Peripheral blood values as predictors of autoimmune status in oral cavity squamous cell carcinoma.
Peripheral blood values as predictors of autoimmune status in oral cavity squamous cell carcinoma.
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DOI：
10.1016/j.tranon.2021.101220
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发表时间：
2021-12
影响因子：
5
通讯作者：
Patel SG
中科院分区：
文献类型：
--
作者：
Pillai A;Valero C;Navas K;Morris Q;Patel SG
口腔癌根据肿瘤而不是宿主特征进行高度分层。宿主外周血数据可以影响口腔癌的预后。自身免疫性疾病是宿主免疫失调的宏观反映。创建了一个称为 AI 分数的分数来描述主机状态。 AI 评分作为口腔癌的预后工具很有用，应该进行更广泛的研究。最近的文献强调了宿主在口腔鳞状细胞癌（OSCC）预后中的作用。自身免疫（AI）疾病代表了宿主状态的宏观描述。本研究的目的是预测 AI“状态”并分析该“状态”作为 OSCC 预后指标的效用。从 OSCC 患者的部门数据库 (n = 1377) 中，确定了 125 名患有 AI 疾病的患者。获得 PBL 值并标准化以供分析。使用 LASSO 回归模型来确定 AI 状态的最佳预测因子，并开发了 AI 评分。然后对各个生存终点的得分进行分析。当AI评分分为二元变量时，最高四分位的患者的总生存期（OS）、局部无复发概率（LRFP）和远处无复发概率（DRFP）显着较差。生存曲线显示 OS、DSS、LRFP 和 DRFP 存在显着差异。人工智能疾病是一种可能在预后中发挥作用的免疫失调疾病。因此，开发人工智能评分对于以普遍的方式描述主机状态是必要的。与连续评分相比，AI 评分作为二元变量在临床环境中可能更实用。这种新颖的工具需要验证并整合到更多的肿瘤和宿主特征中。这项调查显示了这种评分的实用性，类似于 OSCC 预后中的 PBL 数据。未来的研究应该纳入已知影响结果的其他相关变量，并实施更全面的预测模型。
Oral cancer is highly stratified by tumor as opposed to host characteristics. Host peripheral blood data can impact prognosis in oral cancer. Autoimmune diseases are macroscopic reflection of host immune dysregulation. A score was created known as AI score to depict host status. AI score was useful as a prognostic tool in oral cancer and should be more widely investigated. Recent literature has highlighted the role of the host in prognosis in oral squamous cell carcinoma (OSCC). Autoimmune (AI) disease represents a macroscopic depiction of host status. The goal of this study was to predict an AI “status” and to analyze the utility of this “status” as a prognostic indicator in OSCC. From a departmental database of OSCC patients (n = 1377), 125 patients with an AI disorder were identified. PBL values were obtained and standardized for analysis. A LASSO regression model was used to determine the best predictors of AI status and an AI score was developed. The score was then analyzed across various survival endpoints. When AI score was divided into a binary variable, patients in the highest quartile had a significantly worse overall survival (OS), local recurrence-free (LRFP) and distant recurrence-free probability (DRFP). Survival curves showed significant differences for OS, DSS, LRFP, and DRFP. AI diseases are immune dysregulations that could play a role in prognosis. Therefore, development of an AI score is necessary to depict host status in a ubiquitous manner. AI score as a binary variable may be more utilitarian in a clinical setting, compared to the continuous score. This novel tool needs validation and integration into more tumor and host characteristics. This investigation showed utility of such a score, similar to PBL data in OSCC prognosis. Future studies should incorporate other relevant variables known to affect outcome and implement a more comprehensive predictive model.
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影响因子：
45.3
作者：
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通讯作者：
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影响因子：
1.4
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DOI：
10.1001/archotol.128.10.1172
发表时间：
2002-10-01
影响因子：
--
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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