label-free quantitative proteomics reveals the mechanisms of Aurora kinase B in renal cell carcinoma

Background

Renal cell carcinoma is the most common form of kidney cancer which is a global threat to human health, needing to explore effective therapeutic targets and treatment methods. Aurora kinase B acts as an important carcinogenic role in various kinds of tumors, while its mechanism in renal cell carcinoma is indistinct. Herein we explore the underlying mechanism of Aurora kinase B in renal cell carcinoma.

Methods and results

Label-free quantitative proteomics analysis was employed to analyze the differentially expressed proteins in 786-O cells which were treated with si-Aurora kinase B or si-ctrl. In the current study, 169 differentially expressed proteins were identified. The top 10 upregulated proteins were MX2, IFI44L, ISG20, DDX58, F3, IFI44, ECE1, PRIC285, NIT1, and IFIT2. The top 10 downregulated proteins were FKBP9, FSTL1, DDAH1, TGFB2, HMGN3, COIL, FAM65A, PTPN14, ARFGAP2, and EIF2C2. GO enrichment analysis showed that these differentially expressed proteins participated in biological processes, including defense response to virus, response to virus, and type I interferon signaling pathway. These differentially expressed proteins participated in cellular components, including focal adhesion, cell-substrate adherens junction, cell-substrate junction, and endoplasmic reticulum lumen. These differentially expressed proteins participated in molecule functions, including guanyl nucleotide binding, nucleotidase activity, double-stranded RNA binding, 2'-5'-oligoadenylate synthetase activity, and virus receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the significantly changed proteins including OAS3, OAS2, JAK1, TAP1, and RAC1 were involved in Epstein-Barr virus infection.

Conclusions

Taken together, our results demonstrate the possible mechanisms that Aurora kinase B may participate in renal cell carcinoma. These findings may provide insights into tumorigenesis and a theoretical basis for developing potential therapies of renal cell carcinoma.

GSG2 promotes thyroid cancer via stabilizing AURKB and activating AKT pathway

Thyroid cancer stands out as the most prevalent endocrine cancer, with its incidence on a global rise. While numerous studies have delved into the roles of GSG2 in the progression of various malignancies, its involvement in thyroid cancer remains relatively unexplored. Therefore, this study was initiated to assess the functional importance of GSG2 in human thyroid cancer development. Our findings revealed a notable upregulation of GSG2 in both thyroid cancer tissues and cell lines, demonstrating a significant correlation with the pathological stage and patients' prognosis. Depletion of GSG2 in thyroid cancer cells resulted in suppressed malignant cell development and inhibited tumor outgrowth. Crucially, our investigation identified AURKB as a downstream gene of GSG2. GSG2 exhibited its regulatory role by stabilizing AURKB, countering SMURF1-mediated ubiquitination of AURKB. Furthermore, overexpressing AURKB restored the functional consequences of GSG2 depletion in thyroid cancer cells. Additionally, we proposed the involvement of the AKT pathway in GSG2-mediated regulation of thyroid cancer. Intriguingly, the reversal of cell phenotype alterations in GSG2-depleted cells following an AKT activator underscored the potential link between GSG2 and the AKT pathway. At the molecular level, GSG2 knockdown downregulated p-AKT, an effect partially restored after AKT activator treatment. In summary, our study concluded that GSG2 played a pivotal role in thyroid carcinogenesis, underscoring its potential as a therapeutic target for thyroid cancer.

Overexpression of Aurora Kinase B Is Correlated with Diagnosis and Poor Prognosis in Hepatocellular Carcinoma

Aurora kinase B (AURKB) overexpression promotes tumor initiation and development by participating in the cell cycle. In this study, we focused on the mechanism of AURKB in hepatocellular carcinoma (HCC) progression and on AURKB's value as a diagnostic and prognostic biomarker in HCC. We used data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to analyze AURKB expression in HCC. We found that the expression levels of AURKB in HCC samples were higher than those in the corresponding control group. R packages were used to analyze RNA sequencing data to identify AURKB-related differentially expressed genes (DEGs), and these genes were found to be significantly enriched during the cell cycle. The biological function of AURKB was verified, and the results showed that cell proliferation was slowed down and cells were arrested in the G2/M phase when AURKB was knocked down. AURKB overexpression resulted in significant differences in clinical symptoms, such as the clinical T stage and pathological stage. Kaplan-Meier survival analysis, Cox regression analysis, and Receiver Operating Characteristic (ROC) curve analysis suggested that AURKB overexpression has good diagnostic and prognostic potential in HCC. Therefore, AURKB may be used as a potential target for the diagnosis and cure of HCC.

