Zheng X, Chen J, Li MT. Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and systemic immune-inflammation index predict survival in HER2-negative advanced gastric cancer. World J Gastrointest Oncol 2026; 18(9): 124094 [DOI: 10.4251/wjgo.124094]
Corresponding Author of This Article
Xin Zheng, Director, Clinical Laboratory, Department of Laboratory, Chuzhou Hospital Affiliated to Anhui Medical University (Chuzhou First People's Hospital), No. 369 Zuiweng West Road, Nanqiao District, Chuzhou 239000, Anhui Province, China. sunnyacmilan@163.com
Research Domain of This Article
Gastroenterology & Hepatology
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research-article
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Sep 15, 2026; 18(9): 124094 Published online Sep 15, 2026. doi: 10.4251/wjgo.124094
Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and systemic immune-inflammation index predict survival in HER2-negative advanced gastric cancer
Xin Zheng, Jing Chen, Meng-Ting Li, Clinical Laboratory, Department of Laboratory, Chuzhou Hospital Affiliated to Anhui Medical University (Chuzhou First People's Hospital), Chuzhou 239000, Anhui Province, China
Author contributions: Zheng X, Chen J and Li MT designed the research study; Zheng X performed the research; Chen J and Li MT contributed new reagents and analytic tools; Zheng X analyzed the data and wrote the manuscript; and all authors have read and approve the final manuscript.
AI contribution statement: AI tools were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Institutional review board statement: The research was reviewed and approved by the Ethics Committee of the Chuzhou First People's Hospital.
Informed consent statement: All research participants or their legal guardians provided written informed consent prior to study registration.
Conflict-of-interest statement: No conflict of interest is associated with this work.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: No other data available.
Corresponding author: Xin Zheng, Director, Clinical Laboratory, Department of Laboratory, Chuzhou Hospital Affiliated to Anhui Medical University (Chuzhou First People's Hospital), No. 369 Zuiweng West Road, Nanqiao District, Chuzhou 239000, Anhui Province, China. sunnyacmilan@163.com
Received: June 5, 2026 Revised: June 30, 2026 Accepted: August 28, 2026 Published online: September 15, 2026 Processing time: 95 Days and 16 Hours
Abstract
BACKGROUND
Human epidermal growth factor receptor 2 (HER2)-negative advanced gastric cancer lacks convenient prognostic biomarkers. Peripheral blood neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR) and systemic immune-inflammation index (SII) reflect tumour inflammatory status. This study hypothesizes the four indicators independently predict overall survival (OS) of such patients.
AIM
To determine the predictive value of NLR, PLR, LMR, and SII for survival in HER2-negative advanced gastric cancer.
METHODS
We retrospectively enrolled 262 HER2-negative advanced gastric cancer patients (2021-2024) split into disease control (n = 203) and progression (n = 59) groups. Baseline and inflammatory markers were collected. Kaplan-Meier, Cox regressions, nomogram and receiver operating characteristic curve analyses were performed to assess survival predictors.
RESULTS
In the disease progress group, the NLR, PLR and SII were significantly higher than in the disease control group, whilst the LMR was significantly lower; the median OS in the disease control group (19.1 months) was significantly longer than in the disease progress group (9.2 months). Multivariate Cox regression analysis revealed that elevated NLR, elevated PLR and elevated SII were independent risk factors for OS, whilst a reduced LMR was an independent protective factor. The nomogram constructed using these four indicators demonstrated good predictive performance, with a combined area under the curve of 0.764 (95% confidence interval: 0.676-0.853).
CONCLUSION
Pretreatment NLR, PLR, LMR, and SII predict survival in HER2-negative advanced gastric cancer; combined use with a nomogram aids prognosis, screening, and treatment.
Core Tip: This retrospective cohort study of 262 patients with human epidermal growth factor receptor 2-negative advanced gastric cancer demonstrates that pretreatment peripheral blood neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII) are independent predictors of overall survival. Elevated NLR, PLR, and SII were risk factors, while elevated LMR was protective. The combined four-marker nomogram achieved an area under the curve of 0.764, offering a simple, cost-effective tool for early risk stratification and treatment optimization in this population.
