Oncotarget

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Novel inflammation-combined prognostic index to predict survival outcomes in patients with gastric cancer

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Oncotarget. 2023; 14:71-82. https://doi.org/10.18632/oncotarget.28353

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Noriyuki Hirahara _, Takeshi Matsubara, Shunsuke Kaji, Hikota Hayashi, Yohei Sasaki, Koki Kawakami, Ryoji Hyakudomi, Tetsu Yamamoto and Yoshitsugu Tajima

Abstract

Noriyuki Hirahara1, Takeshi Matsubara1, Shunsuke Kaji2, Hikota Hayashi1, Yohei Sasaki3, Koki Kawakami2, Ryoji Hyakudomi1, Tetsu Yamamoto1 and Yoshitsugu Tajima1

1 Department of Digestive and General Surgery, Shimane University Faculty of Medicine, Izumo, Shimane 693-8501, Japan

2 Department of Surgery, Matsue Red Cross Hospital, Matsue, Shimane 690-0886, Japan

3 Department of Surgery, Masuda Red Cross Hospital, Masuda, Shimane 698-8501, Japan

Correspondence to:

Noriyuki Hirahara, email: [email protected]

Keywords: gastric cancer; laparoscopic gastrectomy; novel predictive index; inflammation-combined prognostic index; cancer-specific survival

Received: December 15, 2022     Accepted: January 16, 2023     Published: January 31, 2023

Copyright: © 2023 Hirahara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

Background: We focused on the lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) and devised an inflammation-combined prognostic index (ICPI) as a prognostic marker of cancer-specific survival (CSS).

Methods: We reviewed the clinicopathological data of 480 patients with gastric cancer undergoing curative laparoscopic gastrectomy between 2009 and 2019. This study examined the significance of LMR, NLR, PLR, and ICPI as cancer-specific prognostic markers.

Results: In univariate analysis, tumor diameter, histological differentiation, pathological tumor-node-metastasis (pTNM) stage, LMR, NLR, PLR, C-reactive protein (CRP) level, carcinoembryonic antigen (CEA), and postoperative chemotherapy were significantly associated with CSS. In multivariate analysis, pTNM stage and CEA were the independent risk factors for CSS, although LMR, NLR, and PLR were not the independent risk factors for CSS.

The ICPI formula was constructed using hazard ratios for three inflammation-based biomarkers with worse prognosis identified in the univariate analysis: LMR <4.315, NLR ≥2.344, and PLR ≥212.01, which were each scored as 1, with all remaining values pointed at 0. ICPI was calculated as follows: ICPI = 2.9 × LMR + 2.8 × NLR + 2.8 × PLR. The optimal cutoff value of ICPII was 2.9. On multivariate analysis, pTNM stage, CEA, and ICPI were independent prognostic factors for CSS. In the Kaplan–Meier survival analysis, CSS in the high ICPI group was significantly worse than that in the low ICPI group.

Conclusion: ICPI was devised as a novel predictive index for prognosis, and its usefulness was clarified.


Introduction

Tumor-related systemic inflammation based on host–tumor interaction between cancer loci and individuals is caused not only by local nutritional malabsorption but also by systemic metabolic disorders [1, 2]. Systemic inflammation must be evaluated objectively and dynamically as it changes constantly during multidisciplinary treatment. Against this background, the usefulness of biomarkers has been attracting attention in recent years, and it is desirable to devise biomarkers that can evaluate these dynamic changes more quickly, easily, accurately, and at a lower cost [3, 4]. Biomarkers are generally classified into three categories in cancer treatment: (1) having diagnostic significance for cancer, (2) serving as prognostic indicators, and (3) predicting therapeutic effects or risk of side effects. Although it may not be possible to develop significant markers that predict all three categories, tumor-related systemic inflammation and metabolic malnutrition in patients with cancer occur not only in advanced cancers but also in relatively early-stage cancers and are prognostic factors independent of pathological factors and induce treatment resistance [5, 6]. Therefore, it is reasonable to include systemic inflammation and metabolic nutritional status as indicators when devising biomarkers. If the prognosis can be predicted using the pre-treatment specimens, it will lead to the identification of a group of patients who require multimodal treatment, including aggressive chemotherapy and radiotherapy, which will lead to individualized treatment and improved prognosis [6, 7]. Occasionally, even stage 1 cases may recur after surgery. In this study, we focused on blood cell components that complementarily reflect systemic inflammation and metabolic status and devised a cancer-specific prognostic marker for all patients, not limited to patients in stages II and III.

