The implications of dynamic observation of immunoinflammatory indices in patients with resectable gastric cancer

doi:10.21203/rs.3.rs-4901147/v1

Purpose

Inflammatory immune markers and tumor markers are indicators obtained by laboratory tests and have also been shown to predict the prognosis of patients with gastric cancer, but most of the studies were analyzed by dichotomous classification or by taking cut-off values. In this study, we collected NLR, PLR, LMR, SII, PNI and CEA, AFP, CA125, CA199 from patients preoperatively, 3 months, 6 months, 9 months, and 12 months postoperatively, and analyzed the effect of their trends on the prognosis of patients with gastric cancer by latent category.

Results

A total of 93 gastric cancer patients were collected preoperative and postoperative data on inflammatory markers and tumor markers, and the trends of the data at multiple time points were analyzed by latent categories. On univariate analysis, only PNI trend (P < 0.013), surgical resection site (P < 0.034) and TNM stage (P < 0.017) were correlated with the prognosis of gastric cancer patients (P < 0.013). On multifactorial analysis, the trend change of PNI in different groups (P < 0.046) and TNM stage (P < 0.007) were independent risk factors for gastric cancer patients.

Conclusion

Evaluation of PNI at multiple time points has implications for the prognosis of patients with gastric cancer.

Gastric Cancer

Serum Tumor Marker

Inflammatory Index

Prognosis

Gastric cancer is a prevalent gastrointestinal tumor, the third most commonly diagnosed tumor and the third leading cause of cancer deaths in China[1]. Nevertheless, 31%-87% of patients with a first diagnosis of malignant tumor have associated weight regression[2], and gastric cancer in particular increases the incidence of malnutrition in patients due to dyspepsia, malabsorption and gastrointestinal obstruction[3, 4], tumor also secretes cytokines: IL-1, IL-6 and TNF-a[5, 6], which affect the body's hormones and lead to anorexia, and aggravate muscle wastage and loss of strength[7]. In addition, surgery-related trauma, postoperative intake restrictions and postoperative chemotherapy may exacerbate malnutrition, which can occur in up to 60%-80% of patients with gastric cancer[8]. Malnutrition routinely causes hypoproteinemia, organ hypoplasia and reduced immunity, and is also associated with prolonged hospital stays, poor tolerance of subsequent treatments and reduced survival[9]. Therefore, timely monitoring and intervention of malnutrition early in the course of gastric cancer is particularly important to improve patients' postoperative treatment tolerance and survival.

Several indices are currently used to assess the nutritional and immune status of oncology patients, such as NLR[10], PLR[11], LMR[12], SII[13] and PNI[14], and preoperative variation of these indicators correlates with the prognosis of patients with gastric cancer. However, these studies have focused on the prognostic impact of immunoinflammatory markers at a single preoperative time point in patients, and there is a lack of uniformity in the delineation of these immunoinflammatory markers. In this study, we analyzed the effect of multi-temporal changes in immuno-inflammatory indicators on the prognosis of gastric cancer patients by collecting data from patients at multiple time points before and after surgery.

2.1 Patients

Between January 2017 and December 2018, we retrospectively included 93 patients with gastric cancer who underwent surgical treatment in our department. Inclusion criteria included (1) patients who underwent radical gastrectomy; (2) patients with a pathological diagnosis of gastric adenocarcinoma stages I to III; (3) postoperative rechecks of blood counts, tumor markers, and liver function at one, three, six, nine, and twelve months; and (4) patients with complete preoperative and postoperative laboratory tests. (5) Patients with complete clinical history data and postoperative follow-up data. Exclusion criteria included (1) patients who had been diagnosed with distant metastases or other malignant tumors; (2) patients who had received neoadjuvant chemotherapy or radiotherapy before surgery; (3) patients with a history of autoimmune diseases, inflammatory diseases and haematological diseases before surgery; (4) patients who had received blood transfusion and nutritional supplements within 1 month before blood collection; (5) patients with incomplete clinical and follow-up data.

2.2 Clinical information

Clinical data including age, gender, body mass index, primary site of tumor, site of surgical resection, degree of tumor differentiation, maximum diameter of tumor, number of lymph nodes cleared, TNM stage, nerve invasion, vascular invasion, as well as inflammatory markers, routine blood tests, blood biochemistry, and tumor markers at preoperative and postoperative periods of 1 month, 3 months, 6 months, 9 months and 12 months.

2.3 Clinical follow‑up

Regular clinical follow-up examinations were performed every 3 months for the initial 3 years and every 6 months thereafter. Patients were followed up by telephone with a follow-up date of June 2023, and the main observation for follow-up was overall survival (OS).