Citation: Zheng X, Chen J, Li MT. Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and systemic immune-inflammation index predict survival in HER2-negative advanced gastric cancer. World J Gastrointest Oncol 2026; 18(9): 124094
Gastric cancer is a highly prevalent malignant tumour of the digestive system worldwide; its incidence and mortality rates have long ranked among the highest for all types of cancer. Most patients are diagnosed at an advanced stage, and the overall prognosis is poor[1,2]. Human epidermal growth factor receptor 2 (HER2) is a key driver gene and therapeutic target in gastric cancer. Clinical data show that more than half of patients with advanced gastric cancer are HER2-negative[3]. These patients lack targeted treatment options, mainly chemotherapy, immunotherapy and other means, but the overall prognosis is still not ideal[4,5]. Therefore, it is of great significance to find simple and reliable biomarkers for efficacy evaluation and survival prediction of patients with HER2-negative advanced gastric cancer.
The occurrence and development of tumors not only depend on the malignant proliferation of tumor cells themselves, but also closely related to the body’s systemic immune inflammatory microenvironment[6]. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) and the systemic immune-inflammation index (SII) are inflammatory and immune biomarkers derived from routine blood test parameters. They reflect the body’s inflammatory and immune balance and offer advantages such as ease of testing, low cost and the ability to monitor changes over time[7,8]. Studies have shown that the increase or decrease of the above indicators is related to the prognosis of gastric cancer[9,10], but the research in HER2-negative advanced gastric cancer is still relatively insufficient, and its predictive value is not yet clear.
Based on this, this study retrospectively analyzed the data of patients with HER2-negative advanced gastric cancer, detected the levels of NLR, PLR, LMR and SII in peripheral blood before treatment, explored their correlation with clinicopathological features and overall survival (OS) of patients, and clarified the independent predictive value of the above indicators for the survival outcome of patients, aiming to provide a reference for screening high-risk groups and optimizing treatment options.
MATERIALS AND METHODS
Research subjects
A total of 262 patients with HER2-negative advanced gastric cancer diagnosed and treated in Chuzhou Hospital Affiliated to Anhui Medical University from January 2021 to November 2024 were retrospectively selected as the study subjects.
Inclusion criteria: (1) Diagnosis of gastric cancer confirmed by histopathological or cytological examination[11], and classified as stage IV (advanced) according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system[12]; (2) Confirmed HER2-negative status by immunohistochemistry or in situ hybridisation[13]; (3) Age ≥ 18 years; (4) Have received at least one line of systemic therapy, such as chemotherapy, immunotherapy or combination therapy; (5) Have complete peripheral blood count results obtained within one week prior to treatment following admission, allowing for the calculation of NLR, PLR, LMR and SII; and (6) Have complete clinical and pathological data, treatment records and follow-up information.
Exclusion criteria: (1) Concurrent primary malignant tumours; (2) Active infections, autoimmune diseases or haematological disorders; (3) Severe hepatic or renal failure, heart failure or coagulation disorders; (4) Use of glucocorticoids or immunomodulators within 1 month prior to treatment; (5) Previous radiotherapy to the stomach or anti-HER2 targeted therapy; and (6) Missing clinical data or loss to follow-up. This study protocol has been approved by the Ethics Committee of the Chuzhou First People's Hospital, and the use of all patient clinical data complies with ethical guidelines.
