血清胆碱酯酶与炎症标志物在ⅠA~ⅢA期乳腺癌预后中的作用

摘要/Abstract

摘要：

目的分析接受手术治疗的ⅠA~ⅢA期乳腺癌患者术前、术后血清胆碱酯酶（CHE）水平，探索其及外周血炎症指标在ⅠA~ⅢA期乳腺癌预后预测中的作用。方法回顾性研究2012年1月至2017年12月在徐州医科大学附属淮安医院接受手术和术后辅助治疗的152例ⅠA~ⅢA期乳腺癌患者相关血液指标。使用X-tile 3.6.1软件计算血清CHE水平和外周血炎症指标[全身免疫炎症指数（SII）和全身炎症反应指数（SIRI）]的最佳截断值。根据最佳截断值将患者分为低值组和高值组。采用Kaplan-Meier曲线和Cox回归分析评估CHE及外周血炎症指标与无瘤生存期（DFS）的相关性。采用Spearman相关系数和Wilcoxon检验评估治疗前后CHE和炎症指标的相关性和变化。通过R软件构建基于独立预后因素的列线图预测模型，并用Bootstrap法进行验证。结果治疗前后患者的CHE水平分别为8 645.0（7 251.3， 10 229.3）、9 309.0（7 801.0， 10 835.3）U/L，差异有统计学意义（Z=2.73，P=0.006）。与患者DFS相关的术后CHE（Post-CHE）、术后SII（Post-SII）和术后SIRI（Post-SIRI）的最佳截断值分别为7 773 U/L、741和0.9。单因素分析显示，肿瘤大小（≤2 cm比>2 cm且≤5 cm：HR=2.55， 95%CI为1.30~4.99，P=0.006；≤2 cm比>5 cm：HR=8.95， 95%CI为4.15~19.32，P<0.001）、阳性淋巴结数目（HR=3.84， 95%CI为2.24~6.58，P<0.001）、临床分期（Ⅰ期比Ⅱ期：HR=1.52， 95%CI为0.68~3.39，P=0.309；Ⅰ期比Ⅲ期：HR=8.12， 95%CI为3.76~17.55，P<0.001）、Ki-67表达（HR=2.19， 95%CI为1.24~3.84，P=0.007）、是否放疗（HR=2.05， 95%CI为1.19~3.53，P=0.010）、Post-CHE（HR=6.81， 95%CI为3.94~11.76，P<0.001）、术前中性粒细胞/淋巴细胞比值（Pre-NLR）（HR=1.11， 95%CI为1.02~1.21，P=0.014）、Post-NLR（HR=5.23， 95%CI为2.78~9.85，P<0.001）、Pre-血小板/淋巴细胞比值（PLR）（HR=2.08， 95%CI为1.01~4.26，P=0.046）、Post-PLR（HR=7.11， 95%CI为3.78~13.37，P<0.001）、Pre-淋巴细胞/单核细胞比值（LMR）（HR=0.37， 95%CI为0.20~0.66，P<0.001）、Post-LMR（HR=0.23， 95%CI为0.13~0.41，P<0.001）、Pre-SII（HR=1.81， 95%CI为1.05~3.12，P=0.033）、Post-SII（HR=6.12， 95%CI为3.48~10.76，P<0.001）、Pre-SIRI（HR=2.12， 95%CI为1.24~3.63，P=0.006）、Post-SIRI（HR=4.93， 95%CI为2.87~8.48，P<0.001）与ⅠA~ⅢA期乳腺癌患者DFS相关。