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Prognostic value and clinicopathological significance of pre-and post-treatment systemic immune-inflammation index in colorectal cancer patients: a meta-analysis

World Journal of Surgical Oncology volume 23, Article number: 11 (2025) Cite this article

Abstract

Background

In recent years, the association between systemic immune-inflammation index (SII) and the prognosis of patients with colorectal cancer (CRC) has remained a topic of considerable debate. To address this, the present study was carried out to investigate the prognostic significance of SII in CRC.

Methods

Databases including PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science were scrutinized up to March 27, 2024. The relationship between pre- and post-treatment SII levels and the prognosis of CRC was evaluated. Following literature screening, quality assessment, and extraction of outcome measures, a meta-analysis was conducted using Stata. Publication bias was assessed by funnel plots and Egger’s test.

Results

A total of 27 studies were included in the analysis. Pooled results demonstrated that a high SII level was associated with poor overall survival (OS, HR = 1.78, 95% CI = 1.40–2.26), progression-free survival (PFS, HR = 1.80, 95% CI = 1.26–2.56), disease-free survival (DFS, HR = 1.91, 95% CI = 1.43–2.56), and recurrence-free survival (RFS, HR = 3.29, 95% CI = 1.58–6.88). Notably, the association between pre-treatment SII and OS, PFS, and DFS was stronger than that observed for post-treatment SII, indicating that treatment may attenuate the predictive valueof SII for survival outcomes. Additionally, elevated SII was correlated with poor tumor differentiation, tumor location in the rectum, and larger tumor size ≥ 5 cm.

Conclusion

Our meta-analysis suggested that a high SII is a predictor of poor prognosis in CRC patients. High SII levels were strongly correlated with inferior OS, PFS, DFS, and RFS. The relationship between SII and survival outcomes was attenuated post-treatment compared to pre-treatment. Additionally, elevated SII was correlated with clinicopathological factors in CRC patients. These findings suggest that SII can serve as an independent prognostic indicator for CRC.

Introduction

Colorectal cancer (CRC) is a prevalent malignancy and ranks among the leading causes of cancer-related death globally [1]. In 2022, it was estimated that there would be more than 1.9 million new cases of CRC (including anal cancer) and 904,000 deaths, accounting for nearly one-tenth of all cancer cases and deaths worldwide. Overall, CRC ranks third in terms of morbidity and second in terms of mortality [2]. Despite advancements in treatment methods, the prognosis of CRC has not significantly improved. Integrating effective biomarkers into treatment strategies has the potential to notably enhance the prognosis of patients with CRC [3]. Many prognostic and predictive biomarkers, such as RAS mutation status, BRAF mutation, and microsatellite instability, have been employed for early prediction and prognostic assessment in CRC. However, these biomarkers often require invasive testing and dependence on specialized laboratory equipment [4]. Thus, there is a pressing need to identify easily accessible adjunctive biomarkers to assist clinicians in implementing personalized treatments and enhancing patient prognosis.

Evidence has suggested that chronic inflammation is extensively involved in the occurrence and progression of CRC [5]. Systemic inflammatory responses are associated with the prognosis of various cancers, including gastric, esophageal, colorectal, liver, pancreatic, breast, and bladder cancers [6,7,8]. Systemic inflammation is considered a key component of the tumor immune microenvironment, which plays a critical role in the development and progression of many solid tumors [9, 10] Several studies have demonstrated that inflammation-based prognostic biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), prognostic nutritional index (PNI), and Glasgow prognostic score (GPS), can offer valuable survival information for patients with CRC [11,12,13]. Multiple studies have indicated that the SII is associated with the prognosis of malignant tumors [14]. SII is a promising inflammation-based biomarker, primarily calculated from lymphocyte, neutrophil, and platelet counts, and can be easily measured from venous blood samples, making it more convenientto obtain than other biomarkers [12, 15].SII (formula: SII = Neutrophil Count × Platelet Count/Lymphocyte Count) offers a more comprehensive reflection of the immune environment in patients with CRC compared to NLR and PLR. Chen et al. found that the SII is a superior factor in predicting OS, PFS, and DFS compared to the PLR and NLR, which can demonstrate greater immunological effects [12].

