Predictive value of preoperative blood inflammatory markers for recurrence risk of basal cell carcinoma

doi:10.11958/20240867

Abstract

Abstract:

Objective To investigate the predictive value of preoperative blood inflammatory markers for the recurrence risk in patients with basal cell carcinoma (BCC). Methods A total of 225 patients with BCC were divided into the high-risk recurrence group (155 cases) and the low-risk recurrence group (70 cases). General information and preoperative hematological indicators were collected in the two groups of patients. The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammation marker (SIM) and platelet-to-lymphocyte ratio (PLR) were calculated. Receiver operating characteristic (ROC) curves were used to determine the predictive value of hematological markers with statistically significant differences between the two groups for BCC recurrence and to establish optimal cutoff values. Univariate and multivariate Logistic regression analyses were conducted to identify factors influencing BCC recurrence. A multivariate Logistic regression model was established to predict the recurrence risk of BCC. Area under the curve (AUC) and the Hosmer-Lemeshow test were used to evaluate the prediction efficiency and goodness-of-fit of the model. Results ROC analysis identified that optimal cutoff values for LMR and SIM were 5.12 and 0.86, respectively. Univariate Logistic regression analysis showed that LMR, SIM, ulceration at the primary tumor site, UV exposure and tumor maximum diameter were factors influencing BCC recurrence. Multivariate Logistic regression revealed that SIM ≥ 0.86, tumor maximum diameter ≥ 2.0 cm and UV exposure were risk factors for BCC recurrence, while LMR ≥ 5.12 had a protective effect. The Logistic prediction model for BCC recurrence risk was Logit (P) = -1.598 - 1.517 × LMR + 1.323 × SIM + 2.406 × UV exposure + 3.465 × tumor maximum diameter, with good model fit (P = 0.725) and an AUC of 0.869 (95% CI: 0.822-0.917) for predicting BCC recurrence risk. Conclusion Monitoring preoperative LMR and SIM levels can assist in assessing the risk of recurrence in BCC patients and provide important guidance for clinical decision-making.

Key words: carcinoma, basal cell, neoplasm recurrence, local, ROC curve, Logistic models, lymphocyte-to-monocyte ratio, systemic inflammatory marker

CLC Number:

R739.5

Figures/Tables 5

References 19

[1]	SEIDL-PHILIPP M, FRISCHHUT N, HÖLLWEGER N, et al. Known and new facts on basal cell carcinoma[J]. J Dtsch Dermatol Ges, 2021, 19(7):1021-1041. doi:10.1111/ddg.14580.
[2]	MARZUKA A G, BOOK S E. Basal cell carcinoma:pathogenesis,epidemiology,clinical features,diagnosis,histopathology,and management[J]. Yale J Biol Med, 2015, 88(2):167-179.
[3]	BASSET-SEGUIN N, HERMS F. Update in the management of basal cell carcinoma[J]. Acta Derm Venereol, 2020, 100(11):adv00140. doi:10.2340/00015555-3495.
[4]	ULUDAG S S, SANLI A N, ZENGIN A K, et al. Systemic inflammatory biomarkers as surrogate markers for stage in colon cancer[J]. Am Surg, 2022, 88(6):1256-1262. doi:10.1177/0003134821995059.
[5]	NØST T H, ALCALA K, URBAROVA I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank[J]. Eur J Epidemiol, 2021, 36(8):841-848. doi:10.1007/s10654-021-00752-6.
[6]	NISHIJIMA T F, DEAL A M, LUND J L, et al. Inflammatory markers and overall survival in older adults with cancer[J]. J Geriatr Oncol, 2019, 10(2):279-284. doi:10.1016/j.jgo.2018.08.004.
[7]	WANG Y, ZHANG H, YANG Y, et al. Prognostic value of peripheral inflammatory markers in preoperative mucosal melanoma:a multicenter retrospective study[J]. Front Oncol, 2019, 9:995. doi:10.3389/fonc.2019.00995.
[8]	ZHU M, CHEN L, KONG X, et al. The systemic inflammation response index as an independent predictor of survival in breast cancer patients:a retrospective study[J]. Front Mol Biosci, 2022, 9:856064. doi:10.3389/fmolb.2022.856064.
[9]	ZHOU S, YUAN H, WANG J, et al. Prognostic value of systemic inflammatory marker in patients with head and neck squamous cell carcinoma undergoing surgical resection[J]. Future Oncol, 2020, 16(10):559-571. doi:10.2217/fon-2020-0010.
[10]	ZHANG R, HU C, ZHANG J, et al. Prognostic significance of inflammatory and nutritional markers in perioperative period for patients with advanced gastric cancer[J]. BMC Cancer, 2023, 23(1):5. doi:10.1186/s12885-022-10479-6.
[11]	刘忠, 刘聪, 雷丽华, 等. 基底细胞癌复发风险水平分析[J]. 中国实用医药, 2021, 16(3):11-13.
	LIU Z, LIU C, LEI L H, et al. Analysis of the risk level of recurrence in basal cell carcinoma[J]. Practical Medicine in China, 2021, 16(3):11-13. doi:10.14163/j.cnki.11-5547/r.2021.03.004.
[12]	CHAN J C, CHAN D L, DIAKOS C I, et al. The lymphocyte-to-monocyte ratio is a superior predictor of overall survival in comparison to established biomarkers of resectable colorectal cancer[J]. Ann Surg, 2017, 265(3):539-546. doi:10.1097/SLA.0000000000001743.
[13]	SHAU H Y, KIM A. Suppression of lymphokine-activated killer induction by neutrophils[J]. J Immunol, 1988, 141(12):4395-4402.
[14]	MANTOVANI A, ALLAVENA P, SICA A, et al. Cancer-related inflammation[J]. Nature, 2008, 454(7203):436-444. doi:10.1038/nature07205.
[15]	YILMAZ H, CINAR N B, AVCI I E, et al. The systemic inflammation response index: an independent predictive factor for survival outcomes of bladder cancer stronger than other inflammatory markers[J]. Urol Oncol, 2023, 41(5):256.e1-256.e8. doi:10.1016/j.urolonc.2022.11.011.
[16]	ARMSTRONG B K, KRICKER A. The epidemiology of UV induced skin cancer[J]. J Photochem Photobiol B, 2001, 63(1/3):8-18. doi:10.1016/s1011-1344(01)00198-1.
[17]	TENG Y, YU Y, LI S, et al. Ultraviolet radiation and basal cell carcinoma:an environmental perspective[J]. Front Public Health, 2021, 9:666528. doi:10.3389/fpubh.2021.666528.
[18]	SITUM M, BULJAN M, BULAT V, et al. The role of UV radiation in the development of basal cell carcinoma[J]. Coll Antropol, 2008, 32 Suppl 2:167-170.
[19]	MORGAN F C, RUIZ E S, KARIA P S, et al. Factors predictive of recurrence,metastasis,and death from primary basal cell carcinoma 2 cm or larger in diameter[J]. J Am Acad Dermatol, 2020, 83(3):832-838. doi:10.1016/j.jaad.2019.09.075.

