Prognostic value of systemic immune-inflammation index in patients with localized and advanced renal cell carcinoma: a systematic review and meta-analysis

Introduction

Renal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, with clear cell carcinoma being the predominant pathological subtype. GLOBOCAN 2022 data estimated approximately 435,000 new kidney cancer cases worldwide, accounting for 2.2% of all newly diagnosed cancers (1), and its occurrence is influenced by multiple risk factors (2). Treatment strategies depend on clinical stage: localized disease is treated with surgery, adjuvant immunotherapy may be considered for select high-risk patients after nephrectomy, and advanced disease receives systemic therapy. Although substantial advances have been made in the treatment of metastatic RCC (mRCC) in recent years—particularly with the widespread use of targeted therapy and immunotherapy in improving survival outcomes (3,4)—the expected clinical outcome is still poor. According to data from the Surveillance, Epidemiology, and End Results (SEER) program, the 5-year relative survival rate for these patients is only 20.3% (5). Currently, traditional indicators such as tumor stage, grade, and histological subtype, while possessing certain prognostic value, remain insufficient for accurate individualized prognostic assessment. Although established prognostic models, including the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) and Memorial Sloan Kettering Cancer Center (MSKCC) criteria, are widely used in mRCC, they rely on multiple clinical and laboratory parameters. In this context, the identification of other readily available and reproducible biomarkers—such as the systemic immune-inflammation index (SII)—may complement existing risk stratification tools and improve prognostic accuracy. SII integrates three routinely measured peripheral blood parameters, reflecting the balance between the inflammatory and immune status of the host, and offers practical advantages for clinical application.

Chronic inflammation is considered a hallmark of cancer and serves as a key factor in the formation of the tumor microenvironment, angiogenesis, invasion, and immune evasion in RCC. Complex interactions exist between multiple inflammatory factors and prognosis in patients with various cancers, including RCC (6). Peripheral blood parameters may reflect cancer-related inflammatory processes (7-10). In this context, composite indices based on routine peripheral blood inflammatory cell counts have become a focus of oncological prognostic research because they are noninvasive, reproducible, and cost-effective. The SII is a representative parameter, defined as: (platelet count × neutrophil count) divided by lymphocyte count. Recently, several retrospective studies have focused on the relationship between SII and outcomes such as overall survival (OS) and progression-free survival (PFS) in patients diagnosed with RCC, consistently reporting that elevated SII predicts poorer prognosis (11-13). These results indicate that SII may be a promising prognostic tool; however, inconsistencies among studies remain, and the overall strength of evidence warrants systematic evaluation.

Although previous meta-analyses have analyzed the relationship between SII and patient prognosis in mRCC (14), previous meta-analyses have been limited by smaller sample sizes or narrower scope, and an updated synthesis incorporating recent evidence is warranted. Therefore, to address the current evidence gap, this study aims to perform a comprehensive and up-to-date meta-analysis by systematically reviewing the literature and applying strict quality assessment methods to analyze the prognostic value of SII in patients with RCC, focusing on its associations with OS, cancer-specific survival (CSS), and PFS, and further performing subgroup analyses to verify the consistency of the findings, thereby supporting individualized risk management and treatment strategy development. We present this article in accordance with the PRISMA reporting checklist (15) (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0436/rc).

Methods

Literature search

This study was registered with PROSPERO (CRD420251271926). Two authors, L.Z. and W.W., designed the search strategy. Both investigators independently formulated subject-specific terms and relevant keywords to conduct searches across multiple databases including PubMed, Embase, Web of Science, and the Cochrane Library, covering the period from the inception of the databases to October 2025. The search used a wide range of terms such as “Carcinoma, Renal Cell”, “Renal Cell Carcinomas”, “Renal Carcinoma”, “Renal Cell Cancer”, “systemic immune-inflammation index”, “systemic immune inflammation index”, and “SII”. The PubMed search formula is as follows: ((“Carcinoma, Renal Cell”(Mesh)) OR (((Renal Cell Carcinomas) OR (Renal Carcinoma)) OR (Renal Cell Cancer))) AND (((systemic immune-inflammation index) OR (systemic immune inflammation index)) OR (SII)). Table S1 presents the literature search strategy.

