Wang K, Zhang BX, Li KJ, Guan JM, Li BY, Hong ZH, Zeng XY, Zhao ZL, Chen Y. Preoperative immunonutrition status predicts survival after colorectal cancer resection: Nomogram based on modified systemic inflammation score. World J Gastroenterol 2026; 32(30): 119360 [DOI: 10.3748/wjg.119360]
Corresponding Author of This Article
Yi Chen, MD, Professor, Cancer Research Institute, The Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi 830011, Xinjiang Uygur Autonomous Region, China. chenyicsu@outlook.com
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Wang K, Zhang BX, Li KJ, Guan JM, Li BY, Hong ZH, Zeng XY, Zhao ZL, Chen Y. Preoperative immunonutrition status predicts survival after colorectal cancer resection: Nomogram based on modified systemic inflammation score. World J Gastroenterol 2026; 32(30): 119360 [DOI: 10.3748/wjg.119360]
Kuan Wang, Xiang-Yue Zeng, Ze-Liang Zhao, Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China
Bo-Xiang Zhang, Yi Chen, Cancer Research Institute, The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China
Ke-Jin Li, Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou 510000, Guangdong Province, China
Jun-Min Guan, Department of Gastrointestinal Oncology Surgery, Gastroenterology Center, People’s Hospital of Bortala Mongolian Autonomous Prefecture, Bole City 833499, Xinjiang Uygur Autonomous Region, China
Bo-Yang Li, Ze-Hao Hong, Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Yi Chen, Xinjiang Key Laboratory of Translational Biomedical Engineering, Urumqi 830011, Xinjiang Uygur Autonomous Region, China
Co-corresponding authors: Ze-Liang Zhao and Yi Chen.
Author contributions: Zhao ZL and Chen Y contributed to study concept and design; Wang K, Li KJ, Guan JM, Zeng XY, Li BY and Hong ZH collected clinical data; Wang K and Zhang BX performed data analysis and interpretation; Wang K drafted the manuscript; Chen Y and Zhao ZL critically revised the manuscript for important intellectual content and supervised the study; Wang K and Zhang BX contributed equally to this work as co-first authors; Zhao ZL and Chen Y contributed equally to this work as co-corresponding authors; and all authors approved the final version to be published.
AI contribution statement: No AI writing tools, including ChatGPT, Grammarly, DeepL, or any similar tools, were used at any stage of manuscript preparation. The entirety of the manuscript-including the Abstract, Introduction, Materials and Methods, Results, Discussion, and Conclusion-was written entirely by the authors. No portion of the main text was AI-generated. No AI tools were used for language polishing, translation, data analysis, or any form of writing assistance. All language editing and revision were performed manually by the authors. The study was independently designed, conducted, and interpreted by the research team. No AI tools participated in any aspect of study design or result interpretation. All figures in the manuscript were generated using standard statistical software (SPSS and R). No AI tools were used to generate any images or figures.
Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region, No. 2022D01C297.
Institutional review board statement: The study was authorized by the Ethics Committee of Xinjiang Medical University Cancer Hospital (approval No. K-2024056) and the Ethics Committee of People’s Hospital of Bortala Mongolian Autonomous Prefecture (approval No. LLSH20241221), in accordance with the Declaration of Helsinki.
Informed consent statement: All study participants or their legal guardian provided written informed consent for the collection and use of clinical and personal data prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: Technical appendix, statistical code, and dataset are available from the corresponding author at 3097993756@qq.com. The shared data are de-identified, and all participants provided consent for data sharing.
Corresponding author: Yi Chen, MD, Professor, Cancer Research Institute, The Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi 830011, Xinjiang Uygur Autonomous Region, China. chenyicsu@outlook.com
Received: January 27, 2026 Revised: April 7, 2026 Accepted: April 17, 2026 Published online: August 14, 2026 Processing time: 177 Days and 11.3 Hours
Abstract
BACKGROUND
Colorectal cancer (CRC) imposes a heavy burden on global public health and economic development. Reliable and accessible biomarkers reflecting host-tumor interactions, including immune-nutritional status, are urgently needed to optimize postoperative management in resectable CRC. We hypothesized that the modified systemic inflammation score (mSIS), reflecting immune-nutritional status, could stratify overall survival (OS) after curative resection.
AIM
To develop and externally validate an mSIS-based nomogram for predicting OS after radical resection for CRC.
METHODS
This retrospective two-center cohort study included 489 stage I-III CRC patients undergoing curative-intent resection. The Affiliated Cancer Hospital of Xinjiang Medical University formed the training cohort (n = 293) and People’s Hospital of Bortala Mongolian Autonomous Prefecture the external validation cohort (n = 196). Cutoffs were determined using receiver operating characteristic analyses. OS was evaluated using Kaplan-Meier and Cox regression. The nomogram was assessed by time-dependent receiver operating characteristic curves, calibration, and decision curve analysis.
RESULTS
The optimal cutoffs were 2.96 for neutrophil-to-lymphocyte ratio and 38.95 g/L for albumin. Patients were stratified into three mSIS risk groups, and higher mSIS consistently indicated inferior survival. In the training cohort, 5-year OS rates were 93.75%, 61.26%, and 18.18% for mSIS 0, 1, and 2, respectively (log-rank P < 0.001). mSIS outperformed platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and systemic inflammation response index, with an area under the curve of 0.823, and remained an independent predictor of OS. The nomogram achieved area under the curve values of 0.880/0.874/0.891 (1-/3-/5-year) in the training cohort and 0.902/0.881/0.850 in the validation cohort.
CONCLUSION
The mSIS enables immune-nutritional stratification and individualized OS prediction in stage I-III CRC after curative resection, supporting postoperative risk classification and decision-making.
Core Tip: This study developed and externally validated a nomogram integrating multiple hematological indicators reflecting immune-nutritional status and systemic inflammation to predict overall survival after curative resection for colorectal cancer. The model demonstrated robust predictive performance in both the training and external validation cohorts. As a simple and readily generalizable tool, it may facilitate postoperative risk stratification, support individualized surveillance strategies, and inform precision clinical decision-making, ultimately improving long-term outcomes in patients with colorectal cancer.