In this study, we focused on blood cell components that complementarily reflect systemic inflammation and metabolic status and devised a cancer-specific prognostic marker.

Results

Association between the inflammatory biomarkers and clinicopathological features

The 480 patients were divided into the low and high groups based on the cutoff values of each inflammatory biomarker (Table 1); 207 patients (43.1% [male, 154; female, 53]) showed low LMR (median age, 74 [range, 38–91] years). Moreover, 273 patients (56.9% [male, 183; female, 90]) showed high LMR (median age, 69 [range, 36–89] years). Furthermore, 296 (61.7% [male, 204; female, 92]) patients showed low NLR (median age, 70 [range, 36–91] years), and 184 (38.3% [male, 133; female, 51]) patients showed high NLR (median age, 74 [range, 43–90] years). A total of 407 (84.8% [male, 286; female, 121]) patients showed low PLR (median age, 70 [range, 36–90] years), and 73 (15.2% [male, 51; female, 22]) showed high PLR (median age, 72 [range, 43–91] years).

Table 1: Association between the inflammatory biomarkers and clinicopathological features

CharacteristicsTotal patientsLMRNLRPLR
<4.315 (n = 207)≥4.315 (n = 273)p value<2.344 (n = 296)≥2.344 (n = 184)p value<212.01 (n = 407)≥212.01 (n = 73)p value
Age (years)74 (38–91)69 (36–89)<0.00170 (36–91)74 (43–90)0.00870 (36–90)72 (43–91)0.062
Sex0.0790.4320.944
 Male33715418320413328651
 Female1435390925112122
ASA–PS<0.0010.0020.007
 125520196223
 240917123825915035455
 346311518283115
BMI21.9 (14.0–32.5)22.8 (14.8–40.4)<0.00122.3 (14.7–40.4)22.3 (14.0–32.7)0.75422.4 (14.7–40.4)21.7 (14.0–32.5)0.063
WBC5730 (2870–13700)5630 (510–9830)0.2925460 (510–9280)6115 (3510–13700)<0.0015710 (510–13700)5460 (1830–12730)0.133
Neutrophil3700 (1310–11460)3190 (250–6910)<0.0013010 (250–5100)4270 (2210–11460)<0.0013340 (250–8494)3850 (1100–11460)0.012
Lymphocyte1310 (230–3780)1850 (230–3780)<0.0011845 (230–3780)1255 (230–2270)<0.0011730 (230–3780)960 (230–2020)<0.001
Monocyte408 (210–937)311 (3–727)<0.001339 (3–937)366 (85–829)<0.001348 (3–937)362 (37–829)0.192
Platelet216 (58–726)222 (39–460)0.432220 (39–460)220 (58–726)0.267215 (39–460)283 (119–726)<0.001
Tumor location0.8910.8420.84
 EGJ157887132
 U93435058357617
 M204851191297517430
 L16872961016714424
Tumor diameter (mm)44 (3–176)40 (4–180)0.0140 (3–180)42 (5–176)0.05940 (3–180)50 (16–150)0.001
Differentiation0.6570.440.431
 Well94375763318311
 Moderate177761011096814631
 Poor209941151248517831
Depth of tumor0.0030.01<0.001
 T1a-1b252891631718122626
 26229333923557
 371383339325912
 4a-4b95514447486728
Lymph node meta0.0040.3320.011
 N031412319120311127836
 N157292832254116
 N256322431254610
 N353233030234211
pTNM stage<0.0010.004<0.001
 1a-1b283991841929125528
 2a-2b87503747406720
 3a-3c110585257538525
Operative procedure0.0010.2090.034
 Total101584358437823
 Proximal5014363614464
 Distal32913519420212728346
Operation time (min)390 (204–911)378 (70–808)0.51381 (158–911)386 (70–703)0.5383 (70–911)386 (231–692)0.288
Intraope. blood loss50 (0–3600)20 (0–5850)0.0340 (0–5850)50 (0–2600)0.56840 (0–5850)30 (0–1600)0.921
Postoperative complications0.0360.4860.419
 Present1457372865912025
 Absent33513420121012528748
CRP (mg/dl)0.11 (0.01–11.10)0.06 (0.01–4.26)<0.0010.07 (0.01–5.35)0.11 (0.01–11.1)<0.0010.07 (0.01–11.1)0.16 (0.01–7.09)<0.001
CEA (ng/ml)3.4 (0.7–171.6)3.3 (0.7–86.4)0.233.2 (0.7–106.0)3.6 (0.7–171.6)0.0643.3 (0.7–171.6)3.4 (0.8–163.3)0.566
Adjuvant chemotherapy0.9190.5580.557
 Yes1315675785310922
 No34915119821813129851

