Liu QW, Liu L, Hu JX, Hou JQ, He WB, Shu YS, Wang XL. Nomogram based on a novel nutritional immune-inflammatory status score to predict postoperative outcomes in esophageal squamous cell carcinoma. World J Gastroenterol 2025; 31(4): 101749 [PMID: 39877711 DOI: 10.3748/wjg.v31.i4.101749]
Corresponding Author of This Article
Xiao-Lin Wang, Department of Thoracic Surgery, Northern Jiangsu People's Hospital, No. 98 Nantong West Road, Yangzhou 225000, Jiangsu Province, China. 18051063909@yzu.edu.cn
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Qing-Wen Liu, Jia-Qi Hou, Department of Graduate School, Dalian Medical University, Dalian 116000, Liaoning Province, China
Lin Liu, Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214000, Jiangsu Province, China
Jun-Xi Hu, Wen-Bo He, Clinical Medical College, Yangzhou University, Yangzhou 225000, Jiangsu Province, China
Yu-Sheng Shu, Xiao-Lin Wang, Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225000, Jiangsu Province, China
Co-corresponding authors: Yu-Sheng Shu and Xiao-Lin Wang.
Author contributions: Liu QW and Liu L participated in the conception and design of the study and were involved in the acquisition, analysis, or interpretation of data; Liu QW and Liu L, who contributed equally to this paper, are co-first authors of the paper; Liu QW wrote the manuscript; Shu YS and Wang XL, the co-corresponding authors of the study, oversaw the design, analysis, and review of the research; Wang XL is the main contact person for this paper; all authors participated in the collection and review of the data; all authors were responsible for the decision to submit the manuscript for publication.
Supported by Jiangsu Provincial Health Commission Research Project on Elderly Health, No. LKZ2022019; Yangzhou Social Development and Clinical Frontier Technology Project, No. YZ2023084; and Yangzhou Innovation Capability Building Design Plan Project, No. YZ2022168.
Institutional review board statement: The Ethical Review Board of Northern Jiangsu People's Hospital approved the present study (Approval No. 2024ky317).
Informed consent statement: The need for patient consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: Data are available on request from the authors (18051063909@yzu.edu.cn).
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Lin Wang, Department of Thoracic Surgery, Northern Jiangsu People's Hospital, No. 98 Nantong West Road, Yangzhou 225000, Jiangsu Province, China. 18051063909@yzu.edu.cn
Received: September 25, 2024 Revised: November 13, 2024 Accepted: December 6, 2024 Published online: January 28, 2025 Processing time: 96 Days and 0.3 Hours
Abstract
BACKGROUND
The relationship between patient nutritional, immune, and inflammatory status is linked to tumor progression and prognosis. However, there are limited studies on the prognosis of esophageal squamous cell carcinoma (ESCC) after surgery based on the comprehensive indicators of these factors.
AIM
To develop and validate a novel nomogram based on a nutritional immune-inflammatory status (NIIS) score for predicting postoperative outcomes in ESCC.
METHODS
This retrospective study examined 829 patients with ESCC who underwent radical surgery between June 2016 and June 2020, with 568 patients in the training cohort and 261 patients in the validation cohort. We incorporated comprehensive indicators related to nutrition, immunity, and inflammation to develop the NIIS score, using LASSO regression. Subsequently, a nomogram combining the NIIS score and other clinicopathological parameters was developed and validated using calibration curves, time-dependent area under curves, and decision curve analysis.
RESULTS
We identified eight indicators that constitute the NIIS score. High-risk scores emerged as an independent risk factor for overall survival [training set HR 2.497 (1.802, 3.458), P < 0.001]. A NIIS nomogram for personalized prognostic prediction was developed by integrating the NIIS score with clinicopathological variables, yielding enhanced predictive value relative to individual indicators and the UICC/TNM staging system.
CONCLUSION
The NIIS score provides strong predictive value for postoperative outcomes in ESCC, thus offering a valuable tool for clinical decision-making.