Study selection

To be included in the analysis, studies had to meet the following criteria: (I) pathological evaluation was used to confirm RCC diagnosis; (II) analysis of the prognostic value of SII regarding OS, PFS, or CSS; (III) availability of hazard ratios (HRs) with corresponding 95% confidence intervals (CIs), directly reported or calculable from provided data; (IV) division of patients into high- and low-SII categories was performed using predetermined thresholds; (V) full-text publication status.

Exclusion criteria encompassed the following: (I) review articles, editorial comments, abstracts from conferences, letters, and case reports; (II) publications without complete data to derive HRs and 95% CIs; (III) absence of survival-related outcomes; (IV) data duplication or cross-study overlap. L.Z. and W.W. independently reviewed titles and abstracts as part of the study selection procedure, retrieved the full-text articles, and evaluated them for eligibility. Any discrepancies were resolved through discussion between the reviewers.

Data extraction

L.Z. and W.W. independently conducted the data collection phase of the study. In cases of disagreement, the third author (D.L.) made the final decision. Among the extracted variables were the first author’s name, year of publication, geographic origin, design type, total sample size, treatment approach, age of participants, duration of follow-up, time of SII assessment, applied cut-off value, study period, and HRs (95% CIs) for OS, PFS, or CSS.

Quality assessment

The Newcastle-Ottawa Scale (NOS) (16) was employed to determine the quality of the included studies, according to selection, comparability, and outcome domains. Each study could receive up to nine points. Those achieving scores from 7 to 9 were regarded as having high methodological quality.

Statistical analysis

Data processing and analysis were performed using STATA 15.0 alongside Review Manager 5.4. The analysis combined HRs and 95% CIs to explore the prognostic role of SII in RCC, while heterogeneity was quantified using the Cochran Q test and Higgins’ I² (17). Heterogeneity was regarded as statistically significant if I² exceeded 50% or the P value fell below 0.1. As a result, a random-effects model was applied for all statistical analyses. Furthermore, subgroup and sensitivity analyses were performed to investigate the robustness of the results and identify potential sources of heterogeneity. Publication bias was assessed via funnel plot visualization, complemented by Egger’s test. A P value less than 0.05 was interpreted as statistically significant. For outcomes with publication bias, the trim-and-fill method was used to evaluate its impact on the pooled results.

Results

Study characteristics

The initial screening across databases identified 168 records. After eliminating 74 duplicated entries, a review of the titles and abstracts led to the removal of another 68 studies. A total of 26 full-text articles were reviewed for eligibility. Of these, four were excluded mainly due to the lack of sufficient data necessary for conducting survival analysis. As a result, 22 studies were ultimately included in the final meta-analysis (11-13,18-36) (Figure 1).

Figure 1 Flow chart of literature screening.

Of the 22 studies that met the inclusion criteria, the geographical distribution was as follows: twelve were conducted in Asia (12,13,19,22-24,26,28,32-35), eight in Europe (11,18,20,21,27,30,31,36), and the remaining two were multicenter studies (25,29). In terms of study design, 20 were retrospective cohort studies (11-13,19,20,22-36), while the other two were prospective cohort studies (18,21). All studies were published in English between 2016 and 2025. Regarding sample size, the number of participants varied widely across the studies, with the largest study including 1,635 patients and the smallest including 49. The 22 included studies comprised a total of 9,791 patients with RCC. Of these, 9 studies focused on localized RCC, 13 studies on mRCC. All studies used the SII as an analytical measure and divided participants into high-SII and low-SII groups for comparison. The cut-off values for SII differed among the studies, with thresholds spanning from 99.6 up to 1,375. In terms of prognostic evaluation, 21 studies examined the predictive value of SII for OS, seven explored its prognostic significance for PFS, and three examined the relationship between SII and CSS. The main features of the studies incorporated into the analysis are detailed in Table 1.

