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INTRODUCTION
Inflammation is a complex biological response triggered by infection or tissue injury, involving immune cell activation, release of inf lammatory mediators, and immune cell recruitment to the affected site [12]. Chronic inflammation is a prolonged pathological condition characterized by mononuclear immune cell infiltration, sustained tissue damage, damage-induced cellular proliferation, and tissue repair [34]. Some solid cancers originate from sites of chronic inflammation, and solid tumors can also induce an inflammatory microenvironment [5]. The association between chronic inflammation and cancer development and progression is well-established, encompassing cellular transformation, promotion, survival, proliferation, invasion, angiogenesis, and metastasis [46]. Notably, elevated circulating levels of CRP, indicative of inflammatory status, have been linked to poor prognoses in various cancers, including endometrial, liver, cervical, colorectal, breast, and non-small cell lung cancer [789].
Recent studies have identified several blood-based immune-inflammatory biomarkers that hold predictive value for the prognosis of colorectal cancer (CRC) [101112]. For instance, a high neutrophil-to-lymphocyte ratio, high platelet-to-lymphocyte ratio, and low lymphocyte-to-monocyte ratio have been associated with unfavorable oncological outcomes in patients with CRC [1314]. However, these biomarkers only assess 2 immune-inflammatory cell types, prompting the need for a novel biomarker encompassing all inflammatory cellular components from the peripheral blood.
Bioelectrical impedance analysis (BIA) is a well-known noninvasive anthropometric measurement including body composition, fat index, muscle index, obesity index, and phase angle [15]. And skeletal muscle index (SMI) was calculated to diagnose sarcopenia [16]. Recently, some studies showed the relationship between body composition and a patient’s immune-inflammatory status [171819].
The pan-immune-inflammation value (PIV) is a recently developed biomarker that considers neutrophil, platelet, monocyte, and lymphocyte counts [20]. Emerging studies have reported associations between PIV and oncological outcomes in various solid tumors, such as breast cancer, lung cancer, and melanoma [2122]. However, the relationship between preoperative PIV and surgical outcomes in CRC, including postoperative complications and oncological results, remains unclear. As a result, our study aimed to investigate the impact of preoperative PIV on postoperative and oncological following CRC surgery.
METHODS
Ethical considerations
The study protocol received approval from the Institutional Review Board of Keimyung University Dongsan Medical Center (No. DSMC 2022-12-040). Data acquisition and analysis were carried out with ethical considerations, ensuring the patients’ right to privacy. Informed consent requirements were waived due to the retrospective nature of the study.
Patients and data collection
This retrospective study comprised 210 patients who underwent curative resection for stage I–IV CRC between January 2016 and June 2020. Exclusion criteria encompassed synchronous or prior malignancies, malignancies other than adenocarcinoma, familial adenomatous polyposis, hereditary non-polyposis CRC, and incomplete blood cell count information (Fig. 1).
Data collection and definitions
Various variables were collected from prospectively maintained databases and electronic medical records. These included patient demographics such as age, sex, preoperative CEA level, preoperative CRP level, American Society of Anesthesiologists (ASA) physical status (PS) grade, body mass index, sarcopenia, phase angle, visceral fat area (VFA), tumor location, perioperative clinical outcomes (e.g., operation time, time to gas out, sips of water, time to soft diet, and length of stay), morbidity within 30 days after surgery, Clavien-Dindo (CD) classification, and postoperative pathologic outcomes (e.g., T stage, N stage, M stage, histology, and number examined lymph nodes). The PIV data were retrospectively collected from electronic medical records 2 days before the surgery. PIV was calculated as follows: neutrophil count (×103/mm3) × platelet count (×103/mm3) × monocyte count (×103/mm3) / lymphocyte count (×103/mm3).
The CD classification, a widely used grading system for surgical adverse events, was applied [23]. The TNM stage of each CRC patient was classified according to the 8th edition of the American Joint Committee on Cancer classification system. Overall survival (OS) was defined as the interval between the date of surgery and the most recent follow-up visit or the date of death from any cause. Disease-free survival (DFS) was defined as the interval between the date of surgery and the date of recurrence, with recurrence defined as the radiologically or histologically confirmed presence of a tumor. Local recurrence referred to the recurrence in the surgical field, while local recurrence with concurrent systemic recurrence was considered systemic recurrence.
BIA using the InBody 770 (Biospace) was conducted to evaluate body composition, VFA, appendicular skeletal muscle mass, and phase angle. SMI was calculated by appendicular skeletal muscle mass (kg) divided by the square of height (m2), and sarcopenia was defined as SMI <7.0 kg/m2 in male and <5.7 kg/m2 in female [16]. VFA, the measurement of visceral fat in the abdominal region, was defined as high if >70.2 cm2 in male and >82.4 cm2 in female, based on the median value.