多因素分析显示，肿瘤大小（≤2 cm比>2 cm且≤5 cm：HR=2.86， 95%CI为1.41~5.78，P=0.003；≤2 cm比>5 cm：HR=3.72， 95%CI为1.50~9.26，P=0.005）、阳性淋巴结数目（HR=4.66， 95%CI为2.28~9.54，P<0.001）、Ki-67表达（HR=2.13， 95%CI为1.15~3.94，P=0.016）、Post-CHE（HR=0.18， 95%CI为0.10~0.33，P<0.001）、Post-SII（HR=2.71， 95%CI为1.39~5.29，P=0.004）、Post-SIRI（HR=3.77， 95%CI为1.93~7.36，P<0.001）是ⅠA~ⅢA期乳腺癌患者DFS的独立影响因素。Kaplan-Meier生存曲线分析显示， Ki-67<30%组患者中位DFS未达到， Ki-67≥30%组的中位DFS为89.0个月， 3、5年DFS率分别为84.9 %比75.9%、80.8%比64.3%，差异具有统计学意义（χ²=7.65，P=0.006）；肿瘤大小≤2 cm组患者中位DFS未达到， 2 cm<肿瘤大小≤5 cm组中位DFS为93.5个月，肿瘤大小>5 cm组中位DFS为26.3个月， 3、5年DFS率分别为95.5 %比74.6%比42.1%、86.3%比68.6%比25.3%，差异具有统计学意义（χ²=40.46，P<0.001）；阳性淋巴结数目<4个组患者中位DFS未达到，阳性淋巴结数目≥4个组中位DFS为30.7个月， 3、5年DFS率分别为87.9%比46.4%、81.4%比28.6%，差异具有统计学意义（χ²=47.34，P<0.001）；Post-CHE<7 773U/L组患者中位DFS为47.3个月， Post-CHE≥7 773U/L组中位DFS未达到， 3、5年DFS率分别为52.8 %比88.6%、27.8%比81.2%，差异具有统计学意义（χ²=62.17，P<0.001）；Post-SII<741组患者中位DFS未达到， Post-SII≥741组中位DFS为30.5个月， 3、5年DFS率分别为88.1%比38.5%、80.1%比30.8%，差异具有统计学意义（χ²=50.78，P<0.001）；Post-SIRI<0.9组患者中位DFS未达到， Post-SIRI≥0.9组中位DFS为33.3个月， 3、5年DFS率分别为93.5 %比46.7%、84.9%比39.9%，差异具有统计学意义（χ²=40.67，P<0.001）。Spearman相关性分析发现， Post-CHE与Post-SII无相关性（r=-0.111，P=0.175）， Post-CHE与Post-SIRI呈负相关（r=-0.228，P=0.005）。治疗后CHE较术前升高，且治疗后CHE升高组患者的中位DFS未达到， CHE降低组患者的中位DFS为61.8个月，差异有统计学意义（χ²=25.67，P<0.001）。基于独立预后因素构建的列线图具有良好的预测性能，一致性指数为0.893。结论治疗后患者血清CHE水平显著升高，术后血清CHE联合SII及SIRI可有效预测ⅠA~ⅢA期乳腺癌患者的DFS，且治疗后CHE升高患者预后较好，基于独立预后因素构建的列线图对乳腺癌患者的DFS具有良好的预测性能。

