Random forest-based modelling to detect biomarkers for prostate cancer progression

Tóth, Réka; Schiffmann, Heiko; Hube‐Magg, Claudia; BuÌˆscheck, F.; HoÌˆflmayer, D.; Weidemann, Sören; Lebok, Patrick; Fraune, Christoph; Minner, Sarah; Schlomm, Thorsten; Sauter, Guido; Plass, Christoph; Assenov, Yassen; Simon, Ronald; Meiners, Jan; Gerhäuser, Clarissa

doi:10.1101/602334

Cited by 7 publications

(5 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the high recurrence rate and poor outcome of advanced PCa, and several researchers built the prognosis predict features to judge the outcome and provide more effeteness treatment for PCa patients. Toth et al [36] generated a DNA methylation-based prognosis signature with the AUC of 0.95 in the training cohort, however, the AUC value in two external validation cohorts are only 0.771 and 0.687. Shao et al [37] produced a seven long noncoding RNAs signature to predict the RFS of PCa, with the AUC value of 0.68 and C-index value of 0.63, whereas this study lacks external validation.…”

Section: Discussionmentioning

confidence: 99%

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

Geng¹,

Chen²,

Huang³

et al. 2020

Int. J. Med. Sci.

View full text Add to dashboard Cite

Background: Matrix Metalloproteinases (MMPs) play an indispensable role in the initial alteration and development of PCa. We tried to generate an MMP-related prognostic signature (MMPS) in prostate cancer (PCa). Methods: TCGA-PRAD, MSKCC/GSE21032, GSE116918, GSE70769 cohorts were enrolled to assess the prognostic value of MMPs. The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature. The log-rank test and Kaplan-Meier (K-M) survival curve were applied to show the difference RFS, The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) was plotted to predict the accuracy of signature. CIBERSORT was conducted to analyze the different immune infiltration in MMPS-H and MMPS-L groups. Potential signaling pathways activated in the MMPS-H groups by Metascape. Results: MMP1, MMP7, MMP11, MMP24 and MMP26 were selected by LASSO regression and established the MMPS predict signature. The MMPS showed the high prognostic value in TCGA-PRAD training cohort (AUC=0.714) and validation cohorts (GSE116918: AUC=0.976, GSE70769: AUC=0.738, MSKCC: AUC=0.793). Pid integrin1 pathway, G2M checkpoint, and response to growth factor signaling pathways were activated in MMPS-H group, patients with the high MMPS risk score and low M2 macrophage showed the worst recurrence-free survival (RFS). Conclusion: MMPs involved and played an essential role in the tumorigenesis and biochemical recurrence in PCa patients. The MMPS signature could accurately predict the recurrence of PCa patients and validated in several cohorts.

show abstract

Section: Discussionmentioning

confidence: 99%

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

Geng¹,

Chen²,

Huang³

et al. 2020

Int. J. Med. Sci.

View full text Add to dashboard Cite

show abstract

“…Random forests have been used in various aspects of prostate cancer research. [11][12][13][14][15] The current software represents the response to one of the 8 diseases considered in the Dr. Answer AI project. 9 The intention is to use the Dr. Answer AI software in hospitals from 2020, upon obtaining a software license from the Ministry of Food and Drug Safety.…”

Section: Discussionmentioning

confidence: 99%

“…RF approaches have been used in a variety of healthcare fields, including PCa research. [11][12][13][14][15] There existed a class imbalance problem in this study. The problem of data imbalance can be solved by 1) oversampling, 2) undersampling, and 3) combining oversampling and undersampling.…”

Section: Random Forest For Bcr Prediction Modelmentioning

confidence: 99%

Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

Park

Rho

Moon

et al. 2021

Technol Cancer Res Treat

View full text Add to dashboard Cite

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

show abstract

“…Many studies recently reported that ZIC2 might regulate the development of various tumors, including prostate cancer (23,(34)(35)(36), lung adenocarcinoma (17), breast cancer, clear cell renal cell carcinoma (15,37,38), colorectal cancer (14, 39-41), hepatocellular carcinoma (13,(42)(43)(44), and cervical cancer (10,45). All these tumors, except breast cancer, were upregulated.…”

Section: Discussionmentioning

confidence: 99%

Potential Role of The Upregulation of The Zinc Finger Protein of The Cerebellum 2 In Nasopharyngeal Carcinoma

Peng

Li²,

Ding

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Nasopharyngeal carcinoma (NPC) is a common type of cancer mainly caused by EB virus infection in southern China and Southeast Asia. Zinc finger protein of the cerebellum 2 has been reported to be dysregulated in numerous cancers. However, its role in NPC has not yet been completely investigated. Methods: ZIC2 mRNA expression and clinical role of NPC was explored by Gene Expression Omnibus (GEO) databases and tissue microarrays. Immunohistochemical (IHC) staining was used to assessed the protein expression level of ZIC2 in NPC. Moreover, functional enrichment analyses were performed by the overlapping genes of differentially expressed genes in NPC and ZIC2 related co-expressed genes. Cistrome Data Browser (Cistrome DB) and scatter diagrams displayed the possibility of ATAD2 as a target gene of ZIC2. Last, characteristic phenotype was identified by weighted gene co-expression network analysis (WGCNA).Results: The mRNA expression levels of ZIC2 were significantly increased in NPC within genechips and tissue microarrays (SMD = 2.14; 95% CI = 1.85–2.44). Increased ZIC2 correlated with poor prognosis in NPC patients. The IHC results also showed that ZIC2 protein expression in NPC tissues was remarkably higher than in control tissues. Enrichment analysis showed that genes positively related to ZIC2 are mainly enriched in cell division and in the cell cycle. WGCNA analysis discovered that NPC dataset GSE12452 may be positive correlated with N stage and nagetive correlated with grade. Conclusion: ZIC2 upregulation may promote NPC tumorigenesis through cancer cell proliferation and cell cycle regulation. ATAD2 may produce a marked effect as the downstream target of ZIC2.

show abstract

Random forest-based modelling to detect biomarkers for prostate cancer progression

Cited by 7 publications

References 67 publications

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

Potential Role of The Upregulation of The Zinc Finger Protein of The Cerebellum 2 In Nasopharyngeal Carcinoma

Contact Info

Product

Resources

About