Aurora B facilitates cholangiocarcinoma progression by stabilizing c-Myc

BACKGROUND

Cholangiocarcinoma (CCA), a malignancy that arises from biliary epithelial cells, has a dismal prognosis, and few targeted therapies are available. Aurora B, a key mitotic regulator, has been reported to be involved in the progression of various tumors, yet its role in CCA is still unclarified.

METHODS

Human CCA tissues and murine spontaneous CCA models were used to assess Aurora B expression in CCA. A loss-of-function model was constructed in CCA cells to determine the role of Aurora B in CCA progression. Subcutaneous and liver orthotopic xenograft models were used to assess the therapeutic potential of Aurora B inhibitors in CCA.

RESULTS

In murine spontaneous CCA models, Aurora B was significantly upregulated. Elevated Aurora B expression was also observed in 62.3% of human specimens in our validation cohort (143 CCA specimens), and high Aurora B expression was positively correlated with pathological parameters of tumors and poor survival. Knockdown of Aurora B by siRNA and heteroduplex oligonucleotide (HDO) or an Aurora B kinase inhibitor (AZD1152) significantly suppressed CCA progression via G2/M arrest induction. An interaction between Aurora B and c-Myc was found in CCA cells. Targeting Aurora B significantly reduced this interaction and accelerated the proteasomal degradation of c-Myc, suggesting that Aurora B promoted the malignant properties of CCA by stabilizing c-Myc. Furthermore, sequential application of AZD1152 or Aurora B HDO drastically improved the efficacy of gemcitabine in CCA.

CONCLUSIONS

Aurora B plays an essential role in CCA progression by modulating c-Myc stability and represents a new target for treatment and chemosensitization in CCA.

Development of MPS1 Inhibitors: Recent Advances and Perspectives

Monopolar spindle kinase 1 (MPS1) plays a pivotal role as a dual-specificity kinase governing spindle assembly checkpoint activation and sister chromatid separation in mitosis. Its overexpression has been observed in various human malignancies. MPS1 reduces spindle assembly checkpoint sensitivity, allowing tumor cells with a high degree of aneuploidy to complete mitosis and survive. Thus, MPS1 has emerged as a promising candidate for cancer therapy. Despite the identification of numerous MPS1 inhibitors, only five have advanced to clinical trials with none securing FDA approval for cancer treatment. In this perspective, we provide a concise overview of the structural and functional characteristics of MPS1 by highlighting its relevance to cancer. Additionally, we explore the structure-activity relationships, selectivity, and pharmacokinetics of MPS1 inhibitors featuring diverse scaffolds. Moreover, we review the reported work on enhancing MPS1 inhibitor selectivity, offering valuable insights into the discovery of novel, highly potent small-molecule MPS1 inhibitors.

Construction of a hepatocytes-related and protein kinase-related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase-related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

Drugging Hijacked Kinase Pathways in Pediatric Oncology: Opportunities and Current Scenario

Childhood cancer is considered rare, corresponding to ~3% of all malignant neoplasms in the human population. The World Health Organization (WHO) reports a universal occurrence of more than 15 cases per 100,000 inhabitants around the globe, and despite improvements in diagnosis, treatment and supportive care, one child dies of cancer every 3 min. Consequently, more efficient, selective and affordable therapeutics are still needed in order to improve outcomes and avoid long-term sequelae. Alterations in kinases' functionality is a trademark of cancer and the concept of exploiting them as drug targets has burgeoned in academia and in the pharmaceutical industry of the 21st century. Consequently, an increasing plethora of inhibitors has emerged. In the present study, the expression patterns of a selected group of kinases (including tyrosine receptors, members of the PI3K/AKT/mTOR and MAPK pathways, coordinators of cell cycle progression, and chromosome segregation) and their correlation with clinical outcomes in pediatric solid tumors were accessed through the R2: Genomics Analysis and Visualization Platform and by a thorough search of published literature. To further illustrate the importance of kinase dysregulation in the pathophysiology of pediatric cancer, we analyzed the vulnerability of different cancer cell lines against their inhibition through the Cancer Dependency Map portal, and performed a search for kinase-targeted compounds with approval and clinical applicability through the CanSAR knowledgebase. Finally, we provide a detailed literature review of a considerable set of small molecules that mitigate kinase activity under experimental testing and clinical trials for the treatment of pediatric tumors, while discuss critical challenges that must be overcome before translation into clinical options, including the absence of compounds designed specifically for childhood tumors which often show differential mutational burdens, intrinsic and acquired resistance, lack of selectivity and adverse effects on a growing organism.