Study quality

A quality assessment using the NOS resulted in scores between 6 and 8 for all 22 studies, demonstrating a high overall standard (Table S2).

Meta-analysis results

SII and OS

To evaluate the impact of SII on OS, 21 cohort studies were analyzed. Due to high variability across studies (I²=68%, P<0.001), a random-effects approach was implemented (Figure 2A). The results showed that in patients with RCC, elevated SII was significantly linked to a poorer OS (HR =1.89, 95% CI: 1.64–2.18, P<0.001; Figure 2A). Based on the study design, age, region, cut-off value, and population, we conducted subgroup analyses of OS. SII demonstrated notable prognostic relevance for OS within all subgroups examined. During the heterogeneity analysis, it was found that heterogeneity decreased significantly in the Asian subgroup, in subgroups with cut-off values <800, and in non-mRCC subgroups, suggesting that the heterogeneity of OS is mainly related to geographic region, SII cut-off values, and disease stage. Subgroup analysis data are available in Table 2.

Figure 2 Forest plots for the associations between SII and survival outcomes in patients with renal cell carcinoma. (A) Forest plots for the association between SII and OS. (B) Forest plots for the association between SII and PFS. (C) Forest plots for the association between SII and CSS. CI, confidence interval; CSS, cancer-specific survival; df, degree of freedom; IV, inverse variance; OS, overall survival; PFS, progression-free survival; SE, standard error; SII, systemic immune inflammation index.

SII and PFS

We investigated the relationship between SII and PFS by including a total of seven cohort studies. Given the low heterogeneity observed among the studies (I²=2%, P=0.41), a random-effects model was employed for analysis (Figure 2B). The results demonstrated that in patients with RCC, a high SII was significantly linked to poorer PFS (HR =1.74, 95% CI: 1.53–1.97; P<0.001, Figure 2B).

SII and CSS

We examined the association between SII and CSS by including a total of three cohort studies. Given the substantial heterogeneity observed among the studies (I²=59%, P=0.09), a random-effects model was employed for analysis (Figure 2C). The results demonstrated that in patients with RCC, there was a significant link between elevated SII and poorer CSS (HR =1.63, 95% CI: 1.01–2.63; P=0.04, Figure 2C).

Sensitivity analysis

The robustness of the analytical results concerning the clinical relevance of SII was assessed through a sensitivity analysis, showing that the pooled estimates remained stable following the stepwise exclusion of individual studies. This suggests that the overall results were not driven by any single study disproportionately influencing the outcomes of OS (Figure 3A), PFS (Figure 3B), and CSS (Figure 3C), confirming the reliability of the results.

Figure 3 Sensitivity analysis of (A) OS, (B) PFS and (C) CSS. CI, confidence interval; CSS, cancer-specific survival; OS, overall survival; PFS, progression-free survival.

Publication bias

A comprehensive assessment of potential publication bias was conducted using Egger’s linear regression test combined with funnel plots. Egger’s test indicated publication bias in OS (P<0.001, Figure 4A). No evidence of publication bias was detected in PFS (P=0.051, Figure 4B), while CSS showed publication bias (P=0.02, Figure 4C). In addition, the funnel plots also revealed publication bias in OS (Figure 4A), no bias in PFS (Figure 4B), and bias in CSS (Figure 4C). The trim-and-fill method was used to evaluate the impact of publication bias on OS and CSS. The results showed that, after adjustment, the significant associations between SII and OS (HR =1.61, 95% CI: 1.39–1.86; Figure 5A) and CSS (HR =1.26, 95% CI: 1.07–1.48; Figure 5B) remained unchanged.

Figure 4 Funnel plot for the evaluation of publication bias for (A) OS, (B) PFS and (C) CSS. CSS, cancer-specific survival; OS, overall survival; PFS, progression-free survival; SE, standard error.