To our knowledge, two meta-analyses have analyzed the relationship between SII and OS and PFS in CRC patients [4, 16]. In detail, Dong et al. and Li et al. conducted meta-analyses of 12 studies involving a total of 3919 patients with CRC. Their analyses revealed that higher pre-treatment SII is associated with poorer OS and PFS in patients with CRC, suggesting that SII may serve as a determinant in the determination of clinical treatment regimens for these patients. However, as clinical data continues to evolve, in recent years, an increasing number of studies have investigatedthe relationship between CRC and SII, not only in terms of OS and PFS but also with many other prognostic factors, including the recurrence of CRC patients and DFS. For example, in a retrospective study by Zhang et al. involving a total of 188 CRC patients, it was concluded that SII could effectively predict 1-year, 2-year, and 3-year DFS after surgery and could independently predict postoperative recurrence in CRC patients [12]. In a retrospective study by Chen et al. involving 206 CRC patients, it was found that the change in the SII (ΔSII) is an independent prognostic factor for CRC patients undergoing radical resection.In the future, ΔSII may be used as an important reference indicator to guide personalized treatment [17]. In a study by Sato et al. covering 86 patients with obstructive colorectal cancer (OCRC) at stages I to III, it was noted that lower preoperative PLR, SII, and PIV values were independently associated with poorer RFS [18]. Based on the above findings, the present studywas carried out to utilize recent publications to update the analysis, comprehensively review and summarize all available data, and assess the correlation between SII and OS, PFS, DFS, or RFS in CRC patients, alongside its connection with clinicopathological parameters.

In recent years, meta-analyses have been extensively utilized to assess the prognostic role of the SII in various cancers. Numerous related studies have shown that elevated preoperative SII is significantly associated with a worse prognosis in a range of solid tumors. However, current research on the specific prognostic value of SII in CRC patients remains limited. Existing meta-analyses predominantly focus on the prognostic significance of preoperative SII, with insufficient exploration of the impact of dynamic postoperative SII changes on patient outcomes. To address these limitations, this study was carried out to bridge the research gap by integrating the latest data to comprehensively evaluate the prognostic value of both preoperative and postoperative SII in CRC patients.

Methods

Study design

This study was conducted following the guidelines from PRISMA [19], ensuring transparent and comprehensive reporting of methods and results. Additionally, the study has been registered with PROSPERO (ID: CRD42024535199). As this study entails meta-analysis and systematic review of previously published research, ethical approval was deemed unnecessary.

Inclusion/exclusion criteria

The inclusion criteria were: [1] Patients of all age groups and geographic locations with pathologically confirmed colon cancer, rectal cancer, or metastatic intestinal cancer; [2] Prognostic predictors included SII; [3] The report confirmed the relationship between SII and the prognosis of CRC patients, such as OS, PFS, DFS, or other prognostic factors; [4] The study reports are limited to those published in English.

The exclusion criteria were: [1] Patients with cancers other than CRC or those with metastases to the colon from other cancers; [2] Previous meta-analyses/reviews, animal studies, descriptive studies, case reports, or conference abstracts; [3] Studies without clear prognostic predictors, including the relationship between OS, PFS, or DFS and SII; [4] Data results that are unclear or insufficient information for data analysis; [5] Newcastle-Ottawa Scale (NOS) score less than 5 [20]; [6]Studies with preoperative exclusion or emergency reduction for patients with serious infections.

Search strategy

Until March 28, 2024, two researchers (T.Y.T. and Q.L) independently performed comprehensive searches in PubMed, Embase, the CENTRAL, and Web of Science, without imposing restrictions on literature type, publication date, or publication status. MeSH and free textterms were employed for keyword searches, encompassing all known spellings of “colorectal cancer” and “systemic immune-inflammation index” to ensure comprehensive coverage of the literature. The full search strategy is presented in Additional file 1.

Literature screening

Based on the inclusion and exclusion criteria defined above, two researchers (T.Y.T. and Q.L.) independently conducted the literature screening. Initially, all potentially relevant studies were imported into EndNote 20, and duplicates were subsequently eliminated using both automatic and manual methods. Subsequently, studies that did not meet the inclusion criteria were excluded based on the examination of titles and abstracts. Following this, the full texts underwent further review and screening. Any discrepancies were resolved through discussion or mediation with a third researcher (W.C). Moreover, references of related articles were manually scrutinized to ensure no relevant studies were overlooked.