Prognostic biomarker NEIL3 and its association with immune infiltration in renal clear cell carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is a malignant tumor with a high degree of immune infiltration. Identifying immune biomarkers is essential for the treatment of KIRC. Studies have identified the potential of NEIL3 to modulate the immune microenvironment and promote tumor progression. However, the role of NEIL3 in KIRC remains uncertain. This study was to investigate the effect of NEIL3 on the prognosis and immune infiltration of patients with KIRC.

Methods

TCGA and GEO databases were used to study the expression of NEIL3 in KIRC. Cox regression analysis was used to examine the relationship between the expression of NEIL3 and clinicopathological variables and survival. Furthermore, Gene Set Cancer Analysis (GSCA) was applied to study the impact of NEIL3 methylation on outcomes of KIRC. Through gene ontology (GO) and Gene set enrichment (GSEA) analysis, the biological processes and signal pathways related to NEIL3 expression were identified. In addition, immune infiltration analysis was conducted via CIBERSORT analysis, ssGSEA analysis and TISIDB database.

Results

NEIL3 was overexpressed in KIRC, and it was significantly related with histologic grade, pathologic stage, T stage, M stage, and vital status of KIRC patients (P < 0.001). The expression of NEIL3 was associated with worse outcomes. Univariate and multivariate Cox analysis showed that NEIL3 may be an indicator of adverse outcomes in KIRC. GSEA analysis revealed that NEIL3 may be involved in signal pathways including cell cycle, DNA replication, mismatch repair, P53 signal pathway, and antigen processing and presentation. In addition, immune infiltration analysis showed a positive correlation between NEIL3 expression and multiple immune cells (activated CD8 T cells, activated dendritic cells, myeloid-derived suppressor cells, follicular helper T cells, and regulatory T cells) and immunoinhibitors (PD1, CTLA4, LAG3, TIGHT, IL10, and CD96).

Conclusion

NEIL3 is a potential independent biomarker of KIRC, which is relevant to immune infiltration.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

A Cuproptosis-Related lncRNAs Signature Could Accurately Predict Prognosis in Patients with Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism.

Methods

Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis.

Results

Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration.

Conclusion

We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways.

lncRNA ELFN1-AS1 enhances the progression of colon cancer by targeting miR-4270 to upregulate AURKB

The oncogenic role of lncRNA ELFN1-AS1 has been described in different cancers, including colon cancer (CC). However, how ELFN1-AS1 regulates CC malignancy remains unclear. In this study, ELFN1-AS1, AURKB, and miR-4270 expression levels in CC cells and tissues were determined using RT-qPCR and western blotting. CCK-8 and wound healing assays were also performed to analyze alterations in CC cell proliferation and migration. The expression of apoptosis-related proteins (Bax and Bcl-2) was determined via western blot analysis. RNA immunoprecipitation (RIP) assays coupled with luciferase reporter assays were employed to verify the relationship between miR-4270, ELFN1-AS1, and AURKB. An in vivo assay was performed using xenograft tumors in mice to detect the change of tumor growth. It was found that AURKB and ELFN1-AS1 expression was upregulated, whereas miR-4270 was downregulated in CC cells and tissues. ELFN1-AS1 silencing exhibited anti-proliferative, anti-migratory, and pro-apoptotic effects in CC cells. The tumor-suppressive effect of ELFN1-AS1 silencing was verified using in vivo assays. MiR-4270 was predicted to be a target of ELFN1-AS1 and AURKB as a target of miR-4270. Their interactions were further elucidated using luciferase reporter and RNA RIP assays. More importantly, treatment with a miR-4270 inhibitor not only rescued the tumor-suppressing effect of ELFN1-AS1 silencing but also abrogated the tumor suppressor functions of AURKB silencing in CC cells. Taken together, the ELFN1-AS1/miR-4270/AURKB axis facilitates CC tumorigenesis; therefore, targeting this axis might be a promising intervention in preventing CC progression.

High expression of aurora kinase B predicts poor prognosis in hepatocellular carcinoma after curative surgery and its effects on the tumor microenvironment

BACKGROUND

Currently, the only broadly used biomarker for hepatocellular carcinoma (HCC), alpha fetoprotein (AFP), has multiple limitations and the need for novel biomarkers is urgent. Aurora kinase B (AURKB) is a key mitotic protein kinase which performs a critical function in cell cycle progression. Nonetheless, neither the function nor the mechanism of AURKB in HCC following curative surgery is fully grasped at this time. This study sought to evaluate the impact of AURKB on prognosis and the tumor immune microenvironment (TIME) in HCC.