2.4 Statistical analysis

Statistical analyses were performed using R version 4.3.0 software and SPSS version 26.0. All continuous variables were converted to categorical variables for ease of analysis. Categorical variables were expressed as frequencies and percentages and statistical tests such as chi-square test or Fisher's exact test were performed. We used R (4.3.0) language to classify the repeated measurements and classified each repeated measurement into different subgroups according to the minimum AIC value, and then analyzed the trend of the repeated measurements in different subgroups, and then screened the factors affecting the prognosis of patients with gastric cancer and included the clinically significant single factors into Cox multifactorial analysis to clarify the independent risk factors, all the Statistical tests were two-tailed and statistical significance was set at P < 0.05.

3.1 Baseline features

A total of 93 patients with gastric cancer were included in this study.93 patients received routine blood tests, blood biochemistry and tumor markers before and at 1 month, 3 months, 6 months, 9 months and 12 months after surgery, and received different levels of postoperative adjuvant treatments. The average age of the 93 patients with gastric cancer was 51.83 ± 10.63 years, 69 (74.2%) were male patients, female patients 24 (25.8%), 4 patients (4.3%) with TNM stage I, 27 patients (29.0%) with TNM stage II, 62 patients (66.7%) with TNM stage III, 83 patients (89.2%) with positive neurological invasion, and 77 patients (82.8%) with positive vascular invasion (Table 1).

Table-1 Clinical baseline features

Characteristic

(N = 93)

Age

51.83 ± 10.63

Sex

Male

Female

69 (74.2%)

24 (25.8%)

BMI (kg/m²)

< 18.5

18.5 ~ 24.0

≥ 24.0

6 (6.5%)

53 (57.0%)

34 (36.5%)

Primary location

Upper

Middle

Low

10 (10.8%)

26 (28.0%)

57 (61.2%)

Resection site

Total

Proximal

Distal

31 (33.3%)

6 (6.5%)

56 (60.2)

Histologic type

Well

Moderate

Poor

6 (6.5%)

51 (54.8%)

36 (38.7%)

Largest tumor size (cm)

≤ 2.8

> 2.8

32 (34.4%)

61 (65.6%)

Number of lymph node dissection

≤ 21

> 21

49 (52.7%)

44 (47.3%)

TNM stage

Ⅰ

Ⅱ

Ⅲ

4 (4.3%)

27 (29.0%)

62 (66.7%)

Neural invasion

Positive

Negative

83 (89.2%)

10 (10.8%)

Lymphovascular invasion

Positive

Negative

77 (82.8%)

16 (17.2%)

Note: Value are presented as the median (range) or n (%).

3.2 Latent class classification and trajectory modelling of repeated measures indicators

We collected and calculated repeated measurements of NLR, PLR, LMR, SII, PNI, CEA, AFP, CA125, and CA199 as well as preoperative indices of gastric cancer patients at 1, 3, 6, 9, and 12 months after surgery and classified the repeated measurements indices data into four categories by quartile method, and then analyzed the potential category changes using R software. We classified NLR data changes into 3 categories, PLR data changes into 3 categories, LMR data changes into 3 categories, SII data changes into 2 categories, PNI data changes into 3 categories, CEA data changes into 5 categories, AFP data changes into 4 categories, CA125 data changes into 4 categories, and CA199 data changes into 4 categories (Table 2) (Fig.1). Then, we plotted the trend change of each indicator according to the different data sets of each indicator (Fig.2).

Table-2 Grouping of repeated measures indicators

variable

（N=93）

NLR

NLR cluster 1

NLR cluster 2

NLR cluster 3

31 (33.3%)

8 (8.6%)

54 (58.1%)

PLR

PLR cluster 1

PLR cluster 2

PLR cluster 3

40 (43.0%)

19 (20.4%)

34 (36.6%)

LMR

LMR cluster 1

LMR cluster 2

LMR cluster 3

24 (25.8%)

37 (39.8%)

32 (34.4%)

SII

SII cluster 1

SII cluster 2

45 (48.4%)

48 (51.6%)

PNI

PNI cluster 1

PNI cluster 2

PNI cluster 3

34 (36.6%)

33 (35.5%)

26 (27.9%)

CEA

CEA cluster 1

CEA cluster 2

CEA cluster 3

CEA cluster 4

CEA cluster 5

20 (21.5%)

17 (18.3%)

23 (24.7%)

6 (6.5%)

27 (29.0%)

AFP

AFP cluster 1

AFP cluster 2

AFP cluster 3

AFP cluster 4

22 (23.7%)

17 (18.3%)

26 (28.0%)

28 (30.0%)

CA125

CA125 cluster 1

CA125 cluster 2

CA125 cluster 3

CA125 cluster 4

24 (25.8%)

16 (17.2%)

22 (23.7%)

31 (33.3%)

CA199

CA199 cluster 1

CA199 cluster 2

CA199 cluster 3

CA199 cluster 4

18 (19.4%)