Core Tip: This retrospective, bicentric study aims to investigate the prognostic significance of comprehensive indicators related to nutrition, immunity, and inflammation in patients with esophageal squamous cell carcinoma (ESCC) following surgery. We incorporated these indicators to develop a novel nutritional immune-inflammatory status (NIIS) score. Additionally, we created and validated a nomogram based on the NIIS score and other clinicopathological parameters to predict post-surgical outcomes for patients with ESCC.
Citation: Liu QW, Liu L, Hu JX, Hou JQ, He WB, Shu YS, Wang XL. Nomogram based on a novel nutritional immune-inflammatory status score to predict postoperative outcomes in esophageal squamous cell carcinoma. World J Gastroenterol 2025; 31(4): 101749
Esophageal cancer (EC) is the sixth leading cause of cancer-related mortality worldwide. Nearly 50% of new cases of EC globally each year occur in China, and are predominantly characterized by squamous cell carcinoma[1,2]. The current therapeutic approach involves a multidisciplinary strategy centered on surgical intervention; however, the postoperative recurrence rate remains alarmingly high and prognosis is poor, with a five-year survival rate of only 25%[3,4]. Several common clinical and pathological features, such as tumor size, histological grading, and lymph node metastasis, are utilized for prognostic assessment and risk stratification in patients with EC[5-7]. Nevertheless, the predictive value of these conventional factors in determining outcomes is limited, highlighting a pressing need for novel, precise prognostic indicators.
Numerous recent studies have explored the relationship between cancer and blood markers reflecting nutritional, immune, and inflammatory status, establishing the correlation between tumor prognosis, immune response, inflammatory processes, and nutritional factors[8,9]. For instance, the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR)[10-12], prognostic nutritional index (PNI)[13,14], and albumin (ALB)-to-globulin ratio (AGR)[15,16], alongside a range of comprehensive hematological indices such as the systemic inflammatory response index (SIRI) and systemic immune-inflammation index (SII)[17,18], are correlated with the prognosis of patients with EC. However, a singular blood biomarker fails to encapsulate a patient's overall nutritional, immune, and inflammatory status. This study undertakes a thorough analysis of the prognostic significance of nutritional, immune, and inflammatory factors to develop an innovative predictive model known as the nutritional immune-inflammatory status (NIIS) score. The NIIS score integrates biomarkers related to nutrition, immunity, and inflammation to evaluate the prognosis of patients with esophageal squamous cell carcinoma (ESCC) after surgery. Furthermore, based on the NIIS score and other clinical pathological characteristics, a validated NIIS nomogram is constructed for personalized prediction of patient’s survival probabilities.
MATERIALS AND METHODS
Study design and patients
This research was conducted in accordance with the principles outlined in the Helsinki Declaration, and the research protocol adhered to applicable standards and regulations. The Ethical Review Board of Northern Jiangsu People's Hospital approved the present study. The research framework is illustrated in Figure 1. This study is a retrospective, bicentric study that examined patients with ESCC who underwent radical esophagectomy at Northern Jiangsu People's Hospital and Wuxi People's Hospital from June 2016 to June 2020. A total of 909 cases were recruited from Northern Jiangsu People's Hospital; however, owing to an upgrade of the clinical pathology system, some data from Wuxi People's Hospital prior to June 2018 were unavailable, resulting in 397 patients being recruited from there. The inclusion criteria were as follows: (1) No exposure to chemotherapy, radiotherapy, immunotherapy, or targeted treatments before surgery; (2) Distant metastases to the liver, lungs, or brain ruled out by preoperative examinations including computed tomography, magnetic resonance imaging, bone scans, and color Doppler ultrasound; (3) The tumor's anatomical center was located in the thoracic segment; (4) Patients underwent transthoracic radical esophagectomy, with postoperative pathology confirming ESCC; and (5) Patients for whom comprehensive clinical pathology and follow-up data were available. The exclusion criteria were as follows: (1) Postoperative pathology revealing other tissue types; (2) History of other malignancies prior to surgery; and (3) Loss to follow-up. Ultimately, 568 cases were collected from Northern Jiangsu People's Hospital as the training set, while 261 cases from Wuxi People's Hospital constituted the validation set.
Figure 1 Flow chart of the retrospective study.