关键词:乳腺肿瘤,胆碱酯酶,全身免疫炎症指数,全身炎症反应指数,预后,列线图

Abstract:

ObjectiveTo analyze the preoperative and postoperative serum cholinesterase （CHE）levels in patients with stage ⅠA-ⅢA breast cancer who underwent surgical treatment， and to explore the roles of them and peripheral blood inflammatory markers in the prognostic prediction of stage ⅠA-ⅢA breast cancer.MethodsThe relevant blood indicators of 152 patients with stage ⅠA-ⅢA breast cancer who underwent surgery and postoperative adjuvant therapy from January 2012 to December 2017 at Affiliated Huai'an Hospital of Xuzhou Medical University were retrospectively studied. The optimal cut-off values of serum CHE levels and peripheral blood inflammatory markers [systemic immune-inflammation index （SII）and systemic inflammatory response index （SIRI）] were calculated using X-tile 3.6.1 software. Patients were categorized into low and high value groups based on the optimal cutoff values. Kaplan-Meier curves and Cox regression analysis were used to assess the correlation between CHE and peripheral blood inflammation indexes and disease-free survival （DFS）. Spearman correlation coefficient and Wilcoxon test were used to assess the correlation and changes of CHE and inflammation indexes before and after treatment. In addition to this， a nomogram prediction model was conscturcted based on independent prognostic factors by R software， which was validated by Bootstrap method.ResultsThe CHE levels of patients before and after treatment was 8 645.0 （7 251.3， 10 229.3）and 9 309.0 （7 801.0， 10 835.3）U/L， respectively， with a statistically significant difference （Z=2.73，P=0.006）.The optimal cut-off values for postoperative CHE （Post-CHE）， postoperative SII （Post-SII）， and postoperative SIRI （Post-SIRI）associated with patients' DFS， being 7 773 U/L， 741， and 0.9， respectively. Univariate analysis showed that tumor size （≤2 cmvs.>2 cm and ≤5 cm：HR=2.55， 95%CI：1.30-4.99，P=0.006； ≤2 cmvs.>5 cm：HR=8.95， 95%CI：4.15-19.32，P<0.001）， number of positive lymph nodes （HR=3.84， 95%CI：2.24-6.58，P<0.001）， clinical stage （stage Ⅰvs.stage Ⅱ：HR=1.52， 95%CI：0.68-3.39，P=0.309， stage Ⅰvs.stage Ⅲ：HR=8.12， 95%CI：3.76-17.55，P<0.001）， Ki-67 expression （HR=2.19， 95%CI：1.24-3.84，P=0.007）， whether radiotherapy （HR=2.05， 95%CI：1.19-3.53，P=0.010）， Post-CHE （HR=6.81， 95%CI：3.94-11.76，P<0.001）， Pre-neutrophil to lymphocyte ratio （NLR）（HR=1.11， 95%CI：1.02-1.21，P=0.014）， Post-NLR （HR=5.23， 95%CI：2.78-9.85，P<0.001）， Pre-platelet to lymphocyte ratio （PLR）（HR=2.08， 95%CI：1.01-4.26，P=0.046）， Post-PLR （HR=7.11， 95%CI：3.78-13.37，P<0.001）， Pre-lymphocyte to monocyte ratio （LMR）（HR=0.37， 95%CI：0.20-0.66，P<0.001）， Post-LMR （HR=0.23， 95%CI：0.13-0.41，P<0.001）， Pre-SII （HR=1.81， 95%CI：1.05-3.12，P=0.033）， Post-SII （HR=6.12， 95%CI：3.48-10.76，P<0.001）， Pre-SIRI （HR=2.12， 95%CI：1.24-3.63，P=0.006）， and Post-SIRI （HR=4.93， 95%CI：2.87-8.48，P<0.001）were associated with DFS in patients with stage ⅠA-ⅢA breast cancer. Multivariate analysis showed that tumor size （≤2 cmvs.>2 cm and ≤5 cm：HR=2.86， 95%CI：1.41-5.78，P=0.003； ≤2 cmvs.