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. 2024 Feb 18;10(4):e26176. doi: 10.1016/j.heliyon.2024.e26176

Clinical significance of systemic inflammation response index and platelet–lymphocyte ratio in patients with adenocarcinoma of the esophagogastric junction and upper gastric cancer

Tianyi Fang a,1, Xin Yin a,1, Yufei Wang a,1, Lei Zhang b, Shuo Yang b, Xinju Jiang b, Yingwei Xue a,∗
PMCID: PMC10900425  PMID: 38420481

Abstract

Background

Tumor immunity plays an important role in assessing the tumor progression. The purpose of this study was to investigate the prognostic value of combined systemic inflammation response index (SIRI) and platelet–lymphocyte ratio (PLR) of gastroesophageal junction cancer (AEG) and upper gastric cancer (UGC) patients.

Methods

In this retrospective study, patients from 2003 to 2014 were divided into training and validation sets. The prognostic accuracy of each variable was compared using time-independent ROC analysis. The scoring system was calculated by cut-off values of SIRI and PLR in 5-year. Kaplan-Meier and Log-rank tests were used to analyze overall survival (OS). Chi-square test was used to analyze the association between clinical characteristics and the scoring system. Univariate and multivariate analyses based on the competitive risk regression model were used to analyze independent predictors of death due to AGC and UGC. R software was used to construct the Nomogram model of risk assessment.

Results

Patients with SIRI–PLR = 2 had worse survival time than those with 0 and 1 (P < 0.001) and more suitable for postoperative adjuvant chemotherapy (P = 0.002). High PLR patients were more suitable for proximal gastrectomy (P = 0.049). SIRI-PLR were independent predictors in training set (P < 0.001), which could be combined with age, pTNM stage and postoperative chemotherapy to construct Nomogram for predicting OS.

Conclusions

Preoperative SIRI–PLR score was an independent predictor for patients with AEG and UGC. The Nomogram model constructed by age, SIRI-PLR, pTNM stage and postoperative chemotherapy can correctly predict the prognosis of patients.

Keywords: Gastric cancer, Adenocarcinoma of esophagogastric junction, Upper gastric cancer, Systemic infammation response index, Platelet–lymphocyte ratio, Prognosis

1. Introduction

Gastric cancer (GC) is still the third leading cause of cancer mortality worldwide [1]. The incidence of gastroesophageal junction cancer (AEG) and upper gastric cancer (UGC) has increased annually, especially in Japan, where it has increased from 2.3% to 10% in the past 40 years [2]. For AEG, according to Siewert type, >66% of Siewert type II and 90% of Siewert type III patients are mainly treated by radical gastrectomy [[3], [4], [5], [6]]. AEG and UGC have the clinical features of strong invasion, poor prognosis, advanced clinical stage and high rate of postoperative recurrence [7,8]. Regarding the surgical method, there is debate about total gastrectomy (TG) and proximal gastrectomy (PG). Golematis et al. [9] found that TG can ensure a sufficient distal margin and extended lymph node dissection, which can brings survival benefit. However, Harrison et al. found that patients who underwent TG and PG had no difference in overall survival (OS) rate, although PG led to better postoperative nutritional status [10]. Therefore, it is important to find suitable clinical prognostic factors to help surgeons choose a suitable method of gastrectomy as well as evaluate prognosis after surgery.

Researchers have proposed use of the American Joint Committee on Cancer (AJCC) eighth edition for treatment guidelines for AEG. If the tumor invades the gastroesophageal junction and its center is located beyond 2 cm below the gastroesophageal junction, or if the tumor center is within 2 cm below the gastroesophageal junction but does not invade the gastroesophageal junction, it should be classified according to the gastric cancer stage [11]. We found that the current clinical guidelines are only based on assessment of the tumor's general progression and do not consider the immune response caused by tumor cells. In 2014, Galon et al. [12] first proposed pTNM-I, which combines the immune response in the tumor microenvironment and pTNM stage. In 2018 [13], pTNM-I staging was used to guide postoperative chemotherapy in patients with colon cancer. However, the high degree of heterogeneity makes it difficult for pathologists to assess the immune status of patients individually. Reichert et al. [14] found that changes in the peripheral immune microenvironment of the tumor can be reflected by circulating immune-related cells such as neutrophils (N), platelets (P), monocytes (M) and lymphocytes (L). Systemic immune inflammation index (SII) [[15], [16], [17]], neutrophil–lymphocyte ratio (NLR) [18,19], platelet–lymphocyte ratio (PLR) [20], lymphocyte–monocyte ratio (LMR) [21], and scoring systems that combine with inflammation index, such as C-reactive protein (CRP)–NLR [22], NLR–PLR [23], Fibrinogen (F)–NLR [24] and SIRI–PLR [25] have been confirmed to evaluate accurately the prognosis of GC and other malignant tumors. Therefore, as a new comprehensive inflammatory index scoring system combining lymphocytes, neutrophils, monocytes and platelets, the systemic inflammatory response index (SIRI) and platelet-lymphocyte ratio (PLR) encompass more comprehensive clinical immune factors, which are expected to predict the prognosis of GC more accurately and even help clinical doctors choose appropriate surgical methods for AEG and UGC.

