Integrating nutritional and immune-inflammation-based index predicts disease-free survival of colorectal cancer patients undergoing radical surgery

Introduction

Colorectal cancer (CRC) is the third most common malignancy and the fourth leading cause of cancer-related mortality, accounting for almost 9.2% of death worldwide (1). Data from the National Cancer Center of China indicate that both the incidence and mortality rates of CRC have been increasing in China, driven by a wide range of unfavorable risk factors, including an ageing population and dietary habits of typical high-income countries (2). The reported 5- and 10-year survival rates are approximately 65% and 58%, respectively (3). In addition to tumor characteristics, CRC progression is influenced by numerous factors, such as inflammation levels, nutritional status and immune function. As a result, patients with higher levels of inflammation, poor nutrition and impaired immune function are associated with poorer overall survival in CRC (4-6). Therefore, timely administration of anti-inflammatory treatments and nutritional interventions is important to improve disease progression and lays the foundation for early identification of high-risk patients. Recently, several hematological biomarkers that can be easily calculated from routine blood tests have been utilized to assess inflammatory, immune and nutritional status to identify high-risk patients without the need for expensive equipment or setups. For example, the prognostic nutritional index (PNI) reflecting immune-nutritional status has been employed as a prognostic marker in patients with CRC (7). Other novel markers of systemic inflammation with prognostic relevance are the systemic immune inflammation index (SII) and the Systemic Inflammation Response Index (SIRI), which indicate the balance between the inflammatory response and immune status, and increasing values over time may be signals of disease recurrence or progression (8).

Although the prognostic role has been adequately evaluated as noted earlier, to our knowledge, only a limited number of studies has examined their combined predictive value for CRC progression to improve risk stratification (9,10). Nevertheless, the findings of the previous studies require external validation owing to the small sample sizes or limited follow-up periods. The present study developed a composite model integrating the PNI with the SII or SIRI, termed the nutritional-systemic immune inflammation (N-SII) index and nutritional-systemic inflammation response (N-SIRI) index for survival prediction in CRC patients. We further investigated whether these combined prognostic scores demonstrated superior accuracy in predicting long-term disease-free survival (DFS) compared to either index alone. We present this article in accordance with the STROBE reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-522/rc).

Methods

Study design and participant selection

The ethical approval was authorized by the Ethics Examining Committee of Human Research of the Affiliated First Hospital of Soochow University (No. SC-KY-2025021). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was waived, because all data were retrieved from the existing medical and administrative records to inform treatment. No patients were contacted for the study and no personal data were disclosed.

Between January 1, 2022 and December 31, 2024, patients undergoing radical surgery in our department for the treatment of CRC according to the Pan-Asian adaption of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines were reviewed using the electronic medical records (EMRs) (11). Inclusion criteria were as follows: (I) confirmed diagnosis of primary CRC (11); (II) older than 18 years of age; and (III) completion of primary surgical resection followed by chemotherapy for CRC. Patients were excluded if they had severe cardiovascular, hepatic or renal dysfunction, metabolic disease, a history of inflammatory bowel disease or colorectal adenoma, hypersensitivity to treatment drugs, pregnant or incomplete medical data.

Outcomes measurement

Demographic and clinical data of patients within 72 hours prior to surgery upon admission were extracted from our EMRs. These data included age, gender, body mass index, smoking history, tumor size and location, histological type, tumor stage, lymphatic infiltration, tumor/node/metastasis (TNM) stage, surgical approach, adjunctive chemoradiotherapy and hematological variables such as white blood cell (WBC), hemoglobin (HGB), platelets (PLT), lymphocytes, neutrophils, monocytes, serum albumin, total bilirubin (TBIL), creatinine, blood urea nitrogen, lactate dehydrogenase (LDH), carcinoma embryonic antigen (CEA) and carbohydrate antigen 19-9, 125 and 72-4 (CA19-9, 125 and 72-4). Follow-up data were retrieved from medical records, which included regular clinic visits every 3 months during the first one year, every 6 months for the next two years; and thereafter annually through telephone interviews conducted by our specially trained nurses according to our clinical protocol.

The PNI was calculated by albumin and lymphocyte values using the following formula: [10×serum albumin (g/dL) + 5×total lymphocyte value (10⁹/L)] (12). The systemic immune-inflammation index (SII) was calculated using the formula: [platelet count × (neutrophil/lymphocyte ratio)] (13). The systemic inflammation response index (SIRI) was calculated as follows: (neutrophil count ×monocyte count)/lymphocyte count (14). Patients were classified into low and high categories based on the optimal cut-off values (OCV) of the PNI, SII or SIRI scores, using the Youden index. A composite N-SII model was developed by integrating elements from the PNI and SII scores. Patients were redistributed based on the OCVs of these indices as follows: a score of 2 for both low PNI and high SII; a score of 1 for either low PNI or high SII; and a score of 0 for high PNI and low SII. Similarly, the combined N-SIRI scores were developed using the same approach, assigning a score of 2 for low PNI and high SIRI, a score of 1 for either low PNI or high SIRI, and a score of 0 for high PNI and low SIRI.

