Impact Factor 1.022
Volume 32, 12 Issues, 2022
  Original Article     November 2022  

Comprehensive Analysis of Prognostic Factors in Advanced Gastric Cancer Patients Treated with Chemotherapy

By Eyyup Cavdar, Yakup Iriagac, Okan Avci, Erdogan Selcuk Seber

Affiliations

  1. Department of Medical Oncology, Tekirdag Namik Kemal University, Tekirdag, Turkey
doi: 10.29271/jcpsp.2022.11.1420

ABSTRACT
Objective: To evaluate the strongest prognostic factors in advanced gastric cancer.
Study Design: Observational study.
Place and Duration of Study: Department of Medical Oncology, Tekirdag Namik Kemal University, Tekirdag, Turkey, between March 2012 and April 2022.
Methodology: Adult patients with metastatic cancer who had completed at least two months of chemotherapy, without any other comorbidity were included. Using Kaplan-Meier methodology and Cox regression methods, potential prognostic factors were analysed for overall survival. Two different models were created for multivariate analysis by using statistically significant factors in univariate analysis.
Results: The median overall survival in 216 patients was 7.8 months. The univariate analysis showed that body-mass index, performance status, liver metastasis, albumin, gamma-glutamyl transferase, carcinoembryonic antigen, carbohydrate antigen (CA 19-9), neutrophil-lymphocyte ratio (NLR), systemic immune-inflammation index, albumin-to-alkaline phosphatase ratio, sodium-globulin ratio (SGR) prognostic nutritional index (PNI), albumin-bilirubin ratio, and albumin-globulin ratio were associated with survival. In Model 1, which included only laboratory indices, multivariate analyses revealed that NLR (p=0.001), SGR (p=0.025), and PNI (p=0.032) were prognostic for overall survival. In Model 2, established with all parameters, NLR (p=0.003), albumin (p=0.003), performance status (p<0.001), and CA 19-9 (p<0.001) were found to be independent prognostic factors.
Conclusion: Pretreatment NLR, SGR, PNI, albumin, performance status, and CA 19-9 are strong prognostic factors in patients with advanced gastric cancer. These prognostic factors, which are easily accessible in clinical practice, may be utilised as useful tools for clinicians.

Key Words: Gastric cancer, Prognosis, Overall survival, Chemotherapy, Metastasis, Prognostic biomarkers.

INTRODUCTION

Gastric cancer (GC) is one of the most common types of cancer and the leading cause of cancer-related deaths. It has a high prevalence, especially in Central Asian countries. In recent years, the prevalence of gastric cancer has been increasing, especially in young people.1 In the early-stage, the five-year survival rate is approximately 70% with curative treatment.2 However, most patients are diagnosed at an inoperable stage as the symptoms emerge late in the course of the disease. Despite the advances in treatment options, the 5-year survival rate remains below 25% and median survival is less than 12 months in advanced GC.3,4

Due to its high prevalence and mortality, there is a great need for analyses to be conducted with advanced gastric cancer patients and research that can guide further studies. It is important to determine prognostic factors in order to improve survival, guide proper treatment strategies, and allow an efficient follow-up. GC has a considerably heterogeneous nature; therefore, even patients in the same stage may vary in their response to treatment and survival duration.5 Due to this heterogeneous nature of gastric cancer, it also leads to the identification of many prognostic factors for gastric cancer. Previous studies have investigated various factors such as clinicopathological characteristics, inflammatory markers, hemogram parameters, and serum electrolytes.6,7 However, despite promising developments, the majority of these studies focused on operable gastric cancers and currently there is no consensus on the most appropriate marker for clinical practice.

In this study, the aim was to determine strong prognostic factors by means of a comprehensive prognostic factor analysis in advanced GC patients receiving chemotherapy and to determine the most appropriate prognostic factors that may guide oncologists in clinical practice.