METHODS

We evaluated both the expression profile of AURKB in HCC and its clinical value using online databases and clinical specimens. The prognostic value of AURKB was studied by Kaplan-Meier survival analysis, and the link between AURKB and tumor-infiltrating immune cells (TIICs) were analyzed.

RESULTS

We found the mRNA expression patterns of AURKB were remarkably upregulated in HCC in contrast with adjoining normal tissues (P<0.001). Upregulation of the AURKB protein in HCC was additionally verified by clinical samples. The expression of AURKB was substantially associated with Child-Pugh, microvascular invasion (MVI), Edmondson-Steiner grade, and tumor recurrence. Furthermore, patients diagnosed with HCC who had a low AURKB expression had a better. Our data suggested age [hazard ratio (HR): 1.34], alanine aminotransferase (ALT) (HR: 1.65), tumor size (HR: 1.99), mor number (HR: 1.60), MVI (HR: 1.93), grade (HR: 5.58), and AURKB expression (HR: 3.63) independently functioned as prognostic risk indicators for HCC (P<0.05). Importantly, we also found AURKB expression was inversely linked to resting natural killer (NK) cells, M2 macrophages, activated mast cells, and naive B cells, and positively linked to M0 macrophages, T follicular helper cells (Tfh), regulatory T cells (Treg), and resting myeloid dendritic cells. In addition, AURKB expression was also positively linked to the immune checkpoints of PDCD1, CD274, CTLA4, and LAG3. Finally, 1,696 DEGs were discovered, and were predominantly implicated in chromosome segregation, cell cycle, xenobiotic metabolic process, calcium signaling pathway, bile secretion, tyrosine metabolism, and DNA replication.

CONCLUSIONS

AURKB may be a potential prognostic biomarker for HCC after curative surgery, which correlates with MVI and the TIME in HCC.

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD.

Methods

GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively.

Results

We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores.

Conclusions

Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

Effects of Shenkang Pills on Early-Stage Diabetic Nephropathy in db/db Mice via Inhibiting AURKB/RacGAP1/RhoA Signaling Pathway

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease, so there is an urgent need to suppress its development at early stage. Shenkang pills (SKP) are a hospital prescription selected and optimized from effective traditional Chinese medicinal formulas for clinical treatment of DN. In the present study, liquid chromatography-quadrupole-time of flight-mass spectrometry (LC-Q-TOF-MS) and total contents qualification were applied to generate a quality control standard of SKP. For verifying the therapeutic effects of SKP, db/db mice were administered intragastrically with SKP at a human-equivalent dose (1.82 g/kg) for 4 weeks. Moreover, the underlying mechanism of SKP were analyzed by the renal RNA sequencing and network pharmacology. LC-Q-TOF-MS identified 46 compounds in SKP. The total polysaccharide and organic acid content in SKP were 4.60 and 0.11 mg/ml, respectively, while the total flavonoid, saponin, and protein content were 0.25, 0.31, and 0.42 mg/ml, respectively. Treatment of SKP significantly reduced fasting blood glucose, improved renal function, and ameliorated glomerulosclerosis and focal foot processes effacement in db/db mice. In addition, SKP protected podocytes from injury by increasing nephrin and podocin expression. Furthermore, transcriptome analyses revealed that 430 and 288 genes were up and down-regulated in mice treated with SKP, relative to untreated controls. Gene ontology enrichment analysis revealed that the differentially expressed genes mainly involved in modulation of cell division and chromosome segregation. Weighted gene co-expression network analysis and network pharmacology analysis indicated that aurora kinase B (AURKB), Rac GTPase activating protein 1 (RacGAP1) and SHC binding, and spindle associated 1 (shcbp1) might be the core targets of SKP. This protein and Ras homolog family member A (RhoA) were found overexpression in db/db mice, but significantly decreased with SKP treatment. We conclude that SKP can effectively treat early-stage DN and improve renal podocyte dysfunction. The mechanism may involve down-regulation of the AURKB/RacGAP1/RhoA pathway.

The Regulation of let-7c-5p on the Biological Characteristics of Lung Adenocarcinoma Cells by Targeting AURKB

To study the modulatory mechanism of let-7c-5p on the biological characteristics of lung adenocarcinoma (LUAD) cells by targeting AURKB. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. CCK-8, colony formation, scratch healing, Transwell, and flow cytometry assays were employed to test biological functions of LUAD cells. Western Blot was undertaken to assay the protein level of AURKB, and qRT-PCR was undertaken to test AURKB mRNA and let-7c-5p expression. Dual-luciferase reporter gene method was applied to detect the interaction between AURKB and let-7c-5p. Let-7c-5p was much likely to target AURKB expression. Let-7c-5p was poorly expressed in LUAD cells and suppressed AURKB. Silencing AURKB or overexpressing let-7c-5p both could suppress proliferation, migration, and invasion and stimulate apoptosis, while overexpressing the two simultaneously could reverse such effect. Forced expression of let-7c-5p inhibited proliferation, migration, and invasion and accelerated apoptosis of LUAD cells by inhibiting AURKB, which may provide a new way to understand the malignant progression of LUAD.