Data extraction and assessment of quality

The extracted information encompassed: [1] Basic details such as title, first author’s name, authors’ country, and year of publication; [2] Characteristics of the study design and subjects, including age, mean age, gender ratio, sample size, histological type, tumor TNM stage, SII cut-off value, treatment method, time of SII measurement, survival endpoints, and hazard ratio (HR) with corresponding 95% confidence interval (CI). OS and PFS/DFS served as the primary and secondary endpoints of this meta-analysis, respectively.

Assessment of quality

The methodological quality of the included studies was evaluated using the NOS. Scores on the NOS ranged from 0 to 9, with a score above 7 considered indicative of high quality in this study. In evaluating cohort selection, scoring criteria encompassed the representativeness of the cohort, selection of the non-exposed cohort from the same population, and accuracy of treatment records. Regarding comparability, scrutiny focused on whether exposed and non-exposed cohorts were selected and analyzed based on the most critical factors. For outcome assessment, criteria included independence, blinding, reliance on reliable records, sufficient follow-up time, and complete follow-up of all study subjects.

Data synthesis and statistical analysis

Summarized HRs and corresponding 95% CIs were employed to estimate the association between SII and OS, PFS, DFS, and RFS in CRC patients. Stata 16 was utilized to statistically assess heterogeneity among the included studies. If I2 exceeded 50% or p was less than 0.05 (indicating significant heterogeneity among studies), a random-effects model would be employed. Otherwise, a fixed-effects model would be applied. Subgroup analyses were conducted based on country, age, mean age, sample size, treatment method, tumor type, SII cut-off value, TNM stage, NOS score, and time of SII measurement to identify sources of heterogeneity. To further investigate the relationship between SII and clinicopathological characteristics in CRC patients, Odds ratio (OR)and corresponding 95% CIs were calculated. OR served as the effect size for the association between SII and clinicopathological factors, expressed alongside 95% CIs. Sensitivity analysis was conducted to pinpoint the source of heterogeneity, while Egger’s test was employed to examine potential publication bias. All statistical analyses were performed using Stata 16.0 (Stata Corporation, College Station, TX). A p-value less than 0.05 (two-sided) was deemed statistically significant.

Results

Search results

The characteristics and specific process of study inclusion and exclusion are detailed in Fig. 1 Initially, a total of 223 relevant studies were identified from the aforementioned four databases. Following automated duplication removal of 37 studies and manual elimination of 38 studies, 6 studies (comprising meta-analyses, reviews, guidelines, and conference abstracts) were excluded. Subsequently, the full texts of the remaining 142 studies were assessed for credibility, leading to the exclusion of 115 articles based on predetermined criteria. These criteria included study subjects not being CRC patients, absence of pertinent data on SII and prognostic indicators, studies involving cancers metastatic to the intestine from other sites, insignificant findings, illogical cohort study designs, and unavailability of full-text data. Ultimately, 27 articles were deemed suitable and included.

Fig. 1
figure 1

Identification of studies via databases and registers

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Characteristics of the studies included

Among the included studies, 19 studies were conducted in China [12, 18, 21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37], 4 in Japan [18, 38,39,40], 3 in Italy [28, 41, 42], and 1 in the USA [43]. Of these, 18 studies included patients with primary CRC [12, 18, 21, 22, 26,27,28, 30,31,32,33,34,35,36,37,38,39, 44], while 9 studies focused on recurrent CRC [23,24,25, 29, 40,41,42,43, 45]. Among the studies, 22 analyzed the relationship between SII and OS [12, 18, 22,23,24,25,26,27,28,29,30, 32,33,34, 36, 37, 39,40,41,42,43,44,45], 11 analyzed the relationship between SII and PFS [12, 23, 25, 34,35,36,37, 41,42,43, 45], 8 analyzed the relationship between SII and DFS [21, 22, 26, 27, 30, 33, 42, 44], and 3 analyzed the relationship between SII and RFS [18, 29, 38]. The sample sizes of the 27 studies ranged from 41 to 1383 individuals, with a median age range of 45–68 years. Among the 27 studies, 26 were retrospective cohort studies [12, 18, 21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40, 42,43,44,45], and 1 was a prospective cohort study [41]. The main characteristics of the 27 studies included in our study are presented in Table 1. The NOS scores of the 27 studies ranged from 6 to 9, with scores exceeding 7 indicative of high quality. Reasons for lower quality included inadequate and imprecise surgical records, non-independent or non-blinded outcome assessment, inadequate follow-up duration, incomplete follow-up, and absence of analysis concerning lost follow-up subjects.