Cox regression analysis of inflammatory biomarkers associated with cancer-specific survival (CSS)

In univariate analysis, tumor diameter (p < 0.001), histological differentiation (p = 0.029), pathological TNM (pTNM) stage (p < 0.001), LMR (hazard ratio [HR], 2.866; p < 0.001), NLR (HR, 2.778; p < 0.001), PLR (HR, 2.803; p = 0.001), C-reactive protein (CRP) level (p = 0.003), carcinoembryonic antigen (CEA) (p = 0.011), and postoperative chemotherapy (p < 0.001) were significantly associated with CSS. In multivariate analysis, pTNM stage (HR, 21.452; 95% confidence interval [CI], 2.268–8.151; p < 0.001) and CEA (HR, 2.000; 95% CI, 1.089–3.672; p = 0.025) were identified as independent risk factors for CSS, although LMR, NLR, and PLR were not found to be independent risk factors for CSS (Table 2).

Table 2: Cox regression analysis of inflammatory biomarkers associated with cancer-specific survival

VariablesCategory or
characteristics
Patients
(n = 480)
UnivariateMultivariate
HR95% CIp valueHR95% CIp value
Age(<70/≥70)225/2551.3731.134–1.6620.258
Sex(female/male)143/3371.5340.782–3.0090.196
BMI(≥18.5/<18.5)439/411.0130.364–2.8240.980
Tumor diameter(<5/≥5)285/1954.2992.268–8.151<0.0011.7130.875–3.3540.116
Differentiation(well and mod/poor)271/2091.8951.067–3.3640.0290.8220.447–1.5110.528
pTNM stage(1/2,3)283 /19740.3859.799–166.437<0.00121.4524.549–101.176<0.001
LMR(≥4.315/<4.315)273/2072.8661.585–5.181<0.0011.5230.760–3.0740.234
NLR(<2.344/≥2.344)296/1842.7781.557–4.956<0.0011.5630.782–3.1230.206
PLR(<212.069/≥212.069)407/732.8031.503–5.2270.0011.1700.570–2.4030.669
CRP(≦0.5/>0.5)413/672.6641.409–5.0370.0030.9540.472–1.9290.896
CEA(<5.0/≥5.0)364/1162.2101.231–3.9660.0112.0001.089–3.6720.025
Postope. Complications(absent/present)335/1451.2800.695–2.3580.428
Adjuvant chemotherapy(no/yes)349/1316.2063.329–11.570<0.0011.3710.675–2.7870.383

CSS according to inflammatory biomarkers

The Kaplan–Meier survival curve revealed significantly worse CSS in the low LMR (p < 0.001) (Figure 1A), high NLR (p < 0.001) (Figure 1B), and high PLR (p < 0.001) groups (Figure 1C).

Cancer-specific survival curve based on the inflammatory biomarkers.

Figure 1: Cancer-specific survival curve based on the inflammatory biomarkers. (A) Lymphocyte-to-monocyte ratio, (B) neutrophil-to-lymphocyte ratio, and (C) platelet-to-lymphocyte ratio. Abbreviations: LMR: Lymphocyte-to-monocyte ratio, NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio.