Citation: Wang K, Zhang BX, Li KJ, Guan JM, Li BY, Hong ZH, Zeng XY, Zhao ZL, Chen Y. Preoperative immunonutrition status predicts survival after colorectal cancer resection: Nomogram based on modified systemic inflammation score. World J Gastroenterol 2026; 32(30): 119360
Colorectal cancer (CRC) remains a major global health challenge, imposing a substantial and growing burden on both public health systems and socioeconomic development[1,2]. According to the most recent global cancer statistics, CRC ranks among the most commonly diagnosed malignancies worldwide and is a leading cause of cancer-related mortality, reflecting its high incidence as well as its considerable lethality despite advances in contemporary oncologic care[3,4]. With continuous population aging, lifestyle transitions, and increasing prevalence of metabolic risk factors, the incidence and mortality of CRC are projected to rise further in many regions, underscoring its long-term public health implications[5,6]. Although curative-intent surgery remains the cornerstone for stage I-III disease, long-term outcomes after radical resection remain highly heterogeneous[7]. Even among patients with similar tumor node metastasis (TNM) stage and comparable clinicopathological characteristics, postoperative recurrence risk and overall survival (OS) can vary markedly[8], suggesting that conventional tumor-centered prognostic factors may not fully capture the complexity of patient outcomes[9]. Therefore, beyond optimizing surgical and adjuvant therapeutic strategies, there is an urgent need for reliable, cost-effective, and widely accessible prognostic biomarkers or prediction tools that can support individualized postoperative risk stratification, refine surveillance intensity, and ultimately improve survival outcomes in resectable CRC.
Beyond conventional clinicopathological determinants, accumulating evidence suggests that long-term survival after curative resection for CRC is also shaped by inter-individual heterogeneity in host systemic status, particularly the interplay among systemic inflammation, immune surveillance, and nutritional/metabolic reserve[10,11]. CRC progression is not driven solely by tumor-intrinsic biology but is profoundly influenced by dynamic host–tumor interactions[12]. Tumor-associated inflammation may induce sustained cytokine release and reshape peripheral immune cell composition, thereby facilitating invasion and dissemination while attenuating effective antitumor immunity[13]. In this context, readily available blood-derived indicators have attracted substantial attention due to their noninvasiveness, low cost, and repeatability. Common hematologic indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) have been widely used to reflect the balance between pro-tumor inflammatory activity and adaptive immune competence: Neutrophilia often indicates an inflammation-dominant and immunosuppressive phenotype, whereas lymphopenia suggests compromised immune surveillance and impaired antitumor responses, both of which are associated with unfavorable postoperative outcomes[14-16]. Meanwhile, nutritional status is tightly coupled with inflammation in cancer patients. Serum albumin, traditionally regarded as a marker of nutritional reserve, is also highly sensitive to inflammation-driven acute-phase responses and catabolic states, linking hypoalbuminemia to impaired immune function, reduced tissue repair capacity, and diminished tolerance to surgical stress[17-19]. Given the limitations of single markers, an expanding body of research has focused on composite inflammation-nutrition indices that capture multidimensional host phenotypes more comprehensively[20]. For example, the prognostic nutritional index (PNI) integrates lymphocyte count and serum albumin, reflecting the dual axes of immune competence and nutritional reserve[21,22]. Systemic immune-inflammation index and its derivatives incorporate neutrophils, platelets, and lymphocytes to better quantify systemic inflammatory burden and immune suppression[23]. In addition, emerging integrated scores, such as the systemic inflammation-nutrition index, further combine inflammatory and nutritional components for pragmatic risk stratification[24]. Collectively, these composite metrics aim to approximate a clinically actionable host immune-inflammatory-nutritional phenotype and provide prognostic granularity beyond traditional TNM-based risk assessment.
Despite the growing popularity of composite inflammation-nutrition indices, the current evidence base remains fragmented, and several clinically relevant gaps persist in resected stage I-III CRC. First, many prognostic scores require laboratory parameters that are not consistently available across institutions (e.g., C-reactive protein), limiting routine implementation, particularly in resource-variable settings[25]. Second, the optimal selection and integration of host-related biomarkers remain controversial, as different indices may capture overlapping yet distinct biological dimensions of systemic inflammation and nutritional resilience[26,27]. Under these circumstances, a simplified and widely accessible scoring system that retains mechanistic interpretability and prognostic robustness is highly desirable for postoperative risk stratification. The modified systemic inflammation score (mSIS), integrating NLR and serum albumin, provides such a framework by simultaneously reflecting inflammation-dominant immune imbalance and nutrition-related catabolic vulnerability[28,29]. Notably, mSIS has demonstrated prognostic relevance in several malignancies, including lung cancer, breast cancer, and head and neck cancers, supporting its potential as a broadly applicable host immunonutrition phenotype[30-32]. However, its predictive performance and clinical utility in CRC patients undergoing curative-intent resection have not been sufficiently established, particularly with rigorous external validation. Therefore, we conducted a dual-center retrospective cohort study including stage I-III CRC patients treated with radical resection to evaluate the prognostic significance of preoperative mSIS and to develop and externally validate an mSIS-based nomogram for individualized OS prediction. We further assessed its discrimination, calibration, and clinical net benefit using decision curve analysis (DCA) to support practical postoperative risk stratification and individualized management.
