Genetic variation in cell cycle regulatory gene AURKA and association with intrinsic breast cancer subtype

Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature

Objective

This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD).

Methods

A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.

Results

High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).

Conclusion

Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.

tRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer

Lung cancer, being the most widespread and lethal form of cancer globally, has a high incidence and mortality rate primarily attributed to challenges associated with early detection, extensive metastasis, and frequent recurrence. In the context of lung cancer development, noncoding RNA molecules have a crucial role in governing gene expression and protein synthesis. Specifically, tRNA-derived fragments (tRFs), a subset of noncoding RNAs, exert significant biological influences on cancer progression, encompassing transcription and translation processes as well as epigenetic regulation. This article primarily examines the mechanisms by which tRFs modulate gene expression and contribute to tumorigenesis in lung cancer. Furthermore, we provide a comprehensive overview of the current bioinformatics analysis of tRFs in lung cancer, with the objective of offering a systematic and efficient approach for studying the expression profiling, functional enrichment, and molecular mechanisms of tRFs in this disease. Finally, we discuss the clinical significance and potential avenues for future research on tRFs in lung cancer. This paper presents a comprehensive systematic review of the existing research findings on tRFs in lung cancer, aiming to offer improved biomarkers and drug targets for clinical management of lung cancer.

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

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

AURKA Gene Variants rs1047972, and rs8173 Are Associated With Breast Cancer

PURPOSE

Association between variants rs1047972 and rs8173 of the AURKA gene in healthy women and breast cancer (BC) in a Mexican population.

METHODS

Genomic DNA samples from 409 healthy women and 572 patients with BC were analyzed for variants rs1047972 and rs8173 of the AURKA gene by polymerase chain reaction-restriction fragment length polymorphism.

RESULTS

TT genotype (odds ratio [OR], 2.5; 95% confidence interval [CI], 1.22-5.11; p = 0.0015) and the T allele (OR, 1.16; 95% CI, 1.23-2.12; p = 0.0007) of the rs1047972 variant were associated as risk susceptibility for BC relative to the control group. Contrarily, the GG genotype (OR, 0.64; 95% CI, 0.43-0.94; p = 0.029) was associated as a protective factor of susceptibility of BC of the variant rs8173 of the AURKA gene. Differences were observed in the patients with BC who were carriers of the CT genotype of the rs1047972 variant with overweight, obesity, estrogen receptor-positive plus obesity, Ki-67 (≥ 20%) plus history familial positive of cancer; and for variant rs8173 the BC patients who were CG carriers and presented chemotherapy gastric toxicity, hormonal receptor positive plus chemotherapy gastric toxicity, and menopause status plus chemotherapy gastric toxicity (p < 0.05). Two common haplotypes were identified in the study groups: CG and TC genotypes, were associated as a protective and risk factor, respectively (p < 0.05).

CONCLUSION

Variants rs1047972 and rs8173 of the AURKA gene and the TC haplotype were associated as risk susceptibility factors for BC in this population.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein–protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein–protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients’ prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

MicroRNA-363-3p, negatively regulated by long non-coding RNA small nucleolar RNA host gene 5, inhibits tumor progression by targeting Aurora kinase A in colorectal cancer

MicroRNA-363-3p (miR-363-3p), reportedly, exhibits a tumor-suppressive role in human malignancies. Herein, our research was designed to further explain the functions and molecular mechanisms of miR-363-3p in the progression of colorectal cancer (CRC). With in vitro models, this study found that miR-363-3p was markedly under-expressed in CRC tissues and cells, and its overexpression suppressed the viability, migration, and invasion of CRC cells, and promoted cell apoptosis, whereas inhibiting miR-363-3p expression exhibited an opposite role. Additionally, aurora kinase A (AURKA), capable of counteracting the impacts of miR-363-3p on malignant biological behaviors of CRC cells, was identified as a direct target of miR-363-3p. Besides, miR-363-3p was sponged by long non-coding RNA small nucleolar RNA host gene 5 (SNHG5), which suppressed miR-363-3p expression. This research shows that SNHG5/miR-363-3p/AURKA axis partakes in CRC progression.

