Pan-immune-inflammation value as a novel prognostic biomarker for digestive system cancers: a meta-analysis

Abstract

Background

Digestive system cancers pose a significant global health challenge with high incidence and mortality rates. Inflammation is a key factor in cancer progression, necessitating reliable prognostic indicators. The pan-immune-inflammation value (PIV), as a new biomarker of immune-inflammatory response, has emerged as a potential prognostic biomarker for cancers.

Methods

We performed a meta-analysis to evaluate the prognostic significance of PIV in digestive system cancers. Our search, up to June 2024, included 20 studies from 19 articles with 5037 patients. We extracted and analyzed data on PIV levels and assessed hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), recurrence-free survival (RFS), and cancer-specific survival (CSS) using STATA 14.0.

Results

Our analysis found that high PIV levels were significantly associated with poor prognosis in patients with digestive system cancers. Specifically, high PIV was linked to shorter OS (HR = 2.039, P < 0.001), PFS (HR = 1.877, P = 0.028), DFS (HR = 1.624, P = 0.005), RFS (HR = 2.393, P = 0.037), and CSS (HR = 2.053, P < 0.001). Additionally, the adverse prognostic impact of high PIV on OS was consistent across different cancer types, including digestive tract, colorectal, esophageal, and hepatobiliary pancreatic cancers. Although some heterogeneity was observed, sensitivity and bias analyses confirmed the reliability of these findings.

Conclusions

PIV was a valuable and practical prognostic marker for digestive system cancers, providing significant predictive value across multiple survival metrics. Its simplicity and minimal invasiveness nature support its potential integration into routine clinical practice.

Introduction

Malignant tumors have long been a major health issue affecting people worldwide. Despite significant advancements in various areas of research in recent years, which have improved the conditions for some individuals, the health challenges remain formidable. Among these challenges, the incidence and mortality rates of digestive system cancers continued to be alarmingly high due to factors such as increasing societal pressure and the complexity of dietary habits [1]. As the primary route for nutrient intake, cancers in the digestive system significantly impacted the quality of life. Medical professionals have introduced innovations in surgical methods and pharmaceuticals; however, the psychological and economic burdens on patients remained substantial. Therefore, there was a pressing need to identify regular, affordable, and reliable clinical indicators to assist in the treatment of patients with cancers of the digestive system.

Blood tests have become a nearly universal measure taken before treating cancer patients to quickly understand their general health status. The complete blood count includes a wealth of data that can describe the patient’s inflammatory and immune responses at the time, providing clinical information without adding extra trauma or burden to the patient, while also offering guidance for their treatment [2]. Chronic inflammation, characterized by prolonged immune response and tissue damage, is linked to cancer development and progression. It influences cellular transformation, proliferation, invasion, angiogenesis, and metastasis, creating an inflammatory tumor microenvironment [3]. While inflammation is crucial for cancer prevention, chronic inflammation can exacerbate cancer development by causing DNA damage and genomic instability. This leads to T-cell exhaustion, reducing their ability to combat tumors and promoting cancer progression [4]. Chronic inflammation is linked to various cancers, such as gastric, hepatocellular, and cervical cancer [5]. Tumors evade immune detection by expressing PD-L1 and CTLA-4, reducing MHC class I antigens, and releasing immunosuppressive factors like IDO [6]. The interaction between tumors, inflammation, and immunity has piqued researchers’ interest. Studies have found that composite prognostic scores based on pro-inflammatory cells and/or anti-cancer immune cell counts in peripheral blood, in addition to being applicable to infectious and immune diseases, are also reliably effective for various cancers [7]. These can serve as indirect measures of cancer-related inflammatory stress.

