TPX2 as a Novel Prognostic Indicator and Promising Therapeutic Target in Triple-negative Breast Cancer

Doxorubicin downregulates cell cycle regulatory hub genes in breast cancer cells

Breast cancer (BC) is the leading commonly diagnosed cancer in the world, with complex mechanisms underlying its development. There is an urgent need to enlighten key genes as potential therapeutic targets crucial to advancing BC treatment. This study sought to investigate the influence of doxorubicin (DOX) on identified key genes consistent across numerous BC datasets obtained through bioinformatic analysis. To date, a meta-analysis of publicly available coding datasets for expression profiling by array from the Gene Expression Omnibus (GEO) has been carried out. Differentially Expressed Genes (DEGs) identified using GEO2R revealed a total of 23 common DEGs, including nine upregulated genes and 14 downregulated genes among the datasets of three platforms (GPL570, GPL6244, and GPL17586), and the commonly upregulated DEGs, showed significant enrichment in the cell cycle in KEGG analysis. The top nine genes, NUSAP1, CENPF, TPX2, PRC1, ANLN, BUB1B, AURKA, CCNB2, and CDK-1, with higher degree values and MCODE scores in the cytoscape program, were regarded as hub genes. The hub genes were activated in disease states commonly across all the subclasses of BC and correlated with the unfavorable overall survival of BC patients, as verified by the GEPIA and UALCAN databases. qRT-PCR confirmed that DOX treatment resulted in reduced expression of these genes in BC cell lines, which reinforces the evidence that DOX remains an effective drug for BC and suggests that developing modified formulations of doxorubicin to reduce toxicity and resistance, could enhance its efficacy as an effective therapeutic option for BC.

Integrative analysis based on the cell cycle-related genes identifies TPX2 as a novel prognostic biomarker associated with tumor immunity in breast cancer

BACKGROUND

This study aims to identify the essential cell cycle-related genes associated with prognosis in breast cancer (BRCA), and to verify the relationship between the central gene and immune infiltration, so as to provide detailed and comprehensive information for the treatment of BRCA.

MATERIALS AND METHODS

Gene expression profiles (GSE10780, GSE21422, GSE61304) and the Cancer Genome Atlas (TCGA) BRCA data were used to identify differentially expressed genes (DEGs) and further functional enrichment analysis. STRING and Cytoscape were employed for the protein-protein interaction (PPI) network construction. TPX2 was viewed as the crucial prognostic gene by the Survival and Cox analysis. Furthermore, the connection between TPX2 expression and immune infiltrating cells and immune checkpoints in BRCA was also performed by the TIMER online database and R software.

RESULTS

A total of 18 cell cycle-related DEGs were identified in this study. Subsequently, an intersection analysis based on TCGA-BRCA prognostic genes and the above DEGs identified three genes (TPX2, UBE2C, CCNE2) as crucial prognostic candidate biomarkers. Moreover, we also demonstrated that TPX2 is closely associated with immune infiltration in BRCA and a positive relation between TPX2 and PD-L1 expression was firstly detected.

CONCLUSIONS

These results revealed that TPX2 is a potential prognostic biomarker and closely correlated with immune infiltration in BRCA, which could provide powerful and efficient strategies for breast cancer immunotherapy.

Gains of 20q11.21 in human pluripotent stem cells: Insights from cancer research

The genetic abnormalities observed in hPSC cultures worldwide have been suggested to pose an important hurdle in their safe use in regenerative medicine due to the possibility of oncogenic transformation by mutant cells in the patient posttransplantation. One of the best-characterized genetic lesions in hPSCs is the gain of 20q11.21, found in 20% of hPSC lines worldwide, and strikingly, also amplified in 20% of human cancers. In this review, we have curated the existing knowledge on the incidence of this mutation in hPSCs and cancer, explored the significance of chromosome 20q11.21 amplification in cancer progression, and reviewed the oncogenic role of the genes in the smallest common region of gain, to shed light on the significance of this mutation in hPSC-based cell therapy. Lastly, we discuss the state-of-the-art strategies devised to detect aneuploidies in hPSC cultures, avoid genetic changes in vitro cultures of hPSCs, and strategies to eliminate genetically abnormal cells from culture.

miRNA-27b-3p/TPX2 Axis Regulates Clear Cell Renal Cell Carcinoma Cell Proliferation, Invasion and Migration