NIIS: Nutritional immune-inflammatory status; ESCC: Esophageal squamous cell carcinoma.
Clinicopathologic variables and follow-up
The clinical pathological data for all patients were collected from hospital records. The clinical staging was based on the International Union Against Cancer (UICC)/TNM classification for malignant tumors (8th edition). The pathological characteristics of the patients were categorized into several groups: (1) General information: Age, gender, height, weight, body mass index (BMI), medical history, alcohol use, and smoking history; (2) Surgical details: Type of surgery, length of hospital stay, and postoperative complications; (3) Tumor pathological features: Tumor location, size, grade, vascular invasion, neural invasion, pathological type, surgical margins, and TNM staging (all patients underwent pathological staging post-surgery, pTNM); and (4) Preoperative blood parameters: Within one week prior to surgery, the levels of neutrophil count (NEUT), platelet count (PLT), lymphocyte count (LYC), monocyte count (MONO), hemoglobin (Hb), ALB, and globulin were assessed. The following formulas were used to calculate nutritional immune-inflammatory indices: NLR = NEUT/LYC; PLR = PLT/LYC; LMR = lymphocyte/monocyte ratio; SII = PLT × NEUT/LYC; SIRI = MONO × NEUT/LYC; AGR = ALB/globulin ratio; PNI = ALB (g/L) + 5 × LYC per nanoliter; HB ALB LYC platelet (HALP) = Hb (g/L) × ALB (g/L) × lymphocytes (/L)/platelets (/L). The optimal cutoff values for hematological indicators were determined using the surv_cutpoint function in the R (4.3.2) survminer package.
Overall survival (OS), defined as the interval from the date of surgery to either the end of the study or patient death, was the primary predictive outcome in this research. Patients were followed up via phone or outpatient visits after discharge, with follow-ups every three months for the first two years, every six months from two to five years, and annually thereafter, extending to June 2024.
Construction of the NIIS score
In the initial phase, a univariate Cox regression analysis was conducted on a cohort of 829 individuals to identify prognostically significant preoperative markers related to nutrition, immunity, and inflammation. Indicators with P values < 0.05 were retained for subsequent analysis. Kaplan-Meier (KM) curves were then used to assess the influence of various parameters on survival duration. LASSO Cox regression analysis was used to evaluate the prognostic significance of these prognostic biomarkers. Finally, the NIIS score was calculated based on variables with non-zero coefficients.
Prognostic value of NIIS score
The prognostic value of the NIIS score was evaluated using receiver operating characteristic (ROC) curves for the 1-, 3-, and 5- year OS of patients with ESCC after surgery in the training and validation sets. Patients were divided into high-risk and low-risk groups based on the median NIIS score as a cut-off value. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors independently associated with patient outcomes.
Construction and validation of the nomogram
We conducted a univariate Cox regression analysis on the clinical pathological factors, including NIIS scores, in the training dataset. Variables with a P value of < 0.05 were incorporated into a multivariate Cox regression analysis, using the stepwise backward approach to identify independent prognostic indicators for OS. The identified indicators were employed to develop a nomogram model. The predictive accuracy of the nomogram for 1-year, 3-year, and 5-year OS was assessed using ROC and calibration curves in training. Decision curve analysis (DCA) was performed to compare the nomogram with each constituent index and the UICC/TNM staging system to assess its clinical utility at various time intervals. Its value was further evaluated in the validation cohort.
Statistical analysis
Continuous variables were represented by the median, maximum, and minimum values, while non-parametric rank-sum tests were used for intergroup comparisons. Categorical variables were conveyed through frequency and percentage, with group comparisons conducted via χ2 tests. The optimal cutoff values for hematological indicators were determined using the surv_cutpoint function in the R (4.3.2) survminer package. Univariate and multivariate Cox regression analyses, as well as LASSO regression, ROC, calibration, DCA, and KM curves, were performed or constructed using R packages such as "glmnet", "survival", "forest plot", "survival ROC", "rms", "survminer", and "timeROC". P < 0.05 was considered to indicate statistical significance. Confidence intervals (CI) were calculated at the 95% level.