METHODOLOGY

Data of advanced gastric cancer patients treated at the Department of Medical Oncology, Tekirdag Namik Kemal University, between March 2012 and April 2022 were analysed retrospectively. The Local Ethics Committee approved this study. The patients eligible for inclusion were proven gastric cancer patients aged >18 years having completed at least two months of first-line chemotherapy (platin, taxane, fluorouracil) after the diagnosis; metastasis in other organs confirmed by imaging, absence of concomitant chronic kidney disease, no other current or prior history of malignancy, and absence of active infectious disease, immunosuppressive medication or nutritional support. Patients with oesophageal junction tumour and a known human epidermal growth factor (HER2) receptor pathology were excluded from this study.

Demographic information, clinicopathological characteristics, and serum laboratory parameters measured before the first chemotherapy were obtained from the electronic medical record system. Prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), the De Ritis ratio (aspartate transaminase (AST)-to-alanine transaminase (ALT) ratio), albumin-to-alkaline phosphatase (ALP) ratio (AAPR), sodium-to-globulin ratio(SGR), albumin-to-bilirubin ratio (ABR), and albumin-to-globulin ratio (AGR) were measured and recorded from laboratory data. Globulin value was calculated using the formula “total protein-serum albumin” and PNI value was calculated using the formula “10 x serum albumin (g/dL)+0.005 x lymphocyte count (per mm3)”.

SPSS statistical software version 24 (SPSS Inc., Chicago, IL) was used to perform statistical analyses. Categorical variables were presented as numbers and percentages, and continuous measurements as mean and standard deviation. Optimal cut-off values for SII, SGR, AAPR, PNI and carbohydrate antigen (CA 19-9) were determined by the receiver operating characteristic (ROC) curve. Median cut-off values were used for the other laboratory parameters. These cut-offs were used to differentiate two groups as low and high. Kaplan-Meier and Cox regression methods were used for overall survival (OS) analysis. The Forward: LR method was used for multivariate analyses. Hazard ratios (HR) were presented with the corresponding 95% confidence intervals (CI). OS was taken into account from the date of death or the last available follow-up. Statistical significance was accepted as p <0.05.

RESULTS

A total of 874 patients were examined. Recurrent disease was identified in 108 patients, 387 patients had early-stage disease, and laboratory data before the first treatment could not be found for 126 patients. Eleven patients had concomitant conditions (such as renal impairment, rheumatologic disease, etc.) that could affect laboratory results, and death was attributed to non-cancer causes in 26 patients. After having excluded these patients, the study was completed with 216 patients. The median age was 67 (28–91) years. One hundred and eighty-eight (87%) of the entire patients population died due to cancer-associated reasons. The median OS (mOS) in all patients was 7.8 months (95% CI 6.3-9.3).

According to the ROC-AUC analysis, the ideal cut-off value were 880.84 (AUC: 0.615, 95% CI 0.50-0.73, p=0.05) for SII, 48.81 (AUC: 0.675, 95% CI 0.57-0.78, p=0.003) for SGR, 0.037 (AUC: 0.632, 95% CI 0.52-0.74, p=0.024) for AAPR, 9.82 (AUC: 0.652, 95.5 CI 0.55-0.76, p=0.010) for ABR, 37.11 (AUC: 0.639, 95% CI 0.53-0.75, p=0.018) for PNI, and 34 (AUC: 0.633, 95% CI 0.53-0.74, p=0.023) for CA 19-9. For the OS analysis, median values were considered and the cut-off value was 2.9 for globulin, 138 for sodium, 23 for AST, 17 for ALT, 34 for GGT (gamma-glutamyl transferase), 101 for ALP, 0.46 for total bilirubin, 4 for CEA (carcinoembryonic antigen), 3.06 for NLR, 191.3 for PLR, 1.39 for De-Ritis, and 1.3 for AGR.

Of the patients, 65 (30.1%) were female, and 162 (75%) were over 60 years of age. The most common site of the metastasis was liver (n=122, 56.5%), while 124 (57.4%) patients received only one line of chemotherapy. Table I shows the general features and laboratory data for patients.