Currently, there are scores based on two indicators, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), as well as a score based on three indicators, the systemic immune-inflammation index (SII). These have been extensively studied in various aspects of tumor prognosis and treatment evaluation, yielding promising scientific results [8, 9]. However, research also points out the complex interactions between various blood cells and cancer prognosis, highlighting certain limitations of these biomarkers. This issue has led to the development of a new scoring method called the pan-immune-inflammation value (PIV), which is derived from the counts of neutrophils, platelets, monocytes, and lymphocytes. PIV is calculated using the formula: PIV = (neutrophil count × platelet count × monocyte count) / lymphocyte count [10]. PIV, as a new biomarker of immune-inflammatory response, can better understand the patient’s immune status and improve the prediction of immunotherapy outcomes. Unlike traditional biomarkers, PIV integrates multiple inflammatory signals, providing a comprehensive immune system assessment and improving prognostic accuracy. A recent meta-analysis comprising fifteen studies indicated that high PIV was associated with poor prognosis in cancer patients [11]. However, it was worth noting that this meta-analysis represented a comprehensive review of all cancers in published studies. Among them, only six studies focused on digestive system cancers, and no specific analysis was conducted. Furthermore, the survival metrics used in this analysis were not rigorously defined. These factors not only preclude a complete elucidation of PIV’s role in digestive system cancers but also affect its generalizability.

Therefore, we conducted this comprehensive meta-analysis to evaluate the role of PIV in digestive system cancers, aiming to provide strong evidence for its effectiveness as a predictive biomarker to assist in the treatment and management of cancer.

Materials and methods

Search strategy

Using online English databases such as Web of Science, PubMed, PMC, and Embase, we conducted a search up to June 2024 using the keywords ‘pan-immune-inflammation value’ and ‘cancer’ to retrieve relevant articles (Detailed in the supplementary search strategies). We also included its alias, the aggregate index of systemic inflammation (AISI), in our search strategy. Additionally, we actively screened the reference lists of included articles and relevant reviews to minimize omissions. The search process was independently conducted by two researchers (JT L, and CY M), followed by discussion and synthesis. Details of the protocol for this systematic review were registered on INPLASY (INPLASY202490087) and are available in full on the inplasy.com (https://doiorg.publicaciones.saludcastillayleon.es/10.37766/inplasy2024.9.0087). Furthermore, our meta-analysis adheres to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

Inclusion and exclusion criteria

To be included in this meta-analysis, articles must adhere to our predefined inclusion and exclusion criteria: (1) They should be clinical studies involving patients with digestive system cancers. (2) The definition and computational model of PIV must be correct, based on pre-treatment complete blood count data of patients. (3) Articles should provide a clear cutoff value for PIV and conduct group analyses based on this value. (4) They must include valid prognostic data such as hazard ratio (HR) and 95% confidence interval (CI). (5) Studies such as database analyses, bioinformatics research, animal cell studies, reviews, abstracts, and letters will be excluded.

Data accumulation and quality assessment

Data collection from included articles was independently conducted by two researchers (JT L, and CY M), with synthesis overseen by a third researcher (JH L). Collected data encompassed essential details such as author, publication year, study region, study design, sample size, study period, cancer type, sampling time, cutoff values, outcomes (HR estimation), and analysis method. Clinicopathological characteristics of patients, including gender, age, TNM stage, tumor size, and lymph node involvement, were also gathered. Additionally, the Newcastle-Ottawa Scale (NOS), widely employed in meta-analyses to assess non-randomized study quality, was used to evaluate article quality, with studies scoring above 6 considered high quality [12].

Statistical analysis

We utilized STATA 14.0 software (STATA Corporation, College Station, Texas, USA) for statistical analysis in this meta-analysis. Hazard ratios (HR) and their corresponding 95% confidence intervals for each outcome endpoint were aggregated to assess the prognostic value of PIV in patients with digestive system cancers. As per convention, HRs derived from multivariable analysis were prioritized unless specified otherwise for analysis. Heterogeneity was assessed using the chi-square test and I-squared statistic, with a random-effects model applied for P-values < 0.1 and/or I² > 50%; otherwise, a fixed-effects model was used. Meta-regression analysis was conducted to explore potential sources of heterogeneity. Sensitivity analysis involved sequentially omitting each study to assess the stability of the overall findings. Additionally, publication bias was evaluated using Begg’s and Egger’s tests. A two-sided P < 0.05 was considered statistically significant in this study.

