Ji-Ye Kim, Ibadullah Kahttana, Hyonok Yoon, Sunhee Chang, Sun Och Yoon
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Multidimensional analysis using statistics and machine learning (ML) models assessed prognostic value of established clinicopathologic variables with stromal FOXC1 and tumor pERK1-2 expressions.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Both high FOXC1 stroma group and high pERK1-2 tumor group were significantly associated with shorter progression-free survival (PFS) and overall survival (OS) compared with low group (<i>p</i> = 0.015, 0.034 and <i>p</i> = 0.025, 0.025 each respectively). In multivariable analysis, high FOXC1 stromal expression was an independent prognostic factor of OS (<i>p</i> = 0.037). The addition of stromal FOXC1 and tumor pERK1-2 to the NCCN-IPI score significantly improved prediction of time to death compared with NCCN-IPI score alone (Harrell's C-index = 0.801 vs. 0.764; <i>p</i> = 0.030). ML models reconfirmed the addition of stromal FOXC1 expression and tumor pERK1-2 to NCCN-IPI score had the highest C-index (0.952) among combinations. Stromal FOXC1 and tumor pERK1-2 were determinants of DLBCL prognosis, whose addition significantly improved prognostic performance of the NCCN-IPI.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.70305","citationCount":"0","resultStr":"{\"title\":\"Exploring the Potential of Enhanced Prognostic Performance of NCCN-IPI in Diffuse Large B-Cell Lymphoma by Integrating Tumor Microenvironment Markers: Stromal FOXC1 and Tumor pERK1/2 Expression\",\"authors\":\"Ji-Ye Kim, Ibadullah Kahttana, Hyonok Yoon, Sunhee Chang, Sun Och Yoon\",\"doi\":\"10.1002/cam4.70305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>FOXC1 and ERK1-2 are proteins implicated in aggressive biological behavior of various malignancies including lymphomas.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Material and Methods</h3>\\n \\n <p>We investigate the additive prognostic value of stromal FOXC1 expression and tumor phosphorylated ERK1-2 (pERK1-2) expression to the established National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI), in 92 diffuse large B-cell lymphoma (DLBCL) cases. Multidimensional analysis using statistics and machine learning (ML) models assessed prognostic value of established clinicopathologic variables with stromal FOXC1 and tumor pERK1-2 expressions.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Both high FOXC1 stroma group and high pERK1-2 tumor group were significantly associated with shorter progression-free survival (PFS) and overall survival (OS) compared with low group (<i>p</i> = 0.015, 0.034 and <i>p</i> = 0.025, 0.025 each respectively). In multivariable analysis, high FOXC1 stromal expression was an independent prognostic factor of OS (<i>p</i> = 0.037). The addition of stromal FOXC1 and tumor pERK1-2 to the NCCN-IPI score significantly improved prediction of time to death compared with NCCN-IPI score alone (Harrell's C-index = 0.801 vs. 0.764; <i>p</i> = 0.030). ML models reconfirmed the addition of stromal FOXC1 expression and tumor pERK1-2 to NCCN-IPI score had the highest C-index (0.952) among combinations. 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引用次数: 0
摘要
背景 FOXC1和ERK1-2是与包括淋巴瘤在内的各种恶性肿瘤的侵袭性生物学行为有关的蛋白质。 材料与方法 我们在92例弥漫大B细胞淋巴瘤（DLBCL）病例中研究了基质FOXC1表达和肿瘤磷酸化ERK1-2（pERK1-2）表达对美国国家综合癌症网络国际预后指数（NCCN-IPI）的附加预后价值。利用统计学和机器学习（ML）模型进行多维分析，评估已确立的临床病理变量与基质FOXC1和肿瘤pERK1-2表达的预后价值。 结果 与低表达组相比，高FOXC1基质组和高pERK1-2肿瘤组与较短的无进展生存期（PFS）和总生存期（OS）显著相关（分别为p = 0.015、0.034和p = 0.025、0.025）。在多变量分析中，FOXC1基质高表达是OS的独立预后因素（p = 0.037）。与单用NCCN-IPI评分相比，在NCCN-IPI评分中加入基质FOXC1和肿瘤pERK1-2可显著提高对死亡时间的预测（Harrell's C-index = 0.801 vs. 0.764; p = 0.030）。ML模型再次证实，在NCCN-IPI评分的基础上增加基质FOXC1表达和肿瘤pERK1-2的组合具有最高的C指数（0.952）。基质FOXC1和肿瘤pERK1-2是DLBCL预后的决定因素，它们的加入显著改善了NCCN-IPI的预后性能。
Exploring the Potential of Enhanced Prognostic Performance of NCCN-IPI in Diffuse Large B-Cell Lymphoma by Integrating Tumor Microenvironment Markers: Stromal FOXC1 and Tumor pERK1/2 Expression
Background
FOXC1 and ERK1-2 are proteins implicated in aggressive biological behavior of various malignancies including lymphomas.
Material and Methods
We investigate the additive prognostic value of stromal FOXC1 expression and tumor phosphorylated ERK1-2 (pERK1-2) expression to the established National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI), in 92 diffuse large B-cell lymphoma (DLBCL) cases. Multidimensional analysis using statistics and machine learning (ML) models assessed prognostic value of established clinicopathologic variables with stromal FOXC1 and tumor pERK1-2 expressions.
Results
Both high FOXC1 stroma group and high pERK1-2 tumor group were significantly associated with shorter progression-free survival (PFS) and overall survival (OS) compared with low group (p = 0.015, 0.034 and p = 0.025, 0.025 each respectively). In multivariable analysis, high FOXC1 stromal expression was an independent prognostic factor of OS (p = 0.037). The addition of stromal FOXC1 and tumor pERK1-2 to the NCCN-IPI score significantly improved prediction of time to death compared with NCCN-IPI score alone (Harrell's C-index = 0.801 vs. 0.764; p = 0.030). ML models reconfirmed the addition of stromal FOXC1 expression and tumor pERK1-2 to NCCN-IPI score had the highest C-index (0.952) among combinations. Stromal FOXC1 and tumor pERK1-2 were determinants of DLBCL prognosis, whose addition significantly improved prognostic performance of the NCCN-IPI.
期刊介绍：
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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