In the univariate analysis, gender, age, histological type, peritoneal metastasis, haemoglobin, globulin, sodium, AST, ALT, De Ritis, total bilirubin, ALP, and PLR were not associated with survival. BMI (p=0.030), ECOG PS (p<0.001), liver metastasis (p=0.003), albumin (p=0.003), GGT (p=0.010), CEA (p=0.017), CA 19-9 (p<0.001), NLR (p<0.001), SII (p=0.007), AAPR (p=0.005), SGR (p=0.001), PNI (p=0.001), ABR (p=0.033), and AGR (p=0.007) were found to be significantly predictors for OS in the univariate analysis (Table II).

Using the significant parameters found in the univariate analysis, two different multivariate models were established to accurately assess the predictive factors of OS. Prognostic indices (NLR, SII, AAPR, SGR, PNI, ABR, and AGR) were evaluated in Model 1. In this model, NLR (p=0.001), SGR (HR=0.71, 95% CI 0.52-0.96, p=0.025), and PNI (HR=0.72, 95% CI 0.53-0.97, p=0.032) showed independent predictive properties for OS.

All predictors in the univariate analysis (BMI, ECOG PS, liver metastasis, albumin, GGT, CEA, CA 19-9, NLR, SII, AAPR, SGR, PNI, ABR, and AGR) were evaluated together in Model 2. In this model, NLR (HR=1.61, 95% CI 1.18-2.21, p=0.003), albumin (HR=0.63, 95% CI 0.46-0.86, p=0.003), CA 19-9 (HR=2.04, 95% CI 1.51-2.77, p<0.001), and ECOG PS (HR=3.40, 95% CI 2.20-5.26, p<0.001) exhibited independent predictive properties for OS. Other factors were not found to be independent prognostic in multivariate analysis.

Survival curves were fitted by means of the Kaplan-Meier analysis of prognostic factors identified as independent prognostics in both models. The corresponding mOS values according to NLR, SGR, PNI, albumin, ECOG PS, and CA 19-9 were 10.0 (95% CI 8.7–11.4) vs. 5.9 (95% CI 4.5–7.4) (p<0.001), 10.4 (95% CI 7.7–13.1) vs. 6.1 (95% CI 4.4–7.8) (p<0.001), 10.4 (95% CI 9.0–11.8) vs. 6.0 (95% CI 4.9–7.2) (p=0.001), 10.0 (95% CI 8.8–11.2) vs. 6.0 (95% CI 4.4–7.6) (p=0.003), 9.4 (95% CI 7.9–11.0) vs. 3.8 (95% CI 3.0–4.6) (p<0.001), and 10.1 (95% CI 7.0–13.2) vs. 6.1 months (95% CI 4.4–7.7, p<0.001), respectively (Figure 1).

Table I: Demographic, clinicopathological characteristics, and blood parameters of the patients.

Clinicopathological characteristics

N

%

Age

 

 

<60

54

25

≥60

162

75

BMI

 

 

< 25

132

62

≥25

84

38

Gender

 

 

Male

151

69.9

Female

65

30.1

ECOG PS

 

 

0-1

29

13.4

≥2

187

86.6

Histological type

 

 

Well, Moderately

71

32.9

Poorly, Mucinous

145

67.1

Peritoneal metastasis

 

 

Yes

74

34.3

No

142

65.7

Liver metastasis

 

 

Yes

122

56.5

No

94

43.5

Lung metastasis

 

 

Yes

11

5.1

No

205

94.9

Chemotherapy lines

 

 

1

124

57.4

≥2

92

42.6

Blood parameters

Low* (N/%)

High* (N/%)

Albumin

82 (38)

134 (62)

Globulin

114 (52.8)

102 (47.2)

Sodium

129 (59.7)

87 (40.3)

AST

95 (44)

121 (56)

ALT

96 (44.4)

120 (55.6)

GGT

94 (43.5)

122 (56.5)

ALP (IU/L)

94 (43.5)

122 (56.5)

Total biluribine (mg/dl)