AURKA gene polymorphisms and central nervous system tumor susceptibility in Chinese children

INTRODUCTION

Central nervous system (CNS) tumors comprise 15-20% of all malignancies occurring in childhood and adolescence. Previous researches have shown that overexpression and amplification of the AURKA gene could induce multiple human malignancies, with which the connection of CNS tumor susceptibility has not been extensively studied.

MATERIAL AND METHODS

In this study, we assessed whether and to what extent AURKA gene single nucleotide polymorphisms (SNPs) (rs1047972 C > T, rs2273535 T > A, rs8173 G > C) were associated with CNS tumor susceptibility, based on a case-control analysis in 191 CNS tumor patients and 248 controls. We determined this correlation using odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

AURKA gene rs8173 G > C exhibited a crucial function to CNS tumor susceptibility fall-off (GC/CC vs. GG: adjusted OR = 0.68, 95% CI = 0.46-0.998, P = 0.049). In addition, the combined effect of lowering the risk of developing CNS tumors was more pronounced in carriers with 3 protective genotypes than others (adjusted OR = 0.55, 95% CI = 0.31-0.98, P = 0.044). Further stratification analysis illustrated that the existence of rs8173 GC/CC and three protective genotypes lowered CNS tumor risk in some subgroups.

CONCLUSIONS

Our research suggested that the AURKA gene rs8173 G > C could significantly reduce CNS tumor susceptibility in Chinese children. More functional experiments are needed to explore the role of the AURKA gene rs8173 G > C.

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Objective

To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice.

Methods

For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model.

Results

Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles.

Conclusions

We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.

AURKA and FAM83A are prognostic biomarkers and correlated with Tumor-infiltrating Lymphocytes in smoking related Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) has become the main histologic type, which account for nearly 40% of lung cancer. The present study aimed to investigate the gene expression signature in smoking related LUAD. A total of 45 smoking related DEGs in LUAD were identified and functional enrichment analysis was also performed. Then Cox's regression model and Kaplan-Meier analysis were used to screen potential prognostic genes. Finally, AURKA and FAM83A were left for further immune-related mechanism exploration. Kaplan-Meier analysis indicated survival rates are related to different immune cell (B cell and Dendritic cell) infiltration levels. Mechanistically, we further explore the correlation between AURKA and FAM83A gene expression levels and tumor-infiltrating lymphocytes (TILs) level as well as their response to immunomodulators. The results suggested that AURKA and FAM83A are highly expressed in smoking related LUAD, and negatively correlated to B cell and Dendritic cell infiltration levels. At the same time, B cell and Dendritic cell infiltration levels also related to the prognosis of LUAD. We further revealed AURKA and FAM83A could be novel targets to improve the prognosis of LUAD through regulated the response to immunomodulators.

AURKA rs2273535 T>A Polymorphism Associated With Cancer Risk: A Systematic Review With Meta-Analysis

Aurora kinase A (AURKA) is a cell cycle regulatory serine/threonine kinase that promotes cell cycle progression. It plays an important role in regulating the transition from G2 to M phase during mitosis. The association between the AURKA rs2273535 T>A polymorphism and cancer risk has been investigated, but the results remain inconsistent. To get a more accurate conclusion, we conducted a comprehensive meta-analysis of 36 case-control studies, involving 22,884 cancer cases and 30,497 healthy controls. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to determine the association of interest. Pooled analysis indicated that the AURKA rs2273535 T>A polymorphism increased the overall risk of cancer (homozygous: OR = 1.17, 95% CI = 1.04-1.33; recessive: OR = 1.15, 95% CI = 1.05-1.25; allele: OR = 1.07, 95% CI = 1.02-1.13). Stratification analysis by cancer type further showed that this polymorphism was associated with an increased breast cancer risk. This meta-analysis indicated that the AURKA rs2273535 T>A polymorphism was associated with an overall increased cancer risk, especially breast cancer. Further validation experiments are needed to strengthen our conclusion.

