A novel preoperative inflammation score system established for postoperative prognosis predicting of intrahepatic cholangiocarcinoma

Background Inflammation is implicated in tumorigenesis and has been reported as an important prognostic factor in cancers. In this study, we aimed to develop and validate a novel inflammation score (IFS) system based on 12 inflammatory markers and explore its impact on intrahepatic cholangiocarcinoma (ICC) survival after hepatectomy. Methods Clinical data of 446 ICC patients undergoing surgical treatment were collected from the Primary Liver Cancer Big Data, and then served as a training cohort to establish the IFS. Furthermore, an internal validation cohort including 175 patients was used as internal validation cohort of the IFS. A survival tree analysis was used to divide ICC patients into three groups (low-, median-, and high- IFS-score groups) according to different IFS values. Kaplan-Meier (KM) curves were used to compare the overall survival (OS) and recurrence-free survival (RFS) rates among three different groups. Cox regression analyses were applied to explore the independent risk factors influencing OS and RFS. Results In the training cohort, 149 patients were in the low-IFS-score group, 187 in the median-IFS-score group, and 110 in the high-IFS-score group. KM curves showed that the high-IFS-score group had worse OS and RFS rates than those of the low- and median-IFS-score groups (P < 0.001) in both the training and validation cohorts. Moreover, multivariable Cox analyses identified high IFS as an independent risk factor for OS and RFS in the training cohort. The area under the curve values for OS prediction of IFS were 0.703 and 0.664 in the training and validation cohorts, respectively, which were higher than those of the American Joint Committee on Cancer (AJCC) 7th edition TNM stage, AJCC 8th edition TNM stage, and the Child-Pugh score. Conclusion Our results revealed the IFS was an independent risk factor for OS and RFS in patients with ICC after hepatectomy and could serve as an effective prognostic prediction system in daily clinical practice.


Introduction
Intrahepatic cholangiocarcinoma (ICC) is one of the deadliest malignancies worldwide, which arises from the epithelial cells of intrahepatic bile ducts [1]. ICC has become the second most common primary liver malignancy after hepatocellular carcinoma (HCC) [2]. In recent decades, the incidence of ICC in the United States has been increasing [1,3]. Although surgical resection is recommended as the first-line treatment for ICC, unfortunately, only 30-40% of patients had surgical opportunities because most patients have been found to present regional or distant metastases at the time of diagnosis [4].
More importantly, even after curative-intent resection, the long-term survival of ICC is still dismal because of the high postoperative recurrence rate [4]. Selection of appropriate patients for surgery is one method to improve survival prognosis. Preoperative evaluation of the resectability of patients with ICC is usually based on BCLC stage, TNM stage, and Child-Pugh grade. Common prognostic indicators, like tumor size, tumor number, vascular invasion, and lymph node metastasis, are often acquired by accurate postoperative pathological data. Consequently, obtaining parameters through preoperative low-cost hematological examination is significant and cost-effective, which can also be used for survival prognosis prediction. Thus, finding an accurate prognostic biomarker is necessary and urgent for surgeons to evaluate prognosis and guide the choice making of treatment before hepatectomy, which provides ICC patients with reasonable individual treatment.
In 1863, Virchow first reported the relationship between inflammation and tumorigenesis [5]. Inflammation has been found to be involved in the onset and progression of diverse diseases. The use of preoperative inflammatory markers to provide individualized survival estimates has been the focus on many reports [6][7][8][9][10][11]. It has been confirmed that inflammatory markers played an important role in the progression and prognosis of liver cancer [12,13]. A series of studies have demonstrated that the systemic inflammation score (SIS), the neutrophil-to-lymphocyte ratio (NLR), and the lymphocyte to monocyte ratio (LMR) were all significantly associated with long-term survival in patients with ICC [14]. Nevertheless, there are no studies that combine several inflammation-based indicators to create an integrated prognostic score system for survival prediction in patients with ICC.
In this study, we aimed to develop a novel inflammatory score system that achieves a good survival prediction performance in patients with ICC after surgery using 12 preoperational inflammatory markers. To this end, we used an internal cohort to validate the survival prediction performance of our inflammatory score system at the same time.

