Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

Liu, Heyu; Li, Lirong; Yu, Fan; Lü, Yaping; Zhu, Changhong; Xia, Wei

doi:10.1155/2021/8846951

Cited by 7 publications

(6 citation statements)

References 44 publications

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“…The increased importance placed on the smaller vitamin digestion and absorption gene set in colon cancer is consistent with previous results that found perturbation of gene expression in this set to be closely linked to colon cancer progression [17]. In prostate cancer the nicotine addiction gene set is ranked as the most significantly enriched gene set despite its small size, supporting previous research indicating that this set is strongly perturbed in prostate cancer [18].…”

Section: Our 1df Methods Found Many More Significantly Enriched Gene ...supporting

confidence: 89%

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Makrooni

O’Shea

Geeleher

et al. 2022

Preprint

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Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates to reduce the influence of genes with greater uncertainty in effect size estimate on distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes. We also show that the approach naturally suggests alternative test types that are not usually considered in GSA; it can, for example, be applied to identify differences in effect size distributions between sample subgroups in a gene set of interest. Applying this approach to an analysis of gene expression changes between matched colon tumour and normal samples, we found several gene sets that showed distinct behaviour in patient subgroups with different prognoses. These may help to explain the clinical differences that have been reported between these patient groups.

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Section: Our 1df Methods Found Many More Significantly Enriched Gene ...supporting

confidence: 89%

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Makrooni

O’Shea

Geeleher

et al. 2022

Preprint

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“…Our results suggest the potential usefulness of F5, TMEM126B and EARS2 as potential therapeutic targets. Using PPI network analysis and tissue-specific gene co-expression network analysis, F5 was identified as one of the core genes in PCa 65 . Interestingly, F5 was also associated with an increased risk of breast cancer and the activation of the immune microenvironment 66 .…”

Section: Discussionmentioning

confidence: 99%

Proteomic-based stratification of intermediate-risk prostate cancer patients

Zhong

Sun

Aref

et al. 2023

Preprint

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Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

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“…BUB1B is identified as the top-scoring kinase by RNA interaction. It is possible that a novel antimitotic target involves impaired spindle checkpoint function in any form of cancer, including PCa [ 51 , 57 ]. DNA topoisomerase II alpha (TOP2A) and ribonucleotide reductase regulatory subunit M2 (RRM2) were discovered to be the most significant in PPI network analysis and survival analysis in liver cancer and for PCa [ 76 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, there remain uncertainties in screening for PCa by PSA levels, which are influenced by various stimuli, ranging from inflammation to sexual activity, leading to overdiagnosis and overtreatment of PCa [ 46 , 47 ]. Some studies have determined numerous differentially expressed genes (DEGs) and biomarkers through microarrays analysis in the various biological processes and pathways of PCa progression [ 46 , 48 , 49 , 50 , 51 ]. The lack of understanding of the molecular pathology on the risk of environmental phenol (EP) and paraben (PB) chemical exposures further limits the prevention, diagnosis, and treatment options.…”

Section: Introductionmentioning

confidence: 99%

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

Alwadi

Felty

Roy

et al. 2022

IJMS

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Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005–2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.

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Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

Cited by 7 publications

References 44 publications

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Proteomic-based stratification of intermediate-risk prostate cancer patients

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

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