RESULTS
Basic clinical information
This study included a total of 829 patients, with 568 cases collected from Northern Jiangsu People's Hospital as the training set and 261 cases from Wuxi People's Hospital serving as the validation set. The age of the patients ranged from 44 to 84 years. The sample comprised 629 males (75.9%) and 200 females (24.1%). The median follow-up period was 54 months (ranging from 1 to 83 months), and the OS rates at 1, 3, and 5 years were 90.5%, 70.8%, and 62.6%, respectively. The baseline clinical and pathological characteristics of the training and validation sets are detailed in Table 1, revealing a balanced distribution of baseline data across both cohorts, with no significant differences (P > 0.05).
Table 1 Comparison of clinicopathological characteristics of patients in the training and validation sets.
Variables
All patients (n = 829)
Training set (n = 568)
Validation set (n = 261)
P value
Age (year), median (IQR)
64 (44-84)
64 (44-84)
64 (47-77)
0.433
Gender, n (%)
0.866
Male
629 (75.9)
430 (74.7)
199 (76.2)
Female
200 (24.1)
138 (24.3)
62 (23.8)
Type of surgery, n (%)
0.824
Sweet Esophagectomy
334 (40.3)
232 (40.8)
102 (39.1)
Ivor-Lewis esophagectomy
49 (5.9)
31 (5.5)
18 (6.9)
Thoracoscopic esophagectomy
38 (4.6)
27 (4.8)
11 (4.2)
McKeown esophagectomy
408 (49.2)
278 (48.9)
130 (49.8)
Tumor size (cm), median (IQR)
4 (1-11)
4 (1-11)
4 (1-11)
0.233
Tumor centre location, n (%)
0.731
Upper thoracic segment
34 (4.1)
25 (4.4)
9 (3.4)
Middle thoracic segment
325 (39.2)
219 (38.6)
106 (40.6)
Lower thoracic segment
470 (56.7)
324 (57)
146 (55.9)
Histological grade, n (%)
0.138
G1
111 (13.4)
78 (13.7)
33 (12.6)
G2
534 (54.4)
375 (66)
159 (60.9)
G3
184 (22.2)
115 (20.2)
69 (26.4)
pT stage, n (%)
0.305
T1
243 (29.3)
174 (30.6)
69 (26.4)
T2
178 (21.5)
126 (22.2)
52 (19.9)
T3
246 (29.7)
158 (27.8)
88 (33.7)
T4
162 (19.5)
110 (19.4)
52 (19.9)
pN stage, n (%)
0.281
N0
527 (63.6)
369 (65)
158 (60.5)
N1
176 (21.2)
122 (21.5)
54 (20.7)
N2
101 (12.2)
62 (10.9)
39 (14.9)
N3
25 (3.0)
15 (2.6)
10 (3.8)
TNM stage, n (%)
0.355
I
234 (28.2)
170 (29.9)
64 (24.5)
II
231 (27.9)
155 (27.5)
75 (28.7)
III
303 (36.6)
204 (35.95)
99 (37.9)
IV
61 (7.4)
38 (6.7)
23 (8.8)
Vascular invasion
0.184
No
718 (86.6)
498 (87.7)
220 (84.3)
Yes
111 (13.4)
70 (12.3)
41 (15.7)
Nerve violations
0.462
No
725 (87.5)
500 (88.0)
225 (86.2)
Yes
104 (12.5)
68 (12)
36 (13.8)
Pathological types
0.749
Superficial type
257 (31)
182 (32)
75 (28.7)
Medullary type
321 (38.7)
214 (37.7)
107 (41)
Fungating type
21 (2.5)
14 (2.5)
7 (2.7)
Ulcerative type
221 (26.7)
153 (26.9)
68 (26.1)
Infiltrating type
9 (1.1)
5 (0.9)
4 (1.5)
Surgical margin
0.885
R0
809 (97.6)
554 (97.5)
255 (97.7)
R1-2
20 (2.4)
14 (2.5)
6 (2.3)
Weight (kg), median (IQR)
64.5 (34-102)
64 (34-102)
65 (41-92)
0.975
BMI (kg/m2), median (IQR)
23.03 (15.24-33.87)
23.03 (15.78-33.87)
23.03 (15.24-30.76)
0.645
HB (g/L), median (IQR)
131 (73-171)
131 (73-171)
132 (73-166)
0.856
NEUT (109/L), median (IQR)
3.62 (1.25-9.47)
3.62 (1.27-9.00)
3.62 (1.25-9.47)
0.310
PLT (109/L), median (IQR)
189 (51.0-527.0)
184.5 (61.0-527.0)
195 (51-481)
0.124