Statistical analysis
We determined the optimal cutoff value for PIV in our study using the Contal and O’Quigley method. In survival analysis, the Contal and O’Quigley approach is employed to identify cutoff points in continuous variables [17]. The process involves computing all log-rank statistics and selecting the optimal cut point based on the maximum value of the log-rank statistic. This method was applied to every possible cutoff, and the one with the highest Q statistic was selected for further investigation. Mortality events were considered in the Contal and O’Quigley equations. The optimal cutoff value of PIV for stratifying patients into the low-PIV group and high-PIV group was determined based on OS using the Contal and O’Quigley method, with PIV defined as high if ≥155.90. Continuous outcomes were presented as means with standard deviations, and categorical outcomes as frequencies with percentages. The t-test and Mann-Whitney U-test were employed to analyze the association between PIV and continuous variables, while the Fisher exact test and chi-square test were used for categorical variables. Complications were analyzed using a logistic regression model, and the effects of individual variables on complications were reported as odds ratios (OR) with 95% confidence intervals (CIs). And the variables with univariate regression P-value of <0.1 were included in multivariate regression analysis. Survival analysis was conducted using the Kaplan-Meier method to examine OS and DFS curves, and Cox proportional hazard regression models were used to report the effects of individual variables on patient survival with hazard ratios (HR) and CIs. Statistical significance was set at P < 0.05. Statistical analyses were performed using IBM SPSS Statistics ver. 26 (IBM Corp.).
RESULTS
Baseline characteristics of patients
A total of 203 patients were included in the study. Based on the optimal cutoff value, 85 patients (41.8%) had low PIV, and 118 patients (58.2%) had high PIV (Table 1). The high-PIV group had a significantly higher proportion of males compared to the low-PIV group (77.1% vs. 56.5%, P = 0.002). However, there were no significant differences in age, preoperative CEA, preoperative CRP, ASA PS grade, body mass index, or tumor location. Additionally, there were no significant differences in the preoperative body composition, including sarcopenia, phase angle, and VFA.
Perioperative clinical outcomes
Table 2 presents the perioperative clinical outcomes of the low- and high-PIV groups. There was no significant difference in overall perioperative outcomes, including total operation time, time to gas out, sips of water, time to soft diet, hospital stay, and CD classification of >3a between the 2 groups. However, the high-PIV group exhibited a higher incidence of postoperative complications than the low-PIV group (39.8% vs. 24.7%, P = 0.024).
Postoperative pathologic outcomes
Table 3 displays the postoperative pathological outcomes of the low- and high-PIV groups. High PIV was significantly associated with larger tumor size than low PIV (3.9 ± 2.3 vs. 3.3 ± 1.8, P = 0.044). However, there were no differences between low and high PIV in T stage, N stage, M stage, histology, number of retrieved lymph nodes, proportion of patients with >12 acquired lymph nodes, number of positive lymph nodes, and lymphovascular or perineural invasions.
Univariate and multivariate regression analysis of factors associated with postoperative complications
Univariate analysis identified the male sex, ASA PS grade III, advanced M stage, and high PIV as factors significantly associated with postoperative complications including all events that occurred after surgery (P < 0.1) (Table 4). In the multivariate regression analysis, ASA PS grade III (OR, 2.701; 95% CI, 1.018–7.166; P = 0.046) and high PIV (OR, 1.923; 95% CI, 1.003–3.686; P = 0.049) remained significantly associated with postoperative complications.
Oncologic outcomes
The median follow-up period was 40.8 months in the high-PIV group and 36.1 months in the low-PIV group (P = 0.057) (Supplementary Table 1). The low-PIV group exhibited higher 5-year OS rates (81.8% vs. 98.8%, P = 0.001) and 5-year DFS rates (78.3% vs. 90.1%, P = 0.021) compared with the high-PIV group (Fig. 2). Among the patients, 17 experienced recurrences within the follow-up period, with no significant difference in the recurrence pattern between the 2 groups. The low-PIV group had 5 cases of recurrence (4 systemic and 1 local recurrence), while the high-PIV group had 12 cases of recurrence (9 systemic and 3 local recurrences).
Univariate and multivariate regression analysis for prognostic factors of oncologic outcomes
Supplementary Table 2 presents the prognostic factors associated with 5-year survival. Univariate analysis showed that male sex, high preoperative CRP levels, N and M stages, complications within 30 days after surgery, and high PIV levels were associated with a worse 5-year OS (P < 0.1). Similarly, male sex; higher preoperative CEA levels; longer operation times; advanced T, N, and M stages; and higher VFA and PIV levels were associated with better 5-year DFS (P < 0.1). In the multivariate analysis, a high preoperative PIV level was identified as an independent prognostic factor for the OS (HR, 1.108; 95% CI, 0.366–3.350; P = 0.047), whereas the nodal stage was an independent prognostic factor for both the OS (HR, 9.411; 95% CI, 1.976–44.814; P = 0.005) and DFS (HR, 3.250; 95% CI, 1.027–10.281; P = 0.045) (Table 5).
