炎症负荷指数与胃癌临床关系研究

doi:10.3760/cma.j.cn371439-20231017-00046

摘要/Abstract

摘要：

目的分析炎症负荷指数（IBI）与胃癌诊断及预后的关系。方法选取2017年1月1日至2021年12月31日于西安医学院第一附属医院就诊的180例胃癌患者作为胃癌组，选取同期于本院就诊的180例胃癌前疾病患者作为癌前疾病组，同期于本院体检的180例健康人群作为对照组，分析IBI在胃癌诊断中的价值。采用单因素方差分析比较3组IBI值;通过受试者操作特征（ROC）曲线确定不同指标的最佳截断值，并进一步评估IBI对胃癌的诊断价值。采用R软件包glmnet进行LASSO回归分析筛选变量，对筛选出的变量进行单因素和多因素Cox回归分析，进一步确定影响胃癌患者预后的独立危险因素。结果胃癌组、癌前疾病组和对照组患者的IBI水平分别为46.69±8.38、35.53±6.11、21.47±4.36，差异有统计学意义（F=681.45，P<0.001）。进一步两两比较发现，胃癌组高于癌前疾病组和对照组，癌前疾病组高于对照组，差异均有统计学意义（t=14.43，P<0.001;t=34.15，P<0.001;t=25.13，P<0.001）。ROC曲线分析显示，中性粒细胞与淋巴细胞比值（NLR）的最佳截断值为3.15（AUC=0.65，95%CI为0.54~0.73），特异性为92.31%，敏感性为43.97%;血小板与淋巴细胞比值（PLR）的最佳截断值为137.36（AUC=0.59，95%CI为0.51~0.68），特异性为93.66%，敏感性为34.98%;淋巴细胞与单核细胞比值（LMR）的最佳截断值为5.18（AUC=0.69，95%CI为0.58~0.76），特异性为61.29%，敏感性为73.33%;泛免疫炎症值（PIV）的最佳截断值为276.57（AUC=0.73，95%CI为0.65~0.78），特异性为78.41%，敏感性为59.77%;系统免疫炎症指数（SII）的最佳截断值为654.37（AUC=0.73，95%CI为0.66~0.78），特异性为86.32%，敏感性为62.11%;IBI的最佳截断值为49.12（AUC=0.82，95%CI为0.79~0.92），特异性为85.36%，敏感性为65.82%。LASSO回归分析显示，共有IBI、T分期、N分期、年龄、癌胚抗原（CEA）、SII、PIV、肿瘤分级8个与预后相关的变量。单因素分析结果显示，IBI（HR=4.57，95%CI为3.82~5.32，P<0.001）、T分期（HR=2.54，95%CI为1.75~3.31，P<0.001）、N分期（HR=1.69，95%CI为1.15~2.21，P=0.032）、CEA（HR=1.38，95%CI为1.06~1.75，P=0.032）、肿瘤分级（HR=1.87，95%CI为1.34~2.59，P=0.026）均为胃癌患者预后的影响因素。多因素分析结果显示，IBI（HR=3.96，95%CI为3.51~4.48，P<0.001）、T分期（HR=2.31，95%CI为1.63~3.02，P=0.023）、肿瘤分级（HR=1.49，95%CI为1.12~1.83，P=0.042）均为影响胃癌患者预后的独立危险因素。与低水平IBI胃癌患者相比，高水平IBI患者的死亡风险高达3.96倍。结论 IBI可能是胃癌诊断及预后预测的重要标志物，对胃癌的诊治具有一定的科研和临床价值。

关键词: 炎症负荷指数, 胃肿瘤, 预后

Abstract:

Objective To analyze the relationship between inflammatory burden index （IBI） and the diagnosis and prognosis of gastric cancer. Methods A total of 180 patients with gastric cancer admitted to the First Affiliated Hospital of Xi'an Medical University from January 1， 2017 to December 31， 2021 were selected as the gastric cancer group， 180 cases of gastric precancerous diseases treated in this hospital during the same period were selected as the precancerous disease group， 180 healthy individuals who underwent physical examinations in this hospital during the same period were selected as the control group. The value of IBI in the diagnosis of gastric cancer was analyzed. The IBI values of the three groups were compared by one-way analysis of variance， and the optimal cut-off values of different indicators were determined by receiver operator characteristic （ROC） curve to further evaluate the diagnostic value of IBI for gastric cancer. LASSO regression analysis was performed using the R software package glmnet to screen variables. Univariate and multivariate Cox regression analysis were performed on the screened variables to further determine the independent risk factors affecting the prognosis of patients with gastric cancer. Results The levels of IBI in gastric cancer， precancerous disease， and control group were 46.69±8.38， 35.53±6.11， and 21.47±4.36， respectively， with a statistically significant difference （F=681.45， P<0.001）. Further pairwise comparisons showed that the gastric cancer group was higher than the precancerous disease group and the control group, and the precancerous disease group was higher than the control group， with statistically significant differences（t=14.43， P<0.001; t=34.15， P<0.001; t=25.13， P<0.001）. ROC curve analysis showed that the optimal cut-off value for the neutrophil to lymphocyte ratio （NLR） was 3.15 （AUC=0.65， 95%CI： 0.54-0.73）， with a specificity of 92.31% and a sensitivity of 43.97%. The optimal cut-off value for the platelet to lymphocyte ratio （PLR） was 137.36 （AUC=0.59， 95%CI： 0.51-0.68）， with a specificity of 93.66% and a sensitivity of 34.98%. The optimal cut-off value for lymphocyte to monocyte ratio （LMR） was 5.18 （AUC=0.69， 95%CI： 0.58-0.76）， with a specificity of 61.29% and a sensitivity of 73.33%. The optimal cut-off value for panimmune inflammation value （PIV） was 276.57 （AUC=0.73， 95%CI： 0.65-0.78）， with a specificity of 78.41% and a sensitivity of 59.77%. The optimal cut-off value for the systemic immune inflammatory index （SII） was 654.37 （AUC=0.73， 95%CI： 0.66-0.78）， with a specificity of 86.32% and a sensitivity of 62.11%. The optimal cut-off value for the IBI was 49.12 （AUC=0.82， 95%CI： 0.79-0.92）， with a specificity of 85.36% and a sensitivity of 65.82%. LASSO regression analysis showed that there were 8 prognostic variables related to survival， including IBI， T stage， N stage， age， carcinoembryonic antigen（CEA）， SII， PIV， and tumor grade. The results of univariate analysis showed that IBI （HR=4.57， 95%CI： 3.82-5.32， P<0.001）， T stage （HR=2.54， 95%CI： 1.75-3.31， P<0.001）， N stage （HR=1.69， 95%CI： 1.15-2.21， P=0.032）， CEA （HR=1.38， 95%CI： 1.06-1.75， P=0.032）， and tumor grade （HR=1.87， 95%CI： 1.34-2.59， P=0.026） were prognostic factors for gastric cancer patients. The results of multivariate analysis showed that IBI （HR=3.96， 95%CI： 3.51-4.48， P<0.001）， T stage （HR=2.31， 95%CI： 1.63-3.02， P=0.023）， tumor grade （HR=1.49， 95%CI： 1.12-1.83， P=0.042） were independent risk factors affecting the prognosis of gastric cancer patients. Compared with low-level IBI gastric cancer patients， the risk of death in high-level IBI patients was up to 3.96 times. Conclusion IBI may be an important marker for diagnosis and prognosis prediction of gastric cancer， which has certain scientific and clinical value in the diagnosis and treatment of gastric cancer.

Key words: Inflammatory burden index, Stomach neoplasms, Prognosis

杨琳, 路宁, 温华, 张明鑫, 朱琳. 炎症负荷指数与胃癌临床关系研究[J]. 国际肿瘤学杂志, 2024, 51(5): 274-279.

Yang Lin, Lu Ning, Wen Hua, Zhang Mingxin, Zhu Lin. Study on the clinical relationship between inflammatory burden index and gastric cancer[J]. Journal of International Oncology, 2024, 51(5): 274-279.