LYC (109/L), median (IQR)
1.51 (0.22-3.43)
1.49 (0.22-3.43)
1.53 (0.31-3.41)
0.983
MONO (109/L), median (IQR)
0.4 (0.12-4.8)
0.4 (0.12-4.8)
0.4 (0.16-2.56)
0.836
ALB (g/L), median (IQR)
45 (26-55.8)
45.25 (26-55.8)
44.3 (32.6-54.8)
0.044
NLR, median (IQR)
2.45 (0.44-21.8)
2.42 (0.72-21.8)
2.52 (0.44-18.51)
0.448
PLR, median (IQR)
125.9 (25.66-981.82)
124.81 (25.66-981.82)
126.94 (25.81-674.36)
0.195
LMR, median (IQR)
3.65 (0.19-17.46)
3.68 (0.19-17.46)
3.61 (0.36-13.94)
0.747
SII, median (IQR)
462.55 (92.33-4841.9)
454.03 (95.43-4575.27)
488.37 (92.33-4841.9)
0.099
PNI, median (IQR)
52.75 (35.75-67.15)
52.85 (35.75-67.15)
52.3 (38.45-64.2)
0.095
AGR, median (IQR)
1.59 (0.64-2.85)
1.60 (0.64-2.85)
1.56 (1.04-2.85)
0.051
HALP, median (IQR)
45.80 (6.6-231.87)
46.80 (6.6-231.87)
44.22 (9.36-203.11)
0.090
SIRI, median (IQR)
1.01 (0.15-14.35)
1.00 (0.16-14.35)
1.02 (0.15-12.33)
0.463
NIIS score
Univariate Cox regression analysis was performed on all computed indices related to nutrition, immunology, and inflammation. The results revealed that a high BMI, ALB, LMR, LYC, PNI, AGR, and HALP grades, along with low NLR, PLR, SII, and PLT grades were significantly associated with improved OS (Figure 2). The correlations of the 11 immune, inflammatory, and nutritional biomarkers are shown in Figure 3A. LASSO regression of these eleven indices identified eight biomarkers with non-zero coefficients (including BMI, ALB, PLT, SII, PLR, LMR, AGR, and HALP) associated with ESCC prognosis after surgery (Figure 3B and C). The NIIS score was calculated using the following formula: Risk score = -0.1525 BMI + 0.0011 PLT - 0.0690 ALB + 0.0007 PLR - 0.0509 LMR + 0.0001 SII - 0.0695 AGR - 0.0006 HALP. The ROC analysis enhanced the accuracy of the NIIS score, with 1-, 3-, and 5-year OS for area under curves (AUCs) of 0.730, 0.770, and 0.710 in the training cohort, and 0.640, 0.722, and 0.759 in the validation cohort, respectively (Figure 3D and E). In both the training and validation cohorts, the ROC values for the NIIS score significantly surpassed those for the individual biomarkers (Supplementary Figure 1).
Figure 3 Construction of the nutritional immune-inflammatory status score using the LASSO Cox regression model.
A: Heatmap of the correlations of nutritional-immune-inflammatory related biomarkers; B: LASSO coefficient profiles of the eight features; C: Optimal parameter (lambda) selection in the LASSO model is achieved through ten-fold cross-validation based on minimum criteria, with red points indicating partial likelihood values, gray lines representing standard error (SE), and vertical dashed lines marking the optimal value at 1 standard error (1-s.e.); D-E: The receiver operating characteristic (ROC) curves for predicting OS at 1-, 3-, and 5 years in the training set (D) and the validation set (E). BMI: Body mass index; PLT: Platelet count; LYC: Lymphocyte count; ALB: Albumin; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; SII: Systemic immune-inflammation index; PNI: Prognostic nutritional index; AGR: Albumin-to-globulin ratio; HALP: Hemoglobin albumin lymphocyte platelet score; AUC: Area under curve.