MATERIALS AND METHODS
Study population
This study retrospectively selected 293 primary CRC patients who underwent radical surgery for CRC from January 2016 to December 2017 at the Cancer Hospital of Xinjiang Medical University and 196 primary CRC patients who underwent radical surgery at the People’s Hospital of Bortala Mongolian Autonomous Prefecture of Xinjiang Uygur Autonomous Region. The inclusion criteria were: (1) The patients were confirmed as primary CRC by postoperative histopathology; (2) The patients underwent radical surgery; (3) The patients’ age was > 18 years old; (4) The blood laboratory indexes were available in the preoperative period of 1 week; and (5) The patients’ clinical data were complete and reliable, and they were capable of completing the follow-up. The exclusion criteria were: (1) Having non-primary CRC; (2) With other primary cancers; (3) Patients with unresectable distant metastases; (4) Patients with haematological and autoimmune diseases; (5) Patients with severe hepatic or renal insufficiency or diseases causing malnutrition; (6) Patients receiving parenteral nutritional support prior to the surgical treatment; and (7) Patients who received preoperative neoadjuvant chemoradiotherapy or immunotherapy. This retrospective study was conducted in accordance with the STROBE checklist, ensuring comprehensive and transparent reporting.
The study was authorized by the Ethics Committee of Xinjiang Medical University Cancer Hospital (approval No. K-2024056) and the Ethics Committee of People’s Hospital of Bortala Mongolian Autonomous Prefecture (approval No. LLSH20241221), in accordance with the Declaration of Helsinki, and informed consent was obtained from all participants and/or their legal guardians.
Data collection
The following information was collected from the electronic medical record system of Xinjiang Medical University Cancer Hospital and the People’s Hospital of Bortala Mongolian Autonomous Prefecture of Xinjiang Uygur Autonomous Region: Including basic information (age, gender, height, weight, history of smoking and alcohol consumption), haematological parameters [platelets, lymphocytes, neutrophils, monocytes, haemoglobin, albumin, carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9)] in the 1-week period before the operation, postoperative pathology (degree of tumor differentiation, vascular cancer embolism, neurological invasion, TNM staging) and follow-up information (survival outcome, survival time). All inflammatory and nutritional indicators, including NLR, albumin, and systemic inflammation response index (SIRI), were obtained from a single preoperative blood sample collected within 1 week before surgery. We acknowledge that these biomarkers are inherently dynamic; however, given the retrospective study design, repeated measurements were not available. The pathological cancer stage was documented based on the American Joint Committee on Cancer Staging Manual, 8th Edition (2018).
Patients were followed up via clinic visits and telephone every 3 months for the first 2 years and every 6 months thereafter. Survival data for those unreachable were obtained from the national cancer registry. Follow-up ranged from 60-80 months (median 70 months). Data were censored at last contact for patients lost to follow-up or alive without recurrence. Patients with incomplete clinical or laboratory data essential for calculating inflammation- or nutrition-based indices were excluded at the cohort assembly stage (complete-case approach). Because exclusions were applied during initial data extraction, individual per-variable missingness rates were not recorded separately; however, the number of excluded patients was small at both centers. The final analytic cohort comprised 293 patients in the training set and 196 in the validation set, all with complete data for the variables analyzed.
All statistical analyses were performed using SPSS software (version 29.0) and R programming language (version 4.4.1). For baseline clinical data, categorical variables were presented as n (%), and compared using the χ2 test or Fisher’s exact test as appropriate. Continuous variables were analyzed using Student’s t-test or one-way analysis of variance. For stratified or non-normally distributed data, the Wilcoxon rank-sum test was applied. Comparative analyses of general clinical and pathological characteristics were conducted between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was employed to determine the optimal cut-off values for NLR and albumin. Specifically, 5-year OS status was used as the binary outcome variable: Patients who died within 5 years of surgery were classified as events, and those who survived beyond 5 years or were censored were classified as non-events. This 5-year time horizon was selected as it represents the standard benchmark for long-term prognostic evaluation in resected CRC. Cut-offs were selected according to the maximum Youden index, which maximizes the sum of sensitivity and specificity. We acknowledge that time-dependent ROC methods provide a more comprehensive assessment across the full follow-up trajectory; however, given that our primary objective was to define a single clinically actionable threshold for mSIS construction, the 5-year binarization approach is methodologically appropriate and widely applied in similar prognostic studies. The prognostic validity of the resulting cut-offs was further supported by time-dependent ROC analysis of the nomogram across 1-, 3-, and 5-year time points. The area under the curve (AUC) was also used to compare the predictive ability of mSIS against other inflammation-based indices. Kaplan-Meier survival curves were plotted for NLR and albumin, and differences in OS were assessed using the Log-Rank test. In addition, restricted cubic spline (RCS) models were used to explore potential nonlinear relationships between NLR, albumin, and the risk of mortality. Univariate Cox regression analysis was performed to screen candidate prognostic variables, with P < 0.05 used as the eligibility threshold for multivariable inclusion. mSIS was pre-specified as the primary exposure variable and retained in the multivariable model regardless of univariable significance. Multivariable Cox proportional hazards regression was subsequently performed to identify independent predictors of OS. Since mSIS is derived from NLR and albumin, and SIRI partly overlaps with neutrophil and lymphocyte counts, potential multicollinearity was formally assessed. Pairwise Spearman correlation coefficients and variance inflation factors (VIFs) were computed for all candidate variables (Supplementary Table 1). The strongest correlations were between NLR and SIRI (ρ = 0.821) and between mSIS and albumin (ρ = -0.687), consistent with their shared biological components. All VIFs ranged from 1.73 to 2.86, well below the conventional threshold of 5, indicating acceptable collinearity levels (Supplementary Table 1). Notably, mSIS was modeled as a binary categorical variable (0 vs ≥ 1) in the multivariable Cox regression, as the mSIS = 2 subgroup contained only 22 patients, insufficient for stable three-level coefficient estimation (see results). This categorical coding inherently attenuates its correlation with the continuous components NLR and albumin, further mitigating collinearity. To further assess the robustness of mSIS against potential multicollinearity with its component variables, a supplementary sensitivity analysis was conducted using a restricted model containing only the four inflammation-related variables (mSIS, NLR, albumin, and SIRI), followed by a further two-variable model excluding NLR and albumin. These procedures ensured that multicollinearity did not compromise model interpretability while allowing mSIS to be retained as a composite measure of preoperative immunonutrition status and SIRI as a comparative inflammation-based index. Based on the multivariate results, a nomogram model was developed using the “rms” package in R to predict individual survival probabilities in CRC patients. The performance of the nomogram was evaluated using ROC curves and calibration plots, and its predictive accuracy was validated both internally (training cohort) and externally (validation cohort). Harrell’s concordance index (C-index) was calculated to quantify the overall discriminative ability of mSIS and each comparator variable (albumin, NLR, SIRI, and CA19-9) for OS prediction. The C-index was computed using the full cohort (n = 489), and 95% confidence intervals (CIs) were estimated via 500-iteration bootstrap resampling. A C-index of 0.5 indicates no discriminative ability, while a value of 1.0 indicates perfect discrimination. P value < 0.05 was considered statistically significant in all analyses.