>5 cm：HR=3.72， 95%CI：1.50-9.26，P=0.005）， number of positive lymph nodes （HR=4.66， 95%CI：2.28-9.54，P<0.001）， Ki-67 expression （HR=2.13， 95%CI：1.15-3.94，P=0.016）， Post-CHE （HR=0.18， 95%CI：0.10-0.33，P<0.001）， Post-SII （HR=2.71， 95%CI：1.39-5.29，P=0.004）， and Post-SIRI （HR=3.77， 95%CI：1.93-7.36，P<0.001）were independent influencing factors for DFS in patients with stage ⅠA-ⅢA breast cancer. Kaplan-Meier survival curve analysis showed that the median DFS of patients in the Ki-67<30% group was not reached， and the median DFS of patients in the Ki-67≥30% group was 89.0 months， and the 3- and 5-year DFS rates were 84.9%vs.75.9% and 80.8%vs.64.3%， respectively， with a statistically significant difference （χ²=7.65，P=0.006）； the median DFS of patients in the tumor size≤2 cm group was not reached， the median DFS of the 2 cm5 cm group was 26.3 months， and the 3- and 5-year DFS rates were 95.5%vs.74.6%vs.42.1%， 86.3%vs.68.6%vs.25.3%， with a statistically significant difference （χ²=40.46，P<0.001）； the median DFS of patients in the group with the number of positive lymph nodes<4 was not reached， and the median DFS of the group with the number of positive lymph nodes≥4 was 30.7 months， and the 3- and 5-year DFS rates were 87.9%vs.46.4% and 81.4%vs.28.6%， respectively， with a statistically significant difference （χ²= 47.34，P<0.001）； the median DFS of patients in the Post-CHE<7 773 U/L group was 47.3 months， and the median DFS of patients in the Post-CHE≥7 773 U/L group was not reached， and the 3- and 5-year DFS rates were 52.8 %vs.88.6% and 27.8%vs.81.2%， respectively， with a statistically significant difference （χ²=62.17，P<0.001）； the median DFS was not achieved in patients in the Post-SII<741 group， and the median DFS was 30.5 months in the Post-SII≥741 group， with 3- and 5-year DFS rates of 88.1%vs.38.5% and 80.1%vs.30.8%， respectively， with a statistically significant difference （χ²=50.78，P<0.001）； the median DFS of patients in Post-SIRI<0.9 group was not reached， the median DFS of Post-SIRI≥0.9 group was 33.3 months， and the 3- and 5-year DFS rates were 93.5%vs.46.7% and 84.9%vs.39.9%， respectively， with a statistically significant difference （χ²=40.67，P<0.001）. Spearman correlation analysis revealed that Post-CHE was not correlated with Post-SII （r=-0.111，P=0.175）， and Post-CHE was negatively correlated with Post-SIRI （r=-0.228，P=0.005）. Post-treatment CHE was elevated compared to preoperative and the median DFS was not reached in patients with elevated CHE group and 61.8 months in patients with reduced CHE group after treatment， with a statistically significant difference （χ²=25.67，P<0.001）. The nomogram based on independent prognostic factors had good predictive performance， with a C-index of 0.893.ConclusionThe serum CHE level exhibited a significant increase following treatment. Postoperative serum CHE combined with SII and SIRI can effectively predict DFS in patients with stage ⅠA-ⅢA breast cancer， and the prognosis of patients with elevated CHE after treatment is better. The nomogram constructed based on independent prognostic factors has good predictive performance for DFS in breast cancer patients.

