Dataset Information

Effective Prediction of Prostate Cancer Recurrence through the IQGAP1 Network.

ABSTRACT: IQGAP1 expression was analyzed in: (1) primary prostate cancer, (2) xenografts produced from LNCaP, DU145, and PC3 cells, 3) tumor of PTEN^-/- and TRAMP mice, and (3) castration resistant PC (CRPC) produced by LNCaP xenografts and PTEN^-/- mice. IQGAP1 downregulations occurred in CRPC and advanced PCs. The downregulations were associated with rapid PC recurrence in the TCGA PanCancer (n = 492, p = 0.01) and MSKCC (n = 140, p = 4 × 10^-6) cohorts. Differentially expressed genes (n = 598) relative to IQGAP1 downregulation were identified with enrichment in chemotaxis, cytokine signaling, and others along with reductions in immune responses. A novel 27-gene signature (Sig27gene) was constructed from these DEGs through random division of the TCGA cohort into a Training and Testing population. The panel was validated using an independent MSKCC cohort. Sig27gene robustly predicts PC recurrence at (hazard ratio) HR 2.72 and p < 2 × 10^-1⁶ in two independent PC cohorts. The prediction remains significant after adjusting for multiple clinical features. The novel and robust nature of Sig27gene underlie its great translational potential as a prognostic biomarker to predict PC relapse risk in patients with primary PC.

SUBMITTER: Gu Y

PROVIDER: S-EPMC7865788 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Effective Prediction of Prostate Cancer Recurrence through the IQGAP1 Network.

Gu Yan Y Lin Xiaozeng X Kapoor Anil A Li Taosha T Major Pierre P Tang Damu D

Cancers 20210123 3

IQGAP1 expression was analyzed in: (1) primary prostate cancer, (2) xenografts produced from LNCaP, DU145, and PC3 cells, 3) tumor of PTEN-/- and TRAMP mice, and (3) castration resistant PC (CRPC) produced by LNCaP xenografts and PTEN-/- mice. IQGAP1 downregulations occurred in CRPC and advanced PCs. The downregulations were associated with rapid PC recurrence in the TCGA PanCancer (n = 492, p = 0.01) and MSKCC (n = 140, p = 4 × 10< ...[more]

PMID: 33498739

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Project description:Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of protein-protein interactions, key pathways will be elucidated and shown to be shared.The authors statistically prioritized common interactors between established cancer genes and genes from each prostate cancer signature of poor prognosis independently via a previously validated single protein analysis of network (SPAN) methodology. Additionally, they computationally identified pathways among the aggregated interactors across signatures and validated them using a similarity metric and patient survival.Using an information-theoretic metric, the authors assessed the mechanistic similarity of the interactor signature. Its prognostic ability was assessed in an independent cohort of 198 patients with high-Gleason prostate cancer using Kaplan-Meier analysis.Of the 13 prostate cancer signatures that were evaluated, eight interacted significantly with established cancer genes (false discovery rate <5%) and generated a 42-gene interactor signature that showed the highest mechanistic similarity (p<0.0001). Via parameter-free unsupervised classification, the interactor signature dichotomized the independent prostate cancer cohort with a significant survival difference (p=0.009). Interpretation of the network not only recapitulated phosphatidylinositol-3 kinase/NF-?B signaling, but also highlighted less well established relevant pathways such as the Janus kinase 2 cascade.SPAN methodology provides a robust means of abstracting disparate prostate cancer gene expression signatures into clinically useful, prioritized pathways as well as useful mechanistic pathways.

Dataset Information

Effective Prediction of Prostate Cancer Recurrence through the IQGAP1 Network.

Publications

Effective Prediction of Prostate Cancer Recurrence through the <i>IQGAP1</i> Network.

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