Results

Search results and baseline details

Fig. 1

Flowchart for extracting relevant articles from literature

Based on the established retrieval strategy and by merging potential reference lists from pending articles, we initially obtained 108 articles. After a preliminary review of the titles and abstracts, we excluded 69 articles due to reasons such as duplication, irrelevant types, and mismatched study subjects. Subsequently, we thoroughly reviewed the full texts of 39 articles and further excluded 20 of them (excluded for database analyses and bioinformatics research [n = 11], incorrect cancer types [n = 4], meta-analyses [n = 4], and lack of survival data [n = 1]). Ultimately, 19 eligible articles, comprising 20 distinct studies, were included in this meta-analysis (Fig. 1). These retrospective studies, conducted in Italy [10, 13], Japan [3, 14, 15], Turkey [16,17,18], Spain [19], China [20,21,22,23,24,25,26,27,28], and Korea [29], were published between 2020 and 2024 and included six types of digestive system cancers: colorectal cancer, esophageal cancer, gastric cancer, hepatocellular cancer, oral cavity cancer, and pancreatic cancer. The studies collectively included 5037 patients who received surgery, chemotherapy, targeted therapy, immunotherapy, or radiotherapy between 2000 and 2023. The number of patients in each study ranged from 51 to 853, and they were divided into two groups based on a certain pre-treatment PIV cutoff value. Sixteen studies provided prognostic data on overall survival (OS), five studies on progression-free survival (PFS), five studies on disease-free survival (DFS), two studies on recurrence-free survival (RFS), and two studies on cancer-specific survival (CSS). Of these, 16 studies estimated hazard ratios (HR) through univariate analysis, while 14 studies did so through multivariate analysis. Notably, based on the NOS scores, all included articles were considered high-quality studies. Refer to Table 1 for more information.

Table 1 Main characteristics of studies included in meta-analysis

Relationship between PIV and the prognosis of patients with digestive system cancers

Fig. 2

Forest plot of overall survival in patients with digestive system cancers based on PIV levels

Fig. 3

Forest plots of survival outcomes: (a) progression-free survival, (b) disease-free survival, (c) recurrence-free Survival, and (d) cancer-specific survival by PIV levels

Through meta-analysis, we confirmed that high PIV levels were associated with poor prognosis in patients with digestive system cancers, as shown in Figs. 2 and 3. Specifically, our meta-analysis, which included 16 studies, revealed that patients with high PIV levels had shorter OS compared to those with lower PIV levels (HR = 2.039, 95% CI = 1.630–2.550, P < 0.001, Fig. 2). Additionally, the results demonstrated that high PIV levels were adverse factors for PFS (HR = 1.877, 95% CI = 1.069–3.295, P = 0.028, Fig. 3a) and DFS (HR = 1.624, 95% CI = 1.155–2.285, P = 0.005, Fig. 3b) in the pooled analysis of five studies each. Furthermore, combined analysis on RFS (HR = 2.393, 95% CI = 1.052–5.442, P = 0.037, Fig. 3c) and CSS (HR = 2.053, 95% CI = 1.548–2.723, P < 0.001, Fig. 3d) also confirmed the association between high PIV levels and poor patient outcome.

According to the results of the chi-square test and I² statistic, we found significant heterogeneity in all prognostic analyses except for the comprehensive analysis of the impact of PIV on patient CSS. Since studies on OS were predominant, we further analyzed possible sources of its high heterogeneity (I² = 79.4%, P_h < 0.001). However, based on the meta-regression results, we did not confirm significant sources of heterogeneity. The P-values for cancer type (P = 0.765), sampling time (P = 0.436), cutoff value (P = 0.240), analysis method (P = 0.523), treatment strategy (P = 0.488), study region (P = 0.958), and sample size (P = 0.081) were all greater than 0.05.