Patients
From January 2010 to December 2013, clinical data of 446 patients with ICC who underwent surgical treatment were collected from the Primary Liver Cancer Big Data in Fujian province [15] and used to form the training cohort. For the internal validation cohort, we collected clinical data of 175 patients with ICC from January 2014 to August 2015 from the same institution. These data were retrospectively reviewed. This study was approved by the Institutional Ethics Committee of the Mengchao Hepatobiliary Hospital. Informed consent was obtained from all patients before surgery in this study.

Inclusion and exclusion criteria
The inclusion criteria were as follows: (1) good preoperative performance with an ECOG score of 0-2, (2) no records of ICC treatment before surgery, (3) radical resection (R0 resection), (4) no other malignant diseases, (5) Child-Pugh graded A or B7, (6) no evidence of main portal vein, hepatic artery, biliary duct, or inferior vena cava invasion. The exclusion criteria were as follows: (1) incomplete clinicopathological data and (2) loss of follow-up within 30 days.

Liver resection and definitions
All patients underwent routine preoperative laboratory examinations (routine blood tests, platelets, neutrophils, lymphocyte cell count, tumor serum markers, such as carbohydrate antigen 19-9 [CA19-9], carcinoembryonic antigen [CEA], and serum alpha-fetoprotein [AFP]) and imaging examinations (abdominal ultrasound, contrastenhanced computerized tomography [CT] scan, and/ or magnetic resonance imaging [MRI] of the abdomen). The intent of surgery was to completely remove the macroscopic tumors with adequate resection margins. The detailed surgical procedures are described in our previous studies [16].
The diagnosis of ICC through histopathological examination of surgical specimens was based on the WHO criteria [17]. Major hepatectomy was defined as the removal of ≥ 3 Chouinard's liver segments. Anatomic resection was defined as liver resections based on systematic removal of Couinaud segment(s) containing the tumor together with the tumor-bearing portal vein and corresponding hepatic territory [18]. R0 resection was defined as the absence of macroscopic and microscopic disease at the surgical margin. Microvascular invasion (MVI) was defined as intraparenchymal vascular involvement identified on histological examination [19].

Follow-up
All patients were followed up regularly after discharge using the protocol described previously [21]. The relapse of ICC was diagnosed using the same criteria as the initial disease diagnosis, and a multidisciplinary approach was used to deal with the recurrence lesions [22]. Overall survival (OS) and recurrence-free survival (RFS) were defined as endpoints. OS was defined as the interval between the date of LR to the date censored, the date of the patient's death, or last follow-up. RFS was measured from the date of LR to the date of the first ICC recurrence or last follow-up. This study censored the follow-up on August 30, 2020.

Cut-off value and inflammation score (IFS)
In this study, we stratified NLR, PLR, LMR, GPR, SII, PNI, APRI, ANRI, ALRI, NγLR, dNLR, PMLR, NMLR, SIS, and GAR into two groups based on receiver operating characteristic (ROC) curves. The optimal threshold values of the highest Youden index (specificity + specificity − 1) were used as cut-off points in this study [23]. The calculation of IFS for each patient was based on the value of every inflammatory marker and its coefficient of the Cox analysis.

