The role of systemic immune inflammation index in predicting melanoma.

IF 3.5 3区 医学 Q2 ONCOLOGY
Frontiers in Oncology Pub Date : 2025-09-17 eCollection Date: 2025-01-01 DOI:10.3389/fonc.2025.1612579
Qingxiu Tao, Chunli Wang, Long Zeng, Mengjie Mao, Yingchun Lu, Chunyu Wang, Bin Liu
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引用次数: 0

Abstract

Many malignancies arise in the context of chronic infection, persistent irritation, or unresolved inflammation, and the inflammatory environment is closely associated with tumor cell proliferation and metastatic spread. The systemic immune-inflammation index (SII), calculated from peripheral lymphocyte, neutrophil, and platelet counts, has been investigated as a prognostic biomarker in several solid tumors, but its role in melanoma is not well defined. Data from the 2003-2018 cycles of the National Health and Nutrition Examination Survey (NHANES) were analyzed using multivariable logistic regression to assess the association between SII and melanoma. Subgroup analyses were conducted according to sex, age, marital status, body mass index, hypercholesterolemia, and smoking status. A cross-sectional study including 39,200 participants from eight NHANES cycles (2003-2018) was conducted, and logistic regression was applied to quantify the association between SII and melanoma. After categorizing SII into tertiles, the unadjusted model indicated that individuals in the highest tertile had a 57% higher melanoma risk compared with those in the lowest tertile (OR = 1.57; 95% CI, 1.06-2.34; p = 0.024). After adjusting for potential confounders, the highest SII tertile remained associated with a 48% increased risk (OR = 1.48; 95% CI, 1.01-2.01; p = 0.047). Higher SII levels were also significantly associated with increased risk in the hypercholesterolemia subgroup (OR = 1.33; 95% CI, 1.08-1.64; p = 0.008). These findings indicate a moderate positive association between SII and melanoma incidence, suggesting that SII may be a simple and accessible biomarker for early detection. To address the limitations of cross-sectional analysis, an external validation cohort was established at our tertiary oncology center. Between 2017 and 2018, 101 pathologically confirmed melanoma patients and 207 contemporaneous non-melanoma controls were recruited. In multivariable logistic regression, the highest SII tertile was associated with a 2.6-fold higher melanoma risk compared with the lowest tertile (OR = 2.60; 95% CI, 1.19-5.69; p = 0.017). These external data support SII as a potential indicator of melanoma risk; however, further validation in prospective cohort studies is required.

全身免疫炎症指数在黑色素瘤预测中的作用。
许多恶性肿瘤发生在慢性感染、持续刺激或未解决的炎症的背景下,炎症环境与肿瘤细胞的增殖和转移扩散密切相关。由外周淋巴细胞、中性粒细胞和血小板计数计算的系统性免疫炎症指数(SII)已被研究作为几种实体瘤的预后生物标志物,但其在黑色素瘤中的作用尚未得到很好的定义。使用多变量logistic回归分析2003-2018年国家健康与营养检查调查(NHANES)周期的数据,以评估SII与黑色素瘤之间的关联。根据性别、年龄、婚姻状况、体重指数、高胆固醇血症和吸烟状况进行亚组分析。对来自8个NHANES周期(2003-2018)的39,200名参与者进行了一项横断面研究,并应用逻辑回归来量化SII与黑色素瘤之间的关联。在将SII分类后,未经调整的模型显示,与最低分位数的个体相比,最高分位数的个体患黑色素瘤的风险高出57% (OR = 1.57; 95% CI, 1.06-2.34; p = 0.024)。在调整潜在混杂因素后,最高SII分值仍然与48%的风险增加相关(OR = 1.48; 95% CI, 1.01-2.01; p = 0.047)。高SII水平也与高胆固醇血症亚组风险增加显著相关(OR = 1.33; 95% CI, 1.08-1.64; p = 0.008)。这些发现表明SII与黑色素瘤发病率之间存在中度正相关,表明SII可能是一种简单且易于获得的早期检测生物标志物。为了解决横断面分析的局限性,我们在三级肿瘤中心建立了一个外部验证队列。在2017年至2018年期间,招募了101名病理证实的黑色素瘤患者和207名同期非黑色素瘤对照组。在多变量logistic回归中,最高SII分位数与最低分位数相比,黑色素瘤风险高2.6倍(OR = 2.60; 95% CI, 1.19-5.69; p = 0.017)。这些外部数据支持SII作为黑色素瘤风险的潜在指标;然而,需要在前瞻性队列研究中进一步验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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