Abstract
Preoperative laboratory data indicators significantly affect the prognosis of a variety of tumors. Nevertheless, the combined effect of systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) on overall survival (OS) in patients with esophageal carcinoma remains unclear. Thus, we examined these associations among patients with postoperative staged T3N0M0 esophageal carcinoma. The data of 246 patients with postoperative staged T3N0M0 esophageal carcinoma from January 1, 2010, to December 31, 2022, were retrospectively analyzed. OS was measured from the date of pathological diagnosis until either death or the last follow-up. The Kaplan–Meier method and multivariate Cox regression model were used to analyze the relationship between neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR), Platelet-to-lymphocyte ratio (LMR), SII, PNI, and OS. The predictive value of SII and PNI as a combined index was analyzed by the receiver operating characteristic curve (ROC). A total of 246 patients aged 65.5 ± 7.4 years were included in this study and 181 (73.6%) were male. The univariate analysis revealed that differentiation, vessel involvement, postoperative treatment, NLR, SII, PLR, LMR, PNI were predictors of OS (P < .05). After adjusted for potential confounds, multivariate Cox regression analysis showed that the differentiation, SII, PNI, and postoperative treatment were independent prognostic factors correlated with OS in patients with postoperative staged T3N0M0 esophageal carcinoma (P < .05). SII and PNI, as a combined indicator, have a higher predictive value for OS. The NLR, SII, PLR, LMR, and PNI could all be used as independent predictors of OS in patients with postoperative staged T3N0M0 esophageal carcinoma. The combination of SII and PNI can significantly improve the accuracy of prediction.
Keywords: Overall survival, Preoperative laboratory data indicators, T3N0M0 esophageal carcinoma
1. Introduction
Esophageal cancer, a highly aggressive malignancy with a poor prognosis, ranked 7th in incidence and 6th in mortality worldwide, and its prevalence has steadily increased over the past few decades.[1] Esophageal squamous cell carcinoma (ESCC) is the predominant histological subtype of esophageal cancer in China, accounting for over 90% of all cases.[2] Despite advances in the diagnosis, imaging, and treatment of esophageal cancer, the overall survival (OS) remains low in recent decades, with a 5-year survival rate of 15% to 25%.[3,4] The primary cause is that the majority of patients are diagnosed at an advanced stage, with lower opportunity for curative resection.[5] At the same time, the prognosis of patients remains poor due to local recurrence or distant metastasis. Hence, it is imperative and urgently needed to discover a simple and efficient prognostic indicator that could identify patients with a higher risk of esophageal carcinoma.
Inflammation is a widely recognized characteristic of malignancy, and numerous inflammatory markers have been investigated as prognostic indicators in cancer patients.[6] Additionally, the inflammatory response of the host plays a pivotal role in tumor initiation and progression.[7,8] For instance, the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) have been recognized as prognostic indicators in gastric, lung, colorectal, and hepatocellular cancers due to their inflammatory and immunologic-based scoring systems.[8–11] Systemic immune-inflammation index (SII), as a composite index containing 3 indicators, can represent different inflammatory and immune pathways in the body and has greater stability. Meanwhile, it has recently been shown to be a significant predictor of gastric cancer and non-small cell lung cancer.[12,13]
In recent years, researchers have been dedicated to exploring the correlation between tumors and malnutrition.[14] Patients with malignant diseases frequently experience malnutrition due to loss of appetite and adverse effects of treatment regimens.[15] Malnutrition in turn exacerbates intolerance towards treatment modalities and ultimately impacts recurrence and OS.[16] In a result, the prognostic nutritional index (PNI) has been recently identified as a prognostic marker in various cancers.[17–19]
According to the 8th edition of the Cancer staging manual, patients with T3N0M0 were classified into stages IIA, IIB, which suggested that survival outcomes may vary significantly among these patients.[20] Therefore, the identification of promising prognostic factors and appropriate clinical treatment have the potential to enhance long-term survival rate.
The primary aim of this study was to evaluate the prognostic efficacy of NLR, SII, PLR, LMR, and PNI. Then, we examined the association between these biomarkers and clinical characteristics and further explored whether their combination could improve the prognostic value of patients with postoperative staged T3N0M0 esophageal carcinoma compared with individual utilization.