Figure 5 Trim-and-fill funnel plots for the evaluation of publication bias after adjustment. (A) Trim-and-fill funnel plot for OS. (B) Trim-and-fill funnel plot for CSS. CSS, cancer-specific survival; OS, overall survival; SE, standard error.

Discussion

In this study, we conducted a systematic review and meta-analysis to assess the prognostic significance of SII in patients with RCC. Evidence from the analysis indicated a clear association between high SII levels and worse prognosis in patients with RCC. Specifically, patients with high SII exhibited significantly poorer OS, PFS, and CSS, indicating that SII is a potentially strong independent prognostic predictor in RCC and provides an important reference for clinical prognostic assessment.

This conclusion is consistent with the meta-analysis by Xu et al. (14), which also confirmed that elevated SII is a significant predictor of poor prognosis in patients with RCC. With respect to OS, the pooled HRs of the two studies were highly comparable (1.89 and 1.88, respectively), further validating the robustness of the prognostic effect of SII. On this basis, the present study expands and deepens the existing body of evidence from multiple perspectives. In terms of literature coverage, this study included ten more studies than the meta-analysis by Xu et al. (14), encompassing not only metastatic patients but also non-metastatic populations, thereby extending the prognostic relevance of SII across the entire disease course. Regarding analytical depth, beyond conventional stratification, this study further incorporated key factors such as geographic region, SII cut-off values, and disease stage into subgroup analyses, identifying these variables as major sources of heterogeneity and providing a more nuanced interpretation of the evidence. In terms of outcome assessment, this study additionally performed a pooled analysis of CSS, enabling a more comprehensive and multidimensional evaluation of patient prognosis.

To further explore potential sources of heterogeneity and verify the robustness of the conclusions, this study conducted in-depth subgroup analyses focusing on the relationship between SII and OS in patients diagnosed with RCC. Subgroups were stratified according to study design, patient age, geographic region, SII cut-off values, and disease stage (metastatic vs. non-metastatic). The results showed that the significant prognostic value of SII for OS remained consistent across all predefined subgroups, further confirming its universality and reliability as a prognostic indicator. Notably, heterogeneity analyses revealed that inter-study heterogeneity was markedly reduced in three subgroups: Asian populations, studies using SII cut-off values below 800, and patients with non-mRCC. These findings suggest that geographic differences, the choice of SII cut-off thresholds, and disease stage (metastatic status) may be the main contributors to heterogeneity in OS outcomes observed in this meta-analysis. Specifically, studies conducted in Asian populations may yield more consistent conclusions. Populations in different regions may exhibit distinct baseline levels of systemic inflammation and tumor-associated immune response characteristics due to variations in genetic background, environmental exposures, lifestyle, and medical practice patterns (37). A high prevalence of obesity, metabolic syndrome, or chronic infections in certain regions may generally elevate population-level SII values, thereby affecting the applicability of SII values. Therefore, greater attention should be paid to identifying specific high-risk populations and regions. Current evidence suggests that the following populations may have a higher risk of developing RCC and experiencing poor prognosis: those with a history of chronic kidney disease or end-stage renal disease (38), patients on long-term dialysis (38), those with VHL gene mutations or familial RCC syndromes (39), and patients with metabolic diseases such as hypertension, obesity, or type 2 diabetes mellitus (40). These high-risk populations often concurrently present with a persistent systemic low-grade inflammatory state, in which SII may have greater prognostic discriminative ability. Moreover, from a regional perspective, some low- and middle-income countries or areas may experience delayed diagnosis and poor treatment access due to limited healthcare resources, resulting in RCC being diagnosed at an advanced stage with higher systemic inflammation levels (41). Therefore, promoting the use of low-cost, easily accessible inflammatory markers such as SII for risk stratification and prognostic assessment in these regions may hold greater health-economic value and practical significance. Future research should focus on the validation, calibration, and cut-off optimization of SII in large, multicenter prospective cohorts across diverse geographic regions and ethnicities. By establishing population-specific reference ranges or adjusting model parameters, the generalizability and clinical applicability of SII-based prognostic models can be enhanced, ultimately providing a more reliable basis for individualized and precise risk management in patients with RCC. Relatively lower SII cut-off values (e.g., <800) may demonstrate superior consistency in prognostic discrimination, providing direction for the future establishment of standardized cut-off thresholds. Moreover, the non-metastatic patient population is more homogeneous, and the association between SII and prognosis in this group may be influenced by fewer confounding factors.