29 (31.2%)

18 (19.4%)

28 (30.0%)

Note: Value are presented as the n (%)

Abbreviation: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CEA, carcinoembryonic antigen; AFP, alpha-fetoprotein; CA125, cancer antigen 125; CA19-9, carbohydrate associated antigen 19-9;

3.3 Univariate and multivariate cox regression analyses

We included the grouping of repeated measures as well as clinical data of 93 patients with gastric cancer together in a univariate analysis, and the results showed that PNI (P<0.013), surgical resection site (P<0.034), and TNM stage (P<0.017) were correlated with prognosis of gastric cancer patients. Subsequently, we included the univariate meaningful results into the multifactorial analysis, and the results showed that the trend change of PNI in different groups (P<0.046) and TNM stage (P<0.007) were independent risk factors for gastric cancer patients.

Table-3 Univariate and multivariate cox regression

Characteristic

Univariate analysis

HR (95% CI)

P value

Multivariate analysis

HR (95% CI)

P value

Age

1.02 (0.98-1.05)

0.415

Sex

Male vs Female

1.02 (0.46-2.28)

0.966

BMI (kg/m²)

<18.5

18.5~24.0

≥24.0

0.89 (0.42-1.89)

1

0.99 (0.22-4.17)

0.957

NLR

NLR cluster 1

NLR cluster 2

NLR cluster 3

0.81 (0.38-1.73)

0.28 (0.04-2.06)

1

0.423

PLR

PLR cluster 1

PLR cluster 2

PLR cluster 3

0.56 (0.24-1.31)

1.36 (0.58-3.19)

1

0.160

LMR

LMR cluster 1

LMR cluster 2

LMR cluster 3

1.04 (0.44-2.46)

0.65 (0.28-1.51)

1

0.515

SII

SII cluster 1

SII cluster 2

1.21 (0.60-2.44)

1

0.605

PNI

PNI cluster 1

PNI cluster 2

PNI cluster 3

1.05 (0.45-2.46)

0.70 (0.28-1.76)

1

0.013

0.33 (0.12-0.89)

0.31 (0.11-0.86)

1

0.046

CEA

CEA cluster 1

CEA cluster 2

CEA cluster 3

CEA cluster 4

CEA cluster 5

0.85 (0.33-2.20)

0.12 (0.02-0.93)

0.84 (0.34-2.09)

1.80 (0.57-5.66)

1

0.192

AFP

AFP cluster 1

AFP cluster 2

AFP cluster 3

AFP cluster 4

0.50 (0.19-1.33)

0.43 (0.14-1.31)

0.59 (0.25-1.43)

1

0.325

CA125

CA125 cluster 1

CA125 cluster 2

CA125 cluster 3

CA125 cluster 4

CA199

0.53 (0.18-1.52)

1.16 (0.45-2.99)

0.92 (0.37-2.30)

1

0.576

CA199 cluster 1

CA199 cluster 2

CA199 cluster 3

CA199 cluster 4

0.46 (0.15-1.43)

0.58 (0.24-1.42)

0.90 (0.35-2.27)

1

0.444

Primary location

Upper

Middle

Low

1

0.89 (0.30-2.62)

0.82 (0.25-2.73)

0.949

Resection site

Total

Proximal

Distal

1

0.57 (0.13-2.48)

0.45 (0.22-0.94)

0.034

Histologic type

Well

Moderate

Poor

1

1.11 (0.26-4.78)

0.78 (0.17-3.55)

0.655

Largest tumor size (cm)

≤2.8

>2.8

1

1.67 (0.75-3.73)

0.213

Number of lymph node dissection

≤21

>21

1

0.93 (0.46-1.90)

0.850

TNM stage

Ⅰ

Ⅱ

Ⅲ

0 (0-inf)

0.124 (0.03-0.518)

1

0.017

0 (0-inf)

0.09 (0.02-0.41)

1

0.007

Neural invasion

Positive vs Negative

0.24 (0.03-1.78)

0.163

Lymphovascular invasion

Positive vs Negative

0.65 (0.22-1.86)

0.420

Note: Value are presented as the median (range) or n (%).

Abbreviation: BMI, body mass index; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CEA, carcinoembryonic antigen; AFP, alpha-fetoprotein; CA125, cancer antigen 125; CA19-9, carbohydrate associated antigen 19-9; TNM, tumor-node-metastasis.

In this study, we analyzed the impact of trends in immuno-inflammatory markers and tumor markers on the prognosis of 93 gastric cancer patients with resectable gastric cancer preoperatively and at 3 months, 6 months, 9 months and 12 months postoperatively. Many previous clinical studies have demonstrated that immune-inflammatory markers have an impact on cancer patient outcomes[15-18]. Yet, these study cohorts are usually stratified for immuno-inflammatory indicators by dividing the best cut-off values, but this leads to a lack of generalizability of the cut-off values in such studies. We analyzed the trend changes of immunoinflammatory indicators at different time points by latent categories to explore the impact of changing trends on the prognosis of gastric cancer patients.