2. Methods
This study was conducted in Taizhou, China from January 1, 2010, to December 31, 2022. Patients who underwent radical esophagectomy for esophageal carcinoma at Taixing People’s Hospital were recruited in this study. The inclusion criteria were: postoperative pathology was squamous cell carcinoma of the esophagus with a TNM stage of T3N0M0 based on the American Joint Committee on Cancer 8th edition staging system; preoperative blood routine examination should be conducted within 3 days; patients with detailed clinicopathological data. Exclusion criteria were: patients with neoadjuvant therapy before surgery; patients with comorbidities including acute infections, hematologic diseases, other malignant tumors, and autoimmune disorders; patients undergoing immunization suppression therapy before surgery; patients with any missing clinical, laboratory, and follow-up information. The study was approved by the medical research ethics committee of Taixing People's Hospital (No. XJS2023060) and was performed in accordance with the Declaration of Helsinki. All participants included in this study provided written informed consent.
2.1. Clinical and pathological parameters
The clinical and pathological parameters of the patients were obtained from the medical records, such as age (years), gender (male, female), smoking (No, current smoking), alcohol drinking (No, current alcohol drinking), tumor differentiation degree (low, medium, high), maximum tumor diameter, nerve involvement (negative, positive), vascular involvement (negative, positive), and postoperative treatment (none, radiotherapy, chemotherapy, chemoradiotherapy). All the pathological parameters followed the definition of American Joint Committee on Cancer 8th edition staging system.
2.2. Blood routine examination and definitions
Data on routine blood tests were retrospectively extracted from the medical record within 3 days before surgery, such as the neutrophil, lymphocyte, platelet, and monocyte counts. The serum albumin level was measured by hepatic function test before surgery. NLR, SII, PLR, LMR, and PNI were calculated using the following formula as follows: NLR = neutrophil counts/lymphocyte counts, SII = platelet counts × neutrophil counts/lymphocyte counts, PLR = absolute platelet count/lymphocyte count, LMR = lymphocyte count/monocyte count, PNI = serum albumin level (g/L) + 5 × absolute lymphocyte count (mm3).
2.3. Follow-up assessment
The patients were followed up with regular checks in the outpatient department, such as physical examination, blood routine, and the computed tomography scan of the chest. Patients were reexamined every 3 to 6 months for the initial 2 years and then the patients were reexamined every 6 months from the 3rd to 5th year. The deadline date of follow-up was December 31, 2022. OS was defined as the interval from the first day of pathological diagnosis to the date of all-cancer death or the date of the last follow-up.
2.4. Statistical analysis
All data analyses were conducted using IBM SPSS software version 26.0 (SPSS Inc. Chicago, IL, USA). The best cutoff values of NLR, SII, PLR, LMR, and PNI were selected by drawing the ROC, which used OS time as the endpoint. Independent sample T test and one-way analysis of variance were used for the comparison of measurement data. Enumeration data were compared by the chi-square test. The Kaplan–Meier method and the multivariate Cox regression model analysis were used to analyze the prognostic relationship among NLR, SII, PLR, LMR, PNI, and OS. All statistical tests were two-tailed and a P < .05 was suggested as statistically significant.
3. Results
The ROC curves were used to determine the best cutoff values for these biomarkers in predicting OS. The area under curve (AUC) for NLR, SII, PLR, LMR, and PNI were 0.605 (P = .005), 0.595 (P = .010), 0.580 (P = .030), 0.586 (P = .020), 0.601 (P = .007). The corresponding maximum cutoff values were 3.15, 723.90, 111.74, 4.36, and 48.13. Based on the best cutoff values, we divided the patients into 2 groups, low SII group (<723.90, n = 184), high SII group (≥723.90, n = 62); low NLR group (<3.15, n = 176), high NLR group (≥3.15, n = 70); low PLR group (<111.74, n = 89), high PLR group (≥111.74, n = 157); low LMR group (<4.36, n = 167), high LMR group (≥4.36, n = 79); low PNI group (<48.13, n = 65), high PNI group (≥48.13, n = 181).
