Nomograms Predict Survival Advantages of Gleason Score 3+4 Over 4+3 for Prostate Cancer: A SEER-Based Study

Zhu, Xin; Gou, Xin; Zhou, Mi

doi:10.3389/fonc.2019.00646

Cited by 31 publications

(34 citation statements)

References 28 publications

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“…Among the diagnostically significant parameters, sPSA and GS are well-established independent predictors of high-grade PCa, whereas family history is an independent predictor of treatment outcome. 24 , 25 In this study, none of these covariates was found to be significantly associated with MAOA except sPSA. Because high sPSA is known to be a useful predictor of bone scan-positive metastasis in PCa patients, this finding further favors association of MAOA with skeletal metastasis.…”

Section: Discussioncontrasting

confidence: 52%

Association of Monoamine Oxidase A with Tumor Burden and Castration Resistance in Prostate Cancer

Meenu

Verma

Seth

et al. 2020

Current Therapeutic Research

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Background Metastatic burden and aggressive behavior determine severity stratification and guide treatment decisions in prostate cancer (PCa). Monoamine oxidase A (MAOA) may promote tumor burden and drug/castration resistance in PCa. A positive association will pave the way for MAOA inhibitors such as moclobemide for PCa therapy. Objective To analyze MAOA in peripheral blood mononuclear cells qualitatively and p38, c-Jun N-terminal kinases, nuclear factor kappa B, and their phosphorylated forms, vascular endothelial growth factor (angiogenesis), transforming growth factor beta, interleukin 6, and tumor necrosis factor-α (cytokines), Bcl-2 associated X, B-cell lymphoma 2, and P53 (apoptosis), prostate-specific membrane antigen, and epithelial cell adhesion molecules (surface markers) in plasma of patients with PCa. Methods This was a 1-year pilot study in which patients with PCa were recruited and stratified into 2 groups and subgroups: treatment-naive with (M1) (n = 23) or without (M0) (n = 23) bone metastasis; hormone-sensitive prostate cancer (n = 26) or hormone/castration-resistant prostate cancer (n = 26). MAOA was detected using ELISA and other proteins were detected using immunoblotting technique. Results MAOA was detected in 8.6% of M0 compared with 30.4% of M1 patients, and in 7.7% of hormone-sensitive compared with 27% of hormone/castration resistant PCa patients, associating it with bone metastasis and castration resistance. Multivariable regression analysis showed a correlation of MAOA with serum prostate-specific antigen, a marker for progression in PCa (Pearson correlation coefficient r = 0.30; P < 0.01). In patients with positive MAOA, there was overexpression of p38, phosphorylated-p38, c-Jun N-terminal kinases, phosphorylated c-Jun N-terminal kinases, nuclear factor kappa B, phosphorylated nuclear factor kappa B, transforming growth factor beta, vascular endothelial growth factor, interleukin 6, tumor necrosis factor α, Bcl-2 associated X, B-cell lymphoma 2, prostate-specific membrane antigen, and epithelial cell adhesion molecule in M1 compared with M0 group patients, associating these proteins with tumor burden. Overexpression of Bcl-2 associated X, tumor protein 53, c-Jun N-terminal kinases, nuclear factor kappa B, transforming growth factor beta, vascular endothelial growth factor, and prostate-specific membrane antigen and underexpression of B-cell lymphoma 2 and phosphorylated nuclear factor kappa B were observed in hormone-sensitive prostate cancer compared with hormone/castration-resistant prostate cancer, associating these proteins with castration resistance. Conclusions Association of key molecules of oncogenesis and metastasis with MAOA suggests that MAOA inhibitors such as moclobemide might be effective in the management of PCa.

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Section: Discussioncontrasting

confidence: 52%

Association of Monoamine Oxidase A with Tumor Burden and Castration Resistance in Prostate Cancer

Meenu

Verma

Seth

et al. 2020

Current Therapeutic Research

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“…The predictive capacity of models was summarized using timedependent ROC curves [12,13]. The calibration refers to the closeness between the predicted probabilities and the actual outcomes, and this was validated using calibration plots [14]. To test the clinical value of the predictive new model, we generated a decision curve analysis graph to visualize the potential net benefit between two models at each threshold probability [15,16].…”