Key words:Breast neoplasms,Cholinesterase,Systemic immune-inflammatory index,Systemic inflammatory response index,Prognosis,Nomogram

陈波光, 王苏贵, 张永杰. 血清胆碱酯酶与炎症标志物在ⅠA~ⅢA期乳腺癌预后中的作用[J]. 国际肿瘤学杂志, 2024, 51(2): 73-82.

Chen Boguang, Wang Sugui, Zhang Yongjie. Role of serum cholinesterase and inflammatory markers in the prognosis of stage ⅠA -ⅢA breast cancer[J]. Journal of International Oncology, 2024, 51(2): 73-82.

图/表7

表1

影响152例乳腺癌患者DFS的Cox回归分析"

项目	例数（%）	单因素分析			多因素分析
项目	例数（%）	HR值	95%CI	P值	HR值	95%CI	P值
年龄（岁）
<50	70（46.1）	1
≥50	82（53.9）	0.65	0.38~1.12	0.118
手术方式
保乳	13（8.6）	1
改良根治术	139（91.4）	0.98	0.39~2.45	0.960
肿瘤大小（cm）
≤2	66（43.4）	1			1
>2且≤5	67（44.1）	2.55	1.30~4.99	0.006	2.86	1.41~5.78	0.003
>5	19（12.5）	8.95	4.15~19.32	<0.001	3.72	1.50~9.26	0.005
阳性淋巴结数目（个）
<4	28（18.4）	1			1
≥4	124（81.6）	3.84	2.24~6.58	<0.001	4.66	2.28~9.54	<0.001
临床分期
Ⅰ	46（30.3）	1			1
Ⅱ	72（47.3）	1.52	0.68~3.39	0.309	0.52	0.14~1.92	0.325
Ⅲ	34（22.4）	8.12	3.76~17.55	<0.001	1.27	0.24~6.79	0.777
免疫分型
Luminal A	55（36.2）	1
Luminal B	15（9.9）	1.71	0.71~4.13	0.233
三阴性	19（12.5）	1.44	0.60~3.47	0.420
人表皮生长因子受体2过表达	63（41.4）	1.32	0.71~2.48	0.381
Ki-67表达
<30%	73（48.0）	1			1
≥30%	79（52.0）	2.19	1.24~3.84	0.007	2.13	1.15~3.94	0.016
病理类型
浸润性导管癌	132（86.8）	1
其他	20（13.2）	0.63	0.25~1.57	0.319
是否放疗
是	109（71.7）	1			1
否	43（28.3）	2.05	1.19~3.53	0.010	1.01	0.45~2.26	0.976
Pre-CHE（U/L）
<6 975	124（81.6）	1
≥6 975	28（18.4）	1.80	0.97~3.31	0.061
Post-CHE（U/L）
<7 773	116（76.3）	1			1
≥7 773	36（23.7）	6.81	3.94~11.76	<0.001	0.18	0.10~0.33	<0.001
Pre-NLR
<1.5	34（22.4）	1			1
≥1.5	118（77.6）	1.11	1.02~1.21	0.014	1.25	0.41~3.85	0.696
Post-NLR
<3.8	136（89.5）	1			1
≥3.8	16（10.5）	5.23	2.78~9.85	<0.001	0.69	0.29~1.61	0.390
Pre-PLR
<207.0	136（89.5）	1			1
≥207.0	16（10.5）	2.08	1.01~4.26	0.046	1.21	0.44~3.36	0.717
Post-PLR
<270.8	136（89.5）	1			1
≥270.8	16（10.5）	7.11	3.78~13.37	<0.001	1.25	0.45~3.42	0.671
Pre-LMR
<6.3	83（54.6）	1			1
≥6.3	69（45.4）	0.37	0.20~0.66	<0.001	0.53	0.23~1.21	0.129
Post-LMR
<3.1	22（14.5）	1			1
≥3.1	130（85.5）	0.23	0.13~0.41	<0.001	0.75	0.34~1.65	0.471
Pre-SII
<479	79（52.0）	1			1
≥479	73（48.0）	1.81	1.05~3.12	0.033	1.47	0.49~4.48	0.494
Post-SII
<741	126（82.9）	1			1
≥741	26（17.1）	6.12	3.48~10.76	<0.001	2.71	1.39~5.29	0.004
Pre-SIRI
<0.7	95（62.5）	1			1
≥0.7	57（37.5）	2.12	1.24~3.63	0.006	0.72	0.35~1.45	0.353
Post-SIRI
<0.9	107（70.4）	1			1
≥0.9	45（29.6）	4.93	2.87~8.48	<0.001	3.77	1.93~7.36	<0.001
注：DFS为无瘤生存期；Pre-为术前；Post-为术后；CHE为胆碱酯酶；NLR为中性粒细胞/淋巴细胞比值；PLR为血小板/淋巴细胞比值；LMR为淋巴细胞/单核细胞比值；SII为全身免疫炎症指数；SIRI为全身炎症反应指数

表1

图1

图2

项目	治疗前	治疗后	Z值	P值
CHE（U/L）	8 645.0（7 251.3，10 229.3）	9 309.0（7 801.0，10 835.3）	2.73	0.006
SII	475.1（322.2，707.7）	475.2（358.4，662.7）	0.17	0.869
SIRI	0.6（0.4，0.9）	0.7（0.4，1.0）	0.67	0.503