Subgroup analyses for correlation between PIV and OS

Due to the majority of included articles substantiating the relationship between PIV and OS, we conducted a subgroup analysis to further explore the prognostic value of PIV on OS in patients with digestive system cancers. The results were summarized in Table 2. From the subgroup analysis, we observed that patients with high PIV had significantly shortened OS across various cancer types subgroups, including digestive tract cancer (HR = 1.689, 95% CI = 1.504–1.896, P < 0.001), colorectal cancer (HR = 2.178, 95% CI = 1.786–2.657, P < 0.001), esophageal cancer (HR = 1.656, 95% CI = 1.373–1.996, P < 0.001), and hepatobiliary pancreatic cancer (HR = 2.427, 95% CI = 1.581–3.727, P < 0.001). We also confirmed the adverse prognostic impact of high PIV across different analysis method subgroups (Multivariate analysis, HR = 2.076, 95% CI = 1.578–2.732, P < 0.001; Univariate analysis, HR = 2.630, 95% CI = 1.700-4.068, P < 0.001). Furthermore, we analyzed the timing of blood sample collection, cutoff values for PIV, and sample size. Our findings indicate that under a well-designed study framework, variations in sampling time (Within 1 week before treatment, HR = 1.960, 95% CI = 1.411–2.724, P < 0.001; Before treatment, HR = 2.042, 95% CI = 1.539–2.708, P < 0.001), differences in PIV cutoff values (< 300, HR = 1.631, 95% CI = 1.304–2.039, P < 0.001; ≥ 300, HR = 2.692, 95% CI = 2.466–2.937, P < 0.001), and the size of the study sample (< 300, HR = 2.741, 95% CI = 2.505–2.999, P < 0.001; ≥ 300, HR = 1.686, 95% CI = 1.359–2.092, P < 0.001) did not affect the analytical results. Similarly, subgroup analyses based on different treatment strategies demonstrated that PIV had predictive value in patients undergoing both surgical (HR = 1.885, 95% CI = 1.484–2.394, P < 0.001) and non-surgical (HR = 2.237, 95% CI = 1.656–3.023, P < 0.001) treatments. Additionally, studies conducted in various regions (East Asia, HR = 1.828, 95% CI = 1.450–2.304, P < 0.001; Other region, HR = 2.230, 95% CI = 1.723–2.887, P < 0.001) consistently showed that high PIV levels led to reduced survival times in patients with digestive system cancers.

Table 2 Subgroup analyses of PIV and OS in digestive system cancers

Assessment of the stability and publication bias

Fig. 4

Sensitivity analysis of overall survival in cancer patients

Fig. 5

Evaluation of publication bias in included studies using egger’s test

To deepen our understanding of the stability of meta-analysis regarding the impact of PIV on OS in patients with digestive system cancers, we conducted additional sensitivity analysis. The results indicated that all estimated points fall within the 95% CI (Fig. 4), confirming the stability of this analysis. Furthermore, Begg’s (P = 0.065) and Egger’s (P = 0.258, Fig. 5) tests confirmed the absence of publication bias in this analysis.

Discussion

The burden faced by patients with digestive system cancers is profound and complex. Treatments such as gastrointestinal resection, diversion, and stomas significantly impact their quality of life. Researchers have explored various avenues, including fecal examinations, carcinoembryonic antigen tests, and gastrointestinal endoscopies, to guide treatment for those at risk or undergoing therapy. While these approaches are standard and effective, efforts are underway to uncover additional biomarkers from routine tests that can assist patients without adding to their already heavy load. Notably, findings from this meta-analysis suggested that PIV extracted from pre-treatment complete blood counts could serve as a valuable prognostic indicator for patients with digestive system cancers.

Peripheral blood biomarkers, derived from complete blood counts, have emerged as minimally invasive and cost-effective tools reflecting tumor behavior and the immune status of the tumor microenvironment. Neutrophils, monocytes, platelets, and lymphocytes in the blood can provide insights into the systemic inflammatory state, with implications for cancer prognosis. The dysregulation of inflammatory and immune cells within the tumor microenvironment is a key factor in tumor progression [30, 31]. Neutrophils, the most common innate immune cells, facilitate tumor invasion and metastasis through the secretion of VEGFA, MMPs, IL-6, and TGF-β. Additionally, they impair T cell activation by releasing nitric oxide, arginase, and reactive oxygen species, thus inhibiting the immune response against cancer cells [32,33,34]. Monocytes, particularly those that differentiate into tumor-associated macrophages (TAMs), induce apoptosis in antitumor T cells and promote angiogenesis by producing pro-angiogenic factors [35]. Platelets further the epithelial-mesenchymal transition and angiogenesis via TGF-β, VEGF, and FGF, while also recruiting neutrophils and monocytes to support metastasis [36]. Conversely, lymphocytes, especially cytotoxic T cells, play a crucial role in cancer immune surveillance by inducing cancer cell lysis and apoptosis, with high lymphocyte levels correlating with better prognosis [37]. Inflammation within the tumor microenvironment, driven by pro-inflammatory mediators like TNF-α and IL-6, results in immune exhaustion and evasion. Increased inflammatory markers and TAMs are linked to poor outcomes and treatment resistance across various cancers [38,39,40]. As PIV is calculated from four types of peripheral blood cells, elevated levels of neutrophils, monocytes, and platelets, alongside reduced lymphocytes, could contribute to a higher PIV. In this meta-analysis, it was found to be an effective marker for poor prognosis in cancer patients, which aligning with the potential clinical value of each cell type in the computational model. Thus, PIV, incorporating these cellular components, could offer a valuable predictive tool for patient prognosis in cancers.