95 (44)

121 (56)

Hemoglobin (g/dl)

107 (49.5)

109 (50.5)

CEA (ng/ml)

98 (45.4)

118 (54.6)

CA 19-9 (U/ml)

112 (51.9)

104 (48.1)

NLR

108 (50)

108 (50)

PLR

107 (49.5)

109 (50.5)

SII

88 (40.7)

128 (59.3)

De Ritis

105 (48.6)

111 (51.4)

AAPR

119 (55.1)

97 (44.9)

SGR

112 (51.9)

104 (48.1)

PNI

114 (52.8)

102 (47.2)

ABR

111 (51.4)

105 (48.6)

AGR

110 (50.9)

106 (49.1)

* Low and high values were calculated according to optimal cut-offs.

DISCUSSION

The present study investigated prognostic factors on overall survival in advanced GC patients receiving chemotherapy. Laboratory parameters recorded at the time of diagnosis and indices created thereof were included in the analyses. The study identified BMI, ECOG PS, liver metastasis, albumin, GGT, CEA, CA 19-9, NLR, SII, AAPR, SGR, PNI, ABR, and AGR as prognostic factors. According to the multivariate models created for this study, NLR, SGR, PNI, albumin, ECOG PS, and CA 19-9 were found to have strong prognostic values for survival.

Table II: Univariate analyses of overall survival (OS).

Variables

Category

Univariate analysis

HR (95% CI)

p

Clinicopathologic characters

 

 

Gender

Male / Female

1.09(0.80-1.51)

0.580

Age

<60 / ≥60

1.28(0.90-1.82)

0.164

Histologic type

A/B*

1.36(0.99-1.86)

0.059

ECOG PS

0-1 / ≥2

3.54(2.33-5.39)

<0.001

BMI

<25 / ≥25

0.71(0.53-0.97)

0.030

Peritoneal metastasis

No / Yes

1.01(0.74-1.36)

0.964

Lung metastasis

No / Yes

0.93(0.48-1.81)

0.839

Liver metastasis

No / Yes

1.56(1.16-2.10)

0.003

Chemotherapy lines

1/ ≥ 2

0.76(0.56-1.02)

0.062

Laboratory parameters

 

 

 

Hemoglobin (g/dl)

<10 / ≥10

0.91(0.68-1.21)

0.504

Albumin (g/dl)

<3.5 / ≥3.5

0.64(0.48-0.86)

0.003

Globulin (g/dl)

<2.9 / ≥2.9

1.31(0.98-1.74)

0.067

Sodium (mmol/L)

<138 / ≥138

0.77(0.58-1.04)

0.092

AST (IU/L)

<23 / ≥23

1.31(0.98-1.76)

0.071

ALT (IU/L)

<17 / ≥17

1.03(0.77-1.38)

0.838

Total biluribine (mg/dl)

<0.46 / ≥0.46

1.13(0.84-1.53)

0.422

GGT (IU/L)

<34 / ≥34

1.49(1.10-2.00)

0.010

ALP (IU/L)

<101 / ≥101

1.29(0.95-1.74)

0.099

CEA (ng/ml)

<4 / ≥4

1.43(1.07-1.91)

0.017

CA 19-9 (U/ml)

<34 / ≥34

1.86(1.38-2.50)

<0.001

Indexes

 

 

 

NLR

<3.06 / ≥3.06

1.86(1.38-2.49)

<0.001

PLR

<191.3 / ≥191.3

1.27(0.95-1.69)

0.105

SII

<880.8 / ≥880.8

1.51(1.12-2.03)

0.007

De Ritis

<1.39 / ≥1.39

1.27(0.95-1.69)

0.107

AAPR

<0.037 / ≥0.037

0.66(0.49-0.88)

0.005

SGR

<48.81 / ≥48.81

0.60(0.47-0.80)

0.001

PNI

<37.11 / ≥37.11

0.61(0.46-0.82)

0.001

ABR

<9.82 / ≥9.82

0.73 (0.55-0.97)

0.033

AGR

<1.3 / ≥1.3

0.67(0.50-0.89)

0.007

sSignificant values are indicated in bold.  *A, Well, Moderately; B, Poorly, Mucinous;  NLR, Neutrophil-lymphocyte ratio; PLR, Platelet-lymphocyte ratio; AAPR, Albumin-ALP ratio; SGR, Sodium-globulin ratio; PNI, Prognostic nutritional index; ABR, Albumin-bilirubin ratio; AGR, Albumin-globulin ratio.