Section: Resultsmentioning

confidence: 99%

INHBA is a prognostic predictor for patients with colon adenocarcinoma

Liang

et al. 2020

BMC Cancer

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Background: Colon adenocarcinoma (COAD) is one of the most lethal cancers. It is particularly important to accurately predict prognosis and to provide individualized treatment. Several lines of evidence suggest that genetic factors and clinicopathological characteristics are related to cancer onset and progression. The aim of this study was to identify potential prognostic genes and to develop a nomogram to predict survival and recurrence of COAD. Methods: To identify potential prognostic genes in COAD, microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained from GEO2R. Venn diagram was drawn to select those genes that were overexpressed in all datasets, and survival analyses were performed to determine the prognostic values of the selected genes. New nomograms were developed based on the genes that were significantly associated with prognosis. Clinicopathological data were obtained from The Cancer Genome Atlas (TCGA). Finally, the new nomograms were compared head-to-head comparison with the TNM nomogram.Results: From GSE21510, GSE110223, GSE113513 and GSE110224, a total of 834, 218, 236 and 613 overexpressed DEGs were screened out, respectively. The Venn diagram revealed that 12 genes appeared in all four profiles. After survival analyses, only INHBA expression was associated with both overall survival (OS) and disease-free survival (DFS). Multivariate analyses revealed that age, pathological N and pathological M were significant independent risk factors for OS. Age, pathological N, pathological M and INHBA were significant independent risk factors for DFS. Two prediction models predicted the probability of 3-year survival and 5-year survival for OS and DFS, respectively. The concordance indexes were 0.785 for 3-year overall survival, 0.759 for 5-year overall survival, 0.789 for 3-year disease-free survival and 0.757 for 5-year disease-free survival. The head-to-head comparison according to timedependent ROC curves indicated that the new models had higher predictive accuracy. Decision curve analyses (DCA) indicated that the clinical value of the new models were higher than TNM models for predicting disease-free survival.Conclusion: The combination of INHBA expression with a clinical nomogram improves prognostic power in colon adenocarcinoma, especially for predicting recurrence.

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“…The bioinformatics method based on the TCGA database has been increasingly used to analyze the molecular basis of prostate cancer development and clinical patient prognosis [14][15][16]. By using appropriate analytical software and methods to explore a variety of large-size data of clinical specimen from the TCGA database (including transcriptome sequencing data, gene sequencing data, ATAC-seq data, etc.…”

Section: Introductionmentioning

confidence: 99%

High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells.

Dai

Liu

2021

Preprint

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Background: Tumor metastasis is the main cause of death of cancer patients, and the existence of cancer stem cells is the basis of tumor metastasis. However, the systematic analysis of the stemness of prostate cancer cells is still not abundant. This article explores the effective factors that lead to the stemness of prostate cancer cells with multi-omics data mining.Methods: Gene expression modules that were strongly related to the stemness of prostate cancer cells were obtained with WGCNA and stemness scores, based on the prostate cancer transcriptome data in TCGA. Calculated the copy number variation of stemness genes of prostate cancer and evaluated the difference of transcription factors in prostate cancer and normal tissues, based on CNV (copy number variation) data and ATAC-seq data. The protein interaction network of stemness genes of prostate cancer was constructed with the STRING database. At the same time, the correlation between stemness genes of prostate cancer and immune cells was analyzed.Results: Prostate cancer with high Gleason grade was more stemness. The gene set highly related to prostate cancer stemness had higher CNV in prostate cancer samples than normal samples. The transcription factors of stemness genes had similar expressions but had different contributions in normal and prostate cancer tissues. And particular transcription factors enhanced the stemness of prostate cancer, such as PUM1, CLOCK, SP1, TCF12, and so on. The lower tumor immune microenvironment was conducive to the stemness of prostate cancer, and CD8+ T cells and M1 macrophages might play an important role than other immune cells in the tumor microenvironment. We also found that EZH2 played a central role in stemness genes and was negatively correlated with resting mast cells and positively correlated with activated memory CD4+ T cells.Conclusions: This study found that high copy number variation, specific transcription factors, and low immune microenvironment contributed to the stemness of prostate cancer cells through systematic multi-omics analysis. These findings would provide new clues and directions for future research of prostate cancer stem cells.

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Nomograms Predict Survival Advantages of Gleason Score 3+4 Over 4+3 for Prostate Cancer: A SEER-Based Study

Cited by 31 publications

References 28 publications

Association of Monoamine Oxidase A with Tumor Burden and Castration Resistance in Prostate Cancer

Association of Monoamine Oxidase A with Tumor Burden and Castration Resistance in Prostate Cancer

INHBA is a prognostic predictor for patients with colon adenocarcinoma

High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells.

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