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

Lung adenocarcinoma is one of the most common malignant tumors worldwide. Although efforts have been made to clarify its pathology, the underlying molecular mechanisms of lung adenocarcinoma are still not clear. The microarray datasets GSE75037, GSE63459 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify biomarkers for effective lung adenocarcinoma diagnosis and therapy. The differentially expressed genes (DEGs) were identified by GEO2R, and function enrichment analyses were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The STRING database and Cytoscape software were used to construct and analyze the protein-protein interaction network (PPI). We identified 376 DEGs, consisting of 83 upregulated genes and 293 downregulated genes. Functional and pathway enrichment showed that the DEGs were mainly focused on regulation of cell proliferation, the transforming growth factor β receptor signaling pathway, cell adhesion, biological adhesion, and responses to hormone stimulus. Sixteen hub genes were identified and biological process analysis showed that these 16 hub genes were mainly involved in the M phase, cell cycle phases, the mitotic cell cycle, and nuclear division. We further confirmed the two genes with the highest node degree, DNA topoisomerase IIα (TOP2A) and aurora kinase A (AURKA), in lung adenocarcinoma cell lines and human samples. Both these genes were upregulated and associated with larger tumor size. Upregulation of AURKA in particular, was associated with lymphatic metastasis. In summary, identification of the DEGs and hub genes in our research enables us to elaborate the molecular mechanisms underlying the genesis and progression of lung adenocarcinoma and identify potential targets for the diagnosis and treatment of lung adenocarcinoma.

Hypoxic exosomes facilitate angiogenesis and metastasis in esophageal squamous cell carcinoma through altering the phenotype and transcriptome of endothelial cells

Background

In cancer progression, hypoxia, or low oxygen tension, is a major regulator of tumor aggressiveness and metastasis. However, how cancer cells adapt to the hypoxia and communicate with other mesenchymal cells in microenvironment during tumor development remains to be elucidated. Here, we investigated the involvement of exosomes in modulating angiogenesis and enhancing metastasis in esophageal squamous cell carcinoma (ESCC).

Methods

Differential centrifugation, transmission electron microscopy and nanoparticle tracking analysis were used to isolate and characterize exosomes. Colony formation and transwell assay were performed to assess the proliferation, migration and invasion of human umbilical vein endothelial cells (HUVECs). The tube formation assay and matrigel plug assay were used to evaluate the vascular formation ability of HUVECs in vitro and in vivo respectively. An in vivo nude mice model was established to detect the regulatory role of exosomes in ESCC progression. Microarray analysis was performed to analyze the transcriptome profiles in HUVECs.

Results

Exosomes derived from ESCC cells cultured under hypoxia played a better role in promoting proliferation, migration, invasion and tube formation of HUVECs in vitro and in vivo than exosomes from ESCC cells cultured under normoxia. Moreover, hypoxic exosomes significantly enhanced the tumor growth and lung metastasis compared with normoxic exosomes in nude mice models. Interestingly, endothelial cells were programmed by hypoxic and normoxic exosomes from ESCC cells which altered the transcriptome profile of HUVECs.

Conclusions

Taken together, our data identified an angiogenic role of exosomes from ESCC cells which shed light on the further application of exosomes as valuable therapeutic target for ESCC.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1384-8) contains supplementary material, which is available to authorized users.

Polymorphisms in AURKA and AURKB are associated with the survival of triple-negative breast cancer patients treated with taxane-based adjuvant chemotherapy

Purpose

Triple-negative breast cancer (TNBC) is more than a single disease. Identifying biomarkers to further subdivide TNBC patients with distinct outcome is of great importance. It has been reported that single-nucleotide polymorphisms (SNPs) in Aurora kinase A (AURKA) or Aurora kinase B (AURKB) are associated with the risk and survival of several cancers. But till now, there is no research about these polymorphisms in TNBC patients.

Materials and methods

In this study, we investigated the association between polymorphisms in AURKA or AURKB gene and prognosis of TNBC patients treated with taxane-based adjuvant chemotherapy. A total of 273 TNBC patients were enrolled. Haploview 4.2 software was used to identify Tag SNPs. Genotyping was conducted using the MassARRAY MALDI-TOF system.

Results

We found that AURKA rs6099128 GG genotype carriers had significantly worse overall survival (OS) than TT+ TG genotype carriers (P = 0.003, HR = 12.499, 95% CI = 2.357–66.298). AURKB rs11651993 TT genotype carriers had better disease-free survival (DFS) than TC + CC genotype carriers (P = 0.018, HR = 1.876, 95% CI = 1.116–3.154). AURKB rs2289590 CC genotype carriers had worse DFS than CA + AA genotype carriers (P = 0.021, HR = 0.536, 95% CI = 0.315–0.912). After subgroup analysis, rs11651993 TC + CC genotype predicted worse DFS in subgroups of age ≤ 50, post-menopausal, grade unknown (UK), tumor size >2 cm, and lymph node negative. Rs2289590 CA + AA genotype could predict favorable DFS in pre-menopausal, grade 3 and lymph node-positive patients.