DISCUSSION
This study aimed to analyze the impact of preoperative PIV on clinical and oncologic outcomes after surgery in patients with CRC, including stage 4 cases treated with curative intent. Our results revealed significantly higher OS and DFS rates in the low-PIV group than in the high-PIV group. In the multivariate analysis of postoperative complications, ASA PS grade III and high preoperative PIV were identified as independent risk factors. For oncological outcomes, the advanced N stage and high preoperative PIV levels were independent prognostic factors for the OS, while only the advanced N stage was an independent prognostic factor for the DFS.
Numerous factors can inf luence the postoperative complications in CRC surgery. A meta-analysis by van Kooten et al. [24] highlighted several risk factors for major postoperative complications, including older age, frailty, male sex, high ASA PS grade, elevated preoperative inflammatory biomarkers (e.g., WBC count, CRP), malnutrition, preoperative weight loss, sarcopenia, overweight, and advanced tumor stage. Some studies have specifically investigated immunoinflammatory markers in predicting complications after CRC surgery. For example, Kamonvarapitak et al. [25] found that the preoperative lymphocyte-to-monocyte ratio could predict postoperative infectious complications, whereas Feng et al. [26] demonstrated that the systemic inflammation index is a useful predictor for postoperative infectious complications. Similarly, Sato et al. [27] and Sato et al. [28] reported associations between PIV and postoperative complications in CRC patients. Postoperative complications are reportedly associated with inflammatory response and malnutrition. Past reports have also shown that low albumin and low total lymphocyte count are closely related to the development of an inflammatory response in patients with malignancy. Our study’s findings align with these previous works, as high preoperative PIV was significantly associated with postoperative complications in patients with CRC.
Recent studies have explored the relationship between body composition, sarcopenia, VFA, phase angle, and CRC prognosis [2930]. Body composition can reflect the nutritional status and may have associations with a patient’s immune-inflammatory status [1718]. Song et al. [30] reported that body composition indices, including fat and muscle indices, were related to platelet and lymphocyte ratios in CRC, particularly in male patients. Previous research has indicated that preoperative VFA measured using the BIA could serve as an independent prognostic factor for DFS [1531]. However, in our study, no significant associations were observed between sarcopenia, VFA, phase angle, and PIV. Furthermore, these body composition markers did not exhibit significant correlations with oncologic outcomes. Future research is warranted to further explore the association between body composition and blood-based inflammatory markers, such as PIV, and their impact on oncological outcomes.
Several studies have demonstrated that a high preoperative PIV has prognostic implications for the OS and DFS and for other immune-inflammatory markers like the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and prognostic nutritional index in patients with CRC [1332]. High PIV has also been linked to poor oncological outcomes in breast and lung cancer [2122]. However, the association between elevated PIV and oncological outcomes in CRC has shown slightly varying results in previous studies. Fuca et al. [20] reported that high PIV had the most significant impact on the OS and progression-free survival among immune-inflammatory biomarkers in metastatic CRC. In contrast, Sato et al. [28] found that high preoperative PIV was associated with poor survival in patients with CRC and a stent-inserted in obstructive tumors due to potential tumor cell apoptosis triggered by platelet-derived microparticles infiltrating the solid tumor. The progression of tumors involves the activation of myeloid-derived suppressor cells by neutrophils and monocytes. Monocytes can also transform into tumor-associated macrophages, promoting tumor growth and metastasis. A diminished lymphocyte count indicates a weakened antitumor response, as cytotoxic T-cell infiltration predicts the survival and response of CRC patients to immune therapy [27]. PIV includes these blood components, which interact with other blood components. Therefore, high preoperative PIV, reflecting a compromised immune-inflammatory status, significantly correlated with poorer OS, indicating the need for further investigation.
Our study has a few limitations. It was a retrospective analysis, and certain relevant factors related to the patients’ immune-inflammatory status may not have been included. Additionally, being a single-center study, the median duration of follow-up for the patients was relatively short, limiting our ability to analyze long-term oncological outcomes. Further research is needed to overcome these limitations and provide a more comprehensive understanding of the associations between PIV, immune-inflammatory status, and CRC outcomes.
In conclusion, a high preoperative PIV was associated with more postoperative complications and independently predicted worse OS in CRC patients. PIV shows promise as a comprehensive biomarker with potential applications in risk assessment and decision-making for treatment. Further research is needed to explore its underlying mechanisms and validate its predictive value in larger patient cohorts.
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