RESULTS
Clinicopathological characteristics of patients
A total of 489 patients diagnosed with CRC were enrolled in this study, with detailed clinicopathological characteristics collected and analyzed. The cohort was divided into a training set (n = 293) and a validation set (n = 196), as summarized in Table 1. Most baseline features were comparable, with the exception of drinking history (P = 0.026) (Table 1).
Table 1 Baseline demographic and clinicopathological characteristics of patients in the training and validation cohorts, n (%).
Among all included patients, 284 (58.1%) were male. The median age was 62 years. Regarding tumor stage, based on the TNM classification, 18.0% of patients were diagnosed with stage I disease, 43.6% with stage II, and 38.4% with stage III. Histologically, the majority of tumors (68.3%) were moderately differentiated, while poorly differentiated, undifferentiated, and well-differentiated subtypes accounted for 17.8%, 8.8%, and 5.1% of cases, respectively. Perineural invasion was observed in 82 patients (16.8%), and lymphovascular invasion was identified in 88 patients (18.0%), indicating a substantial proportion of patients with aggressive pathological features. In terms of tumor biomarkers, elevated levels of CEA and CA19-9 were detected in 35.8% and 10.8% of patients, respectively. These biomarkers are commonly used indicators of tumor burden and disease progression in CRC. Additional clinical and laboratory baseline characteristics, including inflammatory and nutritional indices, are presented in detail in Table 2.
Table 2 Baseline demographic and clinicopathological characteristics stratified by serum albumin and neutrophil-to-lymphocyte ratio in the training cohort, n (%).
Optimal cutoff values for NLR and albumin in the training cohort
In the training cohort, the optimal cutoff values for NLR and serum albumin were determined using ROC curve analysis. The AUC for NLR was 0.683, with an optimal cutoff value of 2.958 (rounded to 2.96 for clinical application). For albumin, the AUC was 0.785, and the optimal cutoff value was 38.95 g/L. Accordingly, NLR ≥ 2.96 and < 2.96 were classified as high NLR and low NLR, respectively; albumin ≥ 38.95 g/L and < 38.95 g/L were categorized as high albumin and low albumin, respectively (Figure 1). Furthermore, RCS analysis revealed a gradual and continuous increase in mortality risk with decreasing albumin levels and rising NLR, indicating a stable and dose-dependent prognostic relationship between these biomarkers and OS (Figure 2).
Figure 1 Receiver operating characteristic curves of serum albumin and neutrophil-to-lymphocyte ratio in the training cohort.
Receiver operating characteristic (ROC) analysis was performed to evaluate the discriminatory ability of serum albumin (red curve) and neutrophil-to-lymphocyte ratio (NLR) (black curve) for the study endpoint. The area under the curve (AUC) was 0.785 for albumin and 0.683 for NLR. The optimal cut-off values determined by ROC analysis were 38950 for albumin (sensitivity = 0.781, specificity = 0.718) and 2.958 for NLR (sensitivity = 0.874, specificity = 0.474). The difference between the AUCs was statistically significant (P = 0.032883). ROC: Receiver operating characteristic; AUC: Area under the curve; NLR: Neutrophil-to-lymphocyte ratio.
Figure 2 Restricted cubic spline analysis of the associations of serum albumin and neutrophil-to-lymphocyte ratio with the study endpoint.
A: Restricted cubic spline (RCS) curve showing the dose-response relationship between serum albumin level and hazard ratio (HR); B: RCS curve showing the dose-response relationship between neutrophil-to-lymphocyte ratio (NLR) and HR. The solid red line represents the estimated HR, and the shaded area indicates the 95% confidence interval. The black dashed line indicates the reference (HR = 1.0). The distribution of albumin/NLR is shown by the histogram. P values for overall association and nonlinearity are presented in each panel (albumin: P overall < 0.001, P nonlinear = 0.013; NLR: P overall < 0.001, P nonlinear < 0.001). CI: Confidence interval; NLR: Neutrophil-to-lymphocyte ratio.
Correlation analysis of NLR and albumin with survival rate
In the training cohort, the median OS of the 293 patients was 67 months. Kaplan-Meier survival curves demonstrated that patients in the low albumin group had significantly shorter OS compared to those in the high albumin group (P < 0.001). Similarly, patients with high NLR also exhibited poorer OS, with the difference being statistically significant (P < 0.001) (Figure 3A and B).
Figure 3 Kaplan-Meier overall survival curves.
A and B: Curves stratified by serum albumin and neutrophil-to-lymphocyte ratio (NLR) in colorectal cancer; A: Overall survival curves for patients in the low-albumin and high-albumin groups; B: Overall survival curves for patients in the low-NLR and high-NLR groups. Shaded areas represent 95% confidence intervals. The numbers of patients at risk at each time point are shown below the plots. P values were calculated using the log-rank test (both P < 0.0001); C and D: Curves stratified by the modified systemic inflammation score; C: Overall survival curves for patients with modified systemic inflammation score (mSIS) scores of 0, 1, and 2; D: Overall survival curves for patients with mSIS = 0 and mSIS ≥ 1. Shaded areas represent 95% confidence interval. The numbers of patients at risk at each time point are shown below the plots. P values were calculated using the log-rank test (both P < 0.0001). mSIS: Modified systemic inflammation score; NLR: Neutrophil-to-lymphocyte ratio.