In this study, 371 patients with AEG and UGC who underwent radical surgery at the Cancer Hospital of Harbin Medical University were selected consecutively. The relationship between SIRI–PLR and pathological factors was investigated by cohort study to explore the clinical significance of SIRI–PLR scoring.

2. Materials and methods

2.1. Patients

Tumors that were mainly located in the upper third of the stomach were designated UGC. Tumors that were mainly located at 2–5 cm from the gastroesophageal junction were designated Siewert type Ⅲ GC [26], as well as AEG. This study retrospectively analyzed 371 patients underwent radical surgery at the Harbin Medical University Cancer Hospital from 2003 to 2014. Inclusion criteria were: (a) patients diagnosed with gastric adenocarcinoma by experienced pathologists, (b) tumors mainly located in the upper third of stomach or 2–5 cm from the gastroesophageal junction, (c) patients without neoadjuvant chemotherapy, (d) complete follow-up records (including missed visits and end-point event occurrence) and (e) complete clinical and pathological data. Exclusion criteria were: (a) history of blood transfusion in the last 2 months, (b) thyroid diseases, (c) intravascular coagulation, (d) history of heparin treatment in the last 1 month, (e) connective tissue diseases and (f) active bleeding.

Total patients were divided into two independent cohorts according to admission time, 194 patients from 2003 to 2010 were the training set, and 177 patients from 2011 to 2014 were the validation set. Over the all known died patients, 186 patients died due to GC and 21 patients (11 patients in the training set, 10 patients in the validation set) died from other causes (7 deaths because of heart disease, 4 patients died naturally, 2 patients died from accidents and 8 deaths from unknown causes).

Operation methods and postoperative chemotherapy standards are based on The Japanese gastric cancer treatment guidelines [27]. The range of gastrectomy is determined according to the patient's clinical stage and tumor location. TG is for clinically node-positive (cN+) or T2-T4a. PG is for the patients with acceptable proximal resection margin (>5 cm or frozen section examination of the resection line is desirable). The invasion of the pancreas by tumors requiring pancreaticosplenectomy must also be performed by TG, regardless of the location of the tumor and distance of the surgical margin. TG includes all gastric tissues, including pylorus and cardia, combined with D2 lymph node dissection range (No. 1–7 and No. 8a, 9, 10, 11p, 11d, 12a). Digestive tract reconstruction methods are Roux-en-Y esophagojejunostomy, Jejunal interposition and Double tract. PG involves proximal 2/3 of the gastric tissues, with gastroesophageal junction and pylorus retained, in combination with the D1+ lymph node dissection area (No. 1, 2, 3a, 4sa, 4sb, 7 and No. 8a, 9, 11p, and No.110 dependent on the need for surgery), and the methods of digestive tract reconstruction include Esophagogastrostomy, Jejunal interposition and Double tract method. There were 38 patients with T4b, of which 7 patients had intragastric resection of adjacent organs (2 patients with partial liver resection, 1 with transverse colon, 2 with spleen, and 2 with pancreatic tail). In addition, oxaliplatin + capecitabine (XELOX) or oxaliplatin + S-1 (SOX) are the main treatment options for patients with postoperative pathological stages II-III, which were 336 patients in the study. In order to ensure the accuracy of this study, we included all patients in our institution with a total of 117 patients. The remaining 219 patients were not included in the postoperative chemotherapy patient group. This is because these patients did not complete all postoperative chemotherapy regimens in our institution. Most of the patients returned to the local hospital for treatment after surgery and did not have complete chemotherapy records.

Patients’ clinicopathological data were saved in the Gastric Cancer Information Management System v1.2 of Harbin Medical University Cancer Hospital (Copyright No. 2013SR087424, http://www.sgihmu.com). All patients were re-examined by checking tumor markers or radiological examination [computed tomography (CT), ultrasound and gastroscopy] every 3–6 months, and positron emission tomography-CT were performed as needed.