Prognostic implications of the NIIS score
Subsequently, the patients were classified into low- and high-risk groups based on the medium cut-off value of -6.58. KM analysis suggested that patients in the high-risk group had shorter OS than those in the low-risk group (Figure 4A-D). The results of univariate and multivariate Cox regression analyses conducted on the training cohort are shown in Table 2. After adjusting for other clinical and pathological factors, multivariate Cox regression analysis of patients in the training cohort demonstrated that the NIIS score is an independent prognostic factor affecting survival outcomes in patients with ESCC after surgery (OS: HR 2.232, 95%CI: 1.599-3.117; Figure 4E). The NIIS score of the validation cohort remained consistent (OS: HR 2.351, 95%CI: 1.566-3.530; Figure 4F). These findings indicate that the NIIS score is of paramount importance as an independent risk factor for the prognosis of patients with ESCC after surgery.
Figure 4 Prognostic implications of the nutritional immune-inflammatory status score.
A and B: The Kaplan-Meier curves of overall survival (OS; A) and the distribution of survival status (B) for patients in the low- and high-risk groups based on the nutritional immune-inflammatory status (NIIS) score in the training set; C and D: The Kaplan-Meier curves of OS (C) and the distribution of survival status (D) for patients in the low- and high-risk groups based on the NIIS score in the validation set; E: Forest plot of multivariable Cox regression analysis of OS in the training set; F: Forest plot of multivariable Cox regression analysis of OS in the validation set. 95%CI: 95% confidence intervals.
Table 2 Results of univariate and multivariate Cox regression analysis for overall survival in the training set.
Variables
Univariate Cox regression analysis for OS
Multivariate Cox regression analysis for OS
HR
P value
HR
P value
NIIS score, ≥ -6.58 vs < -6.58
2.497 (1.802, 3.458)
< 0.001
2.232 (1.599, 3.117)
< 0.001
Age
1.085 (1.058, 1.112)
< 0.001
1.073 (1.046, 1.101)
< 0.001
Size
1.264 (1.153, 1.385)
< 0.001
1.194 (1.082, 1.319)
< 0.001
Gender, female vs male
1.170 (0.805, 1.700)
0.412
Type of surgery
Sweet esophagectomy
1
Ivor-Lewis esophagectomy
1.079 (0.556, 2.097)
0.821
Thoracoscopic esophagectomy
1.413 (0.747, 2.672)
0.288
McKeown esophagectomy
0.886 (0.638, 1.232)
0.473
Site
Upper
1
Middle
2.735 (0.859, 8.714)
0.089
Lower
2.893 (0.917, 9.127)
0.07
Grade
G1
1
G2
1.457 (0.831, 2.554)
0.189
1.231 (0.697, 2.176)
0.474
G3
3.594 (1.998, 6.465)
< 0.001
2.124 (1.158, 3.899)
0.015
pT
T1
1
T2
0.960 (0.611, 1.507)
0.859
T3
1.338 (0.897, 1.998)
0.154
T4
1.267 (0.816, 1.967)
0.291
pN
N0
1
N1
1.579 (1.073, 2.324)
0.02
1.581 (0.890, 2.810)
0.118
N2
3.432 (2.306, 5.109)
< 0.001
2.950 (1.517, 5.740)
0.001
N3
6.749 (3.481, 13.084)
< 0.001
6.426 (2.312, 17.864)
< 0.001
TNM
I
1
II
1.290 (0.812, 2.049)
0.281
1.003 (0.626, 1.609)
0.989
III
1.941 (1.288, 2.924)
0.002
0.899 (0.481, 1.680)
0.738
IV
4.099 (2.377, 7.069)
< 0.001
0.842 (0.335, 2.115)
0.714
Venous invasion, No vs Yes
1.390 (0.908, 2.129)
0.129
Perineural invasion, No vs Yes
1.080 (0.683, 1.708)
0.742
Pathological type
Superficial type
1
Medullary type
1.072 (0.746, 1.541)
0.705
Fungating type
1.102 (0.399, 3.046)
0.851
Ulcerative type
0.879 (0.581, 1.329)
0.541
Infiltrating type
1.455 (0.354, 5.970)
0.603
Surgical margin, R0 vs R1-2
1.257 (0.516, 3.062)
0.614
Development and validation of NIIS-nomograms