We calculated NLR, PLR, LMR, SII and PNI in 93 patients with gastric cancer preoperatively and at 3, 6, 9 and 12 months postoperatively and analyzed tumor markers at the same time, and analyzed the types of trends for each indicator at multiple time points by latent category analysis. Subsequently, we observed that among the continuously observed indicators, only PNI trend (P<0.013), surgical resection site (P<0.034) and TNM stage (P<0.017) were correlated with the prognosis of gastric cancer patients (P<0.013), 62 (66.7%) of the 93 patients with gastric cancer had a postoperative pathological stage III, and the increased tumor burden and postoperative chemotherapy in stage III patients would lead to poorer postoperative nutritional status and thus affect the prognosis of the patients. In a multifactorial analysis, trend in PNI (P<0.046) and TNM stage (P<0.007) were an independent risk factor for patients with gastric cancer. Moreover, we found that the change level of PNI group 3 was firstly increased and then decreased and then increased, the change level of PNI group 2 was firstly decreased and then increased, and the overall change level of PNI group 1 was continuously decreased, the average survival time of gastric cancer patients in PNI group 3 was 49 months, and that of gastric cancer patients in PNI group 2 was 52 months, and that of gastric cancer patients in group 1 with the decrease of PNI level was 42 months, We found that gastric cancer patients with consistently lower PNI subgroup 3 at multiple time points had a worse prognosis, but there was no difference in survival between the groups due to small sample size. The main trend in the remaining inflammatory markers NLR, PLR, LMR, SII and the tumor markers CEA, AFP, CA125 and CA199 was a decrease, which could be attributed to the fact that radical surgery as well as adjuvant postoperative radiotherapy eliminated most of the tumor load as well as the haematological impact of radiotherapy.

PNI was firstly proposed by BUZBY[19], and then modified by Japanese scholar ONODERA, the formula is: PNI= serum protein value (g/L)+5×total number of peripheral blood lymphocytes (×109 L-1), and the modified formula has the advantages of low cost, simple calculation, reliable data source, etc. It was initially used as an indicator to assess the nutritional status and surgical risk of gastrointestinal tumor patients, and has been gradually used to predict the prognosis of tumor and adverse reactions in recent years. The modified formula has the advantages of low cost, simple calculation, reliable data source, etc. It was initially used as an index to assess the nutritional status and surgical risk of patients with gastrointestinal tumors, but in recent years, it has been gradually used to predict the prognosis of the tumors and the adverse reaction[20]. Lymphocyte count reflects the level of body immunity in patients with malignant tumors. When the lymphocyte count decreases, it indicates a decline in the body's immune function and poor tolerance to anti-tumor therapy, and the total number of lymphocytes is also an independent prognostic factor for patients with tumors[21]. Therefore, the PNI value calculated from the serum protein and the total number of lymphocytes can predict the prognosis of the disease. Moreover, several studies on tumor patients have shown that the remission rate, disease control rate, 5-year overall survival rate and progression-free survival rate were higher in the high PNI group than in the low PNI group, and the incidence of adverse events was lower than that in the low PNI group, and the univariate and multivariate analyses have shown that the value of PNI is a significant factor influencing the progression-free survival (PFS)[22-24].

In addition, this study is localized: small sample size and retrospective study. However, this study is the first of its kind to analyze trends in inflammatory markers and tumor markers by collecting data on inflammatory markers and tumor markers at multiple time points and using latent category trajectories. In conclusion, our study confirms that a persistent decrease in PNI in patients with gastric cancer predicts a poor prognosis, and whether we can identify such patients by analyzing them and give them treatment.

Author contributions: All authors contributed to the study conception and design. YD designed the study. YD, XC, JS collected the data. YD, XC, JS analyzed the data. YD, XC, JS visualized the data. YD drafted the manuscript. JL, ZW revised the manuscript.

Funding：The authors declare that no funds, grants, or other support was received during the preparation of this manuscript.

Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflict of interest: The authors declare that there is no conflict of interest.

Ethical approval: This is an observational study. The Xijing Hospital Ethics Committee has confirmed that no ethical approval is required.

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No competing interests reported.

The implications of dynamic observation of immunoinflammatory indices in patients with resectable gastric cancer

Status:

Version 1

Abstract

Purpose

Results

Conclusion

Figures

1. INTRODUCTION

2. MATERIAL AND METHODS

2.1 Patients

2.2 Clinical information

2.3 Clinical follow‑up

2.4 Statistical analysis

3. RESULTS

3.1 Baseline features

4. DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1