The prognostic value of SII derives from its integrated quantification of key inflammatory and immune components at the tumor-host interface. Specifically, the three parameters included in its calculation correspond to core biological processes involved in tumor initiation and progression. Activated neutrophils can release reactive oxygen species, proteolytic enzymes, and a range of pro-inflammatory cytokines, directly disrupting the extracellular matrix and promoting local tumor invasion and distant metastasis, thereby creating favorable conditions for tumor growth and dissemination (42,43). Platelets directly promote cancer cell proliferation by releasing multiple growth factors [such as platelet-derived growth factor (PDGF) and transforming growth factor-beta (TGF-β)], and can also shield circulating tumor cells from immune recognition and elimination, while facilitating their adhesion to the vascular endothelium, thereby promoting the formation of metastatic lesions (44,45). Lymphocytes, particularly cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, constitute the core of adaptive immune responses against tumors (46). A reduction in their numbers or functional exhaustion directly reduces the effectiveness of immune recognition and elimination of cancer cells, ultimately leading to failure of immune surveillance.

A high SII value essentially indicates a dysregulated systemic state in which pro-tumorigenic and pro-inflammatory forces (elevated neutrophils and platelets) predominate over anti-tumor immune defenses (reduced lymphocytes). This state closely aligns with key biological processes of tumor progression, reflecting both persistent inflammatory drive and angiogenesis, as well as relative impairment of immune surveillance and tumor cell clearance. Therefore, as an easily accessible composite index, SII can globally quantify this prognostically unfavorable biological milieu, enabling effective prediction of outcomes in patients diagnosed with RCC in clinical practice.

Building upon the established prognostic value of baseline SII, accumulating evidence has shifted focus toward its dynamic changes during the course of treatment. In the context of targeted therapy, Lolli et al. (27) included 335 patients with mRCC receiving first-line sunitinib and found that those whose SII declined from high (≥730) to low at 6 weeks had significantly superior median PFS and OS compared with those who maintained persistently high SII, confirming that early SII reduction is closely associated with survival benefit. In the immunotherapy setting, Stühler et al. (11) reported that, among 49 patients treated with first-line ipilimumab plus nivolumab, an increase in SII of >20% from baseline at 12 weeks was significantly correlated with disease progression at first radiographic assessment (P=0.003), suggesting that early SII elevation may serve as a warning signal of treatment failure. Furthermore, a 2025 study involving 40 patients who received cabozantinib after prior immune checkpoint inhibitor therapy extended dynamic monitoring to combined biomarkers, revealing that the combined dynamic changes in SII and albumin may offer enhanced prognostic stratification (47). The above findings provide important implications for the future direction of SII research: rather than focusing solely on the baseline prognostic value of SII, greater efforts should be directed toward exploring its dynamic changes during treatment and evaluating its potential as a dynamic biomarker for predicting treatment response, monitoring resistance, and tracking therapeutic efficacy, thereby transforming SII from a static stratification tool into an indicator that dynamically guides treatment adjustments.

Metastasis of RCC exhibits organ tropism; in addition to the brain, common sites include bone (especially the spine), lung, and liver. Different metastatic sites may shape or select for distinct local immune microenvironments. Taking spinal metastasis as an example, approximately one-third of patients with advanced clear cell RCC have bone metastases at diagnosis (48), with a very poor prognosis and a 5-year survival rate of only about 12% (49). Single-cell transcriptomic studies have revealed that the bone marrow of patients with bone metastases exhibits an immunosuppressive state, characterized by an increase in exhausted CD8⁺ cytotoxic T cells, regulatory T cells, and tumor-associated macrophages. A population of tumor-associated mesenchymal stromal cells has also been identified, which is significantly associated with epithelial-mesenchymal transition, bone remodeling, and poor survival outcomes (48). In contrast, lung metastases may display a different profile of immune cell infiltration (50). Therefore, it is reasonable to hypothesize that the prognostic value of SII, which reflects systemic immune-inflammatory status, may be heterogeneous among patients with different metastatic patterns (14).