Based on the multivariate Cox regression analysis by backward stepwise selection with the smallest AIC value, the NIIS score, age, tumor size, grade, and pN stage were used in the final nomogram for OS (Figure 5A). The accuracy of the nomogram was assessed by ROC analysis. In the training cohort, the AUCs for 1-, 3-, and 5-year OS were 0.920, 0.824, and 0.757, respectively (Figure 5B). The corresponding values for the validation cohort were 0.909, 0.817, and 0.827 (Figure 5C). The calibration curves further demonstrated consistency between the predicted and observed survival outcomes (Figure 5D and E). Overall, the NIIS-nomogram exhibited both discriminative ability and calibration. Additionally, DCA was conducted to evaluate the clinical applicability of the nomogram by quantifying the net benefits at various threshold probabilities, emphasizing its clinical value at 1, 3, and 5 years and confirming its superiority over any individual component index (Figure 6).
Figure 5 Development and validation of nutritional immune-inflammatory status-nomograms.
A: Nomograms incorporating the nutritional immune-inflammatory status (NIIS) score and other clinicopathological parameters for overall survival (OS); B: NIIS-nomogram receiver operating characteristic (ROC) curve for predicting the 1-, 3-, and 5- years OS in the training set; C: NIIS-nomogram ROC curve for predicting the 1-, 3-, and 5- years OS in the validation set; D: NIIS-nomogram calibration curve: Predicted and observed 1-, 3-, and 5- years OS in the training set; E: NIIS-nomogram calibration curve: Predicted and observed 1-, 3-, and 5- years OS in the validation set. AUC: Area under curve; OS: Overall survival.
Figure 6 Decision curve analysis of overall survival prediction using the nutritional immune-inflammatory status-nomogram and rationality analysis of the nomogram model.
A-F: Decision curve analysis (DCA) of the nomogram, nutritional immune-inflammatory status (NIIS) score, size, age, grade, and pN stage for 1-year overall survival (OS; A); 3-year OS (C); and 5-year OS (E) in the training set. DCA of the nomogram, NIIS score, size, age, Grade, and pN stage for 1-year OS (B); 3-year OS (D); and 5-year OS (F) in the validation set.
The UICC/TNM staging system is widely used in clinical settings to predict the prognosis of patients with EC. We compared the predictive accuracy of the NIIS nomogram against that of the UICC/TNM staging system using time-dependent ROC curves and DCA calibration curves. In both the training and validation cohorts, DCA showed that the NIIS-nomograms were superior to the recognized UICC/TNM staging system in predicting ESCC prognosis (Figure 7A-F). The AUC value of the OS nomogram significantly surpassed that of the UICC/TNM staging system (Figure 7G-H).
Figure 7 Decision curve analysis of overall survival prediction by nutritional immune-inflammatory status-nomogram and the UICC/TNM staging system
A-C: Decision curve analysis (DCA) of the nomogram and the UICC/TNM staging system for 1-year overall survival (OS; A); 3-year OS (B); and 5-year OS (C) in the training set; D-F: DCA of the nomogram and the UICC/TNM staging system for 1-year OS (D); 3-year OS (E); and 5-year OS (F) in the validation set; G and H: Time-dependent area under curves of the nutritional immune-inflammatory status-nomogram and the UICC/TNM staging system for predicting OS in the training set (G) and validation set (H).