The clinicopathological characteristics of 246 patients (age 43–81 years) enrolled in this study were summarized in Table 1, with 181 (73.6%) being male and 65 (26.4%) female. Among all the patients, 119 cases (48.4%) had a history of smoking and 124 cases (50.4%) had a history of Alcohol Drinking. According to the degree of tumor differentiation, 20 (8.1%), 205 (83.3%), and 21 (8.5%) patients were classified in the low, moderate, and high differentiation groups, respectively. At the same time, we found that both NLR, SII, and PLR were significantly associated with tumor length (P < .001). In addition, low PLR group was significantly associated with low differentiation (P = .043) and negative nerve involvement (P = .049). Furthermore, the low LMR group had a higher proportion of males (P < .001) and a higher proportion of smoking (P = .001) and alcohol drinking (P < .001).
Table 1.
Relationships among NLR, SII, PLR, LMR, PNI and clinicopathological characteristics in patients with esophageal carcinoma.
| Variables | Total patients | NLR | SII | PLR | LMR | PNI | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Low (176) |
High (70) |
P | Low (184) |
High (62) |
P | Low (89) |
High (157) |
P | Low (167) |
High (79) |
P | Low (65) |
High (181) |
P | |||
| Age (years, %) | 65.5 ± 7.4 | 65.1 ± 7.3 | 66.4 ± 7.6 | .220 | 65.2 ± 7.3 | 66.2 ± 7.7 | .383 | 65.4 ± 7.2 | 65.5 ± 7.5 | .929 | 65.5 ± 7.5 | 65.3 ± 7.1 | .849 | 66.9 ± 6.5 | 65.0 | .072 | |
| Gender | |||||||||||||||||
| Male | 181 | 127 | 54 | .424 | 132 | 49 | .260 | 66 | 115 | .877 | 137 | 44 | <.001 | 47 | 134 | .787 | |
| Female | 65 | 49 | 16 | 53 | 12 | 23 | 42 | 32 | 33 | 18 | 47 | ||||||
| Smoking | |||||||||||||||||
| No | 128 | 86 | 41 | .169 | 94 | 34 | .558 | 39 | 88 | .065 | 74 | 53 | .001 | 34 | 93 | .898 | |
| Yes | 119 | 90 | 29 | 91 | 28 | 50 | 69 | 93 | 26 | 31 | 88 | ||||||
| Alcohol drinking | |||||||||||||||||
| No | 122 | 87 | 35 | .936 | 92 | 30 | .826 | 41 | 81 | .405 | 70 | 52 | <.001 | 29 | 93 | .349 | |
| Yes | 124 | 89 | 35 | 92 | 32 | 48 | 76 | 97 | 27 | 36 | 88 | ||||||
| Differentiation | |||||||||||||||||
| Low | 20 | 10 | 10 | .061 | 11 | 9 | .104 | 4 | 16 | .043 | 16 | 4 | .423 | 4 | 16 | .545 | |
| Medium | 205 | 149 | 56 | 157 | 48 | 73 | 132 | 138 | 67 | 57 | 148 | ||||||
| High | 21 | 17 | 4 | 16 | 5 | 12 | 9 | 13 | 8 | 4 | 17 | ||||||
| Vascular involvement | |||||||||||||||||
| Negative | 212 | 155 | 57 | .173 | 162 | 50 | .144 | 77 | 135 | .908 | 141 | 71 | .248 | 52 | 160 | .092 | |
| Positive | 34 | 21 | 13 | 22 | 12 | 12 | 22 | 26 | 8 | 13 | 21 | ||||||
| Nerve involvement | |||||||||||||||||
| Negative | 213 | 152 | 61 | .871 | 157 | 56 | .318 | 72 | 141 | .049 | 144 | 69 | .811 | 60 | 153 | .115 | |
| Positive | 33 | 24 | 9 | 27 | 6 | 17 | 16 | 23 | 10 | 5 | 28 | ||||||
| Length (mm, %) | 4.2 ± 1.3 | 4.0 ± 1.1 | 4.9 ± 1.5 | <.001 | 4.0 ± 1.2 | 4.8 ± 1.4 | <.001 | 4.0 ± 1.2 | 4.4 ± 1.3 | .029 | 4.3 ± 1.3 | 4.0 ± 1.2 | .082 | 4.5 ± 1.3 | 4.1 ± 1.2 | .051 | |
| Postoperative treatment | |||||||||||||||||
| None | 151 | 107 | 44 | .565 | 117 | 34 | .622 | 58 | 93 | .817 | 99 | 52 | .817 | 46 | 105 | .112 | |