Grouping method
All patients received first-line systemic therapy (chemotherapy, immunotherapy or combination therapy), and the efficacy was evaluated after 2 treatment cycles (6-8 weeks after the start of treatment). Efficacy evaluation. According to the evaluation criteria of solid tumor efficacy, the measurable lesions were dynamically measured at baseline and after treatment by imaging examination[14]. Based on the status of target lesions and new lesions, overall response is classified into the following four categories: (1) Complete response (CR): Disappearance of all target lesions, no new lesions, and normalisation of tumour markers; (2) Partial response (PR): A reduction of ≥ 30% in the sum of the diameters of target lesions compared with baseline; (3) Stable disease (SD): The target lesions have shrunk but not to the extent of PR, nor have they increased to the criteria for progressive disease (PD); and (4) PD: The sum of the diameters of the target lesions has increased by ≥ 20% (and the absolute increase is ≥ 5 mm) compared with baseline or the post-treatment minimum, or one or more new lesions have appeared[15].
Based on the best overall efficacy of patients during treatment, patients with the best efficacy of CR, PR and SD were included in the disease control group (n = 203), and patients with the best efficacy of PD were included in the disease progress group (n = 59)[16]. The efficacy evaluation was completed independently by two radiologists with more than 5 years of experience.
Data collection
The following clinical data were collected through the hospital electronic medical record system, inspection system and image archiving system: (1) Demographic data: Gender, age, body mass index (BMI), smoking history, alcohol consumption history; (2) Clinical and pathological characteristics: Tumour site, grade of differentiation, sites of distant metastasis, treatment regimen; (3) Peripheral blood inflammatory markers: Collect the patient’s complete blood count results from approximately one week prior to treatment, including neutrophil count, lymphocyte count, platelet count and monocyte count, and use these to calculate the NLR, PLR, LMR and SII; and (4) Survival follow-up data: The starting time of follow-up was the date of the patient's first treatment, and the follow-up deadline was November 30, 2025. OS is defined as the time from the date of first treatment to the date of any cause of death or last effective follow-up. Those who were still alive at the last follow-up were recorded as truncated data. Follow-up methods include: (1) Outpatient follow-up records (once every 3 months, routine imaging and hematological examinations); (2) Hospital medical records review (record each admission treatment and disease changes); and (3) Patients who failed to return on time were followed up by telephone to ask about their survival status, follow-up treatment and whether there was an endpoint event. Telephone follow-up was performed independently by two uniformly trained researchers at intervals of no more than 3 months until the patient’s death or study deadline.
Statistical analysis
Statistical Product and Service Solutions 27.0 (IBM, Armonk, NY, United States) was used to analyze the data. The normality of continuous variables was evaluated by Kolmogorov-Smirnov test, which was consistent with normality, expressed as mean ± SD, and independent sample t test was used for comparison between groups. Count data were expressed as n (%). The χ2 test or Fisher exact probability method was used for comparison between groups. Rank sum test was used for comparison of categorical data between groups. Kaplan-Meier method was used to draw the survival curve and Log-rank test was used to compare the OS of patients with different efficacy groups. Univariate Cox regression was used to preliminarily screen the prognostic factors. Variables with P value < 0.05 were included in multivariate Cox regression model to determine independent prognostic factors. Based on the results of multivariate analysis, a nomogram for predicting OS was constructed. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of combined detection of inflammatory markers for survival outcomes. P value < 0.05 was considered statistically significant.
RESULTS
Comparison of demographic and clinical-pathological characteristics between the two groups
A comparison of the demographic and clinical-pathological characteristics between the two groups is shown in Table 1. The results indicate that patients in the disease progress group were significantly older than those in the disease control group; there was also a statistically significant difference in the distribution of differentiation grades between the two groups, with a higher proportion of poorly differentiated and undifferentiated tumours in the disease progress group. With regard to distant metastases, the incidence of liver and peritoneal metastases in the disease progress group was significantly higher than that in the disease control group (P value < 0.05). There were no statistically significant differences between the two groups in terms of gender, BMI, smoking history, alcohol consumption history, tumour site, lung metastases, bone metastases, or treatment regimens (P value > 0.05).
Table 1 Comparison of demographic data and clinical and pathological characteristics.