表2

图3

图4

图5

参考文献29

[1]	Cao W, Chen HD, Yu YW, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020[J].Chin Med J (Engl),2021,134(7): 783-791. DOI:10.1097/CM9.0000000000001474.
[2]	Lei SY, Zheng RS, Zhang SW, et al. Breast cancer incidence and mortality in women in China: temporal trends and projections to 2030[J].Cancer Biol Med,2021,18(3): 900-909. DOI:10.20892/j.issn.2095-3941.2020.0523.
[3]	Kim MY. Breast cancer metastasis[J].Adv Exp Med Biol,2021,1187: 183-204. DOI:10.1007/978-981-32-9620-6_9. pmid:33983579
[4]	Mitsunaga S, Kinoshita T, Hasebe T, et al. Low serum level of cholinesterase at recurrence of pancreatic cancer is a poor prognostic factor and relates to systemic disorder and nerve plexus invasion[J].Pancreas,2008,36(3): 241-248. DOI:10.1097/MPA.0b013e31815b6b2b.
[5]	Zhao YJ, Wang XY, Wang T, et al. Acetylcholinesterase, a key prognostic predictor for hepatocellular carcinoma, suppresses cell growth and induces chemosensitization[J].Hepatology,2011,53(2): 493-503. DOI:10.1002/hep.24079. pmid:21274871
[6]	Calaf GM. Role of organophosphorous pesticides and acetylcholine in breast carcinogenesis[J].Semin Cancer Biol,2021,76: 206-217. DOI:10.1016/j.semcancer.2021.03.016. pmid:33766648
[7]	Pérez-Aguilar B, Marquardt JU, Muñoz-Delgado E, et al. Changes in the acetylcholinesterase enzymatic activity in tumor development and progression[J].Cancers (Basel),2023,15(18): 4629. DOI:10.3390/cancers15184629.
[8]	Gao HQ, Wan YZ, Fan XY, et al. The role of cholinesterase in differential diagnosis between gastric cancer and benign gastric diseases[J].Clin Lab,2021,67(2): 200525. DOI:10.7754/Clin.Lab.2020.200525.
[9]	中国抗癌协会乳腺癌专业委员会. 中国乳腺癌筛查与早期诊断指南[J].中国癌症杂志,2022,32(4): 363-372. DOI:10.19401/j.cnki.1007-3639.2022.04.010.
[10]	Xu YL, Li FY, Ndikuryayo F, et al. Cholinesterases and engineered mutants for the detection of organophosphorus pesticide residues[J].Sensors (Basel),2018,18(12): 4281. DOI:10.3390/s18124281.
[11]	Silman I. The multiple biological roles of the cholinesterases[J].Prog Biophys Mol Biol,2021,162: 41-56. DOI:10.1016/j.pbiomolbio.2020.12.001.
[12]	Yu HF, Xia HW, Tang QL, et al. Acetylcholine acts through M3 muscarinic receptor to activate the EGFR signaling and promotes gastric cancer cell proliferation[J].Sci Rep,2017,7: 40802. DOI:10.1038/srep40802. pmid:28102288
[13]	Schweitzer N, Kirstein MM, Kratzel AM, et al. Second-line chemotherapy in biliary tract cancer: outcome and prognostic factors[J].Liver Int,2019,39(5): 914-923. DOI:10.1111/liv.14063. pmid:30716200
[14]	Jiang C, Lu YB, Zhang SY, et al. Systemic Immune-Inflammation index is superior to neutrophil to lymphocyte ratio in prognostic assessment of breast cancer patients undergoing neoadjuvant chemotherapy[J].Biomed Res Int,2020,2020: 7961568. DOI:10.1155/2020/7961568.