| Radiotherapy | 17 | 12 | 5 | 12 | 5 | 5 | 12 | 13 | 4 | 3 | 14 | ||||||
| Chemotherapy | 54 | 42 | 12 | 39 | 15 | 18 | 36 | 38 | 16 | 14 | 40 | ||||||
| Chemoradiotherapy | 24 | 15 | 9 | 16 | 8 | 8 | 16 | 18 | 6 | 2 | 22 | ||||||
LMR = lymphocyte-to-monocyte ratio, NLR = neutrophil-to-lymphocyte ratio, PLR = platelet-to-lymphocyte ratio, PNI = prognostic nutritional index, SII = systemic immune-inflammation index.
The correlations between clinical parameters and preoperative laboratory data indicators and OS are shown in Table 2. Univariate Cox regression models showed that the differentiation (P = .005), vessel involvement (P = .046), postoperative treatment (P = .014), NLR (P < .001), SII (P < .001), PLR (P = .044), LMR (P = .002), and PNI (P < .001) were the influencing factors for OS. After adjusted for age, gender, smoking, alcohol drinking, these 9 influencing factors were included in multivariate Cox regression analysis. The results showed that patients with Medium differentiation (HR: 0.54 (0.27, 0.98), P = .043)), high differentiation (HR: 0.19 (0.07, 0.54), P = .002)), and SII (HR: 1.76 (1.03–3.01), P = .039)) were independent risk factors for OS. Chemotherapy treatment (HR: 0.48 (0.29–0.81)), P = .005), chemoradiotherapy treatment (HR: 0.26 (0.10–0.66), P = .004)), PNI (HR: 0.62 (0.40–0.97), P = .037)) were independent protect factors for OS.
Table 2.
Prognostic factors for OS in patients with postoperative staged T3N0M0 esophageal carcinoma.
| Univariate analysis | Multivariate analysisa | |||
|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | |
| Gender | ||||
| Male | 1 | .731 | ||
| Female | 1.08 (0.71,1.63) | |||
| Smoking | ||||
| No | 1 | .559 | ||
| Yes | 1.12 (0.77,1.62) | |||
| Alcohol drinking | ||||
| No | 1 | .268 | ||
| Yes | 1.23 (0.85,1.79) | |||
| Differentiation | ||||
| Low | 1 | .005 | 1 | .006 |
| Medium | 0.48 (0.27,0.85) | .011 | 0.54 (0.27,0.98) | .043 |
| High | 0.21 (0.074,0.57) | .002 | 0.19 (0.07,0.54) | .002 |
| Vessel involvement | ||||
| Negative | 1 | .046 | 1 | .053 |
| Positive | 1.64 (1.01,2.66) | 1.66 (0.99–2.76) | ||
| Nerve involvement | ||||
| Negative | 1 | .977 | 1 | .637 |
| Positive | 0.98 (0.58,1.71) | 1.16 (0.64–2.10) | ||
| Postoperative treatment | ||||
| None | 1 | .014 | 1 | .003 |
| Radiotherapy | 0.88 (0.47,1.83) | .735 | 0.79 (0.36–1.57) | .455 |
| Chemotherapy | 0.56 (0.34,0.92) | .021 | 0.48 (0.29–0.81) | .005 |
| Chemoradiotherapy | 0.31 (0.13,0.77) | .012 | 0.26 (0.10–0.66) | .004 |
| NLR | ||||
| Low | 1 | <.001 | 1 | .116 |
| High | 2.35 (1.61,3.43) | 1.50 (0.91–2.48) | ||
| SII | ||||
| Low | 1 | <.001 | 1 | .039 |
| High | 2.23 (1.51,3.30) | 1.76 (1.03–3.01) | ||
| PLR | ||||
| Low | 1 | .044 | 1 | .850 |
| High | 1.51 (1.01.2.26) | 0.85 (0.52–1.39) | ||
| LMR | ||||
| Low | 1 | .002 | 1 | .072 |
| High | 0.52 (0.33,0.81) | 0.64 (0.40–1.04) | ||
| PNI | ||||
| Low | 1 | <.001 | 1 | .037 |
| High | 0.44 (0.30,0.65) | 0.62 (0.40–0.97) | ||
CI = confidence interval, HR = hazard ratio, LMR = lymphocyte-to-monocyte ratio, NLR = neutrophil-to-lymphocyte ratio, PLR = platelet-to-lymphocyte ratio, PNI = prognostic nutritional index, SII = systemic immune-inflammation index.