Comparison of pre-treatment peripheral blood inflammatory markers between the two groups
The results of the comparison of pre-treatment peripheral blood inflammatory markers between the two groups of patients are shown in Table 2. Compared with the disease control group, patients in the disease progress group had significantly higher NLR, PLR and SII levels, whilst LMR levels were significantly lower (P value < 0.001), indicating that higher pre-treatment levels of NLR, PLR and SII, and lower levels of LMR, are associated with an increased risk of disease progression.
Table 2 Comparison of peripheral blood inflammatory markers.
Comparison of OS across different efficacy subgroups
With follow-up continuing up to November 30, 2025, Kaplan-Meier survival curves showed that the median OS for patients in the disease control group was 19.1 months [95% confidence interval (95%CI): 17.0-20.9], whereas the median OS for patients in the disease progress group was 9.2 months (95%CI: 7.3-10.3). The Log-rank test indicated that OS was significantly longer in the disease control group than in the disease progress group (P value < 0.001), suggesting that patients who achieved disease control following treatment had a better survival prognosis (Figure 1).
Figure 1 Kaplan-Meier survival curves for the two groups of patients.
HR: Hazard ratio; 95%CI: 95% confidence interval.
Univariate and multivariate Cox regression analyses of factors affecting OS
Using OS as the outcome variable, univariate Cox regression analyses were performed on patients’ demographic data, clinical and pathological characteristics, and peripheral blood inflammatory markers. The results showed that liver metastasis, NLR, PLR, LMR and SII were significantly associated with OS. Variables with P value < 0.05 in the univariate analysis were included in a multivariate Cox regression analysis. The results indicated that NLR [hazard ratio (HR) = 1.557, 95%CI: 1.157-2.094], PLR (HR = 1.009, 95%CI: 1.003-1.015), LMR (HR = 0.759, 95%CI: 0.578-0.995) and SII (HR = 1.004, 95%CI: 1.001-1.006) were all independent predictors of OS in patients with HER2-negative advanced gastric cancer (all P value < 0.05). These results indicate that higher pre-treatment NLR, PLR and SII, and lower LMR, are independent risk factors for reduced patient survival (Table 3).
Table 3 Univariate and multivariate Cox regression analyses of factors influencing overall survival.
Construction of OS prediction model based on inflammatory markers
Based on four independent prognostic factors identified through multivariate Cox regression analysis, a nomogram model was developed to predict OS in patients with HER2-negative advanced gastric cancer (Figure 2). This model assigns numerical values to each inflammatory marker to quantify the risk score; the total score is obtained by summing the scores of all markers, thereby enabling the prediction of individualised survival probabilities for patients. By visually integrating multiple inflammatory and immune markers, this nomogram model provides an intuitive and convenient assessment tool for personalised survival prediction in clinical practice.
Figure 2 Nomogram for predicting overall survival based on inflammatory markers.
NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; SII: Systemic immune-inflammation index.
ROC curve analysis of the predictive value of inflammatory markers for OS
ROC curves were used to evaluate the predictive performance of individual and combined inflammatory markers for OS in patients with HER2-negative advanced gastric cancer (Figure 3). The results showed that the area under the curve (AUC) for the combined analysis of the four markers was 0.764 (95%CI: 0.676-0.853). The combined use of peripheral blood inflammatory markers has a moderately high predictive value for survival outcomes in this patient population and may serve as a reference for clinical prognostic stratification.
Figure 3 Receiver operating characteristic curve for the predictive value of inflammatory markers on overall survival.
95%CI: 95% confidence interval; AUC: Area under the curve.
DISCUSSION
Based on the clinical data of patients with HER2-negative advanced gastric cancer, this study evaluated the predictive value of peripheral blood NLR, PLR, LMR and SII on the survival outcome of patients. The results showed that the above four inflammatory immune indicators were closely related to the patient 's disease control status and OS, and were all influencing factors. The prediction model based on this has good predictive efficacy and provides a new idea for clinical prognosis evaluation.