The primary endpoint was prognostic accuracy, assessed by the area under the ROC curve. Second outcomes included the OCVs and hazard ratios (HRs) for each prognostic indicator, as well as long-term DFS, defined as the interval from the first day of surgical resection to the occurrence of any CRC progression, recurrence, death or the last follow-up visit.

Sample size calculation

PASS statistical software, version 22.0 (NCSS, LLC., Kaysville, Utah, USA) was used to calculate the sample size. Based on a consensus among multidisciplinary experts following a series of case discussions, a clinical superiority design was developed to compare the accuracy of the integrated prognostic model for predicting overall survival in patients with CRC against the use of a single model, which yielded an area under the curve (AUC) of 0.75 to 0.80 using ROC analysis. The investigator aimed to detect a superior AUC ranging from 0.80 to 0.85 when using the combined model. The correlation between responses from the two models was approximately 0.6 for both positive and negative associations. To achieve 90% power with a two-sided type I error rate of 5%, the required sample size was calculated to be 1,122. Accounting for an anticipated 20% data loss, the final sample size was adjusted to 1,404.

Statistical analysis

SPSS software version 22.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. Statistical significance was set as P<0.05. Kolmogorov-Smirnov Z test was employed to assess data normality. Normally and non-normally distributed continuous data and categorical data were expressed as mean ± standard deviation (SD) and percentages. Comparisons were conducted using t-tests and Chi-squared test. Kaplan-Meier method was utilized to analyze the survival probabilities, and the log-rank test was applied to compare differences among subgroups. Post-hoc comparisons were performed using Bonferroni Correction. Cox proportional hazards regression was used to calculate HRs and 95% confidence intervals (CIs). ROC curves with AUC values were utilized to assess the predictive effectiveness of PNI, SII, SIRI and the composite N-SII and N-SIRI scores for survival.

Results

Table 1 showed the flowchart of the study cohort. A total of 1,443 patients who met the criteria were included for analysis, among whom 71.9% experienced DFS during the 5-year follow-up. A subset of 433 cases (30%) was used as the testing set for cross-validation. The demographic and clinical characteristics of patients at baseline were showed in Table 2. Tumor size, histological type, TNM stage, tumor stage and adjunctive treatment significantly differed between the cohorts with DFS and without.

As shown in Figure 1, based on the OCVs of 48.3 for PNI, 352 for SII, 1.3 for SIRI, 0.549 for N-SII and 0.567 for N-SIRI, the mean 5-year DFS was significantly longer in patients with low PNI, low SII, low SIRI and an N-SII or N-SIRI score of 2, compared to those with a high-risk isolated score. Specifically, the mean 5-year DFS was 42.62 months (95% CI: 41.27–43.97) vs. 33.20 months (95% CI: 29.73–36.68) for PNI (log-rank P<0.001); 42.78 months (95% CI: 41.42–44.15) vs. 33.57 months (95% CI: 30.37–36.76) for SII (P<0.001); and 42.93 months (95% CI: 41.56–44.31) vs. 35.13 months (95% CI: 31.83–38.43) for SIR (P<0.001). For the integrated scores, the mean 5-year DFS was 44.14 months (95% CI: 42.76–45.52) vs. 31.19 months (95% CI: 28.50–33.89) for N-SII (P<0.001) and 43.95 months (42.58–45.33) vs. 31.24 months (95% CI: 28.46–34.02) for N-SIRI (P<0.001).

Figure 1 The Kaplan-Meier curves showed the 5-year DFS rates of CRC patients categorized by different prognostic index. (A) The high- and low-PNI cohorts (log-rank test P<0.001); (B) the high- and low-SII cohorts (P<0.001); (C) the high- and low-SIRI cohorts (P<0.001); (D) the N-SII score of 0–1 and 2 cohorts (P<0.001); and (E) the N-SIRI score of 0–1 and 2 cohorts (P<0.001). CRC, colorectal cancer; DFS, disease-free survival; N-SII, nutritional-systemic immune inflammation index; N-SIRI, nutritional-systemic inflammation response index; PNI, prognostic nutritional index; SII, systemic immune inflammation index; SIRI, systemic inflammation response index.

Figure 2 illustrated the accuracy of different prognostic indicators in predicting the progression of CRC. The time-dependent ROC curves showed an AUC of 0.829 (95% CI: 0.802–0.856) and 0.850 (95% CI: 0.825–0.874) when using the combined scoring system of N-SII and N-SIRI. This indicated that the combined N-SII and N-SIRI scores had superior predictive value for long-term DFS in patients with CRC compared to the conventional PNI, SII and SIRI scores alone, which had AUCs of 0.760 (95% CI: 0.728–0.793), 0.782 (95% CI: 0.751–0.812) and 0.784 (95% CI: 0.753–0.815), respectively (all P<0.001).