There is a wide variety of cancer cells that can be linked to the presence of TPX2. However, there is not a lot of evidence regarding its role in the development and maintenance of clear cell renal cell carcinoma (ccRCC). In our study, bioinformatics analysis was performed to obtain differentially expressed mRNAs and miR-NAs in ccRCC. Survival curves predicted correlation of TPX2 expression with patient survival. The upstream regulatory miRNA of TPX2 was predicted to be miRNA-27b-3p through database, and dual luciferase assay verified the targeted relationship. qRT-PCR and Western blot were employed for examination of TPX2 mRNA and protein expression in ccRCC cells. Proliferation, invasion, migration and cell cycle were detected by CCK-8, colony formation, wound healing, Transwell, and flow cytometry assays. The results showed that TPX2 showed very high expression in ccRCC, and patients with higher TPX2 expression had shorter relative survival. Low miRNA-27b-3p expression was found in ccRCC. Knockdown of TPX2 or forced expression of miRNA-27b-3p in ccRCC cells inhibited cell proliferation, migration, invasion, and arrested cell division in G0/G1 phase. Dual luciferase reporter presented that miRNA-27b-3p targeted TPX2 to inhibit its expression. Rescue experiments demonstrated that the miRNA-27b-3p/ TPX2 axis affected the biological functions of ccRCC cells. Concurrent overexpression of miRNA-27b-3p and TPX2 inhibited the facilitating effect of TPX2 on ccRCC cell growth. The results revealed novel regulatory mechanisms involved in ccRCC progression, hoping that it may spark an insight for later discovery about the new therapeutic targets for ccRCC.

Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

BACKGROUND

Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis.

METHODS

A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP.

RESULTS

We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC.

CONCLUSION

The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

Role of the TPX2/NCOA5 axis in regulating proliferation, migration, invasion and angiogenesis of breast cancer cells

Breast cancer is a common malignant tumor in women. Increasing evidence has demonstrated that nuclear receptor coactivator 5 (NCOA5) and targeting protein for xenopus kinesin-like protein 2 (TPX2) serve vital roles in the progression of breast cancer. However, to the best of our knowledge, the molecular mechanisms underlying the involvement of TPX2/NCOA5 in the development of breast cancer are not fully understood at present. In the present study, the expression levels of NCOA5 and TPX2 were compared between paired non-tumor and tumor tissues of patients with breast cancer using the TNMplot tool. Expression differences of NCOA5 and TPX2 in human breast epithelial cell lines (MCF10A and MCF12A) and human breast cancer cell lines (MCF7 and T47D) were assessed via reverse transcription-quantitative PCR and western blotting. Additionally, proliferation, migration and invasion of breast cancer cells were determined via Cell Counting Kit-8, would healing and transwell assays. In vitro angiogenesis was determined using a tube formation assay. Furthermore, TPX2 was identified as a high-confidence NCOA5 interactor based on BioPlex network data sets. A co-immunoprecipitation assay was adopted to confirm the interaction between TPX2 and NCOA5. The present study revealed that TPX2 and NCOA5 were highly expressed in breast cancer cells. TPX2 interacted with NCOA5 and there was a positive association between TPX2 and NCOA5 expression. NOCA5 knockdown repressed the proliferation, migration, invasion and in vitro angiogenesis of breast cancer cells. In addition, TPX2 knockdown suppressed the proliferation, migration and invasion of breast cancer cells, and inhibited in vitro angiogenesis, and all of these effects were reversed following NCOA5 overexpression. In conclusion, NCOA5 was a downstream target of TPX2 in enhancing proliferation, migration, invasion and angiogenesis of breast cancer cells.

Comprehensive Analysis of the Oncogenic Role of Targeting Protein for Xklp2 (TPX2) in Human Malignancies

Mitosis and spindle assembly require the microtubule-associated protein Xenopus kinesin-like protein 2 (TPX2). Although TPX2 is highly expressed in several malignant tumor forms, little is known about its role in cancer. In this study, we performed the gene set enrichment analysis of TPX2 in 33 types of cancers and an extensive pan-cancer bioinformatic analysis using prognosis, tumor mutational burdens, microsatellite instability, tumor microenvironment, and immune cell infiltration data. According to the differential expression study, TPX2 was found to be overexpressed across all studied cancer types. Based on the survival analysis, increased TPX2 expression was associated with a poor prognosis for most cancers. The TPX2 expression level was confirmed to correlate with the clinical stage, microsatellite instability, and tumor mutational burden across all cancer types. Furthermore, TPX2 expression has been linked to tumor microenvironments and immune cell infiltration, particularly in bladder urothelial carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma. Finally, the gene set enrichment analysis implicated TPX2 in the regulation of aminoacyl tRNA biosynthesis, which is the most important tumor cell cycle signaling pathway. This comprehensive pan-cancer analysis shows that TPX2 is a prognostic molecular biomarker for most cancers and suggests its potential as an effective therapeutic target for the treatment of these diseases.