[15]	Tang R, Deng JP, Zhang L, et al. Prognostic significance of the skeletal muscle index and systemic inflammatory index in patients with lymph node-positive breast cancer after radical mastectomy[J].BMC Cancer,2022,22(1): 234. DOI:10.1186/s12885-022-09312-x. pmid:35241010
[16]	刘永红, 薛玲博, 白杨, 等. 治疗前全身炎症反应指数对乳腺癌新辅助化疗病理完全缓解的预测价值[J].国际肿瘤学杂志,2022,49(4): 210-215. DOI:10.3760/cma.j.cn371439-20210813-00037.
[17]	Dong J, Sun QQ, Pan YY, et al. Pretreatment systemic inflammation response index is predictive of pathological complete response in patients with breast cancer receiving neoadjuvant chemotherapy[J].BMC Cancer,2021,21(1): 700. DOI:10.1186/s12885-021-08458-4. pmid:34126950
[18]	Hua X, Long ZQ, Huang X, et al. The preoperative systemic inflammation response index (SIRI) independently predicts survival in postmenopausal women with breast cancer[J].Curr Probl Cancer,2020,44(4): 100560. DOI:10.1016/j.currproblcancer.2020.100560.
[19]	Halder N, Lal G. Cholinergic system and its therapeutic importance in inflammation and autoimmunity[J].Front Immunol,2021,12: 660342. DOI:10.3389/fimmu.2021.660342.
[20]	Hubbard RE, O'Mahony MS, Calver BL, et al. Plasma esterases and inflammation in ageing and frailty[J].Eur J Clin Pharmacol,2008,64(9): 895-900. DOI:10.1007/s00228-008-0499-1. pmid:18506436
[21]	徐涵, 杨燕, 吴穷. 乳腺癌药物治疗致肝损害的研究现状[J].癌症进展,2018,16(1): 22-24, 31. DOI:10.11877/j.issn.1672-1535.2018.16.01.06.
[22]	Liu G, Kong X, Dai Q, et al. Clinical features and prognoses of patients with breast cancer who underwent surgery[J].JAMA Network Open,2023,6(8): e2331078. DOI:10.1001/jamanetworkopen.2023.31078.
[23]	Nielsen TO, Leung SCY, Rimm DL, et al. Assessment of Ki67 in breast cancer: updated recommendations from the international Ki-67 in breast cancer working group[J].J Natl Cancer Inst,2021,113(7): 808-819. DOI:10.1093/jnci/djaa201.
[24]	Li W, Lu N, Chen C, et al. Identifying the optimal cutoff point of Ki-67 in breast cancer: a single-center experience[J].J Int Med Res,2023,51(8): 03000605231195468. DOI:10.1177/03000605231195468.
[25]	Zhang Y, Zhao J, Wang Y, et al. Changes of tumor markers in patients with breast cancer during postoperative adjuvant chemotherapy[J].Disease Markers,2022,2022: 1-14. DOI:10.1155/2022/7739777.
[26]	Cheng HW, Wang T, Yu GC, et al. Prognostic role of the systemic immune-inflammation index and pan-immune inflammation value for outcomes of breast cancer: a systematic review and meta-analysis[J].Eur Rev Med Pharmacol Sci,2024,28: 180-190. DOI:10.26355/eurrev_202401_34903.
[27]	Dong J, Sun Q, Pan Y, et al. Pretreatment systemic inflammation response index is predictive of pathological complete response in patients with breast cancer receiving neoadjuvant chemotherapy[J].BMC Cancer,2021,21(1): 700. DOI:10.1186/s12885-021-08458-4. pmid:34126950
[28]	张倩, 彭帅, 王晓敏, 等. T₁期乳腺癌的临床病理特征及预后危险因素分析[J].中国普通外科杂志,2023,32(5): 761-770. DOI:10.7659/j.issn.1005-6947.2023.05.016.
[29]	林文华, 王文文, 杨少玲, 等. 基于术前超声及炎症指标的列线图预测早期乳腺癌腋窝高淋巴结负荷的价值[J].中华超声影像学杂志,2023,32(4): 339-347. DOI:10.3760/cma.j.cn131148-20220915-00630.