Risk stratification and survival outcomes based on mSIS score
In the training cohort, 293 patients were stratified into three groups based on the mSIS score. Group 1 (mSIS = 0, low risk) included patients with NLR < 2.96 and albumin ≥ 38.95 g/L (n = 160, 54.6%). Group 2 (mSIS = 1, intermediate risk) comprised patients with either NLR ≥ 2.96 and albumin ≥ 38.95 g/L or NLR < 2.96 and albumin < 38.95 g/L (n = 111, 37.9%). Group 3 (mSIS = 2, high risk) consisted of patients with NLR ≥ 2.96 and albumin < 38.95 g/L (n = 22, 7.5%) (Table 3). The 5-year OS rates for these three groups were 93.75%, 61.26%, and 18.18%, respectively (P < 0.001, Figure 3C and D).
Table 3 Scoring criteria of the modified systemic inflammation score based on neutrophil-to-lymphocyte ratio and serum albumin.
For multivariable Cox regression analysis, mSIS = 1 and mSIS = 2 were combined into a single group (mSIS ≥ 1, n = 133) because the mSIS = 2 subgroup contained only 22 patients, which was insufficient for stable coefficient estimation in the multivariable model. This post hoc regrouping was adopted to ensure statistical reliability; the full three-group stratification was retained in all Kaplan-Meier and descriptive analyses.
Comparison of mSIS and inflammatory indicators in survival prediction: ROC analysis
ROC curve analysis was performed to compare the prognostic accuracy of mSIS and other inflammation-based indices, including PLR, LMR, and SIRI. The results revealed that mSIS demonstrated a superior predictive performance, with an AUC of 0.823, compared to PLR (AUC = 0.665), LMR (AUC = 0.696), and SIRI (AUC = 0.682) (P < 0.05, Figure 4). These findings indicate that the mSIS score, which incorporates both NLR and serum albumin levels, provides a more robust prognostic value in CRC patients than the other individual inflammatory markers. To further quantify the discriminative performance of mSIS and each comparator, Harrell’s C-index was calculated across the full cohort (n = 489), with 95%CIs estimated via 500-iteration bootstrap resampling. As shown in Table 4, the C-index for mSIS was 0.775 (95%CI: 0.737-0.811), substantially exceeding those of albumin (0.696, 95%CI: 0.657-0.738), NLR (0.659, 95%CI: 0.621-0.701), SIRI (0.640, 95%CI: 0.602-0.683), and CA19-9 (0.512, 95%CI: 0.486-0.540). These findings confirm that the composite mSIS score provides superior prognostic discrimination compared with any individual inflammatory or nutritional marker, and are consistent with the AUC-based comparisons reported above.
Figure 4 Receiver operating characteristic curve analysis of inflammation-related indicators in the training cohort.
A: Receiver operating characteristic (ROC) curve of the modified systemic inflammation score, with an area under the curve (AUC) of 0.823. The optimal cut-off value was 1.500 (sensitivity = 0.698, specificity = 0.872), as indicated in the figure; B: Comparison of ROC curves for platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic inflammation response index (SIRI). The AUCs were 0665 for PLR, 0.696 for LMR, and 0.682 for SIRI. The optimal cut-off values were 157.702 for PLR (sensitivity = 0.740, specificity = 0.564), 2.875 for LMR (sensitivity = 0.837, specificity = 0.462), and 1.709 for SIRI (sensitivity = 0.921, specificity = 0.410). The difference among the ROC curves was statistically significant (P = 0.032883). ROC: Receiver operating characteristic; AUC: Area under the curve; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; SIRI: Systemic inflammation response index.
Table 4 Harrell’s C-index for modified systemic inflammation score and comparator variables (full cohort, n = 489).
Identification of independent prognostic indicators in the training cohort
Univariate and multivariate analyses were performed on the clinicopathological characteristics of CRC patients in the training cohort (Table 5). Univariate analysis identified T stage, N stage, TNM stage, Nerve invasion, Intravascular tumor emboli, CEA, CA19-9, albumin, preoperative NLR, mSIS, PLR, LMR, and SIRI as factors significantly associated with OS (P < 0.05). Multivariate Cox regression analysis demonstrated that CA19-9, albumin, NLR, mSIS, and SIRI were independent prognostic factors for OS (P < 0.05). Patients with normal CA19-9 levels had better OS compared to those with elevated levels [hazard ratio (HR) = 0.39, 95%CI: 0.19-0.80, P = 0.010]. Lower albumin was associated with worse OS, consistent with HR < 1 indicating a protective effect of higher albumin (HR = 0.46, 95%CI: 0.23-0.93, P = 0.03), suggesting that nutritional status plays a critical role in CRC prognosis. Elevated NLR was correlated with decreased OS (HR = 1.26, 95%CI: 1.10-1.40, P < 0.001), further supporting the close link between systemic inflammation and tumor progression. Regarding inflammation-based scoring systems, patients with mSIS ≥ 1 had significantly worse OS compared to those with mSIS = 0 (HR = 4.06, 95%CI: 2.10-7.85, P < 0.001), while those with elevated SIRI exhibited the worst prognosis (HR = 4.70, 95%CI: 1.77-12.44, P = 0.002). These composite indices reflect the systemic inflammatory burden and immune status prior to surgery, with elevated levels typically indicating a tumor-promoting microenvironment that accelerates disease progression and adversely affects survival. In the supplementary sensitivity analysis using a four-variable model containing only mSIS, NLR, albumin, and SIRI, mSIS remained a strong independent predictor of OS (HR = 4.85, 95%CI: 2.24-10.47, P < 0.001). After further excluding NLR and albumin, leaving only mSIS and SIRI in a two-variable model, the HR for mSIS increased to 9.09 (95%CI: 4.62-17.90, P < 0.001), with the magnitude increase reflecting absorption of prognostic effects previously shared with its component variables. These sensitivity results are consistent with the primary multivariable model (HR = 4.06, 95%CI: 2.10-7.85, P < 0.001; Table 5), collectively confirming the independent prognostic value of mSIS.