Overexpression of FRAT1 protein is closely related to triple-negative breast cancer

Purpose

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis and a lack of targeted therapy. Overexpression of FRAT1 is thought to be associated with this aggressive subtype of cancer. Here, we performed a comprehensive analysis and assessed the association between overexpression of FRAT1 and TNBC.

Methods

First, using different web-based bioinformatics platforms (TIMER 2.0, UALCAN, and GEPIA 2), the expression of FRAT1 was assessed. Then, the expression of the FRAT1 protein and hormone receptors and HER2 status were assessed by immunohistochemical analysis. For samples of tumors with equivocal immunoreactivity, we performed silver in situ hybridization of the HER2 gene to determine an accurate HER2 status. Next, we used the R package and bc-GenExMiner 4.8 to analyze the relationship between FRAT1 expression and clinicopathological parameters in breast cancer patients. Finally, we determined the relationship between FRAT1 overexpression and prognosis in patients.

Results

The expression of FRAT1 in breast cancer tissues is significantly higher than in normal tissue. FRAT1 expression was significantly related to worse overall survival (P < 0.05) and was correlated with these clinicopathological features: T stage, N stage, age, high histologic grade, estrogen receptor status, progesterone receptor status, Her-2 status, TNBC status, basal-like status, CK5/6 status, and Ki67 status.

Conclusion

FRAT1 was overexpressed in breast cancer compared to normal tissue, and it may be involved in the progression of breast cancer malignancy. This study provides suggestive evidence of the prognostic role of FRAT1 in breast cancer and the therapeutic target for TNBC.

In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer

Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer

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 was to identify genes significantly associated with TNBC and further analyze their prognostic significance. The Cancer Genome Atlas (TCGA) TNBC database and gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) were used to explore differentially co-expressed genes in TNBC compared with those in normal tissues and non-TNBC breast cancer tissues. Differential gene expression and weighted gene co-expression network analyses identified 24 differentially co-expressed genes. Functional annotation suggested that these genes were primarily enriched in processes such as metabolism, membrane, and protein binding. The protein-protein interaction (PPI) network further identified ten hub genes, five of which (MAPT, CBS, SOX11, IL6ST, and MEX3A) were confirmed to be differentially expressed in an independent dataset (GSE38959). Moreover, CBS and MEX3A expression was upregulated, whereas IL6ST expression was downregulated in TNBC tissues compared to that in other breast cancer subtypes. Furthermore, lower expression of IL6ST was associated with worse overall survival in patients with TNBC. Thus, IL6ST might play an important role in TNBC progression and could serve as a tumor suppressor gene for diagnosis and treatment.

TPX2 mediates prostate cancer epithelial-mesenchymal transition through CDK1 regulated phosphorylation of ERK/GSK3β/SNAIL pathway

Prostate cancer with high Gleason grade is prone to metastasis, which is one of the factors that seriously threaten the survival of patients, and it is also a treatment difficulty. In this study, we first revealed the potential connection between TPX2 and prostate cancer metastasis. We found that TPX2 is highly expressed in high-grade prostate cancer and is significantly related to poor prognosis. Depletion of TPX2 can significantly inhibit cell activity and migration, and in vivo experiments show that knockdown of TPX2 can significantly inhibit tumor growth. In terms of mechanism, we found that knocking down TPX2 can inhibit the expression of CDK1, repress the phosphorylation of ERK/GSK3β/SNAIL signaling pathway, and thereby inhibit tumor epithelial-mesenchymal transition. Subsequently, we found that after rescuing TPX2, all related proteins and phenotype changes were restored, and this effect can be inhibited by CDK1 inhibitor, RO-3306. Our findings suggest the potential of TPX2 as an important target in anti-tumor metastasis therapy, which is conducive to precision medicine for prostate cancer.

