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pmid:32391054
Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management.
Hengyu Chen · Xianxiong Ma · Ming Yang · Mengyi Wang · Lei Li · Tao Huang
Frontiers in genetics · 2020 doi:10.3389/fgene.2020.00333
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Complete file
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Several prognostic and predictive transcription factor (TF) markers have been reported for BC; however, they are inconsistent due to small datasets, the heterogeneity of BC, and variation in data pre-processing approaches. This study aimed to identify an effective predictive TF signature for the prognosis of patients with BC. We analyzed the TF data of 868 patients with BC in The Cancer Genome Atlas (TCGA) database to investigate TF biomarkers relevant to recurrence-free survival (RFS). These patients were separated into training and internal validation datasets, with GSE2034 and GSE42568 used as external validation sets. A nine-TF signature was identified as crucially related to the RFS of patients with BC by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis in the training dataset. Kaplan-Meier analysis revealed that the nine-TF signature could significantly distinguish high- and low-risk patients in both the internal validation dataset and the two external validation sets. Receiver operating characteristic (ROC) analysis further verified that the nine-TF signature showed a good performance for predicting the RFS of patients with BC. In addition, we developed a nomogram based on risk score and lymph node status, with C-index, ROC, and calibration plot analysis, suggesting that it displays good performance and clinical value. 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available", "72 (8.29)", "52 (8.55)", "20 (7.69)", "", ""]], "row_count": 54, "column_count": 6, "numeric_cells": 136, "header_rows": 1, "locator": "section[8]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:32391054:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:43bcd514e568cfc2cbceb1a89d6bb2a2bd49cd2b1922b76b175647923d750f43", "bytes": 1894, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#T2", "version": null, "note": "Univariate Cox regression analysis and multivariate Cox regression analysis outcomes based on methylation risk score and other clinical factors.", "structure": {"pmcid": "PMC7193038", "section_index": 13, "section_title": "Nomogram Development and Assessment", "row_count": 13, "column_count": 9}, "table": {"label": "TABLE 2", "caption": "Univariate Cox regression analysis and multivariate Cox regression analysis outcomes based on methylation risk score and other clinical factors.", "rows": [["", "Univariate Cox regression analysis", "Multivariate Cox regression analysis"], ["Characteristics", "HR", "HR.95L", "HR.95H", "p value", "HR", "HR.95L", "HR.95H", "p value"], ["Score", "2.738045421", "2.048628528", "3.659469066", "1.01E-11", "2.510661624", "1.788829617", "3.523768687", "1.02E-07"], ["Estrogen receptor status", "0.692324993", "0.342239438", "1.400522099", "0.306355543", "0.768993041", "0.260500851", "2.270051305", "0.634356191"], ["Progesterone receptor status", "0.592458976", "0.30230991", "1.161085454", "0.127286775", "0.981504947", "0.33480194", "2.877378671", "0.972862011"], ["Her2 receptor status", "1.257944488", "0.823912985", "1.9206207", "0.287844565", "1.373910032", "0.865382257", "2.181265863", "0.178008111"], ["Tumor", "1.754711187", "0.617255141", "4.988231197", "0.291488237", "1.101502581", "0.3254944", "3.727584669", "0.87648392"], ["Lymph node status", "2.17751336", "1.49200771", "3.177975825", "5.48E-05", "1.651534043", "1.069601814", "2.550074858", "0.023602411"], ["Metastasis status", "2.520159824", "0.343448624", "18.49244717", "0.363359385", "1.858617241", "0.226521566", "15.25001838", "0.563807742"], ["Margin status", "0.7974526", "0.396241415", "1.604907073", "0.525907749", "0.634209273", "0.307262431", "1.309048428", "0.218093787"], ["Ethnicity", "0.634959689", "0.086536008", "4.659029397", "0.655117076", "1.014927373", "0.132510023", "7.773582306", "0.988619118"], ["Age", "0.97574987", "0.948136617", "1.004167323", "0.093730595", "0.979959839", "0.95094581", "1.009859108", "0.186780098"], ["Anatomic neoplasm subdivision", "1.039911088", "0.843603069", "1.281900351", "0.713889548", "0.97189382", "0.782429155", "1.207237219", "0.796654191"]], "row_count": 13, "column_count": 9, "numeric_cells": 92, "header_rows": 2, "locator": "section[13]/table-wrap[1]#T2"}}], "gaps": ["asset:data:geo:GSE2034 not downloaded", 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