Statistical analysis
Continuous variables were reported as mean ± standard deviation (SD), or median and interquartile range (IQR). The differences were compared by one-way analysis of variance (ANOVA) or Kruskal-Wallis H tests (K-W tests). Categorical variables were reported as frequencies and percentages, and differences in different groups were compared using the Chi-square test. OS and RFS rates were analyzed by the Kaplan-Meier (KM) curves, and the differences were compared using the log-rank test. Cox regression analyses were used to identify independent prognostic factors for OS and RFS. Survival tree analysis was applied to divide patients into three differentiated groups (high-, median-, and low-score groups) with different survival outcomes.
The AJCC 7th TNM stage, AJCC 8th TNM stage, and Child-Pugh stage were compared with the IFS system in testing the performance of prognosis prediction via the time-dependent areas under the ROC (Time-ROC) curve and akaike information criterion (AIC). Statistical analysis was performed using the SPSS® version 25.0 (IBM, Armonk, New York, USA), and R program version 3.2.0 (http://www.r-project.org/). A P-value < 0.05 indicated statistical significance. Figure 1a shows the correlation heatmap of 15 inflammatory markers in this study. The darker the yellow, the stronger the correlation (positive or negative correlation). As shown in Fig. 1b, we quantified the correlation of 15 inflammatory markers. We found that the correlation coefficient between the ANRI and ALRI was 1 point. Moreover, we also found that APRI had a strong correlation between ANRI and ALRI, with coefficients of both 0.99. In parallel, the correlations between the NLR and DNLR, SII and PMLR, and PMLR and NMLR were also strong, with coefficients of 0.96, 0.87, and 0.85, respectively. Through the ROC curves, we found that the best cutoff values of the 15  Therefore, we scored every patient using the above 12 positive inflammatory markers, as illustrated in Table 1, according to the marker values and their corresponding coefficients. First, we calculated the median coefficient value of the 12 inflammatory markers for a value of 0.662. Second, we used every marker's coefficient value to divide 0.662 and then rounded it to the whole number (> 0.5 scored 1 point, < 0.5 scored 0 point) to obtain the inflammatory coefficient. Third, a patient was scored 1 point if his or her inflammatory marker > cut-off value; otherwise, 0 points were scored. Finally, we used inflammatory-coefficient times 0 or 1 for each marker and summed the total points for every patient to form IFS.

Patients were divided into three groups according to IFS
As shown in Fig. 3, the OS survival tree divided the patients in training cohort into three groups with different IFS levels. The best two cut-off values were 2 and 6 points, respectively. As a result, patients with IFS ≤ 2 were assigned to the low-IFS-score group (n = 149), > 6 were assigned to the high-IFS-score group (n = 110), and the remaining patients were assigned to the median-IFSscore group (n = 187). Additionally, Fig. 4 demonstrates the heatmap showing the distribution of the values of the 15 inflammatory markers in the training cohort according to the different IFS values and different IFS groups. On the vertical axis, 15 types of inflammatory markers could be clustered into five categories. Table 2 shows the basic clinicopathological characteristics of three groups in the training cohort. As shown in Tables 2 and 149 patients were in the low-IFS-score group (33.4%), 187 patients were in the median-IFS-score group (41.9%), and 110 patients were in the high-IFSscore group (24.7%).
Additionally, Table 4 shows the results of univariate and multivariate Cox analyses for RFS. Multivariate analyses identified high level of CA19-9 (HR = 1.001,

Comparison of different scoring systems
The ROC curve of IFS demonstrated in Fig. 5c had an AUC value of 0.703 (95%CI = 0.658-0.748), which suggested that the IFS system had a good prognosis predictive performance for OS in the training cohort. Time-ROC curves were established to compare the performance of the IFS system with the other three scoring systems (Fig. 5d), which showed that the AUC value of the IFS from 0 to 60 months was better than that of the other three scoring systems in the training cohort. The AIC value of the IFS was also obviously lower than others in the training cohort. The comparison results are listed in Table 5, and we found that the survival predictive ability of the IFS was significantly better than that of the Child-Pugh stage (P < 0.0001), AJCC 7th TNM stage (P < 0.0001), and AJCC 8th TNM stage (P < 0.0001).