2.2. Inflammation index

Blood samples were collected from patients in fasting condition 1 week before surgery. Neutrophil, lymphocyte, monocyte and platelet counts were obtained by hematological examination. For inflammation index, systemic immune inflammation index (SII) = N × P/L [28], neutrophil–lymphocyte ratio (NLR) = N/L, platelet–lymphocyte ratio (PLR) = P/L and systemic inflammation response index (SIRI) = N × M/L [29] (N = Neutrophil count, L = Lymphocyte count, M = Monocyte count and P=Platelets).

2.3. Statistical analysis

Overall survival (OS) was calculated as the time from surgery to death from any cause. If patients were alive at the last follow-up, they were censored. Patient's survival time in each group were shown as mean [95% confidence interval (CI)]. We used R software version 3.6.1 and the ‘survivalROC’ package to investigate the prognostic or predictive accuracy of each variable by time-dependent receiver operating characteristic (ROC) analysis. An optimal cutoff value was defined to classify the patients into two groups (high vs low) for each variable with use of the receiver operating characteristic curve for survival in 5-year, and the maximum value of ‘Youden index (Sensitivity + Specificity - 1)’ is the best cutoff value. Delong nonparametric method was used to estimate the AUC confidence interval. Kaplan–Meier method and Log–rank test were used to analysis survival curves. Median follow up was calculated by the reverse Kaplan–Meier analysis. Yates' correction is selected for data with a theoretical frequency between 1 and 5 and a total sample size greater than or equal to 40; Chisq test is used for data with a theoretical frequency greater than or equal to 5 and a total sample size greater than or equal to 40; For data with normal distribution and homogeneity of variance, T-test was used for comparison between the two groups; For data that does not meet the normal distribution, Wilcoxon test was used for comparison between two groups. Univariate and multivariate analyses based on the competitive risk regression model were used to analyze independent predictors for high risk of death due to AGC and UGC. The R software was used to construct the Nomogram model of risk assessment by ‘SvyNom’ and ‘rms’ packages. Standardized Hazard Ratio and 95% CIs were estimated for each factor. SPSS version 22.0 (SPSS Inc., Chicago, IL, USA) was used for analysis and two-tailed P values < 0.05 were considered statistically significant.

3. Results

3.1. Patient characteristics

Patient characteristics including sex, age, tumor size, tumor location, pTNM stage, blood cell count, histological type, vascular infiltration and adjuvant chemotherapy are shown in Supplementary Table I. The training set comprised 194 patients [166 (85.57%) male and 28 (14.43%) female]. The 5-year survival rate was 39.7%. The validation set comprised 177 patients [144 (81.36%) male and 33 (18.64%)]. The 5-year survival rate was 46.3%. The flowchart of this study is shown in Fig. 1.

Fig. 1.

Fig. 1

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The flowchart of this study.

3.2. SIRI–PLR

The time-dependent ROC curve shows that these variables were continuously keep satisfactory significance (Fig. 2A). For the SIRI and PLR, 0.82 and 134.62 were the cutoff value. The area under the curve (AUC) was 0.677 (95% CI: 0.602–0.752) and 0.678 (95% CI: 0.602–0.754). Similarly, the optimal cutoff values of SII and NLR were 464.23 and 2.46 by ROC curve analysis (Fig. 2B–D). And patients with SIRI ≤0.82 and PLR ≤134.62 were in the score 0 group, patients with SIRI ≤0.82 and PLR >134.62 and SIRI >0.82 and PLR ≤134.62 were in the score 1 group, and patients with SIRI >0.82 and PLR >134.62 were in the score 2 group.

Fig. 2.

Fig. 2

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(A): Time dependent ROC curve of SIRI, PLR, SII and NLR. (B–D): Receiver operating characteristic curve of SIRI, PLR, SII and NLR in 1, 3 and 5-year. (E): Receiver operating characteristic curve of inflammation index.

3.3. ROC analysis of inflammation index

In the training set patients, we compared the SIRI–PLR, NLR and SII by ROC. The AUC of the SIRI–PLR was 0.729 [95% CI: 0.658–0.800], the sensitivity was 47.5% and specificity was 86.3%. The AUC of NLR was 0.658 [95% CI: 0.581–0.735], the sensitivity was 50.5% and specificity was 81.1%. The AUC of SII was 0.669 [95% CI: 0.593–0.746], the sensitivity was 70.7% and specificity was 63.2%. (Fig. 2E) (Table 1).

Table 1.

Relevant results of inflammation index.

