There are several histopathological subtypes of ovarian neoplasms, with epithelial ovarian cancer (OC) accounting for the majority (about 90%) of them (1). Globally, OC is ranked 7th and 8th in terms of cancer morbidity and mortality in women, respectively (2). According to GLOBOCAN estimates, there were 313 959 newly diagnosed cases of OC and 207 252 deaths due to OC in 2020 worldwide (3). Approximately 60% of patients with OC are at an advanced stage or have widespread intra-abdominal metastatic disease at the time of initial diagnosis because there are no specific early symptoms (4). The treatments for OC include surgical resection, radiotherapy, chemotherapy, immunotherapy, and targeted therapy (5, 6). Although systemic treatment of OC is becoming increasingly advanced, there has been no significant improvement in the prognosis of OC patients. The five-year survival rate of patients with early-stage OC is 95%, whereas it is less than 30% in patients with advanced OC (stage III or IV) (7). The poor prognosis of OC is partially due to a lack of effective prognostic markers (8). Consequently, it is important to identify new and accurate biomarkers for predicting OC.

Recently, inflammation was found to exert a critical effect on tumor development, recurrence, and metastasis (9). Existing studies have found that systemic inflammation-related indicators can adequately evaluate and predict tumor development and prognosis. These inflammatory response markers include the neutrophil-to-lymphocyte ratio (NLR) (10), platelet-to-lymphocyte ratio (PLR) (11), lymphocyte-to-monocyte ratio (LMR) (12), and the modified Glasgow prognostic score (mGPS) (13). The systemic immune inflammation index (SII) is a blood test-based inflammatory marker that is determined as follows: SII = neutrophil count × platelet count/lymphocyte count. The SII is valuable for predicting cancer prognosis, including colorectal cancer (CRC) (14), esophageal cancer (15), nasopharyngeal carcinoma (NPC) (16), bladder cancer (17), gastric cancer (GC) (18), and neuroblastoma (19). For example, an elevated SII index could predict the diagnosis of postoperative infectious complications and the long-term prognosis of patients with CRC (14). Preoperative SII could predict the survival of patients with thymoma who underwent radical resection (20). A recent study showed that in patients with NPC, SII is a promising indicator for predicting survival, especially the risk of uncontrolled recurrence (16). Some existing studies have analyzed the role of SII in predicting OC prognosis. However, their results remain conflicting (21–26). Few studies have identified high SII as a significant prognostic factor for survival in OC (21, 25, 26). In contrast, other studies have found an insignificant relationship between the SII and prognostic outcomes of OC (22). Consequently, we conducted a comprehensive literature review to identify the precise role of SII in the prognosis of patients with OC.

The role of SII in predicting OC prognosis remains controversial in existing investigations (21–26). This study collected information from six studies involving 1546 patients and quantitatively identified the role of SII in predicting OS and PFS in patients with OC. Based on the pooled data, an increased SII markedly predicted OS and PFS. Additionally, the SII achieved creditable performance in predicting prognosis, as suggested by sensitivity analysis and publication bias. The SII is a blood-test-derived biomarker and is cheap and simple because neutrophil, lymphocyte, and platelet counts are routinely examined in clinical settings. Therefore, the SII may be an effective inflammatory index to estimate the short- and long-term survival of patients with OC owing to its good discriminatory value. To our knowledge, the present work is the first meta-analysis to investigate the role of SII in predicting OC prognosis.

There is growing evidence that immune cells play an essential role in inflammation, contributing to the generation of chemokines and cytokines that promote tumorigenesis, development, metastasis, and angiogenesis (34, 35). Simultaneously, SII is an inflammatory index derived from neutrophil, lymphocyte, and platelet counts. Therefore, a higher SII may result from higher neutrophil, platelet, and/or lower lymphocyte counts. Moreover, the significant connection between SII and the poor prognosis of OC is interpreted below. First, neutrophils are the first line of natural immune defense that is mobilized by the body when an infection occurs. Previous studies have established that neutrophils regulate tumor cell growth and metastasis by generating different inflammatory factors, such as OSM, TGF-β, HGF, and CXCL8 (36). Neutrophils can help construct an immune-privileged tumor microenvironment, which facilitates cancer cells to escape immune surveillance (37). Second, adenine and adenylate are released by platelets to protect circulatory cancer cells, inducing epithelial-mesenchymal transformation (EMT) while favoring tumor cell colonization and invasion (38). In addition, platelets may promote distal metastasis by activating the NF-κB and Smad pathways, which protect tumor cells against lysis induced by natural killer cells (39, 40). Third, lymphocytes have an important effect on cellular immunity, tumor immune editing, and immune surveillance. An increase in lymphocytes indicates immune pathway activation (41). Tumor-infiltrating lymphocytes (TILs) enhance the suppression of cancer cell growth, invasion, and cytotoxic cell death, thereby having an important effect on cancer defense (42). Impaired innate immunity may result from lymphocytopenia and suppression of lymphocyte activity induced by the systemic immune response (43). As a result, patients with a higher SII had higher neutrophil/platelet counts and lower lymphocyte counts, which are factors that predict a poor prognosis.