Bioinformatics prediction and experimental verification identify MAD2L1 and CCNB2 as diagnostic biomarkers of rhabdomyosarcoma

Background

Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumour. In recent years, the tumour microenvironment (TME) has been reported to be associated with the development of tumours. However, the relationship between the occurrence and development of RMS and TME is unclear. The purpose of this study is to identify potential tumor microenvironment-related biomarkers in rhabdomyosarcoma and analyze their molecular mechanisms, diagnostic and prognostic significance.

Methods

We first applied bioinformatics method to analyse the tumour samples of 125 patients with rhabdomyosarcoma (RMS) from the Gene Expression Omnibus database (GEO). Differential genes (DEGs) that significantly correlate with TME and the clinical staging of tumors were extracted. Immunohistochemistry (IHC) was applied to validate the expression of mitotic arrest deficient 2 like 1 (MAD2L1) and cyclin B2 (CCNB2) in RMS tissue. Then, we used cell function and molecular biology techniques to study the influence of MAD2L1 and CCNB2 expression levels on the progression of RMS.

Results

Bioinformatics results show that the RMS TME key genes were screened, and a TME-related tumour clinical staging model was constructed. The top 10 hub genes were screened through the establishment of a protein–protein interaction (PPI) network, and then Gene Expression Profiling Interactive Analysis (GEPIA) was conducted to measure the overall survival (OS) of the 10 hub genes in the sarcoma cases in The Cancer Genome Atlas (TCGA). Six DEGs of statistical significance were acquired. The relationship between these six differential genes and the clinical stage of RMS was analysed. Further analysis revealed that the OS of RMS patients with high expression of MAD2L1 and CCNB2 was worse and the expression of MAD2L1 and CCNB2 was related to the clinical stage of RMS patients. Gene set enrichment analysis (GSEA) revealed that the genes in MAD2L1 and CCNB2 groups with high expression were mainly related to the mechanism of tumour metastasis and recurrence. In the low-expression MAD2L1 and CCNB2 groups, the genes were enriched in the metabolic and immune pathways. Immunohistochemical results also confirmed that the expression levels of MAD2L1 (30/33, 87.5%) and CCNB2 (33/33, 100%) were remarkably higher in RMS group than in normal control group (0/11, 0%). Moreover, the expression of CCNB2 was related to tumour size. Downregulation of MAD2L1 and CCNB2 suppressed the growth, invasion, migration, and cell cycling of RMS cells and promoted their apoptosis. The CIBERSORT immune cell fraction analysis indicated that the expression levels of MAD2L1 and CCNB2 affected the immune status in the TME.

Conclusions

The expression levels of MAD2L1 and CCNB2 are potential indicators of TME status changes in RMS, which may help guide the prognosis of patients with RMS and the clinical staging of tumours.

Comprehensive analysis of aberrant alternative splicing related to carcinogenesis and prognosis of papillary thyroid cancer

As a key mechanism, alternative splicing (AS) plays a role in the cancer initiation and development. However, in papillary thyroid cancer (PTC), data for the comprehensive AS event profile and its clinical implications are lacking. Herein, a genome-wide AS event profiling using RNA-Seq data and its correlation with matched clinical information was performed using a 389 PTC patient cohort from the project of The Cancer Genome Atlas (TCGA). We identified 1,925 cancer-associated AS events (CASEs) by comparing paired tumors and neighboring healthy tissues. Parent genes with CASEs remarkably enriched in the pathways were linked with carcinogenesis, such as P53, KRAS, IL6-JAK-STAT3, apoptosis, and MYC signaling. The regulatory networks of AS implied an obvious correlation between the expression of splicing factor and CASE. We identified eight CASEs as predictors for overall survival (OS) and disease-free survival (DFS). The established risk score model based on DFS-associated CASEs successfully predicted the prognosis of PTC patients. From the unsupervised clustering analysis results, it is found that different clusters based on AS correlated with prognosis, molecular features, and immune characteristics. Taken together, the comprehensive genome-wide AS landscape analysis in PTC showed new AS events linked with tumorigenesis and prognosis, which provide new insights for clinical monitoring and therapy for PTC.