Table 5 Univariable and multivariable Cox proportional hazards regression analyses for overall survival in the training cohort, n (%).
Construction and validation of a nomogram model based on multivariate cox regression analysis
Based on the results of the multivariate Cox regression analysis, we included all variables with statistical significance (P < 0.05) in the construction of a nomogram to evaluate the individualized prognostic risk of CRC patients. The resulting nomogram revealed that mSIS, CA19-9, and SIRI contributed the most to the prediction of OS. Patients with mSIS ≥ 1, elevated CA19-9 levels, or high SIRI were associated with significantly increased risk of mortality. In addition, elevated NLR and decreased albumin also had a negative impact on patient survival, underscoring the prognostic relevance of systemic inflammation and nutritional status (Figure 5). For instance, a patient with mSIS = 1 and elevated CA19-9 has a total score of about 150 points, corresponding to a predicted 3-year OS of roughly 70% and a 5-year survival of about 55%.
Figure 5 Nomogram for predicting 1-, 3-, and 5-year overall survival in colorectal cancer patients.
The nomogram was developed based on serum albumin, neutrophil-to-lymphocyte ratio (NLR), modified systemic inflammation score (mSIS), carbohydrate antigen 19-9 (CA19-9), and systemic inflammation response index (SIRI). For each variable, points were assigned according to the patient’s status (albumin: ≥ 38.95 vs < 38.95; NLR: < 2.96 vs ≥ 2.96; mSIS: 0 vs 1-2; CA19-9: Normal vs high; SIRI: < 1.71 vs ≥ 1.71). The total points were calculated by summing the points for all variables and were then used to estimate the probabilities of 1-, 3-, and 5-year overall survival. CA19-9: Carbohydrate antigen 19-9; SIRI: Systemic inflammation response index; mSIS: Modified systemic inflammation score; NLR: Neutrophil-to-lymphocyte ratio.
To assess the predictive performance of the nomogram, we first conducted internal validation. The time-dependent ROC analysis showed that the AUCs for 1-year, 3-year, and 5-year OS were 0.880, 0.874, and 0.891, respectively, indicating strong discriminatory power of the model in the training cohort (Figure 6A). The calibration curves demonstrated a high concordance between the predicted and observed OS probabilities at all time points (Figure 7). Moreover, the calibration curves for 1-, 3-, and 5-year OS, along with the DCA curves at the corresponding time points (Figure 8A-F), demonstrated good agreement between predicted and observed outcomes and showed favorable net clinical benefit across a wide range of threshold probabilities, further supporting the clinical applicability and utility of the nomogram.
Figure 6 Time-dependent receiver operating characteristic curves of the nomogram for predicting 1-, 3-, and 5-year overall survival in the training and validation cohorts.
A: Receiver operating characteristic (ROC) curves of the nomogram in the training cohort. The area under the curve (AUC) [95% confidence interval (CI)] were 880% (79.6%-96.5%) at 1 year, 87.4% (82.3%-92.5%) at 3 years, and 89.1% (85.3%-93.0%) at 5 years; B: ROC curves of the nomogram in the validation cohort. The AUCs (95%CI) were 902% (82.0%-98.3%) at 1 year, 88.1% (82.2%-94.0%) at 3 years, and 85.0% (79.6%-90.4%) at 5 years. The diagonal dashed line indicates the reference line. AUC: Area under the curve; CI: Confidence interval.
Figure 7 Calibration plots of the nomogram for predicting 1-, 3-, and 5-year overall survival.
Calibration curves were used to assess the agreement between nomogram-predicted and observed overall survival probabilities. The X-axis represents the nomogram-predicted probability of survival, and the Y-axis represents the actual survival probability. The blue, red, and green curves correspond to 1-year, 3-year, and 5-year overall survival, respectively.
Figure 8 Calibration curves and decision curve analysis of the nomogram for predicting 1-, 3-, and 5-year overall survival in the training cohort and validation cohort.
A-F: In the training cohort: Calibration curve at 1 year (A); Decision curve analysis (DCA) curve at 1 year (B); Calibration curve at 3 years (C); DCA curve at 3 years (D); Calibration curve at 5 years (E); DCA curve at 5 years (F); G-L: In the validation cohort: Calibration curve at 1 year (G); DCA curve at 1 year (H); Calibration curve at 3 years (I); DCA curve at 3 years (J); Calibration curve at 5 years (K); DCA curve at 5 years (L). For calibration, the X-axis indicates predicted risk and the Y-axis indicates observed frequency; the diagonal gray line indicates the ideal reference line and Brier scores are shown. For DCA, the X-axis indicates threshold probability and the Y-axis indicates net benefit; the nomogram (model all) is compared with treat all and treat none strategies.
To further evaluate the robustness and generalizability of the model, we applied it to the validation cohort for external validation. The AUCs for predicting 1-, 3-, and 5-year OS in the validation cohort were 0.902, 0.881, and 0.850, respectively (Figure 6B), comparable to those in the training cohort. The calibration curves and DCA curves for the validation set (Figure 8G-L) further confirmed the model’s predictive value and decision-making benefit in external data. Collectively, both internal and external validations demonstrated that the nomogram possesses excellent prognostic accuracy and broad applicability for predicting OS in CRC patients.
DISCUSSION
In this two-center retrospective cohort study of patients with resected stage I-III CRC, we demonstrated that the mSIS, integrating NLR and serum albumin, provides robust prognostic stratification for long-term outcomes. Higher mSIS was consistently associated with significantly worse OS, with a clear gradient in 5-year OS across mSIS categories. Multivariable Cox regression further confirmed mSIS as an independent predictor of OS after adjustment for established clinicopathological factors. Importantly, mSIS showed superior prognostic discrimination compared with its individual components (NLR or albumin) and other commonly used inflammation-based indices, including PLR, LMR, and SIRI, as reflected by higher AUC values. Building on these findings, we developed and externally validated an mSIS-based nomogram, which exhibited strong accuracy, good calibration, and meaningful clinical net benefit on DCA in both the training and validation cohorts. Collectively, our results highlight the clinical relevance of a host immunonutrition phenotype in shaping postoperative survival.