Inflammation index AUC SE P value 95% CI Sensitivity (%) Specificity (%)
SIRI–PLR 0.729 0.036 <0.001 (0.658–0.800) 47.5 86.3
NLR 0.658 0.039 <0.001 (0.581–0.735) 50.5 81.1
SII 0.669 0.039 <0.001 (0.593–0.746) 70.7 63.2
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AUC the area under the curve, SE Standard Error, CI confidence interval.

3.4. The relationship between SIRI–PLR and patient characteristics

The relationship between SIRI–PLR score and clinical and pathological factors is shown in Table 2. In the training set, SIRI–PLR score had a significant association with tumor size, surgical procedure, NLR, SII and pTNM stage (P = 0.004, P = 0.014, P = 0.002, P < 0.001 and P = 0.037). In the validation set, SIRI–PLR had a significant association with tumor size, NLR and SII (P = 0.003, P < 0.001 and P < 0.001).

Table 2.

Connection between SIRI–PLR score and clinicopathologic factors of AEG and UGC patients in training, validation set.

Clinicopathologic factors Training set
 Validation set
Score 0 Score 1 Score 2 X^2 P value Score 0 Score 1 Score 2 X^2 P value
Sex 5.959 0.051 0.374 0.829
Male 46 66 54 43 64 37
Female 4 18 6 10 13 10
Age (years) 3.545 0.170 1.122 0.571
≤60 29 41 24 26 43 22
>60 21 43 36 27 34 25
Tumor size (mm) 10.900 0.004 11.725 0.003
≤50 35 54 25 29 42 12
>50 15 30 35 24 35 35
Tumor location 4.538 0.360 3.301 0.509
UE 12 14 7 13 12 7
U 31 55 38 29 50 27
UM/EUM/UML 7 15 15 11 15 13
Surgical procedure 8.501 0.014 2.072 0.355
Proximal gastrectomy 35 59 29 28 32 19
Total gastrectomy 15 25 31 25 45 28
NLR 12.479 0.002 43.526 < 0.001
≤2.46 50 63 13 52 62 20
>2.46 0 21 47 1 15 27
SII 82.294 < 0.001 75.729 < 0.001
≤464.23 48 35 6 49 46 3
>464.23 2 49 54 4 31 44
T stage 8.101 0.424 8.029 0.431
T1 2 3 2 6 4 1
T2 4 3 3 6 11 4
T3 4 7 1 18 29 14
T4a 32 62 40 22 29 26
T4b 8 9 14 1 4 2
N stage 15.288 0.054 8.993 0.343
N0 20 23 13 25 32 13
N1 17 18 11 9 18 11
N2 7 26 19 11 16 9
N3a 6 13 13 7 7 9
N3b 0 4 4 1 4 5
pTNM stage 10.194 0.037 6.033 0.197
Ⅰ 5 3 4 9 11 3
Ⅱ 16 24 7 22 33 15
Ⅲ 29 57 49 22 33 29
Histological type 5.958 0.202 6.225 0.183
Well and Moderately differentiated adenocarcinoma 20 41 19 26 32 19
Poorly differentiated adenocarcinoma 23 38 34 18 40 21
Others 7 5 7 9 5 7
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SII Systemic immune inflammation index, NLR neutrophil–lymphocyte ratio, SIRI systemic inflammation response index, UE esophagus and upper stomach, U upper, UM upper and middle, EUM esophagus and upper middle of stomach, UML total stomach.

Histological type, T stage, N stage and pTNM stage are according to the eighth edition of the AJCC Cancer Staging Manual. Tumor location and vascular infiltration were according to the postoperative pathology report. Statistically significant P values are in bold (P < 0.05).

3.5. SIRI–PLR and patient survival

In the training and validation sets, patients with SIRI–PLR score 2 had worse survival than patients with score 0 and 1 (All P < 0.001) (Fig. 3A–C). In the training set, patients with SIRI–PLR score 0, 1, and 2 had the survival times of 54.43 (95% CI: 51.085–57.767), 41.23 (95% CI: 36.590–45.865) and 26.83 (95% CI: 21.710–31.956) months, respectively, and 5-year survival rates of 55.9%, 41.6% and 15.2%, respectively. In the validation set, the survival times were 51.01 (95% CI: 46.764–55.248), 45.44 (95% CI: 40.895–49.985) and 29.51 (95% CI: 23.624–35.386) months, respectively, and 5-year survival rates were 56.0%, 44.0%, and 20.0%, respectively. In the total patients, the survival times were 52.08 (95% CI: 49.377–54.791), 41.42 (95% CI: 38.199–44.635) and 25.34 (95% CI: 21.028–29.641) months, respectively, and 5-year survival rates were 52.8%, 57.1% and 21.3%, respectively.