Conclusion

We first demonstrated that polymorphisms in AURKA or AURKB gene might predict the OS or DFS of TNBC patients treated with taxane-based adjuvant chemotherapy.

Elevated mRNA Levels of AURKA, CDC20 and TPX2 are associated with poor prognosis of smoking related lung adenocarcinoma using bioinformatics analysis

Background and aim: Adenocarcinoma is a very common pathological subtype for lung cancer. We aimed to identify the gene signature associated with the prognosis of smoking related lung adenocarcinoma using bioinformatics analysis. Methods: A total of five gene expression profiles (GSE31210, GSE32863, GSE40791, GSE43458 and GSE75037) have been identified from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using GEO2R software and functional and pathway enrichment analysis. Furthermore, the overall survival (OS) and recurrence-free survival (RFS) have been validated using an independent cohort from the Cancer Genome Atlas (TCGA) database. Results: We identified a total of 58 DEGs which mainly enriched in ECM-receptor interaction, platelet activation and PPAR signaling pathway. Then according to the enrichment analysis results, we selected three genes (AURKA, CDC20 and TPX2) for their roles in regulating tumor cell cycle and cell division. The results showed that the hazard ratio (HR) of the mRNA expression of AURKA for OS was 1.588 with (1.127-2.237) 95% confidence interval (CI) (P=0.009). The mRNA levels of CDC20 (HR 1.530, 95% CI 1.086-2.115, P=0.016) and TPX2 (HR 1.777, 95%CI 1.262-2.503, P=0.001) were also significantly associated with the OS. Expression of these three genes were not associated with RFS, suggesting that there might be many factors affect RFS. Conclusion: The mRNA signature of AURKA, CDC20 and TPX2 were potential biomarkers for predicting poor prognosis of smoking related lung adenocarcinoma.

Combined effect of genetic polymorphisms of AURKA and environmental factors on oral cancer development in Taiwan

Background

Oral squamous cell carcinoma (OSCC) is the sixth and fourth most common cause of cancer death in men worldwide and in Taiwan, respectively. AURKA, which encodes a centrosome-related serine/threonine kinase, is frequently amplified and overexpressed in many human cancers, particularly advanced OSCC. We conducted a hospital-based case-control study to estimate AURKA single-nucleotide polymorphisms (SNPs) and environmental risk factors to determine OSCC susceptibility and clinicopathological characteristics.

Methodology/Principal findings

We enrolled a total of 876 OSCC patients and 1200 controls. Four SNPs of AURKA, namely rs1047972, rs2273535, rs2064863, and rs6024836, were analyzed using real-time polymerase chain reaction (PCR). Among the 1420 smokers, the AURKA polymorphism carriers with the betel nut chewing habit had a higher risk of oral cancer than AURKA wild-type (WT) carriers without the betel nut chewing habit. Patients with the AURKA rs2064863 gene had a 1.365-fold higher risk of stage III or IV OSCC (95% confidence interval [CI] 1.029–1.811) than those with the rs2064863 WT gene. Furthermore, carriers of the AURKA rs1047972/rs2273535/rs2064863 C-A-T haplotype had a 1.736-fold (95% CI 1.110–2.715) higher risk of OSCC than controls (C-T-T, the most common haplotype). Among patients with the betel quid chewing habit, carriers of other haplotypes (C-T-T, C-A-G, T-A-T, T-A-G, T-T-T, and C-T-G) had a 12.857-fold (95% CI 10.731–15.404) increased risk, and carriers of the C-A-T haplotype had the highest risk (AOR: 31.120; 95% CI 13.864–69.850) of OSCC, compared with those without the betel quid chewing who harbored other haplotypes.

Conclusions

In conclusion, betel nut chewing combined with the AURKA C-A-T haplotypes lead to a high risk of OSCC. These findings reveal a novel genetic-environmental predisposition for oral tumorigenesis.