Table 1 Main characteristics of studies included
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Impact of SII on OS in CRC patients

The prognostic analysis of SII and OS was conducted on 8347 patients across 22 studies [12, 18, 22,23,24,25,26,27,28,29,30, 32,33,34, 36, 37, 39,40,41,42,43,44,45]. The data exhibited significant heterogeneity (I2 = 92.0%, p < 0.000; Fig. 2), thus a random-effects model was employed. The results indicated a significant association between SII and OS, with higher SII showing approximately twice the risk compared to lower SII (HR = 1.78, 95% CI = 1.40–2.26, p < 0.001; Fig. 2; Table 2). Subgroup analyses were conducted from the following aspects: region, age, mean age, sample size, TNM stage, treatment method, tumor type, SII cutoff value, NOS score, and time of SII measurement. Regardless of each subgroup, poor OS was always significantly associated with high SII (Table 2). For TNM stages I–III, the hazard ratio of high SII compared to low SII was 2.4 times (HR = 2.40, 95% CI = 1.38–4.18, p = 0.002) (Table 2), while in stage IV, the hazard ratio of high SII compared to low SII was 1.43 times (HR = 1.43, 95% CI = 1.21–1.69, p = 0.000) (Table 2), indicating that tumor TNM staging is a factor affecting the relationship between SII and OS. In the group with SII < 550, the hazard ratio of high SII occurrence was about 2 times higher than that of low SII (HR = 1.94, 95% CI = 1.34–2.820, p = 0.000) (Table 2). In the group with SII ≥ 550, the hazard ratio of high SII occurrence was 1.66 times higher than that of low SII (HR = 1.66, 95% CI = 1.31–2.11, p = 0.000) (Table 2). This also indicated that the determination of the SII cut-off value is one of the factors affecting the relationship between SII and OS. Besides, both pre-treatment and post-treatment SII are associated with OS. The correlation between pre-treatment SII and OS (HR = 1.83, 95% CI = 1.42–2.34, p = 0.000) (Table 2) was stronger than that of post-treatment SII (HR = 1.59, 95% CI = 1.10–2.30, p = 0.014) (Table 2). Furthermore, in subgroup analysis, we found that heterogeneity remained high in some subgroups (I² > 50%, p < 0.05). (Table 2).

Fig. 2
figure 2

Forest Plot of the Association Between SII and OS in Patients with CRC

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Table 2 Synthesized HR and 95% CI for subgroup analysis of SII and OS, PFS, DFS in patients with CRC
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Impact of SII on PFS in CRC patients

Prognostic analysis of SII and PFS was conducted on 3996 patients across 11 articles [12, 23, 25, 34,35,36,37, 41,42,43, 45]. The data exhibited significant heterogeneity, thus a random-effects model was applied (I2 = 96.1%, p < 0.001) (Fig. 3). The analysis also indicated a significant correlation between high SII and poor PFS, with high SII showing approximately double the risk compared to low SII in PFS (HR = 1.80, 95% CI = 1.26–2.56, p = 0.001) (Fig. 3; Table 2). Subgroup analyses were conducted from the following aspects: region, age, mean age, sample size, TNM stage, treatment method, tumor type, SII cutoff value, and time of SII measurement. Poor PFS was always significantly associated with high SII in all subgroups (Table 2). In the subgroup analysis by region, the hazard ratio of high SII compared to low SII in China was 2.30 times (HR = 2.30, 95% CI = 1.95–2.78, p = 0.000) (Table 2). In Italy, the risk of high SII compared to low SII was 1.36 times higher (HR = 1.36, 95% CI = 0.81–2.29, p = 0.241) (Table 2), indicating that region is one of the factors influencing the relationship between SII and PFS. In the group with SII < 550, the hazard ratio of high SII occurrence was 2.26 times higher than that of low SII (HR = 2.26, 95% CI = 1.77–2.89, p = 0.000) (Table 2). In the group with SII ≥ 550, the hazard ratio of high SII occurrence was 1.44 times higher than that of low SII (HR = 1.44, 95% CI = 0.92–2.25, p = 0.108) (Table 2). This also indicated that the determination of the SII cut-off value is one of the factors affecting the relationship between SII and PFS. Moreover, in terms of treatment methods, the hazard ratio of high SII compared to low SII in the neoadjuvant chemotherapy group was 2.50 times higher (HR = 2.50, 95% CI = 1.39–4.50, p = 0.002) (Table 2). In the chemotherapy plus targeted therapy group, the hazard ratio of high SII to low SII was 1.58 times (HR = 1.58, 95% CI = 1.08–2.31, p = 0.017) (Table 2), also indicating that different treatment methods are factors influencing the relationship between SII and PFS. Irrespective of pre- or post-treatment status, SII exhibited a significant correlation with PFS (p < 0.05). Notably, the association between pre-treatment SII and PFS (HR = 1.89, 95% CI = 1.38–2.60, p = 0.000) (Table 2) appeared stronger than that observed with post-treatment SII (HR = 1.62, 95% CI = 1.02–2.57, p = 0.041) (Table 2). Moreover, concerning heterogeneity, the sources influencing the relationship between SII and PFS included region and age, while tumor TNM stage, sample size, SII cut-off value, treatment method, and time of SII measurement did not contribute to heterogeneity.