Inflammation-based prognostic index formula

The inflammation-based prognostic index (ICPI) formula was constructed using HRs for three inflammation-based biomarkers with worse prognosis identified in the univariate analysis. LMR <4.315, NLR ≥2.344, and PLR ≥212.01, which were each scored as 1, with all remaining values pointed at 0.

The ICPI was calculated as follows: ICPI = 2.9 × LMR + 2.8 × NLR + 2.8 × PLR.

Using ROC analysis, the optimal cutoff value of ICPII was 2.9 based on CSS (sensitivity, 0.715%; specificity, 0.583%; area under the curve [AUC], 0.656).

Association between ICPI and clinicopathological features

A total of 329 (68.5% [male, 226; female, 103]) patients showed low ICPI (median age, 74 [range, 38–91] years), and 151 (31.5% [male, 111; female, 40]) patients showed high ICPI. The ICPI was significantly correlated with age, American Society of Anaesthesiologists Physical Status, white blood cell count, neutrophil count, lymphocyte count, monocyte count, platelet count, tumor diameter, tumor depth, lymph node metastasis, pTNM stage, operative procedure, and CRP (Table 3).

Table 3: Relationships between the inflammation-combined prognostic index and clinicopathological features

CharacteristicsTotal patientsICPIp value
≤2.9>2.9
(n = 329)(n = 151)
Age (years)70 (36–90)74 (43–91)0.001
Sex0.281
 Male337226111
 Female14310340
ASA-PS<0.001
 125214
 2409288121
 3462026
BMI22.4 (14.7–40.4)22.0 (14.0–32.5)0.102
WBC5580 (510–9830)5910 (2880–13700)0.001
 Neutrophil3110 (250–6910)4070 (1310–11460)<0.001
 Lymphocyte1820 (230–3780)1190 (230–2100)<0.001
 Monocyte332 (3–937)382 (165–829)<0.001
Platelet218 (39–460)230 (58–726)0.029
Tumor location0.592
 EGJ1587
 U936231
 M20414361
 L16811652
Tumor diameter (mm)40 (3–180)45 (5–176)0.013
Differentiation0.644
 Well946826
 Moderate17712156
 Poor20914069
Depth of tumor<0.001
 T1a-1b25218963
 2624616
 3714328
 4a-4b955144
Lymph node meta0.044
 N031422985
 N1573423
 N2563323
 N3533320
pTNM stage<0.001
 1a-1b28321667
 2a-2b875037
 3a-3c1106347
Operative procedure0.044
 Total1016041
 Proximal503911
 Distal32923099
Operation time (min)380 (70–911)386 (204–703)0.641
Intraoperative blood loss40 (0–5850)50 (0–2600)0.314
Postoperative complications0.252
 Present1459451
 Absent335235100
CRP (mg/dl)0.06 (0.01–5.35)0.12 (0.01–11.10)<0.001
CEA (ng/ml)3.3 (0.7–106.0)3.4 (0.7–171.6)0.134
Adjuvant chemotherapy0.54
 Yes1318744
 No349242107

Comparison of predictive ability of inflammatory biomarkers for CSS

The AUC estimate method was used to compare the predictive ability of the inflammatory biomarkers. The AUCs of LMR, NLR, PLR, and ICPI were 0.594, 0.596, 0.585, and 0.656, respectively. The AUCs of ICPI were significantly higher than those of LMR (p = 0.029), NLR (p = 0.018), and PLR (p = 0.005) (Figure 2).

Receiver operating characteristic curve for cancer-specific survival was plotted to verify the optimum cutoff value of lymphocyte-to-monocyte ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and inflammation-based prognostic index.

Figure 2: Receiver operating characteristic curve for cancer-specific survival was plotted to verify the optimum cutoff value of lymphocyte-to-monocyte ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and inflammation-based prognostic index. Abbreviations: LMR: Lymphocyte-to-monocyte ratio, NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio, ICPI: Inflammation-combined prognostic index.