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参考文献 18

[1]	Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. CA Cancer J Clin, 2023, 73(1): 17-48. DOI: 10.3322/caac.21763.
[2]	郑荣寿, 张思维, 孙可欣, 等. 2016年中国恶性肿瘤流行情况分析[J]. 中华肿瘤杂志, 2023, 45(3): 212-220. DOI: 10.3760/cma.j.cn112152-20220922-00647.
[3]	Ma S, Zhou M, Xu Y, et al. Clinical application and detection techniques of liquid biopsy in gastric cancer[J]. Mol Cancer, 2023, 22(1): 10. DOI: 10.1186/s12943-023-01715-z.
[4]	Xie H, Ruan G, Ge Y, et al. Inflammatory burden as a prognostic biomarker for cancer[J]. Clin Nutr, 2022, 41(6): 1236-1243. DOI: 10.1016/j.clnu.2022.04.019. pmid: 35504166
[5]	姜可伟. 规范全球第二大致死率疾病的诊断——《胃癌诊断标准》解读[J]. 中国卫生标准管理, 2010, 1(4): 26-28.
[6]	Wang Q, Qiao W, Zhang H, et al. Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma[J]. Front Immunol, 2022, 13: 1019638. DOI: 10.3389/fimmu.2022.1019638.
[7]	闫超, 陕飞, 李子禹. 2020年全球胃癌负担分析: 聚焦中国流行现状[J]. 中国肿瘤, 2023, 32(3): 161-170. DOI: 10.11735/j.issn. 1004-0242.2023.03.A001.
[8]	Conti CB, Agnesi S, Scaravaglio M, et al. Early gastric cancer: update on prevention, diagnosis and treatment[J]. Int J Environ Res Public Health, 2023, 20(3): 2149. DOI: 10.3390/ijerph20032149.
[9]	Lee HM, Lee HJ, Chang JE. Inflammatory cytokine: an attractive target for cancer treatment[J]. Biomedicines, 2022, 10(9): 2116. DOI: 10.3390/biomedicines10092116.
[10]	Aoyama T, Hara K, Kazama K, et al. Clinical impact of nutrition and inflammation assessment tools in gastric cancer treatment[J]. Anticancer Res, 2022, 42(11): 5167-5180. DOI: 10.21873/anticanres.16023. pmid: 36288879
[11]	Wang W, Tong Y, Sun S, et al. Predictive value of NLR and PLR in response to preoperative chemotherapy and prognosis in locally advanced gastric cancer[J]. Front Oncol, 2022, 12: 936206. DOI: 10.3389/fonc.2022.936206.
[12]	Tomás TC, Eiriz I, Vitorino M, et al. Neutrophile-to-lymphocyte, lymphocyte-to-monocyte, and platelet-to-lymphocyte ratios as prognostic and response biomarkers for resectable locally advanced gastric cancer[J]. World J Gastrointest Oncol, 2022, 14(7): 1307-1323. DOI: 10.4251/wjgo.v14.i7.1307.
[13]	Uzunoglu H, Kaya S. Does systemic immune inflammation index have predictive value in gastric cancer prognosis?[J]. North Clin Istanb, 2023, 10(1): 24-32. DOI: 10.14744/nci.2021.71324. pmid: 36910431
[14]	Guven DC, Sahin TK, Erul E, et al. The association between the pan-immune-inflammation value and cancer prognosis: a systematic review and meta-analysis[J]. Cancers (Basel), 2022, 14(11): 2675. DOI: 10.3390/cancers14112675.
[15]	Zhu M, Ma Z, Zhang X, et al. C-reactive protein and cancer risk: a pan-cancer study of prospective cohort and Mendelian randomi-zation analysis[J]. BMC Med, 2022, 20(1): 301. DOI: 10.1186/s12916-022-02506-x.
[16]	Que H, Fu Q, Lan T, et al. Tumor-associated neutrophils and neutrophil-targeted cancer therapies[J]. Biochim Biophys Acta Rev Cancer, 2022, 1877(5): 188762. DOI: 10.1016/j.bbcan. 2022.188762.
[17]	Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy[J]. Cancer Cell, 2023, 41(3): 450-465. DOI: 10.1016/j.ccell.2023.02.014.
[18]	Ding P, Wu H, Liu P, et al. The inflammatory burden index: a promising prognostic predictor in patients with locally advanced gastric cancer[J]. Clin Nutr, 2023, 42(2): 247-248. DOI: 10. 1016/j.clnu.2023.01.005. pmid: 36653262

项目	单因素Cox回归分析				多因素Cox回归分析
项目	HR值	95%CI	Wald χ²值	P值	HR值	95%CI	Wald χ²值	P值
IBI
≤49.12	1				1
>49.12	4.57	3.82~5.32	11.34	<0.001	3.96	3.51~4.48	8.57	<0.001
T分期
T_1-2	1				1
T_3-4	2.54	1.75~3.31	7.92	<0.001	2.31	1.63~3.02	5.35	0.023
N分期
N₀	1				1
N₁	1.69	1.15~2.21	4.93	0.032	1.34	0.89~1.81	2.18	0.343
年龄（岁）
≤60	1
>60	1.05	0.38~1.52	1.36	0.241
CEA（ng/ml）
≤5	1				1
>5	1.38	1.06~1.75	4.75	0.032	1.16	0.73~1.54	3.06	0.071
SII
≤654.37	1
>654.37	1.11	0.45~1.64	3.15	0.075
PIV
≤276.57	1
>276.57	1.22	0.75~1.71	1.21	0.278
肿瘤分级
高-中分化	1				1
低-未分化	1.87	1.34~2.59	5.86	0.026	1.49	1.12~1.83	4.38	0.042