Fig. 3.

Fig. 3

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Survival curves of patients with AEG and UGC according to SIRI–PLR score. (A): OS of patients with SIRI– PLR score 0, 1, and 2 in training set (n = 194, P < 0.001), (B–C): validation set (n = 177, P < 0.001) and all patients (n = 371, P < 0.001).

3.6. SIRI–PLR and pTNM

In all training set patients. Patients in stage Ⅰ/Ⅱ and Ⅲ with SIRI–PLR score 2 had a worse survival rate than score 0 and 1. Patients in stage Ⅰ/Ⅱ with SIRI–PLR score 0, 1, and 2 had the survival times were 54.11 (95% CI: 49.290–58.929), 49.66 (95% CI: 42.935–56.379) and 32.68 (95% CI: 20.243–45.115) months, respectively. The 5-year survival rates were 76.2%, 66.7% and 27.3%, respectively (P = 0.002) (Fig. 4A). Patients in stage Ⅲ with SIRI–PLR score 0, 1, and 2 had the survival times were 54.74 (95% CI: 50.088–59.392), 37.09 (95% CI: 31.321–42.857) and 25.51 (95% CI: 19.964–31.056) months, respectively. The 5-year survival rates were 41.4%, 33.3% and 18.4%, respectively (P < 0.001) (Fig. 4B).

Fig. 4.

Fig. 4

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Survival curves based on SIRI–PLR of AEG and UGC patients (TNM stage I–III). (A): Survival curves of patients with TNM stage I/II with SIRI–PLR score 0, 1 and 2 (P < 0.001). (B): Survival curves of patients with TNM stage III with SIRI–PLR score 0, 1 and 2 (P < 0.001).

3.7. SIRI–PLR and postoperative chemotherapy and surgical method

In patients with stage Ⅱ and Ⅲ GC. There was no significant difference between patients with and without postoperative chemotherapy in the score 0 and 1 groups (P = 0.875), and patients without postoperative chemotherapy had shorter survival than patients with postoperative chemotherapy in the score 2 group (P = 0.002) (Fig. 5A and B). In the score 0 and 1 groups, patients with and without postoperative chemotherapy had the survival times of 45.91 (95% CI: 41.687–50.132) and 44.83 (95% CI: 41.608–48.044) months, respectively, and 5-year survival rates were 53.1% and 53.3%, respectively. In the score 2 group, the survival times were 38.75 (95% CI: 31.908–45.601) and 23.211 (95% CI: 18.527–27.795) months, respectively, and 5-year survival rates were 36.7% and 14.6%, respectively.

Fig. 5.

Fig. 5

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Relationship between SIRI–PLR score and benefit from postoperative chemotherapy in total patents with SIRI–PLR score 0,1 (A) and 2 (B). Relationship between SIRI, PLR and benefit from surgical method in patents with low PLR (C), high PLR (D), low SIRI (E) and high SIRI (F).

Although SIRI–PLR cannot guide the surgical method and there was also no significant difference between patients with PG and TG in the low PLR, low SIRI and high SIRI patients (P = 0.271, P = 0.271 and P = 0.260), in the high PLR group, patients with TG had shorter survival than patients with PG (P = 0.049) (Fig. 5C–F). In the high PLR group, patients with PG and TG had the survival times of 37.31 (95% CI: 32.855–41.763) and 29.875 (95% CI: 25.525–34.224) months, respectively, and 5-year survival rates were 36.4% and 25.0%, respectively.

3.8. Univariate and multivariate regression analyses

To identify the independent predictors for OS of patients with AGC and UGC, univariate and multivariate analyses based on competitive risk regression model in the training set. According to univariate analysis, age (P = 0.001), NLR (P < 0.001), SII (P < 0.001), SIRI-PLR (P < 0.001), pTNM stage (P = 0.048) and postoperative chemotherapy (P = 0.016) were significant. According to multivariate analyses, age (P = 0.033), SIRI-PLR (P < 0.001), pTNM stage (P = 0.005) and postoperative chemotherapy (P = 0.001) were independent predictors for high risk of death due to AGC and UGC (Table 3).

Table 3.

Risk factors of patients with AEG and UGC by univariate and multivariate based on competitive risk regression model.