Adjusted for age, gender, smoking, alcohol drinking.
The correlation between inflammation-related markers and OS is shown in Figure 1. The OS rates were significantly higher in the low NLR and SII group than in the high NLR and SII groups (P < .001); The OS rates were significantly higher in the high LMR and PNI group than in the low LMR and PNI group (P < .001). The OS survival rate was significantly higher in the low PLR group than in the high PLR group (P = .041).
Figure 1.
Kaplan–Meier survival curves for OS in patients with postoperative staged T3N0M0 esophageal carcinoma. OS = overall survival.
The prognostic value of assessing coPNI-SII in patients with esophageal carcinoma was shown in Figure 2. Patients with low SII and high PNI were scored 3, patients with high SII and high PNI were scored 2, patients with low SII and low PNI were scored 1, and patients with high SII and low PNI were scored 0. And the results revealed that OS rates were 23.1%, 46.2%, 47.2%, and 64.1% in patients with coPNI-SII scored 0, 1, 2, and 3, respectively.
Figure 2.
Kaplan–Meier survival curves for OS according to coPNI-SII. OS = overall survival.
We assessed the predictive power of NLR, SII, PLR, LMR, PNI and coPNI-SII for OS using ROC curve analysis. The AUC values were as follows: NLR, SII, PLR, LMR, PNI, and coPNI-SII were NLR (0.605, P = .005), SII (0.595, P = .010). PLR (0.580, P = .030), LMR (0.586, P = .020), PNI (0.601, P = .007), coPNI-SII (0.629, P = .001). Among these indicators, coPNI-SII showed the highest AUC, making it the most accurate prognostic predictor of survival. Thus, coPNI-SII could serve as an alternative prognostic staging tool for patients with prostoperative stage T3N0M0 esophageal carcinoma.
4. Discussion
In this study, we examined the association between NLR, SII, PLR, LMR, PNI, and OS in patients with postoperative staged T3N0M0 esophageal carcinoma and compared their predictive accuracy. The results in this study indicated that NLR, SII, PLR, LMR, and PNI were reliable predictors of OS. In addition, Multivariate Cox regression analysis showed that SII and PNI were independent prognostic factors. Thus we found that the new prognostic value combining SII with PNI (0.629, P = .001) exhibited higher accuracy in predicting OS when compared with PNI (0.601, P = .007) and SII (0.595, P = .010) alone. To our knowledge, this is the first study to compare the prognostic value of these 5 preoperative biomarkers in patients with postoperative staged T3N0M0 esophageal carcinoma.