However, because the original studies lacked survival data stratified by specific metastatic sites (e.g., bone-only, lung-only, or multiorgan metastases), we are unable to examine whether the prognostic discrimination ability of SII varies according to metastatic site. This represents an important gap in the current body of evidence. Future studies should endeavor to collect and analyze such granular data, which will not only help validate the generalizability of SII but also deepen our understanding of the complex network linking systemic inflammation, the specific metastatic microenvironment, and patient prognosis, and may provide a basis for developing differentiated immunomodulatory strategies for patients with different metastatic sites.

Although this study provides relatively comprehensive evidence for the prognostic value of SII in RCC through a systematic review and meta-analysis, several limitations should be acknowledged. First, retrospective methodologies predominated among the studies selected for analysis, which could introduce selection bias, information bias, and uncontrolled confounding, thereby limiting the strength of causal inference. Second, there was substantial variability in the optimal SII cut-off values used across studies, with no unified standard, which restricts the direct comparability of results and the generalizability of clinical application. Third, although heterogeneity was addressed using subgroup analyses and random-effects models, differences among studies in patient characteristics, treatment regimens, and follow-up duration remain potential sources of residual heterogeneity. Fourth, most of the included studies did not provide detailed comparisons of baseline clinical characteristics between patients with high and low SII. Therefore, we could not directly assess whether the two groups were balanced with respect to established prognostic factors, such as age, sex, tumor-node-metastasis (TNM) stage, histological subtype, tumor burden, metastatic status, performance status, and treatment modality. This information gap limits the interpretation of the independent prognostic value of SII, as the observed survival differences may be partly attributable to baseline disease severity or treatment-related differences rather than SII itself. In addition, a certain degree of publication bias was detected in the analyses of OS and CSS, suggesting that studies with negative results may have been underreported, potentially resulting in an overestimation of the effect size.

Building on the current evidence and its limitations, future work may advance in the following directions. As a priority, multicenter prospective cohorts of substantial size should be implemented to strengthen the level of evidence regarding SII’s prognostic role and reduce biases inherent in retrospective analyses. Second, future studies should aim to explore and establish standardized SII cut-off values based on large-sample data across different populations (e.g., race, disease stage, and treatment modality) to promote standardized clinical application and comparability. Third, future studies should not only evaluate baseline SII but also provide complete baseline characteristic comparisons stratified by SII level and perform adequately adjusted analyses to clarify the incremental prognostic value of SII. In addition, dynamic changes in SII during treatment should be further investigated in relation to therapeutic response and survival outcomes, so as to evaluate its potential as a predictive and monitoring biomarker for treatment efficacy. Fourth, further research may focus on integrating SII with existing prognostic scoring systems (such as the IMDC and MSKCC scores) to explore whether it can enhance the predictive performance of current models. Fifth, from a translational medicine perspective, in-depth investigations are warranted to elucidate the functional status of specific immune cell subsets and signaling pathways reflected by SII, clarifying the molecular mechanisms underlying its association with RCC biology and providing clues for the development of novel therapeutic targets.

Conclusions

This systematic review and meta-analysis confirm that SII is a strong independent prognostic predictor in patients diagnosed with RCC. Higher SII levels are closely associated with significantly shortened OS, PFS, and CSS, and this association remains consistent across different patient subgroups. As an easily accessible indicator, SII provides an important basis for individualized risk stratification in clinical practice. Future prospective studies are required to establish its standardized value in clinical application.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0436/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0436/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0436/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.

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