Integrated analyses of long noncoding RNAs and mRNAs in the progression of breast cancer

Objective

The objective was to explore the expression and potential functions of long noncoding RNA (lncRNA) and mRNAs in human breast cancer (BC).

Methods

Differentially expressed lncRNAs and mRNAs were identified and annotated in BC tissues by using the Agilent human lncRNA assay (Agilent Technologies, Santa Clara, CA, USA) and RNA sequencing. After identification of lncRNAs and mRNAs through quantitative reverse transcription polymerase chain reaction, we conducted a series of functional experiments to confirm the effects of knockdown of one lncRNA, TCONS_00029809, on the progression of BC.

Results

We discovered 238 lncRNAs and 200 mRNAs that were differentially expressed in BC tissues and para-carcinoma tissue. We showed that differentially expressed mRNAs were related to biological adhesion and biological regulation and mainly enriched in cytokine-cytokine receptor interaction, metabolic pathways, and PI3K-Akt signaling pathway. We created a protein–protein interaction network to analyze the proteins enriched in these pathways. We demonstrated that silencing of TCONS_00029809 remarkably inhibited proliferation, invasion, and migration of BC cells, and accelerated their apoptosis.

Conclusions

We identified a large number of differentially expressed lncRNAs and mRNAs, which provide data useful in understanding BC carcinogenesis. The lncRNA TCONS_00029809 may be involved in the development of BC.

IFN-γ Drives Human Monocyte Differentiation into Highly Proinflammatory Macrophages That Resemble a Phenotype Relevant to Psoriasis

As key cells of the immune system, macrophages coordinate the activation and regulation of the immune response. Macrophages present a complex phenotype that can vary from homeostatic, proinflammatory, and profibrotic to anti-inflammatory phenotypes. The factors that drive the differentiation from monocyte to macrophage largely define the resultant phenotype, as has been shown by the differences found in M-CSF- and GM-CSF-derived macrophages. We explored alternative inflammatory mediators that could be used for in vitro differentiation of human monocytes into macrophages. IFN-γ is a potent inflammatory mediator produced by lymphocytes in disease and infections. We used IFN-γ to differentiate human monocytes into macrophages and characterized the cells at a functional and proteomic level. IFN-γ alone was sufficient to generate macrophages (IFN-γ Mϕ) that were phagocytic and responsive to polarization. We demonstrate that IFN-γ Mϕ are potent activators of T lymphocytes that produce IL-17 and IFN-γ. We identified potential markers (GBP-1, IP-10, IL-12p70, and IL-23) of IFN-γ Mϕ and demonstrate that these markers are enriched in the skin of patients with inflamed psoriasis. Collectively, we show that IFN-γ can drive human monocyte to macrophage differentiation, leading to bona fide macrophages with inflammatory characteristics.

Multi-omics analysis identifies potential mechanisms of AURKB in mediating poor outcome of lung adenocarcinoma

Aurora kinases B (AURKB), which plays a critical role in chromosomal segmentation and mitosis, greatly promotes cell cycle progression and aggressive proliferation of cancers. So far, its role and underlying mechanisms in mediating poor outcome of lung adenocarcinoma (LUAD) remained largely unclear. Analyses on multiple omics data of lung adenocarcinoma cohort in The Cancer Genome Atlas (TCGA) were performed based on AURKB expression, and demonstrated its association with clinical characteristics and the potential of using AURKB as a biomarker in predicting patients' survival. This study found aberrant alterations of genomics and epigenetics, including up-regulation and down-regulation of oncogenic genes and tumor suppressors, pathways involved in the cell cycle, DNA repair, spliceosome, and proteasome, hypermethylation enrichments around transcriptional start sites, which are all related to AURKB expression. We further discovered the possible role of tumor suppressors DLC1 and HLF in AURKB-mediated adverse outcome of LUAD. To conclude, this study proved AURKB as a potential prognostic factor and therapeutic target for lung adenocarcinoma treatment and provide a future research direction.