Figure 1: Kaplan Meier survival curves for Overall Survival according to NLR (A), SGR (B), PNI (C), albumin (D), CA 19-9 (E), and ECOG PS (F).
NLR, Neutrophil-lymphocyte ratio; SGR, Sodium globulin ratio; PNI, Prognostic nutritional index.

The close relationship between inflammation and cancer is well-known. Uncontrolled inflammation weakens immune responses, thereby causing cancer progression.8 Recently, an increasing number of studies have focused on the prognostic properties of inflammatory markers in different types of cancer. SII, PLR, and NLR indices, which are derived from hemogram parameters, are considered important indicators of inflammation and immune system functioning.9,10 Numerous studies have reported that these indices are of prognostic value in non-metastatic gastric cancer.11 However, there is only a limited number of studies on metastatic GC. Among these, previous studies by Murakami et al. and Zhang et al. showed the prognostic value of NLR;12,13 and Chen et al. reported SII as a prognostic factor.14 In the study by Hirahara et al. SII for inflammatory gastric cancers and NLR for non-inflammatory gastric cancers were found to be prognostic factors.11 The present study showed that SII and NLR were prognostic factors, while PLR was not. In addition, NLR was found to be an independent prognostic factor. There is no consensus in the literature for the most accurate and useful hemogram index. Well-designed studies that can reveal the underlying mechanism are needed to determine the most beneficial factor.

GC is recognised as a common cause of cachexia, which is a result of weight loss and decreased nutrition.15 Albumin and globulin are sensitive serum proteins whose levels can change due to both inflammation and nutrition-related reasons. Furthermore, gastric cancer is a systemic disease closely related to changes in serum electrolytes.16,17 The close association of gastric cancer with nausea-vomiting and cachexia further increases the risk of electrolyte disturbances. As a result of all these factors, previous studies have investigated a number of different prognostic factors in patients with advanced GC. Crumley et al. reported low albumin levels as a predictor of poor prognosis,18 Eo et al. showed that PNI was associated with survival.19 Ekinci et al. reported that ABR was not prognostic for metastatic GC.20 Zhang et al. analysed SGR in patients with advanced GC and found that low SGR was associated with poor survival.6 Bozkaya et al. reported AGR as a prognostic factor in advanced GC.21 In a study that included patients with resectable GC, Wang et al. determined that AAPR had prognostic value.22 The present study identified albumin levels, ABR, PNI, SGR, AGR, and AAPR as prognostic factors. Furthermore, albumin, SGR and PNI showed independent prognostic properties in the multivariate analysis. The fact that AGR, AAPR, and ABR did not maintain their predictive feature in the multivariate analysis may be related to the higher proportion of patients with liver metastases in this study compared to previous studies. Because it is known that the liver plays a major role in the production of the parameters included in these indexes.

Performance status is an important prognostic factor in GC, as is the case in many types of cancer.23 CA 19-9 was reported as a prognostic factor for advanced GC.24,25 Similarly, ECOG PS and CA 19-9 were found as independent prognostic factors in this study.

This study is not without limitations. Firstly, the study has a single-centre and retrospective design. Secondly, although the patient selection criteria were chosen carefully, there are various circumstances that could influence laboratory markers. One of the strengths of this study is that multiple factors were investigated together in the same patient population, which were reported to be prognostic in the literature or have been analysed for the first time in this study to the best of authors’ knowledge.