Cox regression analysis of ICPI associated with CSS

On multivariate analysis, pTNM stage (HR, 22.646; 95% CI, 4.826–106.277; p < 0.001), CEA (HR, 2.050; 95% CI, 1.120–2.570; p = 0.020), and ICPI (HR, 2.511; 95% CI, 1.383–4.562; p = 0.003) were confirmed as independent prognostic factors for CSS (Table 4).

Table 4: Cox regression analysis of the inflammation-combined prognostic index associated with cancer-specific survival

VariablesCategory or
characteristics
Patients
(n = 480)
UnivariateMultivariate
HR95% CIp valueHR95% CIp value
Age(<70/≥70)225/2651.3731.134–1.6620.258
Sex(female/male)143/3371.5340.782–3.0090.196
BMI(≥18.5/<18.5)439/411.0130.364–2.8240.980
Tumor diameter(<5/≥5)285/1954.2992.268–8.151<0.0011.6560.851–3.2210.138
Differentiation(well and mod/poor)271/2091.8951.067–3.3640.0290.7980.439–1.4500.460
pTNM stage(1/2,3)283/19740.3859.799–166.437<0.00122.6464.826–106.277<0.001
ICPI(≦2.9/>2.9)329/1513.7572.115–6.674<0.0012.5111.383–4.5620.003
CRP(≦0.5/>0.5)413/672.6641.409–5.0370.0030.9870.496–1.9640.971
CEA(<5.0/≥5.0)364/1162.2101.231–3.9660.0112.0501.120–3.7530.020
Postope. Complication(absent/present)335/1451.2800.695–2.3580.428
Adjuvant chemotherapy(no/yes)349/1316.2063.329–11.570<0.0011.2810.639–2.5700.485

CSS according to ICPI

In the Kaplan–Meier survival analysis, CSS in the high ICPI group was significantly worse than that in the low ICPI group (Figure 3).

Cancer-specific survival curve based on the inflammation-based prognostic index.

Figure 3: Cancer-specific survival curve based on the inflammation-based prognostic index. Abbreviation: ICPI: inflammation based prognostic index.

Furthermore, in stage stratification analysis, the high ICPI group was significantly associated with worse prognosis in stages II and III, whereas the prognosis of stage I patients did not reach statistical significance among the ICPI values (Figure 4A4C).

Cancer-specific survival curve based on the inflammation-based prognostic index in the stage stratification analysis.

Figure 4: Cancer-specific survival curve based on the inflammation-based prognostic index in the stage stratification analysis. (A) Stage I, (B) stage II, and (C) stage III. Abbreviation: ICPI: inflammation based prognostic index.

DISCUSSION

In vivo inflammatory responses are involved in cancer growth, invasion, and metastasis, and the involvement of systemic inflammatory responses and the surrounding microenvironment is intricately intertwined [5, 8, 9]. Tumor necrosis factor-α (TNF-α), granulocyte colony-stimulating factor, interleukin-1 (IL-1), and IL-6 are produced by tumor cells and can induce a tumor-related systemic inflammatory reaction (SIR) [10, 11]. Among them, IL-6 is a multifunctional inflammatory cytokine that causes the proliferation and differentiation of various types of cells, such as immunocompetent and hematopoietic cells [12, 13]. Thus, dynamic changes in SIR resulting from tumor–host interaction can be accurately assessed by direct measurement of cytokines. However, routine measurement of cytokines in patients with cancer in clinical practice is expensive and impractical. In contrast, LMR, NLR, and PLR assessments using neutrophils, lymphocytes, monocytes, and platelets, which are regulated by cytokines, proliferate, and differentiate, are simple methods to evaluate the systemic inflammatory response using blood cell components and are complementary to each other [14, 15].