The validation of IFS system
The internal validation cohort consisted of 175 patients, and the basic clinicopathologic characteristics were listed in Table 6. KM curves illustrated that patients in the high-IFS group presented with poorer OS and RFS rates compared with those in the median-IFS and low-IFS groups. The 1-, 3-, and 5-year OS rates were 13.0%, 7.8%, Fig. 3 Survival tree analysis of OS in the training cohort and 2 point and 6 point were considered to be two best cut-off values and 0.0% for the high-IFS group, 60.9%, 28.6%, and 16.8% for the median-IFS group, and 90.5%, 51.1%, and 32.3% for the low-IFS group, respectively (P < 0.0001). Moreover, the 1-, 3-, and 5-year RFS rates were 15.5%, 7.8%, and 0.0% for the high-IFS group, 33.6%, 16.3%, and 11.4% for the median-IFS group, and 69.0%, 30.3%, and 17.6% for the low-IFS group (P < 0.0001) ( Fig. 6a and b).
The ROC curve of IFS in the internal validation cohort was shown in Fig. 6c, and the AUC value was 0.664 (95%CI = 0.569-0.760). Moreover, we also compared the prognosis predictive performance of several scoring systems through time-ROC curves, which showed that the survival predictive ability of IFS was still better than that of the Child-Pugh stage, AJCC 7th TNM stage, and AJCC 8th TNM stage (Fig. 6d).