Fig. 3
figure 3

Forest plot of the association between SII and PFS in patients with CRC

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Impact of SII on DFS in CRC patients

Prognostic analysis of SII and DFS was conducted on 487 patients across 8 articles [17, 18, 21, 23, 25, 27, 33, 38]. The data exhibited significant heterogeneity, thus a random-effects model was applied (I2 = 71.4%, p < 0.000) (Fig. 4). A significant correlation between high SII and poor DFS was also observed. In terms of DFS, high SII showed nearly twice the hazard ratio compared to low SII (HR = 1.91, 95% CI = 1.43–2.56, p = 0.000) (Fig. 4; Table 2). Subgroup analyses were conducted and poor DFS was always significantly associated with high SII in all subgroups depending on the following aspects: age, mean age, sample size, TNM stage, treatment method, SII cutoff value, and time of SII measurement. (Table 2). Regarding the time of SII measurement, our findings indicated that pre-treatment SII is significantly correlated with DFS (p = 0.000), whereas post-treatment SII showed no significant correlation with DFS (p = 0.181). Furthermore, our analysis revealed that age, treatment method, TNM stage, and sample size contributed to heterogeneity, while the SII cut-off value and time of SII measurement did not.

Fig. 4
figure 4

Forest Plot of the Association Between SII and DFS in Patients with CRC

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Impact of SII on RFS in CRC patients

The prognostic analysis of SII and RFS was conducted on 487 patients across 3 studies [18, 29, 38]. The data exhibited significant heterogeneity, thus a random-effects model was applied (I2 = 58.5%, p = 0.090) (Additional file 2). The results indicated a significant correlation between high SII and poor RFS. In terms of RFS, high SII showed 3 times the hazard ratio compared to low SII (HR = 3.29, 95% CI = 1.58–6.88, p = 0.002) (Additional file 2, Table 2).

Correlation of SII with clinicopathological prognosis in CRC patients

Sixteen studies involving 5541 patients [12, 18, 21, 23, 25, 29, 32,33,34,35, 37,38,39, 41, 42, 45]reported the association of SII with 8 clinicopathological characteristics. The characteristics included: gender (male vs. female), tumor differentiation (poor vs. moderate/well differentiated), tumor location (rectum vs. colon), distant metastasis (yes vs. no), Eastern Cooperative Oncology Group Performance Status (ECOG PS) (1–2 vs. 0), age (older adults vs. middle-aged), and tumor size (≥ 5 cm vs. <5 cm). The synthesized results showed that CRC patients with poorly differentiated tumors (OR = 0.24, 95% CI = 0.10–0.59, p = 0.002), tumor location in the rectum (OR = 0.48, 95% CI = 0.31–0.73, p = 0.001), and tumor size ≥ 5 cm (OR = 0.52, 95% CI = 0.27–0.99, p = 0.002) exhibited relatively high SII. However, high SII is not significantly associated with gender (OR = 1.04, 95% CI = 0.80–1.35, p = 0.768), distant metastasis (OR = 0.68, 95% CI = 0.14–3.17, p = 0.622), ECOG PS (OR = 0.59, 95% CI = 0.26–1.31, p = 0.195), or age (OR = 0.76, 95% CI = 0.40–1.44, p = 0.407) (Table 3, forest plots are provided in Additional file 3).