Neutrophils regulate the tumor microenvironment by producing pro-inflammatory cytokines/chemokines that promote proliferation, invasion, and metastasis of cancer cells, such as matrix metalloproteinase-9 and anti-apoptotic factor (nuclear factor kappa light chain enhancer of activated B cells) [16]. Furthermore, increased neutrophils produce large amounts of nitric oxide, arginase, and reactive oxygen species, which not only impair T-cell activation and reduce extracellular matrix adhesion but also promote angiogenesis and cellular DNA damage and inhibit tumor cell apoptosis [17]. As a result, a favorable microenvironment for tumor cells is established, which promotes tumor growth and metastasis. Lymphocytes function as an important component of the immune complex and serve as an antitumor immune response by inducing cytotoxic cell death and inhibiting tumor cell proliferation and migration. In addition, lymphocytes secrete cytokines, such as interferon-γ and TNF-α, which regulate cancer cell growth and metastasis through cellular and humoral immune mechanisms [18, 19]. It has also been shown to be a useful marker for screening nutritional status. Monocytes in the peripheral blood migrate to tissues, mature, and differentiate into macrophages. In patients with cancer, macrophages infiltrating the stroma of tumor tissues are called tumor-associated macrophages, which suppress tumor immunity and promote cancer cell proliferation by releasing angiogenic factors and inhibiting cytotoxic T cells [20]. Platelets allow circulating tumor cells to escape host immune surveillance via platelet-derived transforming growth factor-β and direct platelet-tumor cell contact to induce epithelial-mesenchymal transition, angiogenesis, and differentiation of cancer-associated fibroblasts and regulatory T cells. As a result, it induces microvascular permeability, which promotes the extravasation of cancer cells and induces distant metastasis [21].

Thus, LMR expresses the immune response in the tumor microenvironment and is an indicator of individual immunity [22, 23]. PLR serves as a marker for the balance between the inflammatory reaction and immune response of the host [24, 25]. NLR was initially reported as a predictor of outcome in critically ill patients admitted to intensive care units, but it has since been reported as an oncological prognostic marker and is the most evidence-accumulating biomarker [26, 27]. Since these biomarkers reflect different pathological conditions in patients with cancer, it is necessary to integrate and evaluate the three biomarkers to predict the prognosis of cancer more accurately. In this study, each inflammatory marker showed significant differences in univariate analysis but was not extracted as an independent prognostic factor in the multivariate analysis. Considering that these inflammatory markers calculated from two types of blood cell components are insufficient as prognostic predictors, we devised a novel biomarker reflecting systemic inflammation.

Since the HR is a numerical value that objectively compares the relative risk, we devised the ICPI, which is a novel prognostic marker calculated by adding the specific gravity provided to the prognosis of each inflammatory marker using the HR in univariate analysis. As a result, it was proven that the AUC value of ICPI was significantly higher than that of each inflammatory marker, demonstrating its high predictive and diagnostic ability. Furthermore, ICPI could be extracted as an independent prognostic factor in multivariate analysis.

We have previously reported the usefulness of an index calculated by adding the number of markers that recognized a significant difference in esophageal cancer, ignoring the specific gravity provided to the prognosis of each inflammatory marker. However, by considering the prognostic significance of each marker, the detection power of the index as a prognostic indicator increased.

Although ICPI is a new prognostic prediction index for cancer, sufficient attention is required for its interpretation. First, the number of cases was relatively small and included cases with a short postoperative follow-up period. Furthermore, some medicines, such as anticoagulants and anti-inflammatory agents, have not been evaluated. It is also necessary to measure inflammatory cytokines associated with tumors, and it is a future issue whether ICPI can be a prognostic index for other carcinomas. Second, we did not histologically examine leukocyte migration and infiltration into the cancer site. Third, the calculation formula is complicated, which impedes the generalization of this marker. Because the inflammatory biomarkers have similar hazard ratios, the ICBI formula may be simplified by unifying the coefficients to 2.8 or 2.9. Alternatively, removing the coefficient and adding the number of risk factor inflammation biomarkers will simplify the formula, but further examination is required in the future. In addition, further usefulness may be found by examining its association with the recurrence pattern.

In this study, the ICPI was devised as a novel predictive index of prognosis, and its usefulness was clarified. However, it is still unclear how active preoperative intervention using the ICPI as an indicator will contribute to improved oncological prognosis. In the future, it will be necessary to conduct a multicenter prospective study to examine the prognostic effect of preoperative interventions, including nutrition.