This meta-analysis included 20 studies from 19 articles, involving a total of 5037 patients with digestive system cancers. Through comprehensive analysis of various survival metrics, we found that patients with higher PIV had shorter survival times compared to those with lower PIV, regardless of OS, PFS, DFS, RFS, or CSS. This indicated that PIV was an effective and stable prognostic marker for patients with digestive system cancers. Additionally, we conducted a subgroup analysis on the impact of PIV on patient OS within the included studies. The results showed that PIV had significant statistical relevance in subgroup analyses for digestive tract, colorectal, esophageal, and hepatobiliary pancreatic cancers. Notably, we also confirmed that PIV held prognostic value not only for surgical patients but also for non-surgical patients. These findings underscored the broad and effective clinical value of PIV in patients with digestive system cancers. Furthermore, subgroup analyses based on different analysis methods, sampling time, sample size, and study region revealed that these variables did not influence the statistical outcomes, confirming that high PIV level was an independent and stable prognostic factor for patients with digestive system cancers. Despite observing high heterogeneity in our analysis, meta-regression did not identify the sources of this heterogeneity, which included cancer type, sampling time, cutoff value, analysis method, treatment strategy, study region, and sample size. Therefore, we employed a random effect model to mitigate some of the impacts. Fortunately, the results of the sensitivity analysis and bias analysis confirmed the reliability and validity of this meta-analysis. Consequently, we believed that PIV could be effectively applied to digestive system cancer patients in the future, aiding their clinical treatment.

Beyond its prognostic value, PIV may have significant therapeutic implications. Elevated PIV levels could indicate an intensified inflammatory response, potentially influencing the effectiveness of therapies such as immunotherapy and targeted treatments [10, 23]. A higher PIV might suggest that the tumor microenvironment was less conducive to immune activation, thereby affecting immunotherapy outcomes [41]. Furthermore, the impact of treatment modalities on PIV’s prognostic value should be considered. Notably, this meta-analysis confirmed that PIV has predictive significance in the treatment of digestive system cancers, including surgery, chemotherapy, targeted therapy, immunotherapy, and radiotherapy. However, while chemotherapy might lead to temporary reductions in inflammatory markers, immunotherapy might interact differently with the immune microenvironment [42], potentially altering PIV levels in patients undergoing these treatments. Therefore, understanding these dynamics could assist in tailoring treatment strategies based on PIV profiles.

Despite our diligent efforts in retrieving, summarizing, and analyzing data to achieve satisfactory results, certain limitations must be acknowledged. First, some types of digestive system cancer, including gallbladder cancer and cholangiocarcinoma, were not included in this meta-analysis. Except for colorectal cancer with seven studies, other single cancer types were insufficiently represented. Second, although we conducted subgroup analyses corresponding to different variables and obtained consistent conclusions, undeniable differences such as cancer types, sampling times, and sample sizes still raised concerns about the reliability of the conclusions. Third, the exploration of the clinical value of PIV was still in its early stages, and the variation in cut-off values currently led to discrepancies in its classification. Fourth, all the included study subjects were from the Eurasian continent, lacking research from other regions. Therefore, further exploration of the clinical utility of PIV requires more comprehensive, scientifically standardized, multi-regional, and multi-center studies in the future.

Conclusion

In summary, this meta-analysis for the first time synthesized studies on the role of PIV in patients with digestive system cancer, confirming that high level of PIV was an independent adverse prognostic factor. Specifically, our findings applied to colorectal cancer, esophageal cancer, gastric cancer, hepatocellular cancer, oral cavity cancer, and pancreatic cancer. Given its simplicity, low cost, and minimal invasiveness nature, PIV could be effectively integrated into clinical practice to aid in the prognosis and treatment of these cancers.