Neural Transcription Factors in Disease Progression

Thaper, Daksh; Vahid, Sepideh; Zoubeidi, Amina

doi:10.1007/978-3-030-32656-2_19

Cited by 3 publications

(3 citation statements)

References 265 publications

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“…Recent theories postulate that as tumor cells display a high plasticity, therapy exposure and environmental signals can induce phenotype switching towards different phenotypes that are associated with drug resistance, a phenomenon named "lineage plasticity" [3,4]. For instance, in 25% of metastatic prostate carcinoma patients treated with anti-androgen therapy, tumor relapses with morphological features of neuroendocrine carcinoma named neuroendocrine prostate cancer (NEPC) [5,6]. It is worthy to note that neuroendocrine differentiation is associated with treatment failure and poor outcome in metastatic prostate cancer [5,7].…”

Section: Introductionmentioning

confidence: 99%

“…Neuroendocrine-like cells secrete peptide hormones and growth factors to support the growth of surrounding tumor cells in a paracrine manner and express high levels of survival genes, conferring resistance to treatments [6]. In addition, neuroendocrine prostate tumors are positive for neuroendocrine markers like neuron specific enolase (NSE), beta-III-tubulin, chromogranin A, or synaptophysin.…”

Section: Introductionmentioning

confidence: 99%

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Androgen Deprivation Induces Reprogramming of Prostate Cancer Cells to Stem-Like Cells

Sánchez

Vara-Ciruelos

Díaz‐Laviada

2020

Cells

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In the past few years, cell plasticity has emerged as a mode of targeted therapy evasion in prostate adenocarcinoma. When exposed to anticancer therapies, tumor cells may switch into a different histological subtype, such as the neuroendocrine phenotype which is associated with treatment failure and a poor prognosis. In this study, we demonstrated that long-term androgen signal depletion of prostate LNCaP cells induced a neuroendocrine phenotype followed by re-differentiation towards a “stem-like” state. LNCaP cells incubated for 30 days in charcoal-stripped medium or with the androgen receptor antagonist 2-hydroxyflutamide developed neuroendocrine morphology and increased the expression of the neuroendocrine markers βIII-tubulin and neuron specific enolase (NSE). When cells were incubated for 90 days in androgen-depleted medium, they grew as floating spheres and had enhanced expression of the stem cell markers CD133, ALDH1A1, and the transporter ABCB1A. Additionally, the pluripotent transcription factors Nanog and Oct4 and the angiogenic factor VEGF were up-regulated while the expression of E-cadherin was inhibited. Cell viability revealed that those cells were resistant to docetaxel and 2-hidroxyflutamide. Mechanistically, androgen depletion induced the decrease in AMP-activated kinase (AMPK) expression and activation and stabilization of the hypoxia-inducible factor HIF-1α. Overexpression of AMPK in the stem-like cells decreased the expression of stem markers as well as that of HIF-1α and VEGF while it restored the levels of E-cadherin and PGC-1α. Most importantly, docetaxel sensitivity was restored in stem-like AMPK-transfected cells. Our model provides a new regulatory mechanism of prostate cancer plasticity through AMPK that is worth exploring.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Androgen Deprivation Induces Reprogramming of Prostate Cancer Cells to Stem-Like Cells

Sánchez

Vara-Ciruelos

Díaz‐Laviada

2020

Cells

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“…Studies have shown that an imbalance in transcription factors can damage the normal prostate transcription network and lead to the progression of malignant diseases [ 25 ]. To explore the relationship between transcription factors and PC, we clustered PRAD into two categories based on the transcription factors (Figures 1(a) and 1(b) ).…”

Section: Resultsmentioning

confidence: 99%

A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

Luo

Huang

Wang

2021

Evidence-Based Complementary and Alternative Medicine

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Background. Prostate cancer (PC) is one of the most critical cancers affecting men’s health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs. Materials and Methods. In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package “pRRophetic” was applied to drug sensitivity (IC50) value prediction. Results. We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified. Conclusions. Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

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GSFM: A genome-scale functional module transformation to represent drug efficacy for in silico drug discovery

Tian,

Liao,

Cao

et al. 2024

Acta Pharmaceutica Sinica B

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Neural Transcription Factors in Disease Progression

Cited by 3 publications

References 265 publications

Androgen Deprivation Induces Reprogramming of Prostate Cancer Cells to Stem-Like Cells

Androgen Deprivation Induces Reprogramming of Prostate Cancer Cells to Stem-Like Cells

A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

GSFM: A genome-scale functional module transformation to represent drug efficacy for in silico drug discovery

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