Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

Adeola, Henry Ademola; Smith, Muneerah; Kaestner, Lisa; Blackburn, Jonathan M.; Zerbini, Luiz Fernando

doi:10.18632/oncotarget.7359

Cited by 40 publications

(36 citation statements)

References 80 publications

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“…30 Dysregulation of this gene is detected in prostate cancer compared with benign prostatic hyperplasia. 31 However, no correlation between this gene and PaC or diabetes have been reported yet, nor the regulation interactions with miRNAs. Based on our study, CCDC33 was predicted as a target of miR-214 and up-regulated in diabetic PaC patients, suggesting this gene might serve as an oncogene during the disease progression.…”

Section: Discussionmentioning

confidence: 96%

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

et al. 2017

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Pancreatic cancer (PaC) is highly associated with diabetes mellitus (DM). However, the mechanisms are insufficient. The study aimed to uncover the underlying regulatory mechanism on diabetic PaC and find novel biomarkers for the disease prognosis. Two RNA-sequencing (RNA-seq) datasets, GSE74629 and GSE15932, as well as relevant data in TCGA were utilized. After pretreatment, differentially expressed genes (DEGs) or miRNAs (DEMs) or lncRNAs (DELs) between diabetic PaC and non-diabetic PaC patients were identified, and further examined for their correlations with clinical information. Prognostic RNAs were selected using KM curve. Optimal gene set for classification of different samples were recognized by support vector machine. Protein-protein interaction (PPI) network was constructed for DEGs based on protein databases. Interactions among three kinds of RNAs were revealed in the 'lncRNA-miRNA-mRNA' competing endogenous RNA (ceRNA) network. A group of 32 feature genes were identified that could classify diabetic PaC from non-diabetic PaC, such as CCDC33, CTLA4 and MAP4K1. This classifier had a high accuracy on the prediction. Seven lncRNAs were tied up with prognosis of diabetic PaC, especially UCA1. In addition, crucial DEMs were selected, such as hsa-miR-214 (predicted targets: MAP4K1 and CCDC33) and hsa-miR-429 (predicted targets: CTLA4). Notably, interactions of 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' were highlighted in the ceRNA network. Several biomarkers were identified for diagnosis of diabetic PaC, such as HOTAIR, CECR7, UCA1, hsa-miR-214, hsa-miR-429, CCDC33 and CTLA4. 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' regulations might be two important mechanisms for the disease progression.

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Section: Discussionmentioning

confidence: 96%

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

et al. 2017

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“…It is tempting to speculate that the CT genes associated to a better prognosis are eliciting an immune response against the tumor, which could be the reason for a better outcome in such patients. ROPN1 has been demonstrated to induce autoantibodies in patients with prostate cancer [ 43 ] and multiple myeloma [ 44 ]. Spontaneous tumor immune response was also detected for SPAG6 in sera from patients with gastric cancer, melanoma and prostate cancer [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Genome-wide identification of cancer/testis genes and their association with prognosis in a pan-cancer analysis

Silva

Fonseca

et al. 2017

Oncotarget

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Cancer/testis (CT) genes are excellent candidates for cancer immunotherapies because of their restrict expression in normal tissues and the capacity to elicit an immune response when expressed in tumor cells. In this study, we provide a genome-wide screen for CT genes with the identification of 745 putative CT genes. Comparison with a set of known CT genes shows that 201 new CT genes were identified. Integration of gene expression and clinical data led us to identify dozens of CT genes associated with either good or poor prognosis. For the CT genes related to good prognosis, we show that there is a direct relationship between CT gene expression and a signal for CD8+ cells infiltration for some tumor types, especially melanoma.

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“…It was reported as one of biomarkers to distinguish prostate cancer from 555 benign prostatic hyperplasia in patients [41]. Moreover, CALM1's mutation was reported to connect with prostate cancer and was one of verifiable biomarkers of prostate analysis using urinary shotgun proteomics [42,43]. Such a meaningful biomarker cap-turing indicates the usefulness of our network marker though more detailed analysis can be done for this network marker to retrieve more comprehensive information.…”

mentioning

confidence: 89%

A novel feature selection for RNA-seq analysis

Han

2017

Preprint

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RNA-seq data are challenging existing omics data analytics for its volume and complexity. Although quite a few computational models were proposed from different standing points to conduct differential expression (D.E.) analysis, almost all these methods do not provide a rigorous feature selection for high-dimensional RNA-seq count data. Instead, most or even all genes are invited into differential calls no matter they have real contributions to data variations or not. Thus, it would inevitably affect the robustness of D.E. analysis and lead to the increase of false positive ratios.In this study, we presented a novel feature selection method: nonnegative singular value approximation (NSVA) to enhance RNA-seq differential expression analysis by taking advantage of RNA-seq count data's non-negativity. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach. It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases. Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis:NSVA-seq, besides conducting network marker discovery.

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Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

Cited by 40 publications

References 80 publications

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

Genome-wide identification of cancer/testis genes and their association with prognosis in a pan-cancer analysis

A novel feature selection for RNA-seq analysis

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