CONCLUSION

Pretreatment NLR, SGR, PNI, albumin, ECOG PS, and CA 19-9 have been identified as strong prognostic factors in advanced gastric cancer patients receiving chemotherapy. In routine clinical practice, these survival predictors can be used as easily accessible, simple and appropriate prognostic factors to guide the clinician in taking necessary actions. However, multi-centre prospective randomised studies with larger patient populations are warranted to be able to generalise these results.

ETHICAL APPROVAL:
The Local Ethics Committee of the Tekirdag Namik Kemal University, Tekirdag, Turkey approved this study.

PATIENTS’ CONSENT:
Since it was designed as a retrospective study, the data were obtained from the electronic medical record system after approval of the Ethics Committee.

COMPETING INTEREST:
The authors declared no competing interest.

AUTHORS’ CONTRIBUTION:
EC: Conceptualisation, data curation, investigation, methodology writing-review, and editing.
YI: Conceptualisation, methodology, visualisation, writing-review, and editing.
OA: Data curation and methodology.
ESS: Writing, investigation, and methodology.
All the authors have approved the final version of the manuscript to be published.

REFERENCES

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: Globocan Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209-49. doi: 10.3322/caac.21660. 
  2. Rawla P, Barsouk A. Epidemiology of gastric cancer: Global trends, risk factors and prevention. Prz Gastroenterol 2019; 14(1):26-38. doi: 10.5114/pg.2018.80001. 
  3. Cuyun Carter G, Kaltenboeck A, Ivanova J, Liepa AM, San Roman A, Koh M, et al. Treatment patterns in patients with advanced gastric cancer in Taiwan. Asia Pac J Clin Oncol 2017; 13(3):185-94. doi: 10.1111/ajco.12497. 
  4. Magalhães H, Fontes-Sousa M, Machado M. Immunotherapy in Advanced Gastric Cancer: An Overview of the Emerging Strategies. Can J Gastroenterol Hepatol 2018; 2018: 2732408. doi: 10.1155/2018/2732408.
  5. Ho SWT, Tan P. Dissection of gastric cancer heterogeneity for precision oncology. Cancer Sci 2019; 110(11):3405-14. doi: 10.1111/cas.14191.
  6. Zhang L, Wang Z, Xiao J, Chen H, Zhang Z, Li H, et al. Sodium to globulin ratio as a prognostic factor for patients with advanced gastric cancer. J Cancer 2020; 11(24): 7320-8. doi: 10.7150/jca.47314. 
  7. Wang K, Diao F, Ye Z, Zhang X, Zhai E, Ren H, et al. Prognostic value of systemic immune-inflammation index in patients with gastric cancer. Chin J Cancer 2017; 36(1):75. doi: 10.1186/s40880-017-0243-2.
  8. Jiang N, Deng J-Y, Liu Y, Ke B, Liu H-G, Liang H. The role of preoperative neutrophil-lymphocyte and platelet-lymphocyte ratio in patients after radical resection for gastric cancer. Biomarkers 2014; 19(6):444-51. doi: 10. 3109/1354750X.2014.926567. 
  9. Yodying H, Matsuda A, Miyashita M, Matsumoto S, Sakurazawa N, Yamada M, et al. Prognostic significance of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in oncologic outcomes of esophageal cancer: A systematic review and meta-analysis. Ann Surg Oncol 2016; 23(2):646-54. doi: 10.1245/s10434-015-4869-5.
  10. Fu S, Yan J, Tan Y, Liu D. Prognostic value of systemic immune-inflammatory index in survival outcome in gastric cancer: A meta-analysis. J Gastrointest Oncol 2021; 12(2): 344-54. doi: 10.21037/jgo-20-252.