Figure 2 Area under the receiver operating characteristic curve of different prognostic nutritional indexes for patients with CRC, including PNI, SII, SIRI, N-SII and N-SIRI scoring system. AUC of N-SII and N-SIRI scores were significantly greater than either of conventional index alone. AUC, area under the curve; CRC, colorectal cancer; N-SII, nutritional-systemic immune inflammation index; N-SIRI, nutritional-systemic inflammation response index; PNI, prognostic nutritional index; SII, systemic immune inflammation index; SIRI, systemic inflammation response index.

As shown in Table 3, univariate Cox regression analysis indicated that the tumor size, histological type, TNM stage, tumor stage, postoperative chemotherapy, PNI, SII, SIRI and the integrated score of N-SII and N-SIRI were independent factors associated with 5-year DFS among patients with CRC. After adjusting for clinical factors using multivariable analysis with the likelihood ratio selection, the combined score of N-SII or N-SIRI was identified as an independent predictor of the 5-year DFS in CRCs. Conversely, tumor size, TNM stage, PNI, SII and SIRI were eliminated from the model, suggesting that the conventional isolated prognostic indicators might be less effective than combined scoring models. Cross-validation was utilized to mitigate overfitting. The Brier score and the calibration-in-the-large (CITL) score for the integrated N-SII and N-SIRI scoring system were presented in Figure 3.

Figure 3 Calibration curves of prediction model predicting the 5-year DFS among patients with CRC. (A) a calibration curve with a Brier value of 0.074 for the integrated N-SII scoring system; and (B) a calibration curve with a Brier value of 0.068 for the integrated N-SIRI scoring system. CITL, calibration-in-the-large; CRC, colorectal cancer; DFS, disease-free-survival; N-SII, nutritional-systemic immune inflammation index; N-SIRI, nutritional-systemic inflammation response index.

Discussion

This study investigated an integrated model of prognostic indices to predict the clinical outcomes of CRC patients over a 5-year follow-up. Our findings demonstrated that the combined scoring system of N-SII and N-SIRI provided better predictive accuracy for DFS in CRC patients, achieving AUC values of 0.829 and 0.850 in the testing set, respectively, outperforming individual assessment indices.

The PNI score, derived from serum albumin levels and lymphocyte counts, reflects both the nutritional and immunological status of patients and has been proposed as an accurate prognostic marker for patients with CRC (15). Nevertheless, beyond nutritional and immune status, CRC progression is driven by complex systemic processes, among which tumor-promoting inflammation is a hallmark. Chronic, dysregulated and persistent systemic inflammation leads to invasion, proliferation, angiogenesis, metastasis, suppression of anti-cancer immunity and drug resistance in CRC patients (16). The systemic inflammatory response can be evaluated by using markers from routine blood tests, including neutrophil, platelet and lymphocyte counts; C-reactive protein levels; neutrophil-to-lymphocyte ratio (NLR); lymphocyte-to-monocyte ratio (LMR); and platelet-to-lymphocyte ratio (PLR) (17). Existing evidence indicates that neutrophils contributes to oxidative DAN double-strand breaks in epithelial cells and suppress the immune system by inhibiting peripheral lymphocyte counts during the inflammatory response, leading to impaired immune function and elevated NLR levels (18). Monocyte counts increase during early tumorigenesis through the secretion of proteases and activators that degrade the extracelluar matrix, facilitating tumor invasion and suppressing the anti-tumor immune response (19). However, neither the NLR, MLR nor PLR has been identified as an independent prognostic factor for DFS in 112 patients with stage I-III CRC in a previous retrospective study (20). Recently, the SII score, a blood-based biomarker reflecting relative changes in neutrophils, platelets and lymphocytes, has been suggested to indicate a pro-tumor inflammatory state and compromised immune surveillance. A meta-analysis of 29 published studies involving 10,091 cases demonstrated the prognostic value of SII in patients with CRC, showing that high SII levels were associated with poor overall survival (HR: 1.75, 95% CI: 1.4–2.19) and poor progression-free survival (HR: 1.25, 95% CI: 1.18–1.33) (8). In addition, the SIRI score, which incorporates peripheral neutrophil, monocyte and lymphocyte counts, has emerged as another important inflammation-based prognostic marker since its introduction in 2016 (14). Previous studies assessing the prognostic role of SIRI on the overall survival in patients with CRC revealed that significant heterogeneity in the data (I²=59% and P=0.06) and found that elevated SIRI scores were linked to a significantly worse disease outcomes, with a pooled HR of 2.65 (95% CI: 1.6–4.38) (21). Our results were consistent with these findings, which reported a significantly longer mean DSF in the low-risk cohorts compared to the high-risk cohorts for PNI, SII, and SIRI (all P<0.001). Additionally, Cox regression analysis showed that a high-risk PNI, SII or SIRI scores were independent negative prognostic factors, with HRs of 1.014, 1.376 and 1.421, respectively.