Consistent with the marked survival gradient across mSIS categories in our cohort, the NLR-driven component of mSIS likely reflects an inflammation-dominant host phenotype characterized by neutrophil expansion and impaired adaptive immunity. Emerging evidence in CRC indicates that tumor-educated neutrophils can be reprogrammed toward a pro-metastatic state and promote neutrophil extracellular trap (NET) formation, thereby facilitating immune evasion and the establishment of metastatic niches, particularly in the liver[33]. Beyond acting as physical scaffolds for tumor cell dissemination, NETs can promote epithelial-mesenchymal transition and metabolic rewiring; mechanistically, NETs have been shown to upregulate LDHA and activate glycolytic programs, ultimately enhancing CRC liver metastasis[34,35].
In parallel, systemic inflammatory phenotypes characterized by elevated neutrophil-related parameters are consistently associated with more advanced tumor stage and progression in primary CRC cohorts[36]. Collectively, these mechanistic insights provide biological plausibility for our observation that elevated mSIS is associated with inferior OS after curative resection, supporting mSIS as a concise surrogate of systemic immune-inflammatory imbalance. Albumin, a key component of the mSIS, serves not only as a marker of nutritional reserve but also helps explain why the composite score provides superior prognostic discrimination compared with albumin alone. In our cohort, the albumin cut-off of 38.95 g/L substantially contributed to postoperative risk stratification, suggesting that hypoalbuminemia reflects a systemic vulnerability phenotype driven by inflammation rather than isolated malnutrition. Evidence from surgical CRC cohorts supports this interpretation. In a stage I-III robotic resection cohort, the neutrophil percentage-to-albumin ratio (NPAR) strongly predicted postoperative complications (AUC approximately 0.91) and independently indicated worse OS (HR approximately 1.60), highlighting the joint impact of inflammatory burden and albumin status on long-term outcomes[37]. Consistently, a larger cohort study demonstrated that high NPAR was significantly associated with inferior progression-free survival and OS, and an NPAR-based nomogram enabled individualized survival prediction[38]. Moreover, a two-center retrospective study showed that platelet–albumin ratio combined with the cancer inflammation prognostic index supported the development and external validation of prognostic nomograms, further underscoring that albumin should be interpreted within an inflammatory context[39]. Mechanistically, interleukin-6/signal transducer and activator of transcription 3 signaling at the host-tumor interface can trigger an acute-phase response and systemic effects including cachexia and hypoalbuminemia, thereby impairing immune competence, tissue repair, and treatment tolerance[40,41]. In parallel, large population-based cohorts have shown a robust dose-response association between postoperative C-reactive protein and overall and CRC-specific survival, providing complementary evidence for the survival relevance of the inflammation-nutrition axis[42].
A key finding of our study is that the mSIS provided stronger prognostic discrimination than any single component marker, including NLR or albumin alone, and also outperformed other commonly used inflammation-based indices such as PLR, LMR, and SIRI. This superiority likely reflects the biological nature of mSIS as an integrated immunonutrition phenotype, capturing both inflammatory immune imbalance and metabolic-nutritional vulnerability. In resected stage I-III CRC, systemic inflammation-related biomarkers are increasingly recognized as clinically meaningful surrogates of host-tumor interaction, rather than merely epiphenomena of tumor burden[43]. Similar CRC studies comparing multiple indices have shown that composite scores integrating inflammation and nutritional reserve often provide more stable and clinically relevant prediction than isolated hematologic ratios[44,45]. Notably, inflammatory-nutritional indices such as PNI, C-reactive protein-albumin-lymphocyte (CALLY), and albumin-to-globulin ratio/PNI-based models have consistently demonstrated independent associations with OS/depth first search (DFS) and have been successfully incorporated into validated nomograms, supporting the concept that host condition adds prognostic granularity beyond conventional staging[46-48]. Our observed steep 5-year OS gradient across mSIS strata (93.75% vs 18.18%) further underscores that patients with comparable tumor stage may experience divergent outcomes driven by systemic host factors. Therefore, mSIS may serve as a pragmatic complement to TNM staging, enabling refined risk stratification and individualized postoperative management after curative CRC surgery[49].
Beyond statistical significance, the nomogram developed in this study provides a clinically actionable framework for individualized postoperative risk stratification in resected stage I-III CRC. A total of 489 patients were included (training cohort, n = 293; external validation cohort, n = 196), and the dual-center design with external validation supports generalizability. The nomogram achieved strong discrimination for OS prediction, with AUCs of 0.880, 0.874, and 0.891 for 1-, 3-, and 5-year OS in the training cohort, and 0.902, 0.881, and 0.850 in the validation cohort. Calibration analyses demonstrated good agreement between predicted and observed survival probabilities, indicating reliable risk estimation over time. DCA further suggested meaningful net benefit across clinically relevant threshold probabilities, highlighting potential utility in guiding surveillance intensity and postoperative management.
Importantly, mSIS demonstrated strong predictive value as an independent prognostic factor. Patients with mSIS = 0 had a 5-year OS of 93.75%, whereas those with mSIS ≥ 1 had a significantly lower 5-year OS of 54.14% (P < 0.001). Compared with individual inflammatory or nutritional markers, mSIS exhibited superior discrimination, with an AUC of 0.823 vs (PLR = 0.665, SIRI = 0.682, LMR = 0.696), reflecting its ability to comprehensively capture the host immunonutrition phenotype. Mechanistically, mSIS integrates systemic inflammation (via NLR) and nutritional reserve (via serum albumin), capturing the interplay between neutrophil-mediated pro-inflammatory activity, lymphocyte-mediated immune surveillance, and overall nutritional and immune status. This integration reduces variability caused by transient changes in single markers and more reliably identifies high-risk patients. Recent mechanistic and clinical studies support that high NLR combined with low albumin reflects immunosuppression, chronic low-grade inflammation, and nutritional deficiency, which synergistically contribute to tumor progression. We acknowledge that CRC includes heterogeneous disease entities, namely colon and rectal cancers, which differ in clinical management, surgical approach, and adjuvant treatment. These differences may theoretically influence immunonutrition status and postoperative outcomes. Although our retrospective dataset did not record tumor location at either center, the mSIS provides a robust composite measure of systemic inflammation and nutritional reserve, allowing identification of patients at higher risk regardless of tumor site.