Table 3 Correlation between SII and Clinicopathological characteristics in patients with CRC
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Sensitivity analysis

Due to significant heterogeneity among the included studies, a sensitivity analysis was conducted on the correlation results between SII and OS, PFS, and DFS (provided in Additional file 4), by excluding individual datasets one at a time. The analysis concluded that the synthesized results were stable.

Meta-regression

Meta-regression analyses were performed to determine sources of heterogeneity and to explore the effects of SII and OS, PFS, DFS in Patients with CRC. No significant differences were found in age, area, sample size, treatment, tumor type, TNM stage, cut-off value of SII, or the selection time of the SII. Detailed results are shown in Additional file 5.

Publication bias

The p-values for Egger’s test regarding OS, PFS, and DFS were 0.001, 0.029, and 0.002, respectively. Funnel plot analysis revealed significant publication bias. Egger’s test results (p < 0.05) further suggested the presence of publication bias among the included studies. However, to evaluate the impact of publication bias on the main findings, a sensitivity analysis was subsequently performed. The publication bias did not affect the research results of OS, PFS, and DFS [OS: before trim and fill method: HR = 0.593, 95% CI = 0.359–0.826, P = 0.000, after trim and fill method: HR = 0.404, 95% CI = 0.200-0.608, P = 0.000; PFS: before trim and fill method: HR = 0.586, 95% CI = 0.230–0.941, P = 0.001, before trim and fill method: HR = 0.488, 95% CI = 0.195–0.782, P = 0.001; DFS: before trim and fill method: HR = 0.649, 95% CI = 0.359–0.940, P = 0.000, before trim and fill method: HR = 0.649, 95% CI = 0.359–0.940, P = 0.000]. The P values before and after trim and fill method were less than 0.05, with statistical significance. Hence, the pooled estimates were stable.

Discussion

This study included 27 studies encompassing a total of 10,779 CRC patients to evaluate the prognostic value of SII in this population. Significant results from various subgroups demonstrated that SII was strongly and consistently associated with OS, PFS, DFS, and RFS, with high SII levels significantly correlating with poorer outcomes for all these survival indicators. The predictive effect of SII on survival outcomes was reduced after surgery compared to before surgery, likely due to changes in the postoperative inflammatory response and immune status.Moreover, high SII in CRC patients was associated with poorly differentiated tumors, tumor location in the rectum, and tumor size ≥ 5 cm.Although significant publication bias was identified during the assessment, sensitivity analysis indicated that its impact on the primary results was minimal, demonstrating the robustness of the study’s main findings. However, we acknowledge that publication bias may influence certain secondary analysis results. Therefore, further high-quality studies are needed to validate these findings.