Characteristics Univariate analysis
 Multivariate analysis
SHR SE P value SHR 95 % CI P value
Sex – – –
Male 1
Female 0.611 0.330 0.140
Age (years) 1.033 0.010 0.001 1.023 (1.002–1.045) 0.033
Tumor location – – –
UE 1
U 1.065 0.270 0.820
UM/EUM/UML 1.351 0.343 0.380
Surgical procedure – – –
Proximal gastrectomy 1
Total gastrectomy 1.300 0.210 0.210
NLR 1.211 0.049 <0.001 1.027 (0.850–1.240) 0.780
SII 1.001 0.0001 <0.001 1.000 (1.000–1.001) 0.480
SIRI-PLR 2.558 (1.816–3.603) <0.001
0 1
1 2.990 0.291 <0.001
2 7.431 0.301 <0.001
pTNM stage 1.798 (1.489–2.172) 0.005
Ⅰ 1
Ⅱ 1.632 0.615 0.430
Ⅲ 3.156 0.582 0.048
Histological type – – –
Well-differentiated and Moderately differentiated adenocarcinoma 1
Poorly differentiated adenocarcinoma 1.066 0.219 0.770
Others 1.523 0.278 0.130
Postoperative chemotherapy 0.426 (0.417–0.435) 0.001
No 1
Yes 0.581 0.225 0.016
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NLR neutrophil–lymphocyte ratio, PLR platelet–lymphocyte ratio, SIRI systemic inflammation response index, SHR subdistribution hazard ratio, SE Standard Error, CI confidence interval, UE esophagus and upper stomach, U upper, UM upper and middle, EUM esophagus and upper middle of stomach, UML total stomach.

Histological type, T stage, N stage and pTNM stage are according to the eighth edition of the AJCC Cancer Staging Manual. Tumor location and vascular infiltration were according to the postoperative pathology report. Statistically significant P values are in bold (P < 0.05).

3.9. Prognostic nomogram for OS

Because age, SIRI, PLR and pTNM stage are independent predictors for patients with AGC and UGC in the training set, we first combined these clinical features in the training set and constructed a Nomogram model of continuous variable data to predict the 3-year and 5-year prognosis (Fig. 6A). Concordance was 0.739 and standard error was 0.017 (Fig. 6B and C). Calibration curves for predicting survival at 3 and 5-year were performed. The AUC were 0.781 [95% CI: 0.714–0.847] (Fig. 6D) and 0.790 [95% CI: 0.727–0.853], respectively (Fig. 6F). The sensitivity were 87.5% and 85.9%, respectively, and the specificity were 57.0% and 60.0%, respectively.

Fig. 6.

Fig. 6

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(A): Nomogram model predicting 3-year and 5-year prognosis of patients in the training set. (B–C): The calibration curve for predicting patient survival in 3-year and 5-year in the training set. (D): ROC curve of Nomogram model predicting 3-year prognosis of patients in the training set. (E): ROC curve of Nomogram model predicting 3-year prognosis of patients in the validation set. (F): ROC curve of Nomogram model predicting 5-year prognosis of patients in the training set. (G): ROC curve of Nomogram model predicting 5-year prognosis of patients in the validation set.

We validate the Nomogram model in the validation set. The AUC related to 3-year prognosis was 0.730 [95% CI: 0.653–0.807], the sensitivity was 81.0%, and the specificity was 57.0% (Fig. 6E). The AUC related to 5-year prognosis was 0.713 [95% CI: 0.637–0.788], the sensitivity was 51.7%, and the specificity was 83.3% (Fig. 6G).

4. Discussion

More than 70% patients with AEG and UGC are diagnosed with advanced GC [30], which make patients who had undergone radical surgery still have a high risk of recurrence and metastasis, and suitable clinical prognostic factors are still needed to help surgeons identify high-risk patients. With recognition of the role of systemic inflammation in promoting tumor growth, progression and metastasis of malignant tumors, C-reactive protein, cytokines, inflammatory proteins and immune cells in the systemic inflammatory response are also considered as potential clinical markers to guide treatment and evaluate the prognosis [[31], [32], [33]]. Recently, the prognostic importance of SIRI for patients with renal, pancreatic and nasopharyngeal cancers has been confirmed [25,[34], [35], [36], [37], [38]]. Patients with high SIRI not only represent worse disease-free survival and OS, but also are more suitable for thoracoscopy in lung cancer patients and mFOLFIRINOX chemotherapy in pancreatic cancer patients. Besides, studies of upper tract urothelial carcinoma also have shown that combination of SIRI and PLR scoring evaluates prognosis more effectively.