Notably, the subgroup analysis demonstrated that the prognostic role of the SII for OS and PFS was not influenced by the treatment methods ( Tables 2 , 3 ). These results have important implications for clinical practice. Treatments for OC include systemic therapies such as surgery, chemotherapy, radiotherapy, targeted therapy, poly (ADP-ribose) polymerase (PARP) inhibitors, and immunotherapy (44). Patients with OC may receive one or more treatments at different disease stages (6). Therefore, SII may be an effective prognostic marker for patients with OC undergoing various therapeutic strategies. The disease stages in the included studies were also not uniform (21–26). One study examined early-stage OC (25), whereas the other four studies examined recurrent/relapsed OC (21–24). We combined these studies for the following reasons: first, all eligible studies were selected based on the same inclusion and exclusion criteria; therefore, they were comparable. Second, the subgroup analysis demonstrated that the prognostic value of the SII was not affected by the various treatment methods.

The SII was calculated as SII = neutrophil count × platelet count/lymphocyte count. Therefore, SII can be considered a combination of NLR and PLR. Moreover, SII is more sensitive than NLR or PLR because it uses the product of neutrophil and platelet count as the numerator. SII is also cost-effective and easily available as other blood test-related markers such as NLR, PLR, LMR, and mGPS. We noticed that the HRs and 95% CIs of Nie’s study (21) were higher than those of the other included studies (22–26). These observations could be attributed to several factors. First, the cut-off value in Nie’s study was the lowest among the included studies ( Table 1 ). Second, the patients in Nie’s study underwent surgery, and their survival may have been longer than that of patients undergoing chemotherapy. Therefore, the HRs and 95% CIs in Nie’s study could be higher than those in other studies.

Recently, numerous meta-analyses have analyzed SII’s performance in predicting various cancer types (28, 30, 45–48). In a meta-analysis of 1768 patients, Wang et al. reported that in breast cancer cases (BC), poor OS and disease-free survival (DFS) were considerably correlated with increased SII (48). As discovered by Fu et al. in their meta-analysis of 6925 patients, an increased SII remarkably predicted poor OS and worse DFS in GC (49). According to a recent meta-analysis of 2132 patients, a higher pretreatment SII score predicted poor OS and low cancer-specific survival (CSS), DFS, and PFS in pancreatic cancer (28). In the meta-analysis involving eight studies carried out by Zhou et al., the higher SII remarkably predicted poor OS and extensive-stage among patients with small cell lung cancer (SCLC) (30). As shown by Cao et al. in a meta-analysis of 11 studies, an increased SII before treatment remarkably predicted dismal OS, CSS, PFS, and recurrence-free survival (RFS) inpatients with bladder cancer (50).

However, this study had some limitations. First, there were variations in the treatment of patients with OC in the enrolled studies, possibly affecting survival and causing heterogeneity. Different chemotherapy treatments were administered to 532 patients (22–25), of whom 108 patients received bevacizumab (23), and 423 patients received platinum-based drugs (22, 24, 25). More studies are needed to investigate the prognostic role of SII in patients with OC who received bevacizumab. Neoadjuvant chemotherapy (NACT) is commonly used to treat OC. The prognostic value of SII in patients with OC treated with NACT + surgery needs to be verified. Second, although the SII was markedly related to OS and PFS, the studies had small sample sizes and few publications. Third, retrospective articles were included in the meta-analysis, which may have induced a selection bias. Therefore, large prospective multiregional studies should be conducted to validate our findings.

In conclusion, the present meta-analysis showed that an increased SII significantly predicted worse OS and PFS in patients with OC. Consequently, we can speculate that SII may have an independent effect on the prognosis of OC.

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. The data that support the findings of this study are available from the corresponding author upon reasonable request.

HM and FY contributed to the study design and data collection. Statistical analyses and interpretation of results were performed by HM and FY. HM drafted the manuscript. FY revised the manuscript and edited the language. All authors made the critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.