Tumor microenvironment plays a pivotal role in promoting tumorigenesis and progression.[21] The catabolic effects of systemic inflammation and malnutrition on host metabolism often promote tumor growth, exacerbate the vicious cycle of poor immunonutrition, and lead to the deterioration of cancer.[22] Therefore, the significance of systemic inflammation and malnutrition in malignant tumors is increasingly acknowledged.[23] Consistent with our findings, recent studies have revealed that patients with low LMR experience significantly worse OS compared to those with high LMR.[24] Feng et al[25] found that preoperative NLR and PLR were significant predictors of OS in patients with ESCC. SII, which is a systemic inflammation score based on neutrophil, platelet, and lymphocyte counts, is an independent predictor for patients with hepatocellular carcinoma, colorectal cancer, and gastric cancer.[26–28] Neutrophilia is an inflammatory response that suppresses the cytolytic activity of immune cells, such as lymphocytes and activated T cells, thereby inhibiting the immune system. Platelets may enhance tumor cell extravasation and promote metastasis.[29] Lymphocytes, as a fundamental component of cellular immunity, exert cytokine-mediated cytotoxicity to inhibit the proliferation and invasion of tumor cells.[8] Therefore, SII is posited as a more objective marker that reflects the equilibrium between host inflammatory and immune response status compared to other systemic inflammation indices such as PLR and LMR. In this study, we found that a higher SII score in patients with postoperative staged T3N0M0 esophageal carcinoma usually indicated poorly OS (P = .039), which is a negative prognostic factor for the clinical outcome.
PNI consists of serum albumin concentration and absolute lymphocyte count. Albumin is produced by hepatocytes and plays a crucial role in stabilizing cell growth and DNA replication, buffering various biochemical changes, as well as maintaining sex hormone homeostasis to prevent tumorigenesis.[30,31] The decrease in lymphocytes count will reduce the immune function of the body, making cancer cells more easily to immune escape and eventually lead to poor prognosis of cancer patients.[32] The reduction of PNI reflects malnutrition and decreased immunity, inhibiting inflammatory response and promoting tumor cell invasion function, thereby promoting tumorigenesis and progression, and ultimately leading to poor prognosis of cancer patients.
Combining PNI and SII could effectively predict the prognosis by revealing both the inflammatory and nutritional status of patients.[33] Patients with postoperative staged T3N0M0 esophageal carcinoma are the most common of all classifications, with a large disease burden, and the survival may differ greatly among such patients.[20] This suggests that OS may vary significantly among these patients. So far, few studies have investigated the relationship between nutritional status and systemic inflammation in this population. In our study, Multivariate Cox regression analysis showed that SII and PNI were independent prognostic factors, but the efficacy of PNI or SII alone as a prognostic predictor was very low. Therefore, we conducted a joint analysis of SII and PNI and found that the coPNI-SII had the highest predictive value. The prognosis of the patients in the low SII and high PNI groups was much higher than that of patients in the high SII and low PNI groups. In the future, the treatment of improving PNI and reducing SII could significantly improve the prognosis of patients with postoperative staged T3N0M0 esophageal carcinoma.
Our study has several limitations. First, this was a retrospective and single-center study with a limited number of patients in China, which may lead to a selection bias. Second, some influencing variables that are relevant to OS, including the Glasgow prognostic score, prognostic index, and C-reactive protein, have not been collected in this study.[34] Thirdly, the long duration of data collection in this retrospective analysis, coupled with advancements in surgical technology during the same period, may have an impact on clinical outcomes.
5. Conclusion
In conclusion, this study showed that preoperative laboratory data indicators, such as NLR, SII, PLR, LMR, and PNI, are simple, noninvasive, reliable, and inexpensive for predicting the prognosis of patients with postoperative staged T3N0M0 esophageal carcinoma. Our findings highlight that the combined SII and PNI were more accurate than either indicator alone in predicting OS. Further research especially population-based prospective cohort study was urgently needed to include more prognostic factors to verify our results.
Acknowledgments
We would like to thank the participants in this study.
Author contributions
Conceptualization: Hui Ma, Songbing Qin.
Data curation: Hui Ma, Songbing Qin.
Formal analysis: Hui Ma.
Methodology: Hui Ma, Songbing Qin.
Project administration: Hui Ma, Songbing Qin.
Software: Hui Ma, Songbing Qin.
Supervision: Hui Ma, Songbing Qin.
Validation: Hui Ma.
Writing – original draft: Hui Ma, Yangchen Liu.
Writing – review & editing: Hui Ma, Hongxun Ye, Fei Gao, Zhu Li, Songbing Qin.
Resources: Yangchen Liu, Hongxun Ye, Fei Gao, Zhu Li, Songbing Qin.
Abbreviations:
- AUC
- area under curve
- CI
- confidence interval
- ESCC
- esophageal squamous cell carcinoma
- HR
- hazard ratio
- LMR
- lymphocyte-to-monocyte ratio
- NLR
- neutrophil-to-lymphocyte ratio
- OS
- overall survival
- PLR
- platelet-to-lymphocyte ratio
- PNI
- prognostic nutritional index
- ROC
- receiver operating characteristic
- SII
- systemic immune-inflammation index.
The authors have no funding to disclose.
Informed consent was obtained from all subjects for this study.
The study was approved by the medical research ethics committee of Taixing People’s Hospital (No. XJS2023060) and was performed in accordance with the Declaration of Helsinki.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
How to cite this article: Ma H, Liu Y, Ye H, Gao F, Li Z, Qin S. The prognostic value of preoperative laboratory data indicators in patients with esophageal carcinoma: An observational study. Medicine 2024;103:24(e38477).
Contributor Information
Hui Ma, Email: 820997233@qq.com.
Yangchen Liu, Email: liuyctx@163.com.
Hongxun Ye, Email: yhx4032@126.com.
Fei Gao, Email: gaofei93257@163.com.
Zhu Li, Email: 992518999@qq.com.
References
- [1].Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. [DOI] [PubMed] [Google Scholar]
- [2].Allemani C, Matsuda T, Di Carlo V, et al.; CONCORD Working Group. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391:1023–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Lagergren J, Smyth E, Cunningham D, Lagergren P. Oesophageal cancer. Lancet. 2017;390:2383–96. [DOI] [PubMed] [Google Scholar]
- [4].Zeng H, Chen W, Zheng R, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health. 2018;6:e555–67. [DOI] [PubMed] [Google Scholar]
- [5].Huang FL, Yu SJ. Esophageal cancer: risk factors, genetic association, and treatment. Asian J Surg. 2018;41:210–5. [DOI] [PubMed] [Google Scholar]
- [6].Han W, Allam SA, Elsawa SF. GLI2-mediated inflammation in the tumor microenvironment. Adv Exp Med Biol. 2020;1263:55–65. [DOI] [PubMed] [Google Scholar]
- [7].Yang L, Karin M. Roles of tumor suppressors in regulating tumor-associated inflammation. Cell Death Differ. 2014;21:1677–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Ishibashi Y, Tsujimoto H, Yaguchi Y, Kishi Y, Ueno H. Prognostic significance of systemic inflammatory markers in esophageal cancer: systematic review and meta-analysis. Ann Gastroenterol Surg. 2020;4:56–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Yodying H, Matsuda A, Miyashita 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;23:646–54. [DOI] [PubMed] [Google Scholar]
- [10].Buergy D, Wenz F, Groden C, Brockmann MA. Tumor-platelet interaction in solid tumors. Int J Cancer. 2012;130:2747–60. [DOI] [PubMed] [Google Scholar]
- [11].Ishibashi Y, Tsujimoto H, Hiraki S, et al. Prognostic value of preoperative systemic immunoinflammatory measures in patients with esophageal cancer. Ann Surg Oncol. 2018;25:3288–99. [DOI] [PubMed] [Google Scholar]
- [12].Liu J, Li S, Zhang S, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal. 2019;33:e22964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].He K, Si L, Pan X, et al. Preoperative systemic immune-inflammation index (SII) as a superior predictor of long-term survival outcome in patients with stage I-II gastric cancer after radical surgery. Front Oncol. 2022;12:829689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Tobberup R, Thoresen L, Falkmer UG, Yilmaz MK, Solheim TS, Balstad TR. Effects of current parenteral nutrition treatment on health-related quality of life, physical function, nutritional status, survival and adverse events exclusively in patients with advanced cancer: a systematic literature review. Crit Rev Oncol Hematol. 2019;139:96–107. [DOI] [PubMed] [Google Scholar]
- [15].Jordan T, Mastnak DM, Palamar N, Kozjek NR. Nutritional therapy for patients with esophageal cancer. Nutr Cancer. 2018;70:23–9. [DOI] [PubMed] [Google Scholar]
- [16].Kang J, Yang G, Wang D, Lin Y, Wang Q, Luo H. The clinical application value of the prognostic nutritional index for the overall survival prognosis of patients with esophageal cancer: a robust real-world observational study in China. Comput Math Methods Med. 2022;2022:3889588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Watanabe H, Yamada T, Komori K, et al. Effect of prognostic nutrition index in gastric or gastro-oesophageal junction cancer patients undergoing nivolumab monotherapy. In Vivo. 2021;35:563–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Dai M, Sun Q. Prognostic and clinicopathological significance of prognostic nutritional index (PNI) in patients with oral cancer: a meta-analysis. Aging (Albany NY). 2023;15:1615–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Xiao FK, Wang L, Zhang WC, Wang LD, Zhao LS. Preoperative prognostic nutritional index is a significant predictor of survival in esophageal squamous cell carcinoma patients. Nutr Cancer. 2021;73:215–20. [DOI] [PubMed] [Google Scholar]
- [20].Rice TW, Ishwaran H, Ferguson MK, Blackstone EH, Goldstraw P. Cancer of the esophagus and esophagogastric junction: an eighth edition staging primer. J Thorac Oncol. 2017;12:36–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther. 2021;221:107753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Sugawara K, Yagi K, Uemura Y, et al. Associations of systemic inflammation and sarcopenia with survival of esophageal carcinoma patients. Ann Thorac Surg. 2020;110:374–82. [DOI] [PubMed] [Google Scholar]
- [23].Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493–503. [DOI] [PubMed] [Google Scholar]
- [24].Huang Y, Feng JF. Low preoperative lymphocyte to monocyte ratio predicts poor cancer-specific survival in patients with esophageal squamous cell carcinoma. Onco Targets Ther. 2015;8:137–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Feng JF, Huang Y, Chen QX. Preoperative platelet lymphocyte ratio (PLR) is superior to neutrophil lymphocyte ratio (NLR) as a predictive factor in patients with esophageal squamous cell carcinoma. World J Surg Oncol. 2014;12:58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Wang C, He W, Yuan Y, et al. Comparison of the prognostic value of inflammation-based scores in early recurrent hepatocellular carcinoma after hepatectomy. Liver Int. 2020;40:229–39. [DOI] [PubMed] [Google Scholar]
- [27].Martinez-Lago N, Fernandez-Montes A, Covela M, et al.; Galician Research Group on Digestive Tumors (GITuD). Effect of antiangiogenic-based treatment and systemic inflammatory factors on outcomes in patients with BRAF v600-mutated metastatic colorectal cancer: a real-world study in Spain. BMC Cancer. 2021;21:64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Inoue H, Kosuga T, Kubota T, et al. Significance of a preoperative systemic immune-inflammation index as a predictor of postoperative survival outcomes in gastric cancer. World J Surg Oncol. 2021;19:173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Gao Y, Guo W, Cai S, et al. Systemic immune-inflammation index (SII) is useful to predict survival outcomes in patients with surgically resected esophageal squamous cell carcinoma. J Cancer. 2019;10:3188–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Liu J, Hu G, Zhai C, et al. Predictive value of nutritional indicators with regard to the survival outcomes in patients with metastatic esophageal squamous cell carcinoma treated with camrelizumab. Oncol Lett. 2023;25:198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Zheng Z, Yang C, Cai C, Zhu H. The preoperative neutrophil lymphocyte ratio and platelet lymphocyte ratio predicts disease-free survival in resectable esophageal squamous cell carcinoma. Cancer Manag Res. 2021;13:7511–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Qi Q, Song Q, Cheng Y, Wang N. Prognostic significance of preoperative prognostic nutritional index for overall survival and postoperative complications in esophageal cancer patients. Cancer Manag Res. 2021;13:8585–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Yu X, Wen Y, Lin Y, et al. The value of preoperative Glasgow Prognostic Score and the C-reactive protein to albumin ratio as prognostic factors for long-term survival in pathological T1N0 esophageal squamous cell carcinoma. J Cancer. 2018;9:807–15. [DOI] [PMC free article] [PubMed] [Google Scholar]