In this study, the AUC of SIRI and PLR curve are 0.677 and 0.678, respectively, and the cut-off values are 0.82 and 134.62, respectively, which is similar to the research by Zheng et al. [25]. But we found the AUC of PLR in our study is higher than that reported by Zhang et al. [39]. According to chi-square analysis between SIRI–PLR with clinical and pathological features it could be found that it was correlated with tumor size, NLR and SII. Many previous studies confirmed that NLR and SII could be used as inflammatory indexes for evaluating prognosis of GC patients, which means that SIRI–PLR is expected to become a more significant clinical biomarker. In our study, ROC analysis of SIRI–PLR, NLR and SII showed that the AUC of SIRI–PLR was not only larger than that of NLR and SII, but also had better specificity. We can conclude that the SIRI–PLR is better to other inflammatory indexes in predicting the prognosis, which was consistent with the study by Li et al. [40] and Huang et al. [41].

The tumor immune microenvironment of GC patients can be reflected by postoperative pathological tissue sections or immune indexes. Postoperative pathological tissue sections can be used to analyze the condition of the tumor immune microenvironment by immunohistochemistry, which had high specificity. However, the randomness of tissue site selection and development of preoperative adjuvant therapy reduce the accuracy of the test. On the other hand, the immune indexes constructed by circulating immune cells were calculated based on preoperative hematological tests by clinical data, which had high accuracy, fast and convenient features. So the immune indexes were easy for clinical application [42,43].

The SIRI–PLR scoring system comprehensively covered the circulating immune cells for calculation of the inflammatory index, which could evaluate more comprehensively the physical inflammatory status of patients than NLR and SII did. The tumor immunity plays an important role in measuring tumor progression. GC cells can induce enrichment of neutrophils in tumors, especially GC positive for programmed death ligand 1 and correlative with Epstein–Barr virus [[44], [45], [46]]. Formation of neutrophils and secretion of interferon-γ and tumor necrosis factor-α can inhibit proliferation of lymphocytes and biological activity of CD4+ and CD8+ T cells, lead to immune escape of cancer cells and distant micro-metastasis of tumor cells. Nie et al. [47] showed that expression of a large number of monocytes can inhibit the immune response of T cells near the tumor and promote immune escape of cancer cells by increasing cyclo-oxygenase-2 expression. Senescence-associated secretory phenotype interaction between platelets also plays an important role in metastasis and invasion of cancer cells [48]. Tumor occurrence, development and metastasis lead to changes in immune cells and inflammation in the tumor microenvironment, which would affect the circulating immune cells with disease progression.

As we all know, choosing appropriate individualized treatment for each patient can greatly improve the quality of life even survival time. Surgery is still generally the first choice intervention in patients with GC [49]. At present, clinicians mainly choose TG and PG methods for gastrectomy according to tumor size, tumor location, clinical stage and surgical experience. However, there is still a part of GC which is consistent with both surgical indications of TG and PG. Moreover, the different surgical methods of gastrectomy affect the quality of life and survival time after operation. PG had high postoperative body mass index, albumin, nutrition index and high risk of gastroesophageal reflux, which may be related to the high rate of tumor recurrence. TG can ensure a sufficient distal surgical margin, which can reduce the risk of recurrence and prolong survival [9,50,51]. However Pu et al. [52] found that there was no difference in survival time between patients with PG and TG. This has led to a controversy about how to choose the operation method for such patient. Therefore, this study suggests that in addition to the conventional surgical guidelines, surgeons can also use an additional inflammatory index to help choose the method of gastrectomy. We found that for patients with high PLR patients. By comparing the postoperative survival of patients who received TG and PG, patients who underwent PG had better prognosis than those who underwent TG. These two methods can effectively remove tumor lesions and reduce the tumor burden, but Rashid et al. [53] showed that effective preservation of lesion-peripheral CD4+ and CD8+ T cells could retain tumor immunity potential after surgery, prolong survival, and reduce postoperative recurrence rate. This might explain the difference in postoperative survival between TG and PG patients.

In addition, researchers worldwide have developed abundant preoperative and postoperative treatments for GC, such as neoadjuvant chemotherapy, postoperative chemotherapy, postoperative radiotherapy and targeted therapy. However, it was difficult to assess the sensitivity of patients to different treatment methods. Yuka et al. found that preoperative PLR can evaluate the sensitivity of postoperative chemotherapy [54]. This study also found that patients with SIRI–PLR score 2 had better prognosis than those who did not receive postoperative chemotherapy. Although patients with high SIRI score are reported not to be suitable for chemotherapy [33], we focused on the scoring system that combined SIRI with PLR, and on GC patients who also had AGC and UGC, which might lead to different results.