A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy

Background: Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to establish a novel predictive model based on apoptosis-related gene signature and clinicopathological parameters that can improve risk stratification for BCR and assist in clinical decision-making. Methods: Expression data and corresponding clinical information were obtained from four public cohorts, one from The Cancer Genome Atlas (TCGA) dataset and three from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate modules closely correlated to BCR, and univariate and multivariate Cox regression analyses were utilized to build the gene signature. Time-dependent receiver operating curve (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic value. Finally, we analyzed the expression of genes in the signature and validated the results using quantitative real-time PCR (qRT-PCR). Results: The novel gene signature we established exhibited a high prognostic value and was able to act as an independent risk factor for BCR [Training set: P < 0.001, hazard ratio (HR) = 7.826; Validation set I: P = 0.006, HR = 2.655; Validation set II: P = 0.003, HR = 4.175; Validation set III: P < 0.001, HR = 3.008]. Nomogram based on the gene signature and clinical parameters was capable of distinguishing high-risk BCR patients. Additionally, functional enrichment analysis showed several enriched pathways and biological processes, which might help reveal the underlying mechanism. The expression results of qRT-PCR were consistent with TCGA results. Conclusion: The apoptosis-related gene signature could serve as a powerful predictor and risk factor for BCR in localized PCa patients after RP.

Biological functions and prognostic value of RNA Binding Proteins in clear cell Renal Cell Carcinoma

Early detection and accurate evaluation were both critical to improving the prognosis of clear cell Renal Cell Carcinoma (ccRCC) patients. More importantly, RNA Binding Proteins (RBPs) play a vital role in the tumorigenesis and progression of numerous cancers. However, the relationship between RBPs and ccRCC is still unclear. Exploring the potential biological functions of RBPs in ccRCC and establishing a prognostic signature to predict the survival probability remains meaningful. In this study, transcriptome profiling and the corresponding clinical information were obtained from the TCGA database, GEO database, and ICGC database. By using the "edgeR" R package, 200 DERBPs were found, including 128 up-regulated and 72 down-regulated RBPs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DERBPs were mainly involved in regulating transcriptional processes and metabolism. Furthermore, there were 4 hub genes (RPS2, RPS14, RPS20, and RPLP0) were found in the PPI network, which may play vital biological roles among those DERBPs. Then we used LASSO regression to construct a prognostic signature and validated the signature in the GEO and ICGC cohort. The time-dependent receiver operating characteristic (ROC) curve showed that the signature could accurately predict the prognosis of ccRCC patients. Then we established a nomogram, and the calibration curve and ROC curve showed that the nomogram could accurately predict 1-year, 3-year, and 5-year overall survival (OS) of ccRCC patients (The AUC value: 0.871, 0.829, and 0.816). In conclusion, we constructed a 10-RBPs-based prognostic signature integrating clinical parameters to predict the prognosis of ccRCC patients. The prognostic signature based on the differentially expressed RBPs (DERBPs) might serve as promising diagnostic and prognostic biomarkers in ccRCC.

Identification of Metastasis-Associated Biomarkers in Synovial Sarcoma Using Bioinformatics Analysis

Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (CENPF, KIF11, KIF23, TTK, MKI67, TOP2A, CDC45, MELK, AURKB, and BUB1) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.

HI-511 overcomes melanoma drug resistance via targeting AURKB and BRAF V600E

Rationale: Melanoma is an aggressive tumor of the skin and drug resistance is still a major problem in melanoma therapy. Novel targets and effective agents to overcome drug resistant melanoma are urgently needed in clinical therapy.

Methods: Gene Expression Omnibus (GEO) database analysis, pathway enrichment analysis, and survival rate analysis were utilized to identify a candidate target. An anchorage-independent cell growth assay, flow cytometry, Western blot, and a xenograft mouse model were used to study the function of Aurora kinase B (AURKB) in both drug-sensitive and drug-resistant melanoma. Next, HI-511, a novel dual-target inhibitor targeting both AURKB and BRAF V600E, was designed and examined by an in vitro kinase assay. Methods as indicated above in addition to a BRAF V600E/PTEN-loss melanoma mouse model were used to demonstrate the effect of HI-511 on melanoma development in vitro and in vivo.

Results: AURKB is highly expressed in melanoma and especially in vemurafenib-resistant melanoma and the expression was correlated with patient survival rate. Knocking down AURKB inhibited cell growth and induced apoptosis in melanoma, which was associated with the BRAF/MEK/ERKs and PI3-K/AKT signaling pathways. Importantly, we found that HI-511, a novel dual-target inhibitor against AURKB and BRAF V600E, suppresses both vemurafenib-sensitive and vemurafenib-resistant melanoma growth in vitro and in vivo by inducing apoptosis and mediating the inhibition of the BRAF/MEK/ERKs and PI3K/AKT signaling pathways.

Conclusion: AURKB is a potential target for melanoma treatment. HI-511, a novel dual-target inhibitor against both AURKB and BRAF V600E, could achieve durable suppression of melanoma growth, even drug-resistant melanoma growth.

