Functional validation of metabolic genes that distinguish Gleason 3 from Gleason 4 prostate cancer foci

Roberto, Domenica; Selvarajah, Shamini; Park, Paul C.; Berman, David M.; Venkateswaran, Vasundara

doi:10.1002/pros.23903

Cited by 9 publications

(8 citation statements)

References 64 publications

(72 reference statements)

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“…PGRMC1 expression has also been shown in several cancer cell, like in breast, prostate and lung, emphasizing the translational aspect of such findings [55,60,61]. Recently, it has been demonstrated that PGRMC1 promotes tumorigenesis, cell proliferation, migration, invasion, and antiapoptosis in the same cancer types [55,[60][61][62]. Additionally, higher expression of PGRMC1 may be useful in the prediction of prognosis of breast cancer patients [63].…”

Section: Discussionmentioning

confidence: 96%

Mifepristone Treatment Promotes Testicular Leydig Cell Tumor Progression in Transgenic Mice

Ponikwicka‐Tyszko

Chruściel

Pulawska

et al. 2020

Cancers

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The selective progesterone receptor modulator mifepristone (MF) may act as a potent antiproliferative agent in different steroid-dependent cancers due to its strong antagonistic effect on the nuclear progesterone receptor (PGR). Hereby, we analyzed the effects of MF treatment on Leydig cell tumor (LCT) progression in a transgenic mouse model (inhibin-α promoter-driven SV40 T-antigen), as well as on LCT (BLTK-1 and mLTC-1) cell proliferation. MF significantly stimulated the proliferation of LCT in vitro. Similarly, a 1-mo MF or P4 treatment stimulated LCT tumor growth in vivo. Traceable/absent classical Pgr or nonclassical membrane PRs α, β, γ and Pgrmc2, but abundant membrane Pgrmc1 expression, was found in LCTs. MF did not activate glucocorticoid or androgen receptors in LCTs. Functional analysis showed that PGRMC1 is required for MF and P4 to stimulate the proliferation and invasiveness of LCTs. Accordingly, MF and P4 induced PGRMC1 translocation into the nucleus and thereby stimulated the release of TGFβ1 in LCT cells. MF and P4 treatments upregulated Tgfbr1, Tgfbr2, and Alk1 expression and stimulated TGFβ1 release in LCT cells. Our findings provide novel mechanistic insights into the action of MF as a membrane PR agonist that promotes LCT growth through PGRMC1 and the alternative TGFβ1 signaling pathway.

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Section: Discussionmentioning

confidence: 96%

Mifepristone Treatment Promotes Testicular Leydig Cell Tumor Progression in Transgenic Mice

Ponikwicka‐Tyszko

Chruściel

Pulawska

et al. 2020

Cancers

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“…One group is the cytochrome b5-like heme/steroid-binding protein family, including four members, of which only the PGRMC1 protein was confirmed to bind with progesterone [ 5 ]. Although PGRMC1 was studied in a variety of human cancers, only one report so far showed its involvement in prostate cancer [ 6 ]. The other group is the Class II PAQR family, also known as membrane progestin receptors (mPRs).…”

Section: Introductionmentioning

confidence: 99%

PAQR6 Upregulation Is Associated with AR Signaling and Unfavorite Prognosis in Prostate Cancers

Yang

Chang

et al. 2021

Biomolecules

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Progesterone-induced rapid non-genomic signaling events have been confirmed through several membrane progesterone receptors (mPR). Some mPRs were reported to correlate with cancer progression and patient prognosis. In this study, we conducted a comprehensive analysis of all progesterone receptor (PGR)-related genes in prostate cancer tissues and examined the correlations of their expression levels with disease progression and patient survival outcomes. We utilized multiple RNA-seq and cDNA microarray datasets to analyze gene expression profiles and performed logistics aggression and Kaplan-Meier survival analysis after stratifying patients based on tumor stages and Gleason scores. We also used NCBI GEO datasets to examine gene expression patterns in individual cell types of the prostate gland and to determine the androgen-induced alteration of gene expression. Spearman coefficient analysis was conducted to access the correlation of target gene expression with treatment responses and disease progression status. The classic PGR was mainly expressed in stromal cells and progestin and adipoQ receptor (PAQR) genes were the predominant genes in prostate epithelial cells. Progesterone receptor membrane component-1 (PGRMC1) was significantly higher than PGRMC2 in all prostate cell types. In prostate cancer tissues, PAQR6 expression was significantly upregulated, while all other genes were largely downregulated compared to normal prostate tissues. Although both PAQR6 upregulation and PAQR5 downregulation were significantly correlated with tumor pathological stages, only PAQR6 upregulation was associated with Gleason score, free-prostate-specific antigen (fPSA)/total-PSA (tPSA) ratio, and patient overall survival outcomes. In addition, PAQR6 upregulation and PGR/PGRMC1 downregulation were significantly associated with a quick relapse. Conversely, in neuroendocrinal prostate cancer (NEPC) tissues, PAQR6 expression was significantly lower, but PAQR7/8 expression was higher than castration-resistant prostate cancer (CRPC) tissues. PAQR8 expression was positively correlated with androgen receptor (AR) score and AR-V7 expression levels but inversely correlated with NEPC score in metastatic CRPC tumors. This study provides detailed expression profiles of membrane progesterone receptor genes in primary cancer, CRPC, and NEPC tissues. PAQR6 upregulation in primary cancer tissues is a novel prognostic biomarker for disease progression, overall, and progression-free survival in prostate cancers. PAQR8 expression in CRPC tissues is a biomarker for AR activation.

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“…Marin-Rubio et al believed that since abnormally expressed metabolites could be detected in various stages of tumor tissues, metabolic genes (MGs) could be used as prognostic markers for cancer [11]. The prognostic value of metabolic genes has been verified in a variety of cancers including prostate cancer, lung adenocarcinoma, liver cancer, head and neck squamous cell carcinoma, rectal cancer and so on [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

Wang

Yao

et al. 2020

Preprint

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Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

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Functional validation of metabolic genes that distinguish Gleason 3 from Gleason 4 prostate cancer foci

Cited by 9 publications

References 64 publications

Mifepristone Treatment Promotes Testicular Leydig Cell Tumor Progression in Transgenic Mice

Mifepristone Treatment Promotes Testicular Leydig Cell Tumor Progression in Transgenic Mice

PAQR6 Upregulation Is Associated with AR Signaling and Unfavorite Prognosis in Prostate Cancers

Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

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