DISCUSSION
In recent years, researchers have increasingly recognized the critical roles that nutritional status, the immune system, and inflammatory responses play in tumor progression and prognosis. These factors not only influence the formation and development of tumors, but are also closely associated with patient survival outcomes[19-21]. However, a singular indicator conveys limited physiological information, and there exists a complex interplay between the immune system, inflammatory microenvironment, and nutritional status[22,23]. Previous studies have demonstrated that compared with individual blood markers, superior prognostic value for intrahepatic cholangiocarcinoma and hepatocellular carcinoma can be obtained by integrating indicators of immunity, inflammation, and nutritional status[8,9]. Yet, no research has explored their prognostic significance in patients with ESCC after surgery. This study incorporates comprehensive indicators reflecting patients' nutritional and immune-inflammatory status and establishes a novel NIIS score based on BMI, PLT, ALB, PLR, LMR, SII, AGR, and HALP, identifying it as an independent risk factor for OS of patients with ESCC after surgery. Consistent with earlier findings, high BMI, ALB, LMR, AGR, and HALP grades correlate with improved prognosis in patients undergoing esophagectomy, while low PLR, SII, and PLT grades indicate better outcomes[12,15,17,18]. Current research highlights the association between malnutrition and poor prognosis in patients with cancer[24-26]: Higher BMI, ALB, and AGR reflect favorable nutritional status, thereby improving prognosis. High LMR and low PLR, SII, and PLT grades suggest increased lymphocyte levels or reduced platelet and NEUTs. A study of the immune microenvironment in EC indicates that CD4 T, CD8 T, and NK cells constitute the primary proliferative cellular components within the tumor microenvironment, with NK cells, regulatory T cells (Tregs), alternatively activated macrophages, and dendritic cells predominating in this environment[27]. These cells play distinct roles in anti-tumor and immune-suppressive responses. Neutrophils secrete numerous inflammatory factors, causing cellular damage, promoting epithelial-mesenchymal transition in tumor cells, and facilitating angiogenesis, thereby enhancing tumor growth and metastasis[28-30]. Extant studies show that high neutrophil levels are associated with increased aggressiveness and poor prognosis in breast, lung, and colorectal cancers[31-33]. The intricate regulatory mechanisms of the immune microenvironment and peripheral blood immune cells contribute to complex anti-tumor and immune-suppressive processes throughout tumor development.
In this study, we compared the prognostic value of the NIIS composite score with that of individual indicators, revealing that the NIIS score significantly outperforms individual indicators in predicting outcomes. Simultaneously, we developed a nomogram based on the NIIS score and other clinicopathological parameters, which demonstrated robust predictive capability. The NIIS nomogram incorporates the NIIS score, age, tumor size, grade, and pN stage. Current research indicates that these factors are adverse risk determinants of prognosis in EC[34,35]. Using the DCA curve, we compared the predictive value of the NIIS-nomogram with that of individual indicators, age, tumor size, grade, and pN stage, for the prognosis of patients undergoing esophagectomy. The results indicate that the predictive value of the NIIS-nomogram significantly surpasses that of the individual indicators.
The UICC/TMN staging system is widely utilized in clinical practice to predict the prognosis of patients with EC. Therefore, we compared the prognostic value of the NIIS-nomogram with that of the UICC/TMN staging system for such patients. The results indicated that both the area under the ROC curve and the DCA curve in the training and validation sets demonstrated superior predictive efficacy over the traditional UICC/TMN staging system.
Despite these contributions, this study does have certain limitations. First, although it is based on data from two large centers, external validation in a larger population is necessary to confirm the validity of the scoring system. Second, patients included in this study did not receive adjuvant immunotherapy; therefore, the potential applicability of the NIIS score in predicting responses to immunotherapy was not evaluated. Third, owing to the retrospective nature of this research, there may be inherent biases. Further prospective cohort studies are essential.
CONCLUSION
The comprehensive index of NIIS score based on nutritional, immune, and inflammatory status demonstrates significant prognostic value for post-surgical outcomes of patients with ESCC. Furthermore, a nomogram based on the NIIS score was effectively developed to predict OS in ESCC after surgery, providing a valuable resource for clinical decision-making and patient management.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C
Novelty: Grade B, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade B
P-Reviewer: Bordonaro M; Cerwenka H S-Editor: Lin C L-Editor: A P-Editor: Zheng XM
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