Identification and validation of the angiogenic genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

Since angiogenesis has an indispensable effect in the development and progression of tumors, in this study we aimed to identify angiogenic genes closely associated with prognosis of HCC to establish diagnostic, prognostic, and recurrence models. We analyzed 132 angiogenic genes and HCC-related RNA sequence data from the TCGA and ICGC databases by Cox and least absolute shrinkage and selection operator (LASSO) regression, and identified four angiogenic genes (ENFA3, EGF, MMP3 and AURKB) to establish prognosis, recurrence and diagnostic models and corresponding nomograms. The prognostic and recurrence models were determined to be independent predictors of prognosis and recurrence (P < 0.05). And compared with the low-risk group, patients in the high-risk group had worse overall survival (OS) rates in training cohort (P < 0.001) and validation cohort (P < 0.001), and higher recurrence rates in training cohort (P<0.001) and validation cohort (P=0.01). The diagnostic models have been validated to correctly distinguish HCC from normal samples and proliferative nodule samples. Through pharmacological analysis we identified piperlongumine as a drug for targeting angiogenesis, and it was validated to inhibit HCC cell proliferation and angiogenesis via the EGF/EGFR axis.

Identification of hub genes associated with outcome of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumor types of the urinary system. Bioinformatics tools have been used to identify new biomarkers of ccRCC and to explore the mechanisms underlying development and progression of ccRCC. The present study analyzed the differentially expressed genes (DEGs) associated with RCC using data obtained from Gene Expression Omnibus datasets and GEO2R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of these DEGs was performed and analyzed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes to identify the hub genes, defined as the genes with the highest degree of interrelation. Subsequently, differential expression and survival analyses of hub genes was performed using The Cancer Genome Atlas database and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Using GEO2R, 1,650 DEGs were identified, including 743 upregulated and 907 downregulated genes. GO and KEGG pathway analyses indicated that the upregulated DEGs were primarily involved in blood vessel and vasculature development, whereas downregulated DEGs were primarily involved in organic acid metabolic processes and carboxylic acid metabolic processes. Subsequently, important modules were identified in the PPI network using Cytoscape's Molecular Complex Detection. The 15 most connected hub genes were identified among DEGs, including glycine decarboxylase (GLDC), enolase 2 (ENO2) and topoisomerase II alpha. GEPIA revealed the association between expression levels of hub genes and survival. Specifically, GLDC and ENO2 were associated with the prognosis of patients with RCC and thus, the effects of GLDC and ENO2 involvement in renal cancer were investigated in vitro. GLDC and ENO2 affected the proliferation and apoptosis of renal cancer cells. These hub genes may reveal a new mechanism underlying development or progression of RCC and identify new markers for its diagnosis and prognosis.

Gene signatures and prognostic analyses of the Tob/BTG pituitary tumor-transforming gene (PTTG) family in clinical breast cancer patients

Breast cancer is the most common cancer type in females, and exploring the mechanisms of disease progression is playing a crucial role in the development of potential therapeutics. Pituitary tumor-transforming gene (PTTG) family members are well documented to be involved in cell-cycle regulation and mitosis, and contribute to cancer development by their involvement in cellular transformation in several tumor types. The critical roles of PTTG family members as crucial transcription factors in diverse types of cancers are recognized, but how they regulate breast cancer development still remains mostly unknown. Meanwhile, a holistic genetic analysis exploring whether PTTG family members regulate breast cancer progression via the cell cycle as well as the energy metabolism-related network is lacking. To comprehensively understand the messenger RNA expression profiles of PTTG proteins in breast cancer, we herein conducted a high-throughput screening approach by integrating information from various databases such as Oncomine, Kaplan-Meier Plotter, Metacore, ClueGo, and CluePedia. These useful databases and tools provide expression profiles and functional analyses. The present findings revealed that PTTG1 and PTTG3 are two important genes with high expressions in breast cancer relative to normal breast cells, implying their unique roles in breast cancer progression. Results of our coexpression analysis demonstrated that PTTG family genes were positively correlated with thiamine triphosphate (TTP), deoxycytidine triphosphate (dCTP) metabolic, glycolysis, gluconeogenesis, and cell-cycle related pathways. Meanwhile, through Cytoscape analyzed indicated that in addition to the metastasis markers AURKA, AURKB, and NDC80, many of the kinesin superfamily (KIF) members including KIFC1, KIF2C, KIF4A, KIF14, KIF20A, KIF23, were also correlated with PTTG family transcript expression. Finally, we revealed that high levels of PTTG1 and PTTG3 transcription predicted poor survival, which provided useful insights into prospective research of cancer associated with the PTTG family. Therefore, these members of the PTTG family would serve as distinct and essential prognostic biomarkers in breast cancer.