Previous studies using serum immune index alone to predict the prognosis of patients or guide therapy have unsatisfactory sensitivity and specificity, which is difficult to individualize evaluation. With the development of Real World Study and Big Data for Cancer Research, the mode combining clinicopathological features and serum immunity markers to predicting the prognosis of GC is widely used in clinical [55,56]. For tumors, many studies currently focus on constructing prognostic models based on molecular expression combined with clinical pathological features. However, it is difficult to achieve unified standards for detecting molecular expression levels, resulting in no standard for evaluating prognosis applied in clinical practice except for the TNM staging system currently. We believe that expanding the sample size and regressing to basic clinical features to construct predictive models is more valuable for studying the real world of tumors. Especially for gastric cancer with high heterogeneity, including tumor volume size, Borrmann classification, Laurent classification, differentiation degree, and combination of serum immune indicators, will be assisted in constructing predictive models in the context of massive data and multicenter retrospective analysis. In this study, we found SIRI–PLR, age, pTNM stage and postoperative chemotherapy were independent predictors for high risk of death due to AGC and UGC in the training set. Age and postoperative chemotherapy had been proved to be related to the prognosis of patients. The older patients, or the patients who didn't receive postoperative chemotherapy will had a significantly increased risk of postoperative recurrence [57,58]. Then, a Nomogram model was constructed in the training set to predict the prognosis for 3 and 5-year. Through ROC analysis, it was found that AUC was 0.781 and 0.790, sensitivity was 87.5% and 85.9%, and specificity was 57.0% and 60.0%. And the constructed Nomogram model also can be used well in the verification set. Although this result may be limited by the small number of patients and the cohort grouped by time of admission, it can still indicate that for the patients with AEG and UGC, the prediction model established by age, SIRI–PLR and pTNM stage and postoperative chemotherapy is worth further clinical verification and application.

5. Limitations

This retrospective study still had some limitations. First, the prevalence of AGC and UGC in non-Asian populations needs further exploration. Second, it was difficult to determine whether preoperative gastritis and Helicobacter pylori infection affected circulatory immune cells in patients with GC, and this also needs further exploration. Third, there are only 35 patients in stage I, it is not enough to analyze separately. Therefore, whether the constructed Nomogram model has the same clinical significance in stage I should be further studied. Lastly, molecular characteristics, such as HER-2 should be included in future research.

6. Conclusion

SIRI–PLR score is an independent predictor of survival in patients who undergo curative surgery for AEG and UGC. It can further subgroup patients with stage Ⅰ/Ⅱ and Ⅲ to supplement the eighth edition of the AJCC guidelines. Patients with SIRI–PLR score 2 with postoperative chemotherapy have better survival than patients with SIRI–PLR score 0 or 1. Patients with high PLR with PG have better survival than patients with TG. SIRI–PLR may help clinicians to decide upon individualized treatment for patients with AEG or UGC. The Nomogram with combination of SIRI-PLR, age, pTNM stage and postoperative chemotherapy can predict postoperative survival.

Declarations

The informed consent was obtained from all patients for the publication of all their data. This study was approved by the Ethics Committee of the Harbin Medical University Cancer Hospital (Approval Number: SHGC-1029). All experiments were performed in accordance with established ethical guidelines, and informed consent was obtained from the patients to confirm their voluntary participation.

Patient consent statement

All patients signed informed consent when their clinical information was collected.

Funding information

This work was supported by Nn10 program of Harbin Medical University Cancer Hospital from Yingwei Xue (No. Nn10 PY 2017-03), Haiyan Research Fund of Harbin Medical University Cancer Hospital from Tianyi Fang (No. JJQN2021-06) and Fundamental Research Funds for the Provincial Universities from Tianyi Fang (No. 2022-KYYWF-0286).

Data availability statement

The datasets used in this study are available from the corresponding author on reasonable request. More information can also be obtained from the Gastric Cancer Information Management System v1.2 of Harbin Medical University Cancer Hospital (Copyright No. 2013SR087424, http://www.sgihmu.com/).

CRediT authorship contribution statement

Tianyi Fang: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing – original draft. Xin Yin: Conceptualization, Methodology, Formal analysis, Writing – original draft. Yufei Wang: Conceptualization, Formal analysis, Methodology. Lei Zhang: Formal analysis, Methodology. Shuo Yang: Data curation, Formal analysis. Xinju Jiang: Data curation. Yingwei Xue: Conceptualization, Funding acquisition, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e26176.

Appendix A. Supplementary data

The following is/are the supplementary data to this article:

Multimedia component 1
mmc1.docx (20.9KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

The datasets used in this study are available from the corresponding author on reasonable request. More information can also be obtained from the Gastric Cancer Information Management System v1.2 of Harbin Medical University Cancer Hospital (Copyright No. 2013SR087424, http://www.sgihmu.com/).

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