组别		n	性别（男/女）		年龄			紫外线暴露
组别		n	性别（男/女）		≤60岁	>60岁		紫外线暴露
低危组		70	23（32.9）/47（76.1）		17（24.3）	53（75.7）		55（78.6）
高危组		155	52（33.5）/103（66.5）		27（17.4）	128（82.6）		140（90.3）
χ²			0.010		1.445			5.763^＊
组别			肿瘤最大直径		溃疡			NLR
组别			<2.0 cm	≥2.0 cm	溃疡			NLR
低危组			67（95.7）	3（4.2）	5（7.1）			1.76（1.35，2.19）
高危组			107（69.0）	48（31.0）	35（22.6）			1.83（1.46，2.58）
χ²或Z			19.585^＊＊		7.862^＊＊			1.224
组别	LMR			SIM			PLR
低危组	5.44（4.84，6.84）			0.68（0.48，0.77）			112.97（95.8，148.8）
高危组	4.39（3.43，4.89）			0.87（0.56，1.11）			122.29（92.3，158.1）
χ²或Z	6.682^＊＊			4.515^＊＊			0.809

组别		n	性别（男/女）		年龄			紫外线暴露
组别		n	性别（男/女）		≤60岁	>60岁		紫外线暴露
低危组		70	23（32.9）/47（76.1）		17（24.3）	53（75.7）		55（78.6）
高危组		155	52（33.5）/103（66.5）		27（17.4）	128（82.6）		140（90.3）
χ²			0.010		1.445			5.763^＊
组别			肿瘤最大直径		溃疡			NLR
组别			<2.0 cm	≥2.0 cm	溃疡			NLR
低危组			67（95.7）	3（4.2）	5（7.1）			1.76（1.35，2.19）
高危组			107（69.0）	48（31.0）	35（22.6）			1.83（1.46，2.58）
χ²或Z			19.585^＊＊		7.862^＊＊			1.224
组别	LMR			SIM			PLR
低危组	5.44（4.84，6.84）			0.68（0.48，0.77）			112.97（95.8，148.8）
高危组	4.39（3.43，4.89）			0.87（0.56，1.11）			122.29（92.3，158.1）
χ²或Z	6.682^＊＊			4.515^＊＊			0.809

预测因子	截断值	AUC	95%CI	敏感度	特异度
LMR	5.12	0.778	0.710~0.847	0.774	0.729
SIM	0.86	0.688	0.615~0.761	0.606	0.829
预测模型	-	0.869	0.822~0.917	0.852	0.757

预测因子	截断值	AUC	95%CI	敏感度	特异度
LMR	5.12	0.778	0.710~0.847	0.774	0.729
SIM	0.86	0.688	0.615~0.761	0.606	0.829
预测模型	-	0.869	0.822~0.917	0.852	0.757

变量	β	SE	Wald χ²	P	OR（95%CI）
溃疡	1.333	0.502	7.041	0.008	3.792（1.417~10.148）
紫外线暴露	0.934	0.398	5.502	0.019	2.545（1.166~5.557）
肿瘤最大直径	2.304	0.615	14.033	<0.001	10.019（3.000~33.454）
LMR	-2.220	0.330	45.136	<0.001	0.109（0.057~0.208）
SIM	2.008	0.357	31.596	<0.001	7.448（3.698~15.001）