Discussion
In this study, we developed a new inflammatory score system, abbreviated as the IFS system based on the weight of different inflammatory markers impact on OS in the training cohort, to make a prognostic prediction for ICC after surgery. When patients were divided into high-, median-, and low-IFS groups according to different IFS levels, we found that patients in the high-IFS group had worse OS and RFS rates than those in the median-and low-IFS groups, and the same outcomes were observed in the internal validation cohort. In addition, we found that a high IFS score was an independent risk factor for OS and RFS after Cox regression analyses in the training cohort. More importantly, the IFS score had a good performance of prognostic prediction in ICC with an AUC value of 0.703 and 0.664 in the training and internal validation cohorts, respectively, which were better than those of other scoring systems. In summary, our newly constructed IFS system is a good prognostic predictor for ICC, and a high IFS is significantly associated with worse OS and RFS in patients after hepatectomy.
Extensive studies have revealed that inflammatory responses play decisive roles in tumor development and progression [24]. Over time, an increasing number of inflammation-related indicators have been constructed to serve as predictors of survival after therapies in many tumors. SIS, PNI, NLR, PLR, and Glasgow prognostic score (GPS) are all markers that have been proven to have good prognostic predictive abilities in malignant liver disease [25,26]. However, most studies used only one or two inflammatory markers in their studies to predict survival in patients with ICC. Inconsistent with these findings, we are the first to use 12 common inflammatory markers as an integrated indicator (known as IFS) to predict survival for patients with ICC. Additionally, the establishment of IFS is based on the weight of impact of every inflammatory marker on OS so that we could guarantee that the scoring system is more reasonable and comprehensive.
In our study, we included 15 inflammatory markers and finally selected 12 markers, including NLR, PLR,  used them to build an IFS system. Those markers are a series of scoring systems that can reflect the inflammation-related characteristics of ICC. Previous studies have found that about 25% of cancers are associated with chronic inflammation, which is caused by infections or inflammatory conditions, such as hepatitis infection or prostatitis. Moreover, the existence of inflammatory cells or mediators not only participates in promotion of angiogenesis, extracellular matrix restructuring, and pre-metastatic niche formation, but also can induce some non-epidemiological characteristics in tumors [27]. Detailed exploration of the forms of cancer-related inflammation is a useful approach to discover important prognostic markers for malignant tumors in daily clinical practice. Regulating the systemic inflammatory response might become an important therapeutic target for the tumor itself in future.
Recently, some studies have demonstrated the prognostic utility of NLR, PLR, and LMR in patients with ICC after surgery [26,28,29]. In our study, we found that  (a and b). ROC curves of IFS system (c), and the time-dependent ROC curves for IFS system and other clinical staging systems in the training cohorts (d)  elevated NLR and PLR were negative predictors of OS in the training cohort, while low LMR was a protective factor of OS, which was consistent with the aforementioned studies. In addition, PNI and γ-glutamyl transferaseassociated enzymes, such as GPR and GAR, have been reported to be associated with the prognosis of ICC [30][31][32]. Our study also revealed that GPA, GAR, and PNI were parameters that were significantly associated with the prognosis of ICC in the training cohort. As we know, a high level of GGT usually reflects the disease severity of bile tract disarrangement, and low PNI levels can reflect a patient's nutritional status in ICC.
In 2019, Wang et al. reported that in early recurrent HCC, only the SII (P < 0.001) was an independent predictor for post-recurrence survival in multivariate analyses [33]. Another study also found that increased SII was associated with decreased survival (P < 0.01) in ICC [8]. The SII score is based on lymphocytes, neutrophils, and platelets. Known studies have demonstrated that neutrophils and platelets can promote tumor cell proliferation and metastasis [34,35]. Lymphocytes can lead to cell apoptosis and suppress cancer cell proliferation, migration, and invasion [36]. All the above studies supported our findings that SII was a risk factor for ICC survival after surgery.
Li et al. found that in 724 patients with HCC undergoing curative resection, increased NγLR is an independent risk factor for OS and progression-free survival [37]. The  [38][39][40][41]. However, several studies also concluded that dNLR was not an independent risk factor for survival in renal cell carcinoma [42] and gastric cancer [43]. In our study, we found that a high dNLR level, which was a part of the IFS system, was a risk factor for OS in ICC.   Table 6 Baseline clinicopathological characteristics of the three groups in the internal validation cohort found that NMLR and PMLR were associated with OS and RFS in gastric cancer [45]. NMLR and PMLR are inflammatory markers involved in various immune cells, which might reflect the synergistic effects or complex interaction between different immune cells in the tumor milieu. In 2019, another study from China found that the SIS, which is based on preoperative serum CA19-9 and LMR, was a powerful prognostic biomarker in ICC. In their study, Zhang et al. found that SIS was an independent risk factor for OS and TTR. Moreover, high SIS was significantly associated with aggressive tumor behavior, such as high TNM stage, large tumor size, multiple tumors, and LNM [20]. In our study, we found that SIS was a risk factor for OS in the training cohort, and IFS was an independent risk factor for OS and RFS in ICC.
Our study had several limitations. First, inflammatory marker such as C-reactive protein (CRP) was not included in this analysis because preoperative CRP is not a routine test in our institution, so we lack the data to complete the relevant statistical analysis. CRP is considered to have a higher sensitivity, but a poorer specificity, and CRP values are susceptible to many factors.  (a and b). ROC curves of IFS system (c), and the time-dependent ROC curves for IFS system and other clinical staging systems in the internal validation cohort (d)  Therefore, we believe that CRP is not suitable for being an excellent inflammatory marker parameter to predict the prognosis of patients with ICC. Second, collinearity exists on some inflammatory markers, which is a complex problem to avoid, especially including too many variates.
In this study, we combined all the inflammatory markers for a single indicator (IFS) according to their prognostic weights to assess the role of inflammatory markers systematically and comprehensively. In this manner, the effect of covariance can be reduced to a minimum level. Third, we only used tumor differentiation instead of the histological type (mass-forming, periductal-infiltrating, intraductal-growing), which is a shortcoming of this study. Finally, it was a retrospective study. Further validation from more external cohorts is needed.

Conclusion
Collectively, the IFS established based on 12 common inflammatory markers in our study is a potentially powerful prognostic predictor in patients with ICC after curative resection. IFS is an independent risk factor for OS and RFS in ICC and has a more accurate predictive ability than the AJCC 7th edition, AJCC 8th edition, and Child-Pugh score, which underlines the necessity of considering IFS as an important prognostic factor for ICC in daily clinical practice.