These findings suggest that preoperative mSIS could be used to identify patients at higher risk for poor postoperative outcomes, enabling clinicians to consider tailored perioperative management strategies, closer postoperative monitoring, or nutritional and immunological interventions. The robust performance of mSIS across dual-center cohorts and its incorporation into the nomogram underscore its potential utility as a clinically meaningful prognostic tool, complementing traditional tumor-based staging.
Our findings also have direct clinical implications. Because the mSIS is derived entirely from routine preoperative blood tests, it can be readily implemented before surgery, enabling early identification of patients at high risk for long-term mortality. In our cohorts, mSIS provided marked prognostic stratification, with a pronounced gradient in 5-year OS across mSIS risk groups, suggesting a clinically meaningful “window of opportunity” for perioperative optimization. Biologically, a high mSIS may reflect a host phenotype dominated by systemic inflammation, impaired antitumor immune surveillance, and catabolic or nutritional vulnerability. These features could partly explain why patients with similar tumor burden may experience substantially different outcomes. From a translational perspective, mSIS-guided risk assessment may support individualized perioperative strategies, such as intensified nutritional prehabilitation, inflammation-aware optimization, and tailored postoperative surveillance intensity. Moreover, high-risk patients identified by mSIS could be prioritized for closer evaluation of adjuvant treatment feasibility and tolerance, thereby facilitating more precise and proactive postoperative management.
Several limitations should be considered when interpreting our findings. First, given the retrospective nature of this study, the observed association between mSIS and OS should be regarded primarily as prognostic rather than causal, and may be influenced by selection bias, incomplete documentation, and unmeasured confounders. Second, OS was used as the primary endpoint. We acknowledge that OS may be influenced by non-cancer-related mortality, and that DFS or CSS could provide more disease-specific prognostic information. However, DFS and CSS data were not consistently available across our retrospective cohorts. Despite this limitation, OS is widely accepted as a clinically meaningful endpoint in CRC, and the prognostic value of mSIS was evident. Third, all inflammatory and nutritional indicators were measured only once before surgery. As these biomarkers are inherently dynamic, this single time-point measurement may introduce potential information bias or random variation. However, the consistent prognostic performance of mSIS across two independent institutional cohorts suggests that, despite this inherent limitation, the preoperative values captured a prognostically relevant and reproducible signal. Fourth, the multivariable Cox regression model included approximately 25 degrees of freedom with only 78 OS events in the training cohort, yielding an events per variable (EPV) of 3.12, which falls below the commonly recommended threshold of 10. We acknowledge that a low EPV may increase the risk of biased coefficient estimates and unstable confidence intervals. However, two considerations mitigate this concern: (1) The overfitting risk is partially mitigated by the fact that mSIS was pre-specified as the primary exposure based on established literature, rather than selected through data-driven screening, although we acknowledge that its regression coefficient remains subject to estimation uncertainty under low-EPV conditions; and (2) The model demonstrated consistent discriminative performance in an independent external validation cohort, with time-dependent AUCs of 0.902, 0.881, and 0.850 at 1, 3, and 5 years comparable to or exceeding those in the training cohort providing empirical evidence against clinically meaningful overfitting. Future prospective multicenter studies with larger event counts are warranted to further validate and stabilize the model. Fifth, a complete-case approach was used, excluding patients with incomplete laboratory data at the cohort assembly stage. Although the number of excluded cases was small and missingness was considered unlikely to be systematically related to prognosis, individual per-variable missingness rates were not recorded, and the potential for selection bias cannot be entirely excluded. Sixth, although the nomogram was externally validated in two independent cohorts, both centers were located within the Xinjiang Uygur Autonomous Region, which may limit generalizability across different ethnic structures, baseline nutritional profiles, and healthcare delivery patterns. Additionally, tumor location (colon vs rectum) was not recorded in the retrospective databases at either center, precluding site-specific subgroup analyses. Although NLR and albumin are systemic rather than site-specific biomarkers, future prospective studies should systematically record tumor location to enable site-stratified prognostic evaluation and to determine whether mSIS retains independent prognostic value in colon and rectal cancer separately. Finally, detailed adjuvant therapy information and key molecular stratifiers (microsatellite instability status, KRAS/BRAF alterations) were largely missing, which may introduce residual confounding and limit integration of tumor biology into host-tumor risk modeling. Future prospective, multicenter studies should incorporate standardized perioperative management, adjuvant treatment, and molecular subtyping, and directly compare mSIS with established inflammation-nutrition scores (PNI, modified Glasgow prognostic score, CALLY).
CONCLUSION
In this dual-center retrospective cohort study of patients with resected stage I-III CRC, we demonstrated that the mSIS provides robust prognostic stratification for long-term OS. Higher mSIS was consistently associated with markedly inferior survival and remained an independent predictor after adjustment for clinicopathological factors. Given that mSIS can be readily obtained from routine preoperative blood tests, it may facilitate risk-adapted postoperative surveillance and individualized management. Prospective multicenter studies incorporating treatment variables and molecular stratification are warranted to further confirm its generalizability and clinical utility.
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P-Reviewer: Wang RN, Assistant Professor, China; Xia M, MD, Adjunct Professor, China; Zakaria AD, MD, Professor, Malaysia S-Editor: Fan M L-Editor: A P-Editor: Lei YY