Materials and Methods

Patients

We conducted a retrospective study of patients with gastric cancer who underwent curative laparoscopic gastrectomy between January 2009 and December 2019 at our institution. The average follow-up period for survival was 1743.3 days, and the median follow-up period was 1709 days (interquartile range, 969–2304). Clinical patients’ clinicopathological data and laboratory records were collected using an electronic medical records platform. Blood biochemical examination was performed within 1 week prior to the surgery. All patients were eligible for laparoscopic surgery, but we excluded patients with severe adhesion in the abdominal cavity. Furthermore, laparoscopic surgery was not indicated for patients in whom gastrectomy could not be performed without grasping the cancer site with forceps.

Gastrectomy and lymphadenectomy were usually performed according to the guidelines of the Japanese Gastric Cancer Association [28]. The postoperative stage was based on the 7th edition of the tumor-node-metastasis (TNM) system [29]. The severity of postoperative complications was graded according to the Clavien–Dindo (CD) classification [30]. CD grade II or higher complications were defined as the occurrence of any complications. Postoperative adjuvant chemotherapy with tegafur/gimeracil/oteracil potassium (S-1) was recommended for patients with stage II or higher gastric cancer, usually for 1 year. Furthermore, 5-fluorouracil-based chemotherapy regimens (cisplatin plus S-1 or capecitabine) were recommended to the majority of patients with recurrent gastric cancer according to the Japanese Gastric Cancer Treatment Guidelines (Version 4) [28].

Inflammatory biomarkers

The lymphocyte-to-monocyte ratio (LMR) was calculated by dividing the absolute peripheral lymphocyte count by the absolute monocyte count, neutrophil-to-lymphocyte ratio (NLR) was calculated by dividing the absolute peripheral neutrophil count by the absolute lymphocyte count, and platelet-to-lymphocyte ratio (PLR) was calculated by dividing the absolute platelet count by the lymphocyte count.

The optimal cutoff values of the LMR, NLR, and PLR were determined via the receiver operating curve (ROC) analysis. The optimal cutoff values of LMR, NLR, and PLR for predicting cancer-specific survival (CSS) were 4.315, 2.344, and 212.1, respectively.

Statistical analyses

CSS was defined as the date of gastrectomy until death due to gastric cancer.

Student’s t-test was used when assessing continuous variables, and the chi-squared test or Fisher’s test was used when assessing categorical variables. The survival rate was calculated using Kaplan–Meier analysis, and statistical analysis was performed using the log-rank test. Significantly associated variables (p < 0.05) in univariate analysis were included in the multivariate analysis using the Cox proportional hazards model to identify the independent factors. Probability values less than 0.05 were defined as statistically significant factors. Statistical analyses were performed using JMP software (version 16.0; SAS Institute, Cary, NC, USA).

Abbreviations

AUC: area under the curve; CEA: carcinoembryonic antigen; CD: Clavien–Dindo; CI: confidence interval; CRP: C-reactive protein; CSS: cancer-specific survival; HR: hazard ratio; ICPI: inflammation-combined prognostic index; IL: interleukin; NLR: neutrophil-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio; PLR: platelet-to-lymphocyte ratio; pTNM: pathological tumor-node-metastasis; ROC: receiver operating curve; SIR: systemic inflammatory reaction; TNF-α: tumor necrosis factor-α.

Author contributions

NH was the lead author and conceived this study. TM, SK, HH, YS, KK, RH, TY and WT collected data, performed analysis. YT reviewed paper. All authors read and approved the final manuscript.

CONFLICTS OF INTEREST

Noriyuki Hirahara, Takeshi Matsubara, Shunsuke Kaji, Hikota Hayashi, Yohei Sasaki, Koki Kawakami, Ryoji Hyakudomi, Tetsu Yamamoto, Wataru Tanaka, and Yoshitsugu Tajima have no conflicts of interest or financial ties to disclose.

Ethical statement and consent

All procedures performed in the study involving human participants were in accordance with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study was registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000030472). The protocol of this retrospective study was approved by the Ethical Review Board of Shimane University, Faculty of Medicine (Shimane, Japan). The protocol of this retrospective study was approved by the Ethical Review Board of Shimane University, Faculty of Medicine (Shimane, Japan).

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