The development and progression of tumours result from the interaction between tumour cells and the body’s immune and inflammatory microenvironment; systemic immune and inflammatory dysregulation is one of the core mechanisms driving the progression of advanced gastric cancer and influencing treatment response[17]. This study found that patients in the disease progress group had significantly higher NLR, PLR and SII levels than those in the disease control group, whilst their LMR was significantly lower. An elevated NLR indicates that a predominance of neutrophil-mediated pro-inflammatory responses and suppression of lymphocyte-mediated anti-tumour immune function[18]. Neutrophils can promote tumor proliferation, invasion and metastasis by secreting pro-tumor factors such as vascular endothelial growth factor and matrix metalloproteinase[19,20]. The decrease in the number of lymphocytes indicates that the body’s immune response to tumors is reduced, thereby weakening the synergistic effect of immunotherapy and chemotherapy[21,22]. Elevated PLR indicates excessive activation of platelets. Activated platelets can induce epithelial-mesenchymal transition by releasing cytokines such as transforming growth factor-β, and assist tumor cells to evade immune surveillance of NK cells[23,24]. The decrease of LMR indicates that the relative increase of monocytes. Tumor-associated monocytes can be polarized into M2 macrophages, secrete inhibitory cytokines such as IL-10 and TGF-β, and form an immunosuppressive microenvironment[25,26]. As a comprehensive indicator, SII integrates three indicators of neutrophils, platelets, and lymphocytes, and its increase can more fully reflect the body’s pro-tumor inflammatory state[27]. It is worth noting that the aforementioned inflammatory markers may have a more distinctive prognostic significance in HER2-negative gastric cancer. HER2-negative gastric cancer comprises multiple molecular subtypes, each of which differs in terms of immune microenvironment infiltration characteristics and the intensity of the inflammatory response[28]. The levels of inflammatory markers may reflect distinct immune landscapes across different molecular backgrounds. Previous studies have shown that elevated NLR and SII are associated with a poorer prognosis in CIN-type gastric cancer, whereas their predictive value may be attenuated by immune activation effects in the EBV-type[29]. Furthermore, it has been demonstrated that baseline inflammatory burden can influence the efficacy of PD-1/PD-L1 inhibitors; a highly inflammatory state may attenuate the antitumour activity of immune checkpoint inhibitors by inducing the accumulation of myeloid suppressor cells and regulatory T cells. Consequently, markers such as NLR and SII not only reflect systemic inflammation levels but may also serve as indirect biomarkers of resistance to immunotherapy[30]. It should be noted that the peripheral blood NLR, PLR, LMR and SII are merely blood count-derived indicators that indirectly reflect the systemic inflammatory and immune status; they are not tumour-specific markers. Abnormalities in these indicators are not solely determined by the biological behaviour of the tumour itself; non-tumour factors such as latent infections, malnutrition, chronic underlying inflammation and previous hormone use may all cause fluctuations in these indicators. Clinicians should not rely solely on these indicators to assess the malignancy of a tumour.
The median OS in the control group was significantly longer than that in the disease progress group, and there were significant differences in inflammatory markers between the two groups, suggesting that pre-treatment peripheral blood inflammatory markers can predict treatment response in advance. Multivariate Cox regression analysis further confirmed that elevated NLR, PLR and SII are independent risk factors for OS, whilst an elevated LMR is an independent protective factor. HER2-negative advanced gastric cancer mainly depends on chemotherapy, immunotherapy and combined regimens. There is a lack of biomarkers for early prediction of efficacy in clinical practice[31,32]. The four inflammatory markers in this study can evaluate the immune inflammatory state of patients before the start of treatment, identify high-risk patients with poor efficacy and poor prognosis, help the clinic to adjust the treatment strategy in time and improve the accuracy of diagnosis and treatment.
