Cancer stem cells as a therapeutic target in bladder cancer

Aghaalikhani, Nazi; Rashtchizadeh, Nadereh; Shadpour, Pejman; Allameh, Abdolamir; Mahmoodi, Marzieh

doi:10.1002/jcp.26916

Cited by 70 publications

(51 citation statements)

References 103 publications

(146 reference statements)

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“…Although many studies have focused on BRCA diagnosis and treatment in recent years, therapeutic strategies to prevent and treat this malignancy are still inadequate and ineffective. With the emergence of the tumour CSC hypothesis, cancer cells are now considered likely to originate from a cell population called stem cells, which has self-renewal capacity; in addition, CSCs have been reported to be involved in tumour progression, therapeutic resistance and recurrence [15]. Thus, it is fairly important and urgent to identify the key genes driving the crucial cellular processes involved in the transformation from quiescent stem cells to non-renewing cancer cells with unlimited proliferative potential.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis

Pei

Wang

2020

J Transl Med

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Background: With the gradual unveiling of tumour heterogeneity, cancer stem cells (CSCs) are now being considered the initial component of tumour initiation. However, the mechanisms of the growth and maintenance of breast cancer (BRCA) stem cells are still unknown. Methods:To explore the crucial genes modulating BRCA stemness characteristics, we combined the gene expression value and mRNA expression-based stemness index (mRNAsi) of samples from The Cancer Genome Atlas (TCGA), and the mRNAsi was corrected using the tumour purity (corrected mRNAsi). mRNAsi and corrected mRNAsi were analysed and showed a close relationship with BRCA clinical characteristics, including tumour depth, pathological staging and survival status. Next, weighted gene co-expression network analysis (WGCNA) was applied to distinguish crucial gene modules and key genes. A series of functional analyses and expression validation of key genes were conducted using multiple databases, including Oncomine, Gene Expression Omnibus (GEO) and Gene Expression Profiling Integrative Analysis (GEPIA). Results:This study found that mRNAsi and corrected mRNAsi scores were higher in BRCA tissues than that in normal tissues, and both of them increased with tumour stage. Higher corrected mRNAsi scores showed worse overall survival outcomes. We screened 3 modules and 32 key genes, and those key genes were found to be strongly correlated with each other. Functional analysis revealed that the key genes were related to cell fate decision events such as the cell cycle, cellular senescence, chromosome segregation and mitotic nuclear division. Among 32 key genes, we identified 12 genes that strongly correlated with BRCA survival. Conclusions:Thirty-two genes were found to be closely related to BRCA stem cell characteristics; among them, 12 genes showed prognosis-oriented effects in BRCA patients. The most significant signalling pathway related to stemness in BRCA was the cell cycle pathway, which may support new ideas for screening therapeutic targets to inhibit BRCA stem characteristics. These findings may highlight some therapeutic targets for inhibiting BRCA stem cells. BackgroundBreast cancer is one of the most common and lethal cancers in women. According to the latest cancer statistics, the number of estimated new cases and deaths from breast cancer was 268,600 and 41,760, respectively, and the incidence and mortality rates of breast cancer were nearly 30% and 15%, respectively, among all cancers in

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Section: Discussionmentioning

confidence: 99%

Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis

Pei

Wang

2020

J Transl Med

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“…Cancer stem cells are the cellular sources of unlimited proliferation and recurrence of malignant tumors. It is reported that tumor stem cells are involved in the process of tumor cell proliferation, invasion, metastasis, and therapeutic resistance [ 25 ], which ultimately predicts the low survival rate. Generally, CSCs are resistant to currently available treatments [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

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

Suo

Zuo

Zhang

et al. 2020

BioMed Research International

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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.

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“…Nevertheless, we observed that mRNAsi tend to increase with the progression of tumor grade and stage, indicating that the number of CSCs in cancer tissue increases as the tumor progression, which is consistent with the characteristic of CSCs promoting the malignant process of cancer cells. 25 In view of the close relationship between CSCs and tumor formation or development, we constructed a weighted gene co-expression network to screen genes related to the CSCs characteristic to explore the molecular mechanism of CSCs affecting the formation and development of HNSCC. The GO functional enrichment analysis showed that genes in turquoise module mainly participated in DNA replication, chromatin assembly, mitosis.…”

Section: Discussionmentioning

confidence: 99%

Identification of genes associated with cancer stem cell characteristics in head and neck squamous cell carcinoma through co‐expression network analysis

et al. 2020

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Background Head and neck squamous cell carcinoma (HNSCC) is a type of invasive malignancy and the seventh most common cancer in the worldwide. Cancer stem cells (CSCs) are self‐renewal cells in tumors and can produce heterogeneous tumor cells, which play an important role in the development of HNSCC. In our research, we aimed to identify genes related to the CSCs characteristics in HNSCC. Methods Messenger RNA (mRNA) expression‐based stiffness index (mRNAsi) can be used as a quantitative characterization of CSCs. We used one‐class logistic regression machine learning algorithm (OCLR) to calculate the mRNAsi and investigate the relationship between mRNAsi and clinical characteristics of HNSCC. Then, a weighted gene co‐expression network analysis (WGCNA) and protein‐protein interaction (PPI) network was constructed to screen hub genes related to mRNAsi of HNSCC. Results The results indicated that the score of mRNAsi in HNSCC tissues is higher than in paracancer tissues, while the mRNAsi was not statistically correlated with the prognosis and clinical characteristics of HNSCC. Six positive and six negative hub genes related to mRNAsi of HNSCC were selected, which may act as therapeutic targets for inhibiting CSCs within HNSCC. Conclusions In conclusion, our research selected 12 hub genes related to mRNAsi of HNSCC through weighted gene co‐expression network analysis. These genes may become therapeutic targets to inhibit the CSCs of HNSCC in the future.

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Cancer stem cells as a therapeutic target in bladder cancer

Cited by 70 publications

References 103 publications

Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis

Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis

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

Identification of genes associated with cancer stem cell characteristics in head and neck squamous cell carcinoma through co‐expression network analysis

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