Nevertheless, each of the aforementioned indices has been reported to have limited prognostic efficacy in CRCs. As previously mentioned, the AUC from the ROC analysis for the PNI alone and SIRI alone was 0.62 and 0.69, respectively, indicating relatively low sensitivity and specificity in metastatic CRC patients (21,22). Considering these results, the ultimate survival of CRC patients was determined by nutritional, immunological and inflammatory statuses in addition to oncological and treatment factors. Recently, several clinical studies integrated the immune-nutritional PNI with one of the inflammatory biomarkers, reporting that the combined scoring indices improved prognostic accuracy for cancer patients. Specially, patients with both low PNI and high SII or SIRI exhibited worse prognoses (23,24). Consistent with prior findings, our Kaplan-Meier analysis confirmed that patients with an N-SII or N-SIRI score of 2 had a significantly shorter DFS compared to those with scores of 0 or 1. Furthermore, Cox regression analysis supported the strong predictive value of these scores for poor DFS outcomes, with HR values of 2.442 and 2.527, respectively. According to a previous study combining the pre-treatment PNI and SII scores to predict chemotherapy response and prognosis in gastric patients, those with a score of 2 (both low PNI and high SII) had a 2.758-fold (95% CI: 1.167–4.276) increased risk of cancer progression during follow-up, with a mean DFS of 6.3 months (P=0.003), compared to patients with a score of 0 or 1 (25). Based on our time-dependent ROC curves, both the integrated N-SII and N-SIRI scoring systems demonstrated superior prognostic efficacy in distinguishing survival among CRC patient subgroups, achieving AUC values of 0.829 and 0.850, respectively. These results outperformed conventional markers such as PNI (AUC =0.760), SII (AUC =0.782) and SIRI (AUC =0.784) alone (all P<0.001). In alliance with our findings, a retrospective research employing combined nutritional and immune indices also identified these models as effective prognostic markers for patients with stage I-III CRC, showing significantly higher relapse-free and overall survival rates in the low-risk cohort (two P<0.001) (10). Moreover, several trials compared the accuracy of combined nutritional-inflammatory indices with the isolated nutritional indicators in patients with gastric cancer. Their results demonstrated that the combined model improved prognostic accuracy, achieving a superior AUC of 0.890 (95% CI: 0.865–0.914), compared to the inflammation model (AUC =0.662; 95% CI: 0.673–0.706) and the nutritional model (AUC =0.666; 95% CI: 0.650–0.719) (26-28). Interestingly, a similar study investigating the combined PNI and SIRI also proved to be an accurate tool for evaluating the clinical prognosis in patients with CRCs, with a lower AUC of 0.767, which might be due to its limited sample size (22). The results mentioned above suggest that new models, such as N-SII or N-SIRI, demonstrated superior discriminatory performance compared to single-indicator models. This improvement may be attributed to the integration of the distinct prognostic significance of nutritional, immune, and inflammatory scores, which overcomes the limitations of relying on current values in clinical practice and enhances the diagnostic accuracy of these well-established screening modalities.

This study has several limitations. First, investigators were not blinded to outcomes due to the retrospective nature of the study, which might introduce confounding bias. Second, external validation was not performed, limiting the generalizability to the findings to all patients with CRC. Third, there was no universal cut-off value for all indices because of variations in ethnicity, cancer type or stage; our OCVs were derived solely from our data. Fourth, the study did not examine changes in the integrated N-SII or N-SIRI scores following postoperative chemotherapy, which might have predictive value for the prognosis of patients with CRC. Future research should include well-designed, randomized, controlled trials to validate our results.

Conclusions

In conclusion, our findings suggested that nutritional and systemic inflammatory-immune indicators had significant prognostic value in patients with CRC. This study highlights the importance of integrating these two types of indices for effective risk stratification in routine CRC prognostic assessments in clinical settings. The combined N-SII and N-SIRI model demonstrated superior prognostic accuracy compared to current one-size-fits-all indicators, potentially facilitating personalized postoperative management for high-risk CRC patients.

Acknowledgments

The authors gratefully thank Professor Zhonghui Guan for providing the language editing.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-522/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-522/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-522/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-522/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by the Ethics Examining Committee of Human Research of the Affiliated First Hospital of Soochow University (No. SC-KY-2025021), and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consents were waived, because all data were retrieved from the existing medical and administrative records to inform treatment.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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