The risk-stratification value of the aforementioned inflammatory markers can be translated into specific intervention strategies. For high-risk patients with a pre-treatment NLR ≥ 3.5, SII ≥ 650 and LMR ≤ 4.0, a more intensive follow-up monitoring regimen is recommended; for example, shortening routine imaging follow-ups from every three months to every 6-8 weeks, in order to detect signs of disease progression at an earlier stage. At the same time, such patients may be prioritised for chemotherapy combined with immunotherapy rather than chemotherapy alone, or, where conditions permit, may be encouraged to actively participate in clinical trials. Furthermore, for patients with a high inflammatory burden, supportive measures such as enhanced nutritional support, correction of hypoalbuminaemia, and the appropriate use of anti-inflammatory drugs may help to improve their prognosis. The feasibility of the above strategies still requires validation through prospective studies; however, the model developed in this study provides clinicians with a preliminary risk assessment tool.
This study developed a predictive model based on four independent prognostic factors, achieving the visual integration of multiple indicators. Clinicians can calculate a total score by assigning values to each indicator, thereby directly obtaining an individualised survival probability for each patient. ROC curve analysis showed that the AUC for the combined assessment of the four indicators was 0.764, indicating a predictive performance that is slightly above average. Combined detection can reduce the interference of individual differences and improve the prediction accuracy. In clinical practice, the above indicators can be routinely detected to construct a simple risk scoring system for early identification of high-risk patients. In addition, the model only needs blood routine data before treatment, without additional testing costs, and is easy to operate and promote. It is suitable for application in primary hospitals and clinical routine diagnosis and treatment.
However, this study has the following limitations, which need to be improved in the follow-up study. First of all, this study is a retrospective, single-center design, and the sample size is relatively limited, especially in the disease progression group, only 59 cases, which may introduce selective bias, affect the statistical efficiency and the robustness of the results, and the inherent confounding factors of retrospective studies are difficult to completely eliminate. In the future, multi-center, large-sample prospective cohort studies are needed to verify the external applicability of the conclusions of this study. Secondly, whilst the starting point for both efficacy assessment and survival analysis is the date treatment commences, patients must have completed at least two treatment cycles (6-8 weeks) before they can be categorised into efficacy groups. This means that only patients who have survived for a sufficient period and been assessed as having disease control can be included in the disease control group. This time bias may, to some extent, exaggerate the strength of the association between disease control and survival. Thirdly, peripheral blood inflammation indicators are susceptible to a variety of non-tumor factors, such as recessive infection, stress state, drug effects, etc.[33], and potential residual confounding cannot be completely avoided. In addition, this study only collected blood routine data at a single time point before treatment, and did not dynamically monitor the change trend of the above indicators during treatment. In the future, a dynamic prediction model can be constructed in combination with multi-time point detection. Fourthly, this study did not include other key indicators that may influence prognosis, such as the ECOG performance status score, tumour burden (e.g., number and size of metastases), serum albumin levels and routine tumour markers. As these variables were not adjusted for in this study, they may have affected the independence of the prognostic assessment. Fifthly, there is no further distinction between HER2 zero expression and low expression. In recent years, studies have found that there may be differences in biological behavior and immunotherapy response between the two[34]. In the future, the expression level of HER2 can be more finely stratified. Finally, the follow-up time is not long enough, and the maturity of long-term survival data needs to be improved. Follow-up studies should be extended to verify the predictive ability of the model for long-term survival.
CONCLUSION
The peripheral blood NLR, PLR, LMR and SII before treatment have independent predictive value for the survival outcome of patients with HER2-negative advanced gastric cancer. Among them, the increase of NLR, PLR and SII is an independent risk factor for the shortening of OS, and LMR is an independent protective factor. The median OS of patients in the disease control group was significantly better than that in the disease progression group. The combined detection of four inflammatory indicators has good predictive efficacy. The prediction model based on the above indicators is used to identify high-risk patients early, guide prognostic stratification and treatment optimization, and provide reference for the clinical management of HER2-negative advanced gastric cancer.
Yang Y, Shao Y, Wang J, Cheng Q, Yang H, Li Y, Liu J, Zhou Y, Zhou Z, Wang M, Ji B, Yao J. Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer.Front Oncol. 2023;13:1185240.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 2][Reference Citation Analysis (0)]
Mildanoglu MM, Kutlu Y, Bas O, Koylu B, Dae SA, Sakin A, Erdem D, Sendur MAN, Tasci ES, Dane F, Kaya AO, Aykan MB, Ergun Y, Biter S, Dizdar O, Disel U, Selcukbiricik F, Kose F, Olmez OF, Demir G, Yalcin S, Bilici A. Prognostic and predictive value of systemic inflammatory markers in patients with metastatic gastric and GEJ adenocarcinoma with PD-L1 CPS score ≥ 5: Turkish Oncology Group (TOG) study.Sci Rep. 2025;15:25336.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in RCA: 6][Reference Citation Analysis (0)]
Banks M, Graham D, Jansen M, Gotoda T, Coda S, di Pietro M, Uedo N, Bhandari P, Pritchard DM, Kuipers EJ, Rodriguez-Justo M, Novelli MR, Ragunath K, Shepherd N, Dinis-Ribeiro M. British Society of Gastroenterology guidelines on the diagnosis and management of patients at risk of gastric adenocarcinoma.Gut. 2019;68:1545-1575.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 524][Cited by in RCA: 492][Article Influence: 70.3][Reference Citation Analysis (16)]
Cascinu S, Bodoky G, Muro K, Van Cutsem E, Oh SC, Folprecht G, Ananda S, Girotto G, Wainberg ZA, Miron MLL, Ajani J, Wei R, Liepa AM, Carlesi R, Emig M, Ohtsu A. Tumor Response and Symptom Palliation from RAINBOW, a Phase III Trial of Ramucirumab Plus Paclitaxel in Previously Treated Advanced Gastric Cancer.Oncologist. 2021;26:e414-e424.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 4][Cited by in RCA: 5][Article Influence: 1.0][Reference Citation Analysis (0)]
Aoyama T, Hashimoto I, Maezawa Y, Hara K, Kazama K, Numata M, Kamiya N, Tamagawa H, Sawazaki S, Kawahara S, Komori K, Tamagawa A, Cho H, Morita J, Otani K, Kato A, Tanabe M, Oshima T, Yukawa N, Saito A, Rino Y. The Clinical Impact of the Pretreatment Platelet-to-Lymphocyte Ratio in Gastric Cancer Patients Who Receive Curative Treatment.In Vivo. 2023;37:2696-2703.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 1][Reference Citation Analysis (0)]
Yi W, Muhetaer K, Chen L, Ma X, Wei Y, Cao L, Gao Y, Qu C, Hairoula M, Zhang L. Immunotherapy combined with chemotherapy as first-line treatment for HER2-negative advanced gastric or gastroesophageal junction cancer: systematic review and Bayesian network meta-analysis based on specific PD-L1 expression levels.BMC Cancer. 2026;26:780.
[RCA] [PubMed] [DOI] [Full Text][Reference Citation Analysis (0)]
Yildirim S, Sever ON, Turkoglu E, Sariyar Busery N, Dogan A, Baydas T, Sakin A, Akdag Topal G, Topal A, Alan O, Bayhan AZ, Uzun M, Canaslan K, Ünek İT, Akbas S, Sever N, Sahin E, Oğuz A, Elçiçek ÖF, Özkan FE, Demir H, Şanci PC, Uygun K, Arpaci E, Sakalar T, Surmeli H, Isik D, Ay Ersoy S, Basoglu T, Odabas H, Artaç M, Turan N, Gumus M. Prognostic Impact and Clinicopathological Correlates of HER2 Status in Gastric Cancer: A Multicenter Retrospective Turkish Oncology Group Study.Clin Med Insights Oncol. 2026;20:11795549261445290.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Reference Citation Analysis (0)]
Footnotes
Peer review: Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: China
Peer-review report’s classification
Scientific quality: Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or innovation: Grade B, Grade B
Scientific significance: Grade C, Grade C
P-Reviewer: Park JM, MD, South Korea; Takahashi T, MD, Japan S-Editor: Lin C L-Editor: A P-Editor: Wang WB