The SII, calculated using specific counts of peripheral lymphocytes, neutrophils, and platelets, reflects the interplay between immune and inflammatory responses within the tumor microenvironment. SII provides a more precise and comprehensive assessment of immune and inflammatory activity, establishing it as a novel inflammatory biomarker. In recent years, SII has also been utilized in the prognostic prediction of various other solid tumors. For example, Salazar-Valdivia et al. concluded that SII could be used as a predictor of OS and PFS in patients with testicular cancer [46].Zhang et al. found that high SII values were an independent prognostic factor for low OS in patients with primary invasive bladder cancer [47]. Qiu et al. concluded that higher SII prior to treatment was significantly associated with poorer OS in patients with gastric cancer, as well as advanced tumor stage, positive lymph node metastasis, higher T-stage, and larger tumor size [48]. These findings suggested that SII could be used as an independent and effective prognostic biomarker for various cancers, enabling the stratification of cancer patients by risk. Our study results found that higher pre-treatment SII is associated with poorer OS and PFS in CRC patients, which are consistent with previous findings: Dong et al. and Li et al. collected data from 12 studies involving 3,919 CRC patients, demonstrating that, a high SII level indicates a poor prognosis and higher malignancy of the disease in CRC, suggesting that SII can serve as a determinant in the determination of clinical treatment regimens for these patients [4, 16]. Tumor cells can survive only by evading immune detection. Lymphocytes, integral to the immune system, primarily facilitate the lysis and apoptosis of target cells, exerting anti-tumor effects. Tumor-infiltrating lymphocytes recognize cancer cells through cytotoxic functions, inducing apoptotic cell demise andcontributing to the assault against micrometastases and residual tumor cells [49]. Neutrophils, in contrast, exhibit pro-tumor functions, and neutrophilia is associated with poor prognosis in cancer patients [50]. Tumor-associated N2 neutrophils, characterized by pro-tumor behaviors, can generate reactive nitrogen species (RNS) and reactive oxygen species (ROS), inducing genetic instability and DNA damage, and potentially instigating tumorigenesis [51, 52]. Platelets, a critical component of the tumor microenvironmentstroma, are reliable predictors of tumor prognosis [53]. Tumor cells activate platelets by secreting tissue factors and thrombin, which enable tumor cells to evade surveillance by natural killer (NK) cells [54]. Therefore, elevated platelet and neutrophil counts, along with decreased lymphocyte counts, indicate tumor growth towards infiltration, recurrence, or metastasis, and are associated with poor patient prognosis. The increase in SII is due to these cellular changes — elevated platelets and neutrophils, and reduced lymphocytes — signifying an unfavorable prognosis for patients. Although the effects of immunity and inflammation on tumors are not simply promotional or inhibitory, the balance of immune and inflammatory factors does affect the biological behaviors of tumors.

Recent studies have emphasized that personalized treatment, including surgery, chemotherapy, targeted therapy, and the latest immunotherapy, plays a crucial role in improving the prognosis of CRC patients. For example, Botrel et al. demonstrated that chemotherapy combined with bevacizumab improved response rates, progression-free survival, and overall survival in patients with metastatic CRC who had not previously received chemotherapy [55].Brenner et al. concluded that surgical techniques and adjuvant chemotherapy and/or radiotherapy increased the cure rate of patients after tumor resection and decreased the surgical mortality rate, which may improve survival in cancer patients [56]. Our study also found that the relationship between SII and prognostic survival of CRC patients was significantly attenuated after treatment. Although the relationship between SII and prognostic indicators varied across treatment modalities, SII remained a significant predictor of prognosis in CRC patients. This study suggested that SII can serve as a potential biomarker for evaluating the prognosis of CRC patients, though its application may be influenced by the patient’s disease stage, treatment selection, and SII cutoff value. This enables more precise risk assessments and tailored treatment regimens, potentially enhancing survival rates and quality of life for patients with tumors. Co-infection status has been widely recognized as an important factor affecting the SII. Infection may significantly increase neutrophil counts and platelet counts while reducing lymphocyte counts, leading to elevated SII values. However, since some of the included studies did not clearly report co-infection status, the impact of this factor on the study results cannot be completely ruled out. Therefore, future studies should more clearly include or exclude co-infected patients to improve data reliability.

This study explored the sources of heterogeneity through subgroup analysis and meta-regression analysis. Although some subgroup analyses (such as by region and treatment method) effectively reduced heterogeneity, the heterogeneity of some subgroups was still high (I² > 50%). Meta-regression analysis revealed that region and treatment method were key factors contributing to heterogeneity. This may be attributed to differences in medical conditions, treatment regimens, and measurement methods of the SII across regions. In addition, while the effects of sample size and SII cutoff values on heterogeneity are relatively small, their lack of standardization may still impact the interpretation of research findings. This study provides a preliminary discussion regarding the problem of heterogeneity but has not completely resolved the influence of high heterogeneity. Therefore, future studies should focus on further standardizing SII measurement methods, unifying the definition of cutoff values, and adopting more consistent treatment regimens to reduce variability between studies.

The results of this study showed that elevated preoperative SII was significantly associated with poor prognosis in CRC patients. Unlike previous studies, this research further investigated the potential impact of postoperative changes in SII on patient prognosis. It was found that the predictive value of postoperative SII for survival outcomes was diminished. This suggests that the inflammatory response and immune status of postoperative patients may be complexly affected by treatment methods, thereby altering the prognostic relevance of SII. Moreover, through subgroup analysis, this study identified the moderating effects of region and treatment methods on the relationship between SII and prognosis, offering important insights and directions for future research.

Limitations

However, this study has several limitations. Given the retrospective nature of most included studies, heterogeneity may have arisen. Further prospective studies focusing on relevant patient populations are warranted, as variations in SII cutoff values and measurement methods among the included studies could lead to inconsistencies in SII levels and subsequent outcomes. A major limitation of this study is that there was significant heterogeneity among the included studies (I² > 50%). Although subgroup analysis and meta-regression were used to explore potential sources of heterogeneity, some factors contributing to the heterogeneity could not be fully explained. This may be affected by differences in study design, patient characteristics, and data collection standards. Furthermore, the high level of heterogeneity may limit the generaliz ability of the findings. Therefore, it is recommended that future studies be conducted in broader regional and ethnic contexts to validate the results. One of the limitations of this study is that the coinfection status of all included patients was not fully controlled. Because infection may significantly affect SII values, future studies need to further refine the inclusion criteria and explicitly exclude coinfected patients or use them as confounding variables for correction analysis. Another major limitation of this study is that the included studies were mainly concentrated in East Asia, which may limit the generalize ability of the study results to other regions. This difference in geographical distribution may reflect differences in patient characteristics, medical practices, reference ranges of inflammatory indicators, and treatment regimens in different regions. Therefore, although this study revealed the potential application value of SII in East Asian CRC patients, further validation is required in studies involving diverse geographical and ethnic populations to enhance the universality and reliability of the conclusions. In addition, most of the included studies were retrospective studies or based on single-center data, which may lead to a low level of evidence. The absence of randomized designs and strict intervention controls in some studies could introduce selection bias and confounding factors, potentially affecting the robustness of the study conclusions. Therefore, future research should focus on conducting high-quality multicenter RCTs to validate the effectiveness of SII as a prognostic indicator for CRC patients.

Conclusion

In conclusion, this study reaffirms that high SII in CRC patients is associated with poorer OS, PFS, DFS, and RFS. The association between pre-treatment SII and OS, PFS, and DFS was stronger compared to post-treatment measures, indicating that treatment substantially attenuates the correlation between SII and survival outcomes in patients with CRC. Additionally, high SII in CRC patients is associated with poorly differentiated tumors, rectal tumor location, and tumor size ≥ 5 cm. These findings underscore the potential of SII as a prognostic biomarker for CRC patients.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SII:

Systemic immune-inflammation index

CRC:

Colorectal cancer

PFS:

Progression-free survival

DFS:

Disease-free survival

RFS:

Recurrence-free survival

CRC:

Colorectal cancer

NLR:

Neutrophil-to-lymphocyte ratio

PLR:

Platelet-to-lymphocyte ratio

LMR:

Lymphocyte-to-monocyte ratio

SIRI:

Systemic inflammation response index

PNI:

Prognostic nutritional index

GPS:

Glasgow prognostic score

OCRC:

Obstructive colorectal cancer

HR:

Hazard ratio

CI:

Confidence interval

OR:

Odds ratio

RNS:

Reactive nitrogen species

ROS:

Reactive oxygen species

NK:

Natural killer

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  1. Yueting Tan and Bei’er Hu contributed equally to this work and share first authorship.

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  1. Hunan University of Traditional Chinese Medicine, The Second Affiliated Hospital of Hunan University of Traditional Chinese Medicine, No. 233, Cai’e North Road, Kaifu District, Changsha, Hunan, 410005, China

    Yueting Tan, Bei’er Hu, Qian Li & Wen Cao

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All authors contributed to the study conception and design. Writing - original draft preparation: Y.T.; Writing - review and editing: Q.L.; Conceptualization: Y.T.; Methodology: Y.T. and B.H.; Formal analysis and investigation: Y.T. and B.H.; Funding acquisition: Y.T.; Resources: Q.L.; Supervision: W.C. and B.H. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Tan, Y., Hu, B., Li, Q. et al. Prognostic value and clinicopathological significance of pre-and post-treatment systemic immune-inflammation index in colorectal cancer patients: a meta-analysis. World J Surg Onc 23, 11 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12957-025-03662-z

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World Journal of Surgical Oncology

ISSN: 1477-7819

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