Identification of breast cancer prognostic modules via differential module selection based on weighted gene Co-expression network analysis

Breast cancer is a complex cancer which includes many different subtypes. Identifying prognostic modules, i.e., functionally related gene networks that play crucial roles in cancer development is essential in breast cancer study. Different subtypes of breast cancer correspond to different treatment methods. The purpose of this study is to use a new method to divide breast cancer into different prognostic modules, so as to provide scientific basis for improving clinical management. The method is based on comparing similarities between modules detected from different weighted gene co-expression networks. The method was applied on genomic data of breast cancer from The Cancer Genome Atlas database and was applied to select differential modules between two groups of patients with significant differences in survival times. It was compared with a previously proposed module selection method. The result shows that our method outperforms the previously proposed one. Moreover, within the identified two differential modules, the first one is highly enriched with genes involved in hormone responds, the second one is highly related with biological process engaged in M-phase. The two modules were further validated by log-rank test in the validation dataset GSE3494. Both of the two modules show significantly different with p-values less than 0.02. The identified two modules confirmed previous findings including importance of biological networks in breast cancer involved in hormone response and M-phase. Out of the top twenty hub genes in the two modules, fifteen genes were previously shown to be prognostic markers for breast cancer.

Coexpression Network Analysis of Genes Related to the Characteristics of Tumor Stemness in Triple-Negative Breast Cancer

Cancer stem cells (CSCs) are subsets of cells with the ability of self-renewal and differentiation in neoplasm, which are considered to be related to tumor heterogeneity. It has been reported that CSCs act on tumorigenesis and tumor biology of triple-negative breast cancer (TNBC). However, the key genes that cause TNBC showing stem cell characteristics are still unclear. We combined the RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and mRNA expression-based stemness index (mRNAsi) to further analyze mRNAsi with regard to molecular subtypes, tumor depth, and pathological staging characteristics of breast cancer (BC). Secondly, we extract the differential gene expression of tumor vs. normal group and TNBC vs. other subtypes of BC group, respectively, and intersect them to achieve precise results. We used a weighted gene coexpression network analysis (WGCNA) to screen significant gene modules and the functions of selected genes including BIRC5, CDC25A, KIF18B, KIF2C, ORC1, RAD54L, and TPX2 were carried out through gene ontology (GO) functional annotation. The Oncomine, bc-GenExMiner v4.4, GeneMANIA, Kaplan-Meier Plotter (KM-plotter), and GEPIA were used to verify the expression level and functions of key genes. In this study, we found that TNBC had the highest stem cell characteristics in BC compared with other subtypes. The lower the mRNAsi score, the better the overall survival and treatment outcome. Seven key genes of TNBC were screened and functional annotation indicated that there were strong correlations between them, relating to nuclear division, organelle fission, mitotic nuclear division, and other events that determine cell fate. Among these genes, we found four genes that were highly associated with adverse survival events. Seven key genes identified in this study were found to be closely related to the maintenance of TNBC stemness, and the overexpression of four showed earlier recurrence. The overall survival (OS) curves of all key genes between differential expression level crossed at around nine-year follow-up, which was consistent with the trend of the OS curve related to mRNAsi. These findings may provide new ideas for screening therapeutic targets in order to depress TNBC stemness.

miR‑425‑5p is associated with poor prognosis in patients with breast cancer and promotes cancer cell progression by targeting PTEN

Breast cancer (BC) is the most common invasive cancer in women, and it imposes a heavy burden on patients. microRNAs (miRNAs/miRs) have been found to play an important role in the development of tumors, but their role in the malignant progression of BC is unclear. In the present study, the expression level of miR‑425‑5p was examined in patients with BC, and its association with prognosis was investigated. In vitro experiments were performed to examine role of miR‑425‑5p in the development of BC cells. A downstream target gene of miR‑425‑5p was predicted using a miRNA target prediction tool and validated with a luciferase reporter assay. It was found that miR‑425‑5p expression was increased in BC tissues and cell lines, and was associated with tumor size, clinical stage, lymph node metastasis, distant metastasis and poor overall survival in patients with BC. Knockdown of miR‑425‑5p in BC cell lines inhibited proliferation and migration. PTEN was identified as a downstream target gene of miR‑425‑5p. Overexpression of PTEN was demonstrated to partially inhibit the promotional effect of miR‑425‑5p on cell proliferation and migration. Taken together, miR‑425‑5p is associated with poor prognosis, and promotes cell proliferation and migration via PTEN. Thus, miR‑425‑5p may serve as a therapeutic and prognostic marker for BC.