  11. Hirahara N, Matsubara T, Fujii Y, Kaji S, Kawabata Y, Hyakudomi R, et al. Comparison of the prognostic value of immunoinflammation-based biomarkers in patients with gastric cancer. Oncotarget 2020; 11(27):2625-35. doi: 10.18632/oncotarget.27653.
  12. Murakami Y, Saito H, Shimizu S, Kono Y, Shishido Y, Miyatani K, et al. Neutrophil-to-Lymphocyte ratio as a prognostic indicator in patients with unresectable gastric cancer. Anticancer Res 2019; 39(5):2583-9. doi: 10.21873/ anticanres.13381.
  13. Zhang Y, Lu JJ, Du YP, Feng CX, Wang LQ, Chen MB. Prognostic value of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in gastric cancer. Medicine (Baltimore) 2018; 97(12):e0144. doi: 10.1097/MD. 0000000000010144.
  14. Chen L, Yan Y, Zhu L, Cong X, Li S, Song S, et al. Systemic immune-inflammation index as a useful prognostic indicator predicts survival in patients with advanced gastric cancer treated with neoadjuvant chemotherapy. Cancer Manag Res 2017; 9:849-67. doi: 10.2147/CMAR.S151026. 
  15. Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. Cancer-associated cachexia. Nat Rev Dis Prim 2018; 4:17105. doi: 10.1038/nrdp.2017.105.
  16. Boku N, Ohtsu A, Nagashima F, Muto M, Shinkai T, Yoshida S. Retrospective study of hyponatremia in gastric cancer patients treated with a combination chemotherapy of 5-fluorouracil and cisplatin: A possible warning sign of severe hematological toxicities? Jpn J Clin Oncol 2001; 31(8): 382-7. doi: 10.1093/jjco/hye085.
  17. Rosner MH, Dalkin AC. Electrolyte disorders associated with cancer. Adv Chronic Kidney Dis 2014; 21(1):7-17. doi: 10.1053/j.ackd.2013.05.005.
  18. Crumley ABC, Stuart RC, McKernan M, McMillan DC. Is hypoalbuminemia an independent prognostic factor in patients with gastric cancer? World J Surg 2010; 34(10):2393-8. doi: 10.1007/s00268-010-0641-y.
  19. Eo WK, Chang HJ, Suh J, Ahn J, Shin J, Hur J-Y, et al. The prognostic nutritional index predicts survival and identifies aggressiveness of gastric cancer. Nutr Cancer 2015; 67(8):1260-7. doi: 10.1080/01635581.2015.1082112.
  20. Ekinci F, Erdogan PA. Can albumin bilirubin ratio predict survival in metastatic gastric cancer? EJMI 2022; 6(1): 106-10. doi.org/10.14744/ejmi.2021.51056.
  21. Bozkaya Y, Erdem GU, Demirci NS, Yazıcı O, Ozdemir NY, Kostek O, et al. Prognostic importance of the albumin to globulin ratio in metastatic gastric cancer patients. Curr Med Res Opin 2019; 35(2):275-82. doi: 10.1080/0300 7995.2018.1479683.
  22. Wang Y, Xiong F, Yang J, Xia T, Jia Z, Shen J, et al. Decreased albumin-to-alkaline phosphatase ratio predicted poor survival of resectable gastric cancer patients. J Gastrointest Oncol 2021; 12(4):1338-50. doi: 10.21037/ jgo-21-430.
  23. Lee J, Lim T, Uhm JE, Park KW, Park SH, Lee SC, et al. Prognostic model to predict survival following first-line chemotherapy in patients with metastatic gastric adenocarcinoma. Ann Oncol Off J Eur Soc Med Oncol 2007; 18(5):886-91. doi: 10.1093/annonc/mdl501.
  24. Jo JC, Ryu MH, Koo DH, Ryoo BY, Kim HJ, Kim TW, et al. Serum CA 19-9 as a prognostic factor in patients with metastatic gastric cancer. Asia Pac J Clin Oncol 2013; 9(4):324-30. doi: 10.1111/ajco.12019.
  25. Wang Q, Yang Y, Zhang YP, Zou Z, Qian X, Liu B, et al. Prognostic value of carbohydrate tumor markers and inflammation-based markers in metastatic or recurrent gastric cancer. Med Oncol 2014; 31